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VOLUME 119

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MARCH 2019

The Southern African Institute of Mining and Metallurgy !+ ! ! + + + + !

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*Deceased

! ! !" " Mxolisi Mgojo President, Minerals Council South Africa ! ! " !" " Gwede Mantashe Minister of Mineral Resources, South Africa Rob Davies Minister of Trade and Industry, South Africa Mmamoloko Kubayi-Ngubane Minister of Science and Technology, South Africa !" " A.S. Macfarlane !" " " M.I. Mthenjane

" ! " !" " Z. Botha ! " !" " V.G. Duke ! " !" " I.J. Geldenhuys " " !" " S. Ndlovu " " " !"! R.T. Jones ! ! !" !"! V.G. Duke

! ! " "! I.J. Geldenhuys C.C. Holtzhausen W.C. Joughin G.R. Lane E. Matinde H. Musiyarira G. Njowa

S.M Rupprecht N. Singh A.G. Smith M.H. Solomon D. Tudor A.T. van Zyl E.J. Walls

!" " "! N.A. Barcza R.D. Beck J.R. Dixon M. Dworzanowski H.E. James R.T. Jones G.V.R. Landman C. Musingwini

J.L. Porter S.J. Ramokgopa M.H. Rogers D.A.J. Ross-Watt G.L. Smith W.H. van Niekerk R.P.H. Willis

G.R. Lane–TPC Mining Chairperson Z. Botha–TPC Metallurgy Chairperson K.M. Letsoalo–YPC Chairperson G. Dabula–YPC Vice Chairperson

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W. Bettel (1894–1895) A.F. Crosse (1895–1896) W.R. Feldtmann (1896–1897) C. Butters (1897–1898) J. Loevy (1898–1899) J.R. Williams (1899–1903) S.H. Pearce (1903–1904) W.A. Caldecott (1904–1905) W. Cullen (1905–1906) E.H. Johnson (1906–1907) J. Yates (1907–1908) R.G. Bevington (1908–1909) A. McA. Johnston (1909–1910) J. Moir (1910–1911) C.B. Saner (1911–1912) W.R. Dowling (1912–1913) A. Richardson (1913–1914) G.H. Stanley (1914–1915) J.E. Thomas (1915–1916) J.A. Wilkinson (1916–1917) G. Hildick-Smith (1917–1918) H.S. Meyer (1918–1919) J. Gray (1919–1920) J. Chilton (1920–1921) F. Wartenweiler (1921–1922) G.A. Watermeyer (1922–1923) F.W. Watson (1923–1924) C.J. Gray (1924–1925) H.A. White (1925–1926) H.R. Adam (1926–1927) Sir Robert Kotze (1927–1928) J.A. Woodburn (1928–1929) H. Pirow (1929–1930) J. Henderson (1930–1931) A. King (1931–1932) V. Nimmo-Dewar (1932–1933) P.N. Lategan (1933–1934) E.C. Ranson (1934–1935) R.A. Flugge-De-Smidt (1935–1936) T.K. Prentice (1936–1937) R.S.G. Stokes (1937–1938) P.E. Hall (1938–1939) E.H.A. Joseph (1939–1940) J.H. Dobson (1940–1941) Theo Meyer (1941–1942) John V. Muller (1942–1943) C. Biccard Jeppe (1943–1944) P.J. Louis Bok (1944–1945) J.T. McIntyre (1945–1946) M. Falcon (1946–1947) A. Clemens (1947–1948) F.G. Hill (1948–1949) O.A.E. Jackson (1949–1950) W.E. Gooday (1950–1951) C.J. Irving (1951–1952) D.D. Stitt (1952–1953) M.C.G. Meyer (1953–1954) L.A. Bushell (1954–1955) H. Britten (1955–1956) Wm. Bleloch (1956–1957)

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H. Simon (1957–1958) M. Barcza (1958–1959) R.J. Adamson (1959–1960) W.S. Findlay (1960–1961) D.G. Maxwell (1961–1962) J. de V. Lambrechts (1962–1963) J.F. Reid (1963–1964) D.M. Jamieson (1964–1965) H.E. Cross (1965–1966) D. Gordon Jones (1966–1967) P. Lambooy (1967–1968) R.C.J. Goode (1968–1969) J.K.E. Douglas (1969–1970) V.C. Robinson (1970–1971) D.D. Howat (1971–1972) J.P. Hugo (1972–1973) P.W.J. van Rensburg (1973–1974) R.P. Plewman (1974–1975) R.E. Robinson (1975–1976) M.D.G. Salamon (1976–1977) P.A. Von Wielligh (1977–1978) M.G. Atmore (1978–1979) D.A. Viljoen (1979–1980) P.R. Jochens (1980–1981) G.Y. Nisbet (1981–1982) A.N. Brown (1982–1983) R.P. King (1983–1984) J.D. Austin (1984–1985) H.E. James (1985–1986) H. Wagner (1986–1987) B.C. Alberts (1987–1988) C.E. Fivaz (1988–1989) O.K.H. Steffen (1989–1990) H.G. Mosenthal (1990–1991) R.D. Beck (1991–1992) J.P. Hoffman (1992–1993) H. Scott-Russell (1993–1994) J.A. Cruise (1994–1995) D.A.J. Ross-Watt (1995–1996) N.A. Barcza (1996–1997) R.P. Mohring (1997–1998) J.R. Dixon (1998–1999) M.H. Rogers (1999–2000) L.A. Cramer (2000–2001) A.A.B. Douglas (2001–2002) S.J. Ramokgopa (2002-2003) T.R. Stacey (2003–2004) F.M.G. Egerton (2004–2005) W.H. van Niekerk (2005–2006) R.P.H. Willis (2006–2007) R.G.B. Pickering (2007–2008) A.M. Garbers-Craig (2008–2009) J.C. Ngoma (2009–2010) G.V.R. Landman (2010–2011) J.N. van der Merwe (2011–2012) G.L. Smith (2012–2013) M. Dworzanowski (2013–2014) J.L. Porter (2014–2015) R.T. Jones (2015–2016) C. Musingwini (2016–2017) S. Ndlovu (2017–2018)

! ! "! Botswana DRC Johannesburg Namibia Northern Cape Pretoria Western Cape Zambia Zimbabwe Zululand

Vacant S. Maleba Vacant N.M. Namate F.C. Nieuwenhuys R.J. Mostert L.S. Bbosa D. Muma C. Sadomba C.W. Mienie

%#%*$* + ) $ + " (&)*& Scop Incorporated "('%*& Genesis Chartered Accountants ) *)'$*()& The Southern African Institute of Mining and Metallurgy Fifth Floor, Minerals Council South Africa Building 5 Hollard Street, Johannesburg 2001 • P.O. Box 61127, Marshalltown 2107 Telephone (011) 834-1273/7 • Fax (011) 838-5923 or (011) 833-8156 E-mail: journal@saimm.co.za

!"('%*($ + %$*" R.D. Beck P. den Hoed M. Dworzanowski B. Genc R.T. Jones W.C. Joughin H. Lodewijks C. Musingwini S. Ndlovu J.H. Potgieter N. Rampersad T.R. Stacey M. Tlala

VOLUME 119 NO. 3 MARCH 2019

Contents

!"('%*($ + %#& '$#' D. Tudor

)&)'+$#"+ (& )"+ The Southern African Institute of Mining and Metallurgy P.O. Box 61127 Marshalltown 2107 Telephone (011) 834-1273/7 Fax (011) 838-5923 E-mail: journal@saimm.co.za

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President’s Corner: Treating the mineral asset like an asset by A.S. Macfarlane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The ORCiD initiative – Author registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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PROFESSOR DICK MINNITT SPECIAL EDITION A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value by J. Githiria and C. Musingwini. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A stochastic cut-off grade optimization model that extends Lane’s deterministic theory for calculating optimal cut-off grades over the life of mine (LOM) is presented. The model uses realistic grade-tonnage realizations and commodity price distribution to account for uncertainty. An improved project value by using stochastic optimization approaches was demonstrated.

*(#')"+ + Camera Press, Johannesburg

" )*'(&(# + ) *)&)#'$'( ) Barbara Spence Avenue Advertising Telephone (011) 463-7940 E-mail: barbara@avenue.co.za ISSN 2225-6253 (print) ISSN 2411-9717 (online)

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An empirical long-term commodity price range for Mineral Reserve declarations to minimize impairments in gold and platinum mines by V. Maseko and C. Musingwini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An assessment of modifying factors for converting Mineral Resources to Mineral Reserves revealed that Mineral Reserves were most sensitive to long-term commodity prices. The findings from the analysis of the assessment suggests that long-term commodity price assumptions within Âą5% improve confidence in Mineral Reserve declarations and minimize impairments.

(*) '%* +% + )#+ )&& % *#$ &

THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS. CopyrightŠ 2018 by The Southern African Institute of Mining and Metallurgy. All rights reserved. Multiple copying of the contents of this publication or parts thereof without permission is in breach of copyright, but permission is hereby given for the copying of titles and abstracts of papers and names of authors. Permission to copy illustrations and short extracts from the text of individual contributions is usually given upon written application to the Institute, provided that the source (and where appropriate, the copyright) is acknowledged. Apart from any fair dealing for the purposes of review or criticism under , of the Republic of South Africa, a single copy of an article may be supplied by a library for the purposes of research or private study. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior permission of the publishers. U.S. Copyright Law applicable to users In the U.S.A. The appearance of the statement of copyright at the bottom of the first page of an article appearing in this journal indicates that the copyright holder consents to the making of copies of the article for personal or internal use. This consent is given on condition that the copier pays the stated fee for each copy of a paper beyond that permitted by Section 107 or 108 of the U.S. Copyright Law. The fee is to be paid through the Copyright Clearance Center, Inc., Operations Center, P.O. Box 765, Schenectady, New York 12301, U.S.A. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale.

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Journal Comment by W. Assibey-Bonsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Application of geometallurgical modelling to mine planning in a copper-gold mining operation for improving ore quality and mineral processing efficiency by C. Beaumont and C. Musingwini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Due to a lack of proper integration between technical and functional areas that form the mine value chain at Kansanshi, the ore classification system that was being used unintentionally misclassified ore. A geometallurgical analysis was therefore undertaken to review the classification system and a new geometallurgical ore classification system was developed, which led to a decrease in the waste tons mined and an increase in the ore tons.

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A spatial mine-to-plan compliance approach to improve alignment of short- and long-term mine planning at open pit mines by T.J. Otto and C. Musingwini. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The development and implementation of a spatial mine-to-plan compliance reconciliation approach for an open pit mine is described. Applying this approach to a case study of an open pit iron ore mine in South Africa indicated a significant improvement in the spatial mine-toplan compliance, thus improving the alignment between short-term plans, long-term plans, and life-of-mine plans.

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#')*#$'(%#$ + " (&%* + %$*" R. Dimitrakopoulos, McGill University, Canada D. Dreisinger, University of British Columbia, Canada E. Esterhuizen, NIOSH Research Organization, USA H. Mitri, McGill University, Canada M.J. Nicol, Murdoch University, Australia E. Topal, Curtin University, Australia D. Vogt, University of Exeter, United Kingdom

VOLUME 119

NO. 3

MARCH 2019

VOLUME 119 NO. 3 MARCH 2019

Evaluation of government equity participation in the minerals sector of Tanzania from 1996 to 2015 by P.R. Lobe, A.S. Nhleko, and H. Mtegha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Tanzanian government’s equity participation in the minerals sector was evaluated for the period 1996 to 2015. The study highlights a number of challenges faced by the government, and recommends measures to be taken to ensure a more effective government equity role. Benefits of including resistivity data in a resource model – an example from the Postmasburg Manganese Field by J. Perold and C. Birch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The manganese resources determined by using drilling data exclusively were compared to those determined from a combination of drilling and resistivity data. The results showed that including resistivity data can reduce exploration costs significantly due to the more accurate siting of boreholes, which decreases the number of boreholes, samples, and analyses required. Furthermore, a better forecast of the net present value of an operation can be obtained due to the more accurate estimation of manganese resources and stripping ratios. Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat by M.A. Mahboob, B. Genc, T. Celik, S. Ali, and I. Atif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The unique spectral reflectance and absorption characteristics of remotely sensed Landsat data in the visible, nearinfrared (NIR), shortwave-infrared (SWIR) and thermal -infrared (TIR) regions of the electromagnetic spectrum were used in different digital satellite image processing techniques. The methodology developed in this study, based on reflectance spectroscopy of satellite data, is cost-effective and time saving, and can be applied to inaccessible and/or new areas where on-the-ground knowledge is limited. The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery, South Africa by M. Katuruza and C. Birch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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An unmanned aircraft system (UAS) was used at Isibonelo Colliery to conduct highwall mapping using a digital photogrammetry technique. The collected data was rapidly processed and a 3D model of the mapped pit area produced. The results showed a good correlation between the resource model and the UAS data model.

PAPERS OF GENERAL INTEREST

Improved ultrafine coal dewatering using different layering configurations and particle size combinations by M.E.C. Snyman and N. NaudÊ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Major problems with belt-filter dewatering operations are caused by the presence of ultrafines in the feed stream. This work examined the effect of coal particle size and layering during super-fines belt-filter dewatering using a range of blends. This was determined in practise using a vacuum filter to simulate belt filtration. The best layering configuration was with fine material at the bottom and coarse material on top. Contributors to fatigue at a platinum smelter in South Africa by J. Pelders and G. Nelson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This study assesses the associations between demographic, work, living and socioeconomic conditions, and lifestyle characteristics and fatigue at a platinum smelter. Information was gathered from interviews and questionnaires. Various demographic, lifestyle, and wellness-related factors were found to be associated with fatigue. Both work and non-work contributors should be addressed in fatigue management plans. South Africa’s options for mine-impacted water re-use: A review by T. Grewar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Large volumes of treated mining-impacted water are produced, often with no end use in mind other than discharge or disposal. The most suitable option for re-using treated mining-impact water is within agriculture, specifically the irrigation of crops (food, forage, or energy crops), which currently and unnecessarily uses the majority of South Africa’s high-quality resources of potable water.

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Optimization of conditions for the preparation of activated carbon from olive stones for application in gold recovery by M. Louarrat, G. Enaime, A. Baçaoui, A. Yaacoubi, J. Blin, and L. Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The purpose of this study was to produce an activated carbon from olive stones to be used for the recovery of gold. The effect of four process parameters on the production of the activated carbon was studied in order to optimize the yield, gold recovery capacity, and mechanical strength. The activated carbon produced under the optimal conditions had a gold adsorption capacity similar to that of commercial activated carbon and performed well in CIL tests.

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his volume of the Journal recognizes the immense contribution of Professor Dick Minnitt, of the School of Mining Engineering at the University of the Witwatersrand (Wits), where he is one of their four NRF-rated academics and one of only three Professor Emeritus appointments in the School. He spent most of his career at Wits, supervising not only postgraduate (MSc/PhD) candidates and publishing his own research, but also presenting courses in geostatistics and mineral economics for mining investment in his specialized fields of mineral economics and mineral resource evaluation. Over the years he not only materially contributed to, but also supported and maintained a close association with, the Southern African Institute of Mining and Metallurgy (SAIMM). Though ‘retired’, he continues to provide valuable insights and inputs for ongoing mineral resource research to both industry and academia. Professor Minnitt was appointed as the JCI Professor of Mineral Resources and Reserves in 2001, and over the years has received many merit awards, including three silver medals from the SAIMM in 2008, 2014, and 2016, and most recently the Pierre Gy Sampling Gold Medal in 2017, awarded for excellence in teaching and application of the Theory of Sampling by the World Conference in Sampling and Blending, at the Eighth World Conference on Sampling and Blending 2017. He is a Fellow of the SAIMM, a Fellow of the AusIMM and the Geological Society of South Africa (GSSA), and a life member and past president of the Geostatistical Association of South Africa (GASA). His accolades, awards, and contributions are indeed numerous, illustrating both dedication and much sacrifice of his private life, yet he still finds time to go above and beyond, as shown in 2016 through his membership of the organizing committee for the 35th International Geological Conference. Professor Minnitt has published some 65 technical papers both locally and overseas. The variety and diversity of his contributions to research and industry as reflected in this Journal, deserves mention, from aspects that include mine planning (36%), geology and resource estimation (21%), government and mining policy (21%), to technology, metallurgy, and environmental papers that make up the final 22% of this volume. Most of the contributions are from Wits University, showcasing ongoing research at Wits, with the balance of the papers from industry. Mine planning aspects cover subjects including stochastic cut-off grade derivation aimed at quantifying uncertainty with regards to input parameters for cut-off grade modelling, assessment of modifying factors for reserve declaration, and spatial mine planning reconciliation. The latter provides possible solutions to the practitioners’ challenge with regards to long-term/life-of-mine versus short-term spatial mine reconciliation and compliance. This volume also highlights how geology and resource modelling underpin economic block modelling and demonstrates how stochastic processes, which quantify uncertainty pertaining to geology and economic parameters, could reduce the risks in resource/reserve and mine financial evaluation. The geology and resource modelling papers further show the advantages of accessing additional information for geological modelling, including resistivity data for manganese resource modelling and reflectance spectroscopy for mapping hydrothermal minerals. The policy papers present some interesting contributions including an evaluation of government participation in the mineral sector in Tanzania and South Africa, as well as the role of women in the mining sector. The technology, metallurgy, and environmental topics range from the use of unmanned aircraft systems for highwall mapping in open pits to environmental dust controls. It is hoped that readers will benefit from this volume, which recognizes the immense contributions that Professor Dick Minnitt has made to both research and industry.

W. Assibey-Bonsu Group Geostatistician and Evaluator, Gold Fields Limited

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he papers in this edition are all related to Mineral Resource Management, the field in which Professor Richard Minnitt practised for many years in the Chair in Mineral Resources at the University of the Witwatersrand. Professor Minnitt has followed the great luminary, the late Danie Krige, in pioneering many aspects of this field, especially in the areas of geostatistics and mineral evaluation. It is most fitting that this special edition be dedicated to this area, and to Professor Minnitt. The field of Mineral Resource Management is one which has developed over the last two decades, with its initial development (in a South African context) finding its roots in what was then the Gold Division of Anglo American. The initial premise was that work in the fields of geology, evaluation, mine surveying and mine planning was conducted in ‘silos’, and was normally seen as a ‘service’ function to mining. This relegation to a service function meant that the work done by these departments was seen as purely technical and carried out at a low level in the organization. Work in rock engineering, ventilation, and safety was similarly subordinated. The result of this perspective was that these departments submitted reports on observations, and made recommendations, which all too often went unheeded, rendering these departments ineffective. The advent of value chain thinking, coupled with the application of Levels of Work Theory, as developed by Elliott Jaques in his book entitled Requisite Organization (Cason Hall & Co., 1989) and developments in mining technical information systems created the idea that Mineral Resource Management (MRM) as a concept would on the one hand break down the functional silos and create an integrated approach to technical services, and on the other hand would elevate the profile and accountability of MRM. Benchmarking against international operations indicated that in technical services departments such integration already existed, and the levels of competence and professionalism were considerably higher than had been the case traditionally in South African operations. Companies such as Codelco and BHP Billiton populated these departments with competent senior mining engineers and general managers, and focused on areas aligned to the full value chain, such as business planning, mine design, optimization, metal accounting, and geometallurgy. It was also observed that these departments were considered as the ‘brains trust’ of the operations, with mining departments having little discretion in terms of ‘where to mine’. These observations and conclusions were the basis for establishing MRM at mines and creating MRM departments in South Africa. At the same time, academic research and developments resulted in the establishment of postgraduate courses in MRM at the University of the Witwatersrand, and in Mineral Throughput Management at the University of the Free State. As all of this work progressed, so it started to give direction to the further development of MRM technical systems, with an increasing emphasis on economics and optimization. So where is MRM now? It is my opinion that while good progress was made in developments in MRM, and excellent studies and research have been done in various areas of technical focus (as illustrated by the papers in this edition), there is much work still to be done. The concept of MRM may have reached its limit, and a transition to Mineral Asset Management would seem to be appropriate. It is obvious that the principal asset of a mining company is the Mineral Resource over which the company has rights. Within this asset is the metal or commodity that should be identified, scheduled, extracted, and beneficiated in order to realize optimum value from the underlying asset. This focus on ‘treating the asset like an asset’ leads one in the direction of asset management principles, wherein one would apply the same asset management principles to this principal asset as one would to an asset such as a haul truck. Let us consider some of these. Assets should be the subject of life cycle costing. When applied to the mineral asset, this should cover the entire process of discovery, feasibility, design, scheduling, development, operation, and closure. A life cycle costing approach seeks to derive the maximum value from the asset over its life through the optimal combinations of capital, operating levels, operating costs, and profitability, under constantly varying revenue conditions. This approach recognizes that the old ‘cradle to grave’ approach to the life of mine through a once-a-year rework is obsolete, and that a more dynamic approach is required which constantly re-evaluates whether the extraction strategy is still appropriate, and when the asset should be either sold or replaced. This approach should also ensure that the right metrics are applied at the appropriate stage of the life of the mine. Clearly, long-term measures are not appropriate when the asset is in a harvesting or closure phase.

President’s Corner (continued) The question arises as to whether the traditional metrics applied to the life of mine, in terms of net present value (NPV) and internal rate of return (IRR), are still appropriate. This consideration leads to a conclusion that these metrics may be too ‘constant’ in that they rely on a set of considerations that are static rather than dynamic, unless constant re-planning and valuation is done. Alternatively, an economic value added (EVA) approach (short term), coupled with market value add (MVA) (long term), combines short-term cash flow optimization with long-term value, based on value-adding decisionmaking, and incorporating risk and volatility. In each phase, critical asset management value-adding questions should be asked, such as: Ž Is the asset available? This means the development of appropriate metrics and controls to ensure that planning has resulted in sufficient mineable face length being available, and that sufficient flexibility has been created in the orebody, to ensure that short- and medium-term plans can be realized. Ž Is the asset being utilized optimally? This will place focus on the mining process, mining efficiency, and all the leading indicators of productivity. In this case, productivity includes a holistic approach covering capital, people, systems, and assets. Ž Is the asset reliable? Answering such a question requires that sufficient work has been done to ensure that those aspects that make the asset unreliable are addressed. This could include the level of geological uncertainty, the risk of interruption due to seismicity, potential for the loss of the asset as it is extracted and transported, and the reliability of all relevant data related to the asset and its use. Ž What is the mean time between failures? This involves the development of controls that monitor the mean time between failures due to geological interferences such as dykes, potholes and washouts, and other unwanted events. These measures indicate the appropriate levels of flexibility required. Ž Is the asset optimized? The issue of optimization is where the topic becomes really exciting. One of the tools in the toolbox of the Mineral Asset Manager is the grade-tonnage curve and the use and application of cut-off grades. Traditionally, mines have worked with the pay limit, as espoused by Storrar (South African Mine Valuation, Chamber of Mines, 1977). The pay limit has been used as a measure to define Mineral Resources and Mineral Reserves, usually on an annual basis and mainly for shareholder reporting. This does not serve as an optimization tool. Cut-off grades, on the other hand, allow more focused application to specific mining areas where costs can be accurately allocated. This has been used for short-term optimization through estimating available reserves and mining mix, under varying price conditions. Kenneth Lane (The Economic Definition of Ore, Mining Journal Books, 1988) extended the use of cut-off grades to longterm optimization and dynamic optimization. Juan Camus in his book Management of Mineral Resources (SME. 2001) examined the work of Lane, and realized that Lane’s dynamic cut-off grade optimization was actually based on the EVA equation. He used this equation to perform optimization by maximizing the cash flow of short-term extraction (by varying volumes, grades etc.) and optimizing the value of the remaining life of the asset. This created a very important linkage between cut-off grades, EVA, MVA, and optimization. This two-stage, iterative optimization process intuitively suggests another important link, which is that to real option analysis. This is because the Black-Scholes formula for real option evaluation splits the value of a real option into the value of the remaining asset and the capitalization required to be applied to it. So, what does this mean? It creates an opportunity, in that the use of an EVA/MVA based optimization tool currently relies on an iterative, manual calculation. Thus, the need to develop this into a useable model provides an opportunity to link this directly to the grade/tonnage curve and asset management. Secondly it provides a fascinating problem for a postgraduate study, to identify the missing link between dynamic cut-off grades, EVA and MVA, and real option analysis. Third, these should become important tools in the toolbox of the modern Mineral Asset Manager. These opportunities, as well as the establishment of new Mineral Asset Management metrics, are easier to develop now because of the greater level of real-time data that is possible and available. These initiatives should all help to contribute towards restoring confidence of investors, who have seen a paucity of dividend returns from local mining stocks over many years.

A.S. Macfarlane President, SAIMM

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http://dx.doi.org/10.17159/2411-9717/2019/v119n3a1

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value by J. Githiria1 and C. Musingwini1

Cut-off grade is a decision-making criterion often used for determining the quantities of material (ore and waste) to be mined, ore processed, and saleable product. It therefore directly affects the cash flows from a mining operation and the net present value (NPV) of a mining project. A series of different cut-off grades that are applied over the life of mine (LOM) of an operation defines a cut-off grade policy. Due to the complexity of the calculation process, previous work on cut-off grade calculation has mostly focused on deterministic approaches. However, deterministic approaches fail to capture the uncertainty inherent in input parameters such as commodity price and grade-tonnage distribution. This paper presents a stochastic cut-off grade optimization model that extends Lane’s deterministic theory for calculating optimal cut-off grades over the LOM. The model, code-named ‘NPVMining’, uses realistic grade-tonnage realizations and commodity price distribution to account for uncertainty. NPVMining was applied to a gold mine case study and produced an NPV ranging between 7% and 186% higher than NPVs from deterministic approaches, thus demonstrating improved project value from using stochastic optimization approaches. @<$"8965 optimization, cut-off grade policy, deterministic approach, heuristic approach, stochastic approach, grade-tonnage realization, uncertainty.

;3 298476>87>34:#8//>29;6<>3;.34.;:=87 ;76>8-:=1= ;:=87 Cut-off grade is a decision-making criterion that is generally used in mining to distinguish ore from waste material. Consequently, it is used to determine the quantities of material (ore and waste) to be mined, ore processed, and saleable product. A series of different cutoff grades that are applied over the life-ofmine (LOM) of a mining operation defines the cut-off grade policy for that particular operation. The cut-off grade therefore directly affects the cash flows to be produced and the net present value (NPV) of a project at the mine planning or feasibility study stage. Pioneering work on cut-off grade calculation can be attributed to Mortimer’s (1950) work on grade control for gold mines in South Africa. Although Mortimer’s work is given relatively little recognition, it established the fundamental principle that not only must rock at the lowest grade cover its cost of extraction, but that the average grade of the

1 University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Aug. 2018.

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rock must provide a certain minimum profit per ton processed. Later in the 1960s, work on cut-off grade calculation again appeared, including that published by Henning (1963), Lane (1964), and Johnson (1969). Lane (1988) subsequently published an updated version of his 1964 work as a comprehensive book on the use of cut-off grade to economically define ore using NPV as a proxy for value. To date, it is Lane’s work on value-based cut-off grade optimization that has received the most attention among the mining fraternity. Lane’s work placed more emphasis on optimizing cutoff grade in order to improve the economic viability of mining projects and operations. The cut-off grade algorithm developed by Lane (1964, 1988) was more elaborate than others as it took into account constraints associated with the capacities of the mine, mill, and market, resulting in the derivation of six potential cut-off grades from which an optimal cut-off grade could be selected. Three of the six cut-off grades are described as limiting cutoff grades while the other three are denoted as balancing cut-off grades. Limiting cut-off grades are derived by assuming that each of the three stages (mining, processing, and refining) is an individual and independent constraint on throughput due to production capacity limitations, operating costs, and price attributable to the output product. Balancing cut-off grades are determined by assuming that two out of the three stages are concurrently operating at their capacity limits. Despite its fairly comprehensive structure, Lane’s cut-off grade optimization algorithm had some shortcomings. For example, it could not be used to determine cut-off grades for polymetallic deposits. This shortcoming is now addressed through the concept of net smelter

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value return (NSR) for evaluating polymetallic deposits, as for example in the work of Shava and Musingwini (2018) who developed an NSR model for a zinc, lead, and silver mine. Shava and Musingwini (2018) then related the NSR values to the applicable cut-off grades for each of the three constituent metals. Table I summarizes some studies that have attempted to address different shortcomings in Lane’s cut-off grade theory. Despite making improvements to Lane’s original cut-off grade theory, the models in Table I are deterministic and therefore fail to capture the economic, technical, and geological uncertainties that mining operations continually face. The uncertainties include those associated with commodity price and grade-tonnage distribution. Due to this shortcoming, the NPVs generated from these models are suboptimal. There is, therefore, a need for a stochastic cut-off grade optimization approach that can capture uncertainty in parameters such as commodity price and grade-tonnage distribution. This challenge had been long-recognized in other studies not related to Lane’s framework, but which attempted to use other stochastic and/or dynamic programming (DP) approaches to address the challenge.

:830;5:=3>;76>6$7;1=3>-9829;11=72>;--98;30<5>:8 34:#8//>29;6<> Several studies that have applied stochastic and/or DP approaches in the calculation of cut-off grades have incorporated the dynamic nature of input parameters. Table II summarizes some studies that have attempted to address uncertainty in parameters for cut-off grade determination. The studies summarized in Table II, except for the model by Asad and Dimitrakopoulos (2013), were not based on Lane’s framework and tended to consider only one input parameter as being stochastic. The study presented in this

paper therefore extended Lane’s algorithm to develop a stochastic cut-off grade model by concurrently considering variability in both commodity price and grade-tonnage distribution. The model that was developed is code-named ‘NPVMining’.

86=/=3;:=875>:8> ;7< 5>34:#8//>29;6<>:0<89$>/89>:0< 5:830;5:=3> 186<. Lane’s theoretical framework is premised on a schematic material flow as illustrated in Figure 1. Depending on the applicable cut-off grade, material from the mine can be classified as waste and sent to the waste dump; as ore and sent for milling in the processing plant; or as low-grade material and sent to a stockpile for processing later during the LOM. The milling process produces a concentrate which is sent to the refinery to produce the final saleable product, which is then marketed. The production capacities of the different stages in the mining complex are denoted by M, C, and R, for the mining, milling, and refinery capacities, respectively. Lane’s framework uses the notations in Table III. The input parameters in Table III were then modified to account for variability as explained in the following sections. Given a set of equally probable grade-tonnage curves (w), the stochastic approach develops a cut-off grade policy by determining the cut-off grade (G) from time periods 1 to N. The NPV of future cash flows is maximized subject to mining, processing, and refining capacity constraints. The objective function for the cut-off grade optimization remains unchanged from Lane’s original formulation, as represented by Equation [1]. [1]

Table I

;1-.<5>8/>5:46=<5>;669<55=72>581<>5089:381=725>=7> ;7< 5>34:#8//>29;6<>:0<89$ :46$

411;9$>8/>=1-98 <1<7:

Taylor (1972)

Taylor’s approach for balancing cut-off grades used statistical parameters to describe the grade distribution of the orebody, since grade is variable throughout an orebody.

Taylor (1985)

The approach incorporated a stockpiling stage into Lane’s framework so that instead of dumping low-grade material as waste, it is kept as stockpiles as future ore feed to the mill.

Dagdelen (1992, 1993)

Dagdelen developed an analytical method for finding balancing cut-off grades which was more efficient than Lane’s graphical.

Whittle and Wharton (1995)

The approach, which is incorporated in the open-pit mining software Geovia Whittle 4-XŽ, added linear programming (LP) optimization into Lane’s theory to simultaneously incorporate stockpiling of mined material, ore blending, and account for multiple minerals.

Asad (1997, 2005)

Asad developed a cut-off grade optimization algorithm based on Lane’s work, but with a stockpiling option for open-pit mining operations with two economic minerals.

King (2001)

King modified Lane’s approach by incorporating variations in throughput for different ore types in order to cater for polymetallic deposits.

Dagdelen and Kawahata (2008)

The approach applied mixed-integer linear programming (MILP) to improve the efficiency of the calculation process in Lane’s algorithm and was used develop the OptiPitŽ.mining software package

Gholamnejad (2008, 2009)

Gholamnejad modified Lane’s algorithm to cater for the trade-off between an increase in the average mill grade and a concomitant increase in rehabilitation costs. As the cut-off grade is increased, the amount of material mined and dumped as waste also increases, leading to increased rehabilitation costs.

Osanloo, Rashidinejad, and Rezai (2008)

The study incorporated environmental requirements into Lane’s cut-off grade optimization by incorporating waste and tailings disposal costs.

King (2011)

King modified the earlier (King, 2001) version by incorporating operating and administrative costs.

Githiria, Muriuki, and Musingwini (2016)

The study developed a computer-aided application based on Lane’s algorithm and improved the efficiency of the cut-off grade calculation process.

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A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value Table II

;1-.<5>8/>5:46=<5>;669<55=72>473<9:;=7:$>=7>34:#8//>29;6<>6<:<91=7;:=87 :46$

411;9$>8/>5:830;5:=3>89> >;--98;30

Dowd (1976)

The approach was based on DP to optimize cut-off grade. It incorporated the interaction of cut-off grades and fluctuating commodity prices, but ignored variability in costs.

Krautkraemer (1988)

Krautkraemer analysed the effect of stochastic metal prices on the selection of cut-off grades, depending on the rate of metal price change relative to the discount rate, and concluded that fluctuations in metal prices critically affect the selection of cut-off grades.

Cetin and Dowd (2002, 2013, 2016)

The approach applied genetic algorithms (GAs) and DP to optimize cut-off grades for polymetallic mines, while assuming a constant grade-tonnage distribution for the entire orebody. Cetin and Dowd (2016) compared GA, the grid search method, and DP when deriving optimal cut-off grades for deposits with up to three constituent minerals and concluded that GAs are more robust in optimizing cut-off grade for multi-mineral deposits under technical and economic constraints only.

Asad (2007)

Asad optimized cut-off grade for an open-pit mining operation through an NPV-based algorithm that considered metal price and cost escalation but ignored geological uncertainty.

Li, Yang, and Lu (2012)

The approach optimized cut-off grade using stochastic programming in open-pit mining but considered commodity price as the only dynamic variable.

Asad and Dimitrakopoulos (2013)

Asad and Dimitrakopoulos modified Lane’s approach into a heuristic cut-off grade model to account for geological uncertainty in ore supplied to multiple processing streams. However, the approach was limited to a single mine.

Thompson and Barr (2014)

The approach optimized cut-off grade using stochastic programming in open-pit mining, but considered commodity price as the only dynamic variable.

Myburgh, Deb, and Craig (2014)

The approach was a hybrid heuristic approach to maximize NPV through cut-off grade optimization. It is incorporated in the software package, Maptek EvolutionÂŽ. The approach employs an evolutionary GA to optimize cut-off grade and extraction sequence by managing two lower-level algorithms. The first one is LP-based and determines the optimal flow of material through multiple processing streams and manages stockpiles. The second is a local search technique for deriving the best production schedule.

However, the cash flow equation changes to incorporate the uncertainty of both the metal price and grade-tonnage distribution in the orebody, as indicated in Equation [2].

[2]

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8:;:=875>45<6>=7> ;7< 5>;.289=:01>*;6;-:<6>/981 ,=:0=9=;)>'%(&+ 8:;:=87 i N CC s r m c f y d M C R Qm Qc Qr

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7=:

Year Mine life Capital cost Selling price Refining cost Mining cost Milling cost Annual fixed costs Recovery Discount rate Mining capacity Milling capacity Refining capacity Material mined Ore processed Concentrate refined

Years $ million $/g $/oz $/ton $/ton $/year % % t/a t/a t/a t/a t/a t/a

where d is the discount rate and Pwi is the cash flow generated in period i by extracting the orebody based on a grade-tonnage curve w.

Using the optimum cut-off grades obtained in the algorithm for the given grade-tonnage curve w, a yearly production schedule that shows the cut-off grade, quantity mined (Qmw), quantity processed (Qcw), quantity refined (Qrw), profit, and NPV is calculated. Figure 2 illustrates the steps involved in the algorithm used in this research study. The algorithm developed from Figure 2 was coded using the C++ programming language in Microsoft Visual Studio 2017 Integrated Development Environment (IDE) on a standard computer to produce the application code-named ‘NPVMining’. The code for NPVMining is given in Appendix 1. The application is an executable file and will run on computers that run applications with .exe extension. The computer must have Visual Studio 2017 and Microsoft Office 2013 installed on it. Microsoft Visual Studio 2017 (VC++) supports two versions of the C++ programming language, which are the ISO/ANSI standard C++, and C++/CLI (Common Language Infrastructure). C++/CLI has a highly-developed design capability that enables the assembly of the entire graphical user interface (GUI), and the code that creates it being generated automatically (Githiria, Muriuki, and Musingwini, 2016). The C++ programming language was used in the implementation of the algorithm to enable the execution of VOLUME 119

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Table III

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value the application on Windows-based computers with different architectures and/or platforms. This compatibility aspect enables easy portability of the application to make it usable by mine planners working on different computing platforms. The output from the model is easily exported to Microsoft Excel 2013 for comparison and analysis. The flow diagram in Figure 2 was then modelled as follows: i. Identify the methods of data entry for the grade-tonnage curves, commodity price, and costs. ii. Develop software to input the grade-tonnage curves, commodity price, and costs variations and run tests for validation. iii. Implement the algorithm for calculating cut-off grade and NPV using the inputs provided. iv. Test and validate the output and provide the output in a format that will display the results appropriately. Mathematical functions of commodity price against time were applied in the above procedure. A random number generator was developed to generate values within a specified range so that input values are stochastic but within realistic ranges.

3. 4.

5.

(m), milling cost (c ), refining cost (r ), recovery (y), annual fixed costs ( f ), and discount rate (d ). Introduce variability in the input parameters (metal price and grade-tonnage). Determine the optimum cut-off grade to be used in year i using the cut-off grade equations. If the initial NPVi is not known set the NPVi to zero. Determine the tons of ore (qow), tons of waste (qww), and average grades of the ore associated w the optimum cut-off grade (gavg). Set: Qcwi = C if qow is greater than the milling capacity (C), otherwise Qcwi = qow. Using Equations {3]–[7], calculate the quantity to be mined (Qm) and refined (Qr). Find the limiting capacity and the mine life (N) from the following: [3] [4] [5] [6]

,<7<9;.>5:<-5>=7>:0<> 5:830;5:=3>;.289=:01 The steps to determine a cut-off grade policy as outlined by Lane (1964), but modified as illustrated in Figure 2 to incorporate uncertainty in NPVMining, are as follows (Githiria, 2018): 1. 2.

Formulate multiple realizations of grade-tonnage distribution for the entire deposit. Input the parameters to be used in the cut-off grade policy, such as the mining capacity (M), milling capacity (C), refining capacity (R), selling price (P), mining cost

[7] 6.

Determine the yearly profit using Equation [8]. [8]

7.

Compute the NPV using the formula below by discounting the profits at a given discount rate (d) for the time calculated as the LOM. [9]

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This value of V becomes the second approximation of V (the first was V=0) for use in the formulae to calculate the optimum cut-off grade. 8. Repeat the computation from step 5 until the value, V, converges. 9. Adjust the grade-tonnage distribution by subtracting the ore tons from the grade-tonnage distribution intervals above the optimum cut-off grade (G) and the waste tons (Qmw – Qcw) from the intervals below the optimum cut-off grade (G) in proportionate amounts. This is to ensure that the distribution remains unchanged, otherwise it will change to a different grade-tonnage distribution. For each of the multiple realizations of the grade-tonnage distribution the current grade-tonnage curve being used at that specific period will be altered simultaneously. This will cater for the intertemporal dependencies that alter the gradetonnage distribution with time. 10. If it is the first iteration then, knowing the profits obtained in each year, find the yearly NPV by discounting back those profits and go to step 4. If it is the second iteration, then stop.

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value 11. Use the net present values obtained in step 10 as the initial NPV for each of the corresponding years for the second iteration.

9=</>6<539=-:=87>8/> NPVMining caters for: (i) economic and operational parameters, (ii) grade-tonnage distribution, and (iii) the cutoff grade policy or production schedule as shown in Figure 3. One of the source files, NPVMiningDlg.cpp, contains the code that executes the algorithm through the user interface. Appendix 1 contains the code for NPVMining. The input parameters (price and costs) used in the calculations are uploaded to the application using Microsoft ExcelÂŽ spreadsheets. Other parameters are entered into the user interface via the dialog boxes. The metal price variability is obtained from factoring either a fixed or geometric range. After calculating the optimum cut-off grade, a cut-off grade policy is generated showing the three main economic indicators: annual profit, NPV, and LOM. The NPVMining user interface has five dialog boxes: (i) grade category window (Figure 4), (ii) price criteria window (Figure 5), (iii) other parameters window (Figure 6), (iv) limiting capacity selection window (Figure 7), and (v) cut-off grade calculation window (Figure 7). The grade-tonnage distribution, economic and operational parameters are the input data used in the computer-aided application. The grade-tonnage distribution is entered into the grade category window as shown in Figure 4. The grade category input window has several dialog boxes describing the lower grade limit, upper grade limit, and quantity of ore per increment for the multiple realizations. The economic and operational parameters are keyed on the user interface using the price criteria and other parameters window as shown in Figures 5 and 6. This data is uploaded using Microsoft ExcelÂŽ worksheets containing technical data, while other data is entered in the dialog box as required. This process simplifies data entry, which is tiresome if done manually. The two input windows in Figures 5 and 6 have several dialog boxes that represent economic and operational parameters such as metal price, mining cost, milling cost, refining cost, mining capacity, processing capacity, refining capacity, recovery, discount rate, and fixed cost.

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!=249<> 45<9>=7:<9/;3<>*,=:0=9=;)>'%(&+

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value The break-even cut-off grade and Lane’s limiting cut-off grade are selected and calculated as shown in Figure 7, and the output is displayed in the output window as shown in Figure 8. The main economic indicators of the project (annual profit, NPV, and LOM) are displayed on the user interface. After clicking the ‘calculate’ button, the results of the cut-off grade policy calculations are displayed in the output window and generated on an Microsoft ExcelŽ spreadsheet as shown in Figure 8. The spreadsheet consists of seven main columns: optimum cut-off grade, quantity of material to be mined (Qm), quantity of ore to be milled (Qc), quantity of product refined (Qr), mine life, annual profit, and NPV. The resultant cut-off grade policy is exported to Microsoft ExcelŽ for ease of use and interpretation. The NPVMining application was tested on the data-set for a case study of the McLaughlin gold mine, which is a defunct gold mine in northern California, USA. The data-set was obtained from the mining library compiled by Espinoza et al. (2012) for research purposes. This data-set was selected because it has previously been used in other cut-off grade optimization studies, therefore allowing comparison of the output from NPVMining with those studies. !=249<>& 4:-4:>< -89:<6>:8>;> =39858/:> 3<. 5-9<;650<<:> *,=:0=9=;)>'%(&+

<539=-:=87>8/>:0<> 3 ;420.=7>28.6>1=7<>3;5<>5:46$ 6;:;#5<:> Tables IV and V provide the simulated grade-tonnage distribution and the economic, design, and operational parameters, respectively, for the McLaughlin gold mine case

Table V

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!=249<> <.<3:=87>8/>:0<>.=1=:=72>3;-;3=:$>;76>34:#8//>29;6<>-8.=3$ 45<6>>*,=:0=9=;)>'%(&+

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1847:>

Capital cost Mining cost Milling cost Refining/Selling cost Annual fixed costs Recovery Discount rate Mining capacity Milling capacity Refining capacity

$ million $/ton $/ton $/oz $/year Decimal Decimal t/a t/a t/a

105 000 000 1.20 19.00 655.00 8,350,000 0.90 0.15 Unlimited 1 050 000 Unlimited

Table IV

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, (

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,

,

,

,

0.000-0.020 0.020-0.025 0.025-0.030 0.030-0.035 0.035-0.040 0.040-0.045 0.045-0.050 0.050-0.055 0.055-0.060 0.060-0.065 0.065-0.070 0.070-0.075 0.075-0.080 0.080-0.100 0.100-0.358 Total

70 000 7 257 6 319 5 591 4 598 4 277 3 465 2 438 2 307 1 747 1 640 1 485 1 227 3 598 9 574 125 523

70 022 7 263 6 325 5 595 4 603 4 279 3 466 2 439 2 309 1 748 1 643 1 488 1 229 3 598 9 578 125 585

70 081 7 257 6 332 5 616 4 628 4 306 3 488 2 468 2 335 1 754 1 647 1 494 1 237 3 625 9 579 125 847

70 003 7 364 6 405 5 632 4 608 4 318 3 502 2 438 2 318 1 740 1 637 1 481 1 246 3 636 9 641 125 969

69 640 7 890 6 860 6 100 4 950 4 570 3 680 2 630 2 520 1 900 1 770 1 560 1 270 3 810 10 330 129 480

70 900 7 000 5 700 5 200 4 600 4 300 3 000 2 200 2 100 1 500 1 400 1 200 800 3 300 9 800 123 000

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A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value

A<54.:5>/981>:0<>;--.=3;:=87>8/> :8>:0< 3 ;420.=7>28.6>1=7<>3;5<>5:46$> The stochastic cut-off grade model (NPVMining) is used to calculate the optimum cut-off grade that maximizes NPV in the shortest mine life possible using the price variations in Table VI and the grade-tonnage curves in Table IV. A summary of the best-case scenario showing the calculated

Table VI

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;.4<>* 8 + 1250 1270 1290 1310 1330 1356

NPV for six equally probable grade-tonnage curves and a range of metal prices is generated as shown in Table VII. The resultant cut-off grade policies from the model shows a significant difference between the minimum and maximum NPV generated through the equally probable grade–tonnage curves. The resultant cut-off grade policies generate the optimal NPV in approximately 9 years for the six gradetonnage curves. The application (NPVMining) also generates possible outcomes in relation to the change in gold price. The six possible outcomes for each price category generate different outcome as shown in Table VII. The resultant cutoff grade policies are generated by varying the grade–tonnage curves for all mineable ore in every grade interval and the metal prices against time. The solution was generated in 14.45 seconds after running the NPVMining code on Microsoft Visual Studio 2017. The relationship between NPV and the metal price was modelled using a linear regression approach (Figures 9 and 10). The linear regression model shown in Figure 9 is applied to identify the relationship between NPV and the response variables (metal price) when all the other variables in the model are held fixed. The correlation coefficient in the relationship between metal price and NPV is about 0.97, indicating that metal price variation has a high significance for the NPV.

Table VII

<:>-9<5<7:> ;.4<5>2<7<9;:<6>45=72> "0<7> ;9$=72>-9=3<>;76>29;6<#:877;2<>6=5:9= 4:=87 * <5: 3;5<>53<7;9=8+>*,=:0=9=;)>'%(&+ >* +> 467 172 060.50 467 172 060.49 466 990 510.59 468 922 686.82 488 180 335.90 475 864 529.70 487 959 524.30 487 959 524.30 487 777 974.39 489 777 241.30 509 740 604.86 496 886 282.14 508 746 988.10 508 746 988.10 508 565 438.18 510 631 795.78 531 300 873.82 517 908 034.58 529 534 451.90 529 534 451.88 529 352 901.98 531 486 350.25 552 861 142.78 538 929 787.03 550 321 915.70 550 321 915.68 550 140 365.78 552 340 904.73 574 421 411.74 559 951 539.47 571 109 379.50 571 109 379.47 570 927 829.57 573 195 459.21 595 981 680.69 580 973 291.91

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9=3<>*8 :87+

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1250 1250 1250 1250 1250 1250 1270 1270 1270 1270 1270 1270 1290 1290 1290 1290 1290 1290 1310 1310 1310 1310 1310 1310 1330 1330 1330 1330 1330 1330 1350 1350 1350 1350 1350 1350

125 523 125 585 125 847 125 969 129 480 123 000 125 523 125 585 125 847 125 969 129 480 123 000 125 523 125 585 125 847 125 969 129 480 123 000 125 523 125 585 125 847 125 969 129 480 123 000 125 523 125 585 125 847 125 969 129 480 123 000 125 523 125 585 125 847 125 969 129 480 123 000

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study. Table VI provides the price variations used in the calculations. Six grade-tonnage curves (GTC1 to GTC6) for the McLaughlin deposit, as illustrated in Table IV, were used to determine balancing cut-off grades as described in Lane’s cut-off grade theory (Lane, 1964, 1988). The grade-tonnage curve data was used to calculate the ratios and cumulative values in relation to the different stages in a mining complex. Over-estimation or under-estimation of the grade, volume, or tonnage and other parameters related to a deposit is common in most conventional and deterministic orebody models. This is detrimental to the planning of the mining operation, which consequently leads to loss of profits. There are several statistical methods for measuring uncertainty of the orebody in relation to the geological characteristics. Stochastic approaches are employed to characterize the geological uncertainty by modelling and estimating the orebody more reliably. This study employed the Monte Carlo method to simulate the grade-tonnage distribution of the orebody. It used statistical and graphical techniques, including linear and nonlinear modelling, to simulate the probable distribution of the data. It is evident from the simulated multiple realizations of the orebody that the tonnages decrease with increasing grade ranges. The McLaughlin mining operation case study incorporates a mine, processing plant, and waste dump, following the three main stages presented in Figure 1, excluding the stockpiling option. The mine produces sulphide and oxides ores mixed with waste material in controlled quantities. The ore goes through several processing stages such as gravity concentration, flotation, and leaching. In the subsequent refining step, the gold is recovered from solution and the waste material is sent to the waste dump. The operation is assumed to have an unrestricted potential to mine and refine/market the annual gold production, while the processing capacity is set to be at 1.05 Mt/a of ore (Table V). Gold price uncertainty applied in this study was assumed to have a range (2% per period) that is increasing yearly, as shown in Table VI, to be within the generally accepted longterm gold price of around US$1300 per ounce.

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value Ž 7% better than the Cut-off Grade Optimiser Ž 35% better than OptiPitŽ Ž 13% better than Maptek EvolutionŽ. The superior NPV generated by NPVMining can be attributed to its incorporation of stochasticity of input parameters, which is not incorporated in the other models. This demonstrates that superior results are obtained by incorporating uncertainty into Lane’s cut-off grade theory.

873.45=87 !=249<> =7<;9>9<.;:=8750=-> <:"<<7>1<:;.>-9=3<>;76> > *,=:0=9=;)>'%(&+

The relationship between NPV and the other parameter (grade-tonnage distribution) when all the other variables in the model are held fixed is shown in Figure 10. The correlation coefficient in the relationship between gradetonnage distribution and NPV is about 0.13, indicating that grade-tonnage distribution has a low impact on NPV.

81-;9=587>8/> :8>8:0<9>34:#8//>29;6< ;--98;30<5 In deterministic cut-off grade optimization approaches applied in the mining industry, the outcomes are determined through known parameters without any room for random variation. This limits the flexibility of a mine plan in cases where commodity price and operational costs fluctuate through the LOM. However, the self-adaptation in stochastic algorithms contributes greatly to their ability to address successfully most complex real-world problems. This is due to their strategic outlook on mining problems that allows them to be easily applied to complex mining situations. Table VIII summarizes the results of a comparison conducted between NPVMining and the following cut-off grade approaches:

The stochastic NPVMining model was applied to a gold mine case study data-set to ascertain its benefits in an operational mine. NPVMining was used to generate six cut-off grade policies, indicating that a change in grade-tonnage distribution has an overall effect on NPV. A comparison between NPVMining and other cut-off grade optimization models demonstrated and validated the efficiency of the model. Using an Intel dual-core processor running at 3.00GHz and with 4.00GB RAM, the model generated results for each simulation within 5 seconds. The improvements in NPV generated by NPVMining ranged between 7% and 186%, demonstrating the value of using stochastic approaches to cut-off grade optimization. Ignoring the commodity price and geological uncertainties in daily mining operations may have very serious negative economic implications for a mining project.

3 78".<621<7:> The work reported in this paper is part of a PhD research study in the School of Mining Engineering at the University of the Witwatersrand. The financial support obtained from the Julian Baring Scholarship Fund (JBSF) for the PhD study is greatly acknowledged.

(i)

Break-even cut-off grade model (Githiria and Musingwini, 2018) (ii) Cut-off Grade Optimiser (Githiria and Musingwini, 2018) (iii) OptiPitÂŽ (Dagdelen and Kawahata, 2008) (iv) Maptek EvolutionÂŽ (Myburgh, Deb, and Craig, 2014). Table VIII shows that NPVMining generated the highest NPV. Compared to the other cut-off grade approaches, NPVMining produced results that were: ÂŽ 186% better than the break-even cut-off grade model

!=249<>(% =7<;9>9<.;:=8750=-> <:"<<7>29;6<#:877;2<>6=5:9= 4:=87> ;76> >*,=:0=9=;)>'%(&+

Table VIII

81-;9=587>8/>:0<>1=7<>.=/<)>3;50>/.8")>;76> >/981>6=//<9<7:>34:#8//>29;6<>186<.5>*,=:0=9=;)>'%(&+ <:<91=7=5:=3>34:#8//>29;6<>;--98;30<5

LOM (years) Profits/cash flow ($ million) NPV($ million)

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35 863 163.42

10 825.44 435.52

18 885.60 347.08

10 760.10 413.84

9.12 887.09 467.17

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ASAD, M.W.A. 1997. Multi mineral cut-off grade optimization with option to stockpile, MSc thesis. Colorado School of Mines, Golden, CO. ASAD, M.W.A. 2002. Development of generalized cut-off grade optimisation algorithm for open pit mining operations. Journal of Engineering and Applied Sciences, vol. 21, no. 2. pp. 119–127. ASAD, M.W.A. 2005. Cut-off grade optimisation algorithm with stockpiling option for open pit mining operations of two economic minerals. International Journal of Surface Mining, Reclamation and Environment, vol. 19, no. 3. pp. 176–187. ASAD, M.W.A. 2007. Optimum cut-off grade policy for open pit mining operations through net present value algorithm considering metal price and cost escalation. Engineering Computations, vol. 24, no. 7. pp. 723–736. ASAD, M.W.A. and DIMITRAKOPOULOS, R. 2013. A heuristic approach to stochastic cut-off grade optimisation for open pit mining complexes with multiple processing streams. Resources Policy, vol. 38. pp. 591–597. CETIN, E. and DOWD, P.A. 2002. The use of genetic algorithms for multiple cutoff grade optimisation. Proceedings of the 32nd International Symposium on Application of Computers and Operations Research in the Mineral Industry (APCOM). Society for Mining, Metallurgy & Exploration, Littleton, CO. pp. 769–779. CETIN, E. and DOWD, P.A. 2013. Multi-mineral cut-off grade optimization by grid search. Journal of the Southern African Institute of Mining and Metallurgy, vol. 113, no.8. pp. 659–665. CETIN, E. AND DOWD, P.A. 2016. Multiple cut-off grade optimization by genetic algorithms and comparison with grid search method and dynamic programming. Journal of the Southern African Institute of Mining and Metallurgy, vol. 116, no.7. pp. 681–688. DAGDELEN, K. 1992. Cut-off grade optimisation. Proceedings of the 23rd International Symposium on Application of Computers and Operations Research in the Minerals Industry (APCOM). Society for Mining, Metallurgy & Exploration, Littleton, CO. pp. 157–165. DAGDELEN, K. 1993. An NPV optimisation algorithm for open pit mine design. Proceedings of the 24th International Symposium on Application of Computers and Operations Research in the Mineral Industry, Montreal, Quebec, Canada. Canadian Institute of Mining, Metallurgy and Petroleum, Montreal. pp. 257–263. DAGDELEN, K. anD KAWAHATA, K. 2008. Value creation through strategic mine planning and cut-off grade optimization, Mining Engineering, vol. 60, no. 1. pp. 39–45. DOWD, P.A. 1976. Application of dynamic and stochastic programming to optimise cut-off grades and production rates. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, vol. 85, no. 1. pp 22–31. ESPINOZA, D., GOYCOOLEA, M., MORENO, E., and NEWMAN, A. 2012. MineLib: a library of open pit mining problems. Annals of Operations Research, vol. 206. pp. 93–114. GHOLAMNEJAD, J. 2008. Determination of the optimum cut-off grade considering environmental cost. Journal of International Environmental Application and Science, vol. 3, no. 3. pp.186–194. GHOLAMNEJAD, J. 2009. Incorporation of rehabilitation cost into the optimum cutoff grade determination. Journal of the Southern African Institute of Mining and Metallurgy, vol. 108, no. 2. pp. 89–94. GITHIRIA, J.. 2018. A stochastic cut-off grade optimisation algorithm. PhD thesis, University of the Witwatersrand, South Africa. GITHIRIA, J., MURIUKI, J., and MUSINGWINI, C. 2016. Development of a computeraided application using Lane’s algorithm to optimise cut-off grade. Journal of the Southern African Institute of Mining and Metallurgy, vol. 116, no. 11. pp. 1027–1035. GITHIRIA, J. and MUSINGWINI, C. 2018. Comparison of cut-off grade models in mine planning for improved value creation based on NPV. Proceedings of the 6th Regional Conference of the Society of Mining Professors (SOMP), Johannesburg, South Africa. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 347–362. HENNING, U. 1963. Calculation of cut-off grade. Canadian Mining Journal, vol. 84, no. 3. pp. 54–57. JOHNSON, T.B. 1969. Optimum open-pit mine production scheduling. A Decade of Digital Computing in the Mineral Industry. Weiss A. (ed.). AIME, New York, pp. 539–562. KING, B. 2001. Optimal mine scheduling policies. PhD thesis, Royal School of Mines, Imperial College, London University, UK.

KING, B. 2011. Optimal mining practice in strategic planning. Journal of Mining Science, vol. 47, no. 2. pp. 247–253. KRAUTKRAEMER, J.A. 1988. The cut-off grade and the theory of extraction. Canadian Journal of Economics, vol. 21, no. 1. pp. 146–160. http://doi:10.2307/135216 LANE, K.F. 1964. Choosing the optimum cut-off grade. Colorado School of Mines Quarterly, vol. 59, no. 4. pp. 811–829. LANE, K.F. 1988. The Economic Definition of Ore: Cut-off Grade in Theory and Practice. Mining Journal Books, London. LI, S., YANG, C., and LU, C. 2012. Cut-off grade optimization using stochastic programming in open-pit mining. Proceedings of the Sixth International Conference on Internet Computing for Science and Engineering, Henan, China. IEEE, New York. pp. 66–69. doi:10.1109/ICICSE.2012.24 MORTIMER, G. 1950. Grade control. Transactions of the Institution of Mining and Metallurgy, vol. 59. pp. 1–43. MYBURGH, C.A., DEB, K., and CRAIG, S. 2014. Applying modern heuristics to maximising net present value through cut-off grade optimisation. Proceedings of Orebody Modelling and Strategic Mine Planning Symposium, Perth, WA. Australasian Institute of Mining and Metallurgy, Melbourne. pp. 155–164. OSANLOO, M., RASHIDINEJAD, F., and REZAI, B. 2008. Incorporating environmental issues into optimum cut-off grades modeling at porphyry copper deposits. Resources Policy, vol. 33, no. 4. pp. 222–229. SHAVA, P. and MUSINGWINI, C. 2018. A net smelter return model for a polymetallic deposit. Proceedings of the 6th Regional Conference of the Society of Mining Professors: Overcoming Challenges in the Mining industry through Sustainable Mining Practices, Johannesburg, South Africa, 12-13 March 2018. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 279–291. TAYLOR, H.K. 1972. General background theory of cut-off grades. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, vol. 81. pp. A160–A179. TAYLOR, H.K. 1985. Cut-off grades – some further reflections. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, vol. 96. pp. 204–216. THOMPSON, M. and BARR, D. 2014. Cut-off grade: a real options analysis. Resources Policy, vol. 42. pp. 83–92. WHITTLE, J. and WHARTON, C. 1995. Optimising cut-offs over time. Proceedings of the 25th International Symposium on the Application of Computers and Mathematics in the Mineral Industries, Brisbane, Australia. Australasian Institute of Mining and Metallurgy, Melbourne. pp. 261–265.

--<76= >(?> 86<>/89> *,=:0=9=;)>'%(&+ ! double CNPVMiningDlg::determineStripRatio(double dCutoff_grade, int iPos) { //determine breakeven cutoff grade policy double dSRatio; int g = 0; double dCurrentPrice = dValArray[iPos]; //getCurrentPrice(); POSITION pos = listGradeCat.GetHeadPosition(); dTotalWaste = 0.0; dTotalOre = 0.0; while (pos) { GradeCategory sGradeCat = listGradeCat.GetNext(pos); if (sGradeCat.dLowerLimit < dCutoff_grade) dTotalWaste += sGradeCat.dQuantity; else dTotalOre += sGradeCat.dQuantity; } dTotalOre -= dMinedOre; dSRatio = dTotalWaste / dTotalOre; dSRatio = round(dSRatio*100)/100; return dSRatio; }

! double CNPVMiningDlg::average_grade(double dCutoff_grade) { VOLUME 119

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A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value double dResult = 0.0; POSITION pos = listGradeCat.GetHeadPosition(); while (pos) { GradeCategory sGradeCat = listGradeCat.GetNext(pos); if (sGradeCat.dLowerLimit >= dCutoff_grade) { dResult += sGradeCat.dMidpoint * sGradeCat.dQuantity; dResult = round(dResult * 1000) / 1000; } } dResult /= dTotalOre; dResult = round(dResult * 1000) / 1000; return dResult; }

! ! void CNPVMiningDlg::determineOptimumGrade() { dTotalWaste = 0.0; POSITION pos = listGradeCat.GetHeadPosition(); while (pos) { GradeCategory sGradeCat = listGradeCat.GetNext (pos); sGradeCat.dOreQty = dTotalQty - dTotalWaste; sGradeCat.dWasteQty = dTotalWaste; dTotalQty -= sGradeCat.dQuantity; dTotalWaste += sGradeCat.dQuantity; sGradeCat.dmc = sGradeCat.dOreQty / (sGradeCat.dOreQty + sGradeCat.dWasteQty); sGradeCat.dG = determineGSum(pos) / sGradeCat.dOreQty; sGradeCat.dmr = (sGradeCat.dOreQty * sGradeCat.dG) / (sGradeCat.dOreQty + sGradeCat.dWasteQty); sGradeCat.dcr = sGradeCat.dG; } }

! ! double CNPVMiningDlg::limitingcutoff_grade(int iPos) { double dResult = 0.0; double dPrice = 0.0; double dNPV = cObj->get_NPV(); double dAFixedCost = cObj->get_annualFixedCost(); double dMillCapacity = cObj->get_millingCapacity(); double dMineCapacity = cObj->get_miningCapacity(); double dRefCapacity = cObj->get_refiningCapacity(); //using the first value in the array dPrice = dValArray[iPos]; if (m_boolMillCap == true) { dResult = (cObj->get_millingCost() + ((dAFixedCost + (cObj->get_discountedRate() * dNPV)) / dMillCapacity)) / ((dPrice - cObj->get_refiningCost()) * cObj>get_recovery()); } else if (m_boolMineCap == true) { dResult = (cObj->get_millingCapacity() + ((dAFixedCost + (cObj->get_discountedRate() * dNPV)) / dMineCapacity)) / ((dPrice - cObj->get_refiningCost()) * cObj>get_recovery()); }

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else if (m_boolRefCap == true) { dResult = (cObj->get_refiningCost() + ((dAFixedCost + (cObj->get_discountedRate() * dNPV)) / dRefCapacity)) / ((dPrice - cObj->get_refiningCost()) * cObj>get_recovery()); } dResult = round(dResult * 100) / 100; return dResult; }

! ! double CNPVMiningDlg::breakevencutoff_grade(int iPos) { double dResult = 0.0; double dPrice = 0.0; //using the first value in the array dPrice = dValArray[iPos]; dResult = cObj->get_millingCost() / ((dPrice - cObj>get_refiningCost()) * cObj->get_recovery()); dResult = round(dResult * 100) / 100; return dResult; }

! bool CNPVMiningDlg::checkGradeCatDuplicate(double lowerlimit, double upperlimit) { bool bFound = false; for (int g = 0; g < m_listGradeCat.GetItemCount(); g++) { if (_wtof(m_listGradeCat.GetItemText(g, 1)) == lowerlimit || upperlimit == _wtof(m_listGradeCat.GetItemText(g, 2))) { bFound = true; break; } } return bFound; }

! bool CNPVMiningDlg::checksubCatDuplicate(CString cat, CString subcat) { bool bFound = false; for (int g = 0; g < m_listPriceCat.GetItemCount(); g++) { if (m_listPriceCat.GetItemText(g, 1) == cat && subcat == m_listPriceCat.GetItemText(g, 2)) { bFound = true; break; } } return bFound; }

! ! ! ! ! int CNPVMiningDlg::factorial(int n) { return (n == 1 || n == 0) ? 1 : factorial(n - 1) * n; } int CNPVMiningDlg::combinationcount(int n, int r)

A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value { return (factorial(n) / (factorial(r)*factorial(n - 1))); } int CNPVMiningDlg::getusedcategoriesList() { int iTotal = 0, iLast = 0; int iCat = m_listPriceCat.GetItemCount(); for (int v = 0; v < 30; v++) szUsedCatItems[v] = L""; for (int t = 0; t < iCat; t++) { CString szTempCat = m_listPriceCat.GetItemText(t, 1); for (int k = 0; k < 30; k++) { if (k == iLast) { if (szTempCat == szUsedCatItems[iLast - 1]) break; else { szUsedCatItems[iTotal] = szTempCat; iTotal += 1; iLast += 1; break; } } else if (k < iLast) continue; } } return iTotal; } int CNPVMiningDlg::combinationtotals() { CString szArrayVals[100][2]; CString szArrayCat[20]; int iArrayCatSubItems[20] = { 0 }; int iArrayCatSubItemsCmb[20] = { 0 }; int iArrayCatSubItemsCmbTotals = 0; int iCurrVal = 0, iCurrPos = 0; //determine how many categories exist int iCat = m_cmbPriceCat.GetCount(); for (int u = 0; u < iCat; u++) { m_cmbPriceCat.GetLBText(u, szArrayCat[u]); } //determine the number of combinations expected int iCatCmb = combinationcount(iCat, 1); //loop through all the data and identify the subcategories in each category and store them in an array for (int t = 0; t < m_listPriceCat.GetItemCount(); t++) { szArrayVals[t][0] = m_listPriceCat.GetItemText(t, 1); szArrayVals[t][1] = m_listPriceCat.GetItemText(t, 2); for (int k = 0; k < iCat; k++) { if (szArrayVals[t][0] == szArrayCat[k]) iArrayCatSubItems[k] += 1; } } //determine the number of combinations for each category for (int d = 0; d < iCat; d++)

if (iArrayCatSubItems[d] > 1) { iArrayCatSubItemsCmb[d] = combinationcount(iArrayCatSubItems[d], 1); } else if (iArrayCatSubItems[d] == 1) { iArrayCatSubItemsCmb[d] = 1; } } //get the summation of all the combinations for (int j = 0; j < iCatCmb; j++) { for (int i = 0; i < iCatCmb; i++) { iCurrVal = iArrayCatSubItemsCmb[iCurrPos]; if (i <= iCurrPos) continue; else iArrayCatSubItemsCmbTotals += iCurrVal*iArrayCatSubItemsCmb[i]; } iCurrPos += 1; } return iArrayCatSubItemsCmbTotals; } void CNPVMiningDlg::fillcombinationlist(COleSafeArray *m_combinationList) { long index1[2]; int iArrayCatLastSubItems[20] = { 0 }; int iLastPosArray[20] = { 0 }; //get a list of the category combinations int iCat = iUsedCatNum; for (int r = 0; r < 100; r++) { for (int e = 0; e < 2; e++) { szArrayVals[r][e] = L""; } } for (int b = 0; b < 20; b++) iLastPosArray[b] = -1; int iItemCount = m_listPriceCat.GetItemCount(); for (int t1 = 0; t1 < iItemCount; t1++) { CString szCatItem = m_listPriceCat.GetItemText(t1, 1); szArrayVals[t1][0] = szCatItem; CString szSubCatItem = m_listPriceCat.GetItemText(t1, 2); szArrayVals[t1][1] = szSubCatItem; for (int k = 0; k < iCat; k++) { if (szArrayVals[t1][0] == szUsedCatItems[k]) { iArrayCatLastSubItems[k] = t1; } } } int iCurrRow = 2, iCurrCol = 1; int i = 0; while (1) VOLUME 119

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A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value {

} } else //no number on its right { if (iLastPosArray[j] != iArrayCatLastSubItems[j]){ if (t <= iLastPosArray[j]) continue; } } index1[0] = iCurrRow + i; index1[1] = iCurrCol + j; if (j == 0) { long numpos[2]; numpos[0] = index1[0]; numpos[1] = 0; CString szNum; szNum.Format(L"%d", numpos[0] - 1); BSTR bstr = szNum.AllocSysString(); m_combinationList->PutElement(numpos, bstr); SysFreeString(bstr); } BSTR bstr1 = szArrayVals[t][1].AllocSysString(); m_combinationList->PutElement(index1, bstr1); SysFreeString(bstr1); iLastPosArray[j] = t; break; }

for (int j = 0; j < iCat; j++) { for (int t = 0; t < iItemCount; t++) { if (szArrayVals[t][0] != szUsedCatItems[j]) continue; if (j == 0)// has a number on its right { if (iLastPosArray[j] != iArrayCatLastSubItems[j]) { int iTemp = j; bool bTemp = true; loopj: if (iLastPosArray[iTemp + 1] == iArrayCatLastSubItems[iTemp + 1]) if (iTemp + 1 == iCat - 1) { if (t <= iLastPosArray[j] && bTemp == true) { continue; } } else { iTemp += 1; if (iLastPosArray[iTemp + 1] < iArrayCatLastSubItems[iTemp + 1]) bTemp = false; goto loopj; } } if (t < iLastPosArray[j]) continue; } else { if (t < iArrayCatLastSubItems[j]) continue; else { int iTemp = j; bool bTemp = true; loopj1: if (iLastPosArray[iTemp + 1] == iArrayCatLastSubItems[iTemp + 1]) { if (iTemp + 1 == iCat - 1) { if (t <= iLastPosArray[j] && bTemp == true) { //start from the first sub item if (j > 0) t = iArrayCatLastSubItems[j - 1] + 1; else return; } } else { iTemp += 1; if (iLastPosArray[iTemp + 1] < iArrayCatLastSubItems[iTemp + 1]) bTemp = false; goto loopj1; } } }

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} i++; } }

! double CNPVMiningDlg::dgetAnnualProfit(int iPos) { double dResult = 0.0; double dPrice = 0.0; //using the first value in the array dPrice = dValArray[iPos]; dResult = ((dPrice - cObj->get_refiningCost()) * cObj>get_refiningCapacity()) - (cObj->get_millingCapacity() * cObj->get_millingCost()) - (cObj->get_miningCapacity() * cObj->get_miningCost()) - cObj->get_annualFixedCost(); dResult = round(dResult * 100) / 100; return dResult; }

double CNPVMiningDlg::dgetNPV(double dMinelife, double dAnnualProfit) } double dResult = 0.0; dResult = (dAnnualProfit * ((round(pow(1 + cObj>get_discountedRate(), dMinelife) * 1000) / 1000) - 1)) / ((round(pow(1 + cObj->get_discountedRate(), dMinelife) * 1000) / 1000) * cObj->get_discountedRate()); dResult = round(dResult * 100) / 100; return dResult; }

N

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a2

An empirical long-term commodity price range for Mineral Reserve declarations to minimize impairments in gold and platinum mines by V. Maseko1 and C. Musingwini2

When considered collectively, Mineral Resources and Mineral Reserves are arguably a key asset for any mining company. Mineral Reserves are the economically mineable portions of Mineral Resources. They therefore provide a good indication of the economic prospects in the short to medium term and associated non-financial impairments for mining companies, hence a focus on Mineral Reserves. An assessment of modifying factors for converting Mineral Resources to Mineral Reserves revealed that Mineral Reserves were most sensitive to long-term commodity prices, hence the focus on long-term commodity prices. The justification for selecting gold and platinum mines is that they collectively make a significant contribution to South Africa’s earnings from mining. The introduction of the South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (SAMREC Code) in 2000 informed the basis for analysing long-term commodity price assumptions for major South African gold and platinum mining companies between 2000 and 2016. The analysis revealed that the least number of non-financial impairments occurred when long-term commodity prices were within ¹5% of spot prices. This finding suggests that this range is the ideal range for long-term commodity price assumptions to improve confidence in Mineral Reserve declarations and minimize impairments. @' B>8; modifying factors, long-term commodity prices, spot prices, impairment, reporting codes.

D=>B867=CBD The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (SAMREC Code) defines a Mineral Resource as ‘a concentration or occurrence of solid material of economic interest in or on the Earth’s crust in such form, grade or quality and quantity that there are reasonable prospects for eventual economic extraction’ (SAMREC, 2016, p. 18). Mineral Resources are categorized according to increasing levels of geoscientific confidence or knowledge, from Inferred to Indicated and lastly, Measured categories. The SAMREC Code defines a Mineral Reserve as ‘the economically mineable part of a Measured and/or Indicated Mineral Resource’ (SAMREC, 2016, p. 25). In some of the other reporting codes such as the Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves

1 Ernst & Young Advisory Services Limited, South Africa. 2 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Aug. 2018; revised paper received Jan. 2019.

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(the JORC Code), a Mineral Reserve is synonymous with an Ore Reserve. The conversion of Mineral Resources to Mineral Reserves involves applying modifying factors, which are non-resource considerations. The modifying factors used in the Mineral Reserve estimation process include mining, metallurgical, processing, infrastructure, economic, marketing, legal, environmental, social, and governmental factors. Each of these modifying factors consists of several components which require due consideration in the estimation of Mineral Reserves. Table I shows some of the components typically considered for each of the modifying factors. It can be argued that, considered collectively, Mineral Resources and Mineral Reserves are the single most significant asset or among the most significant assets for any mining company (Njowa and Musingwini, 2018). International reporting codes provide the minimum standards and guidelines for the estimation, declaration, and public reporting of Exploration Results, Mineral Resources, and Mineral Reserves. The reporting of Exploration Results, Mineral Resources, and Mineral Reserves serves the needs of governments, international organizations, investors and potential investors and their advisors (SAMREC, 2016). International organizations are interested in the in situ estimates of mineral inventories to inform world perspectives on strategic and critical minerals, while governments rely on these estimates to design policies for safeguarding their mineral endowments and ensuring that their citizens can benefit from the exploitation of those minerals (Musingwini, 2018). Investors are

An empirical long-term commodity price range for Mineral Reserve declarations Table I

'5C7A<E7B:5BD@D=;E:A CD4E65E:B8C,'CD4E,A7=B>;E/A8A5=@8E,>B:E*55<@'A>8EAD8E(:C=#2E9??3. '5C7A<E7B:5BD@D=;E7BD;C8@>@8

+B8C,'CD4E,A7=B> Mining

Mining method, dilution and mining recovery, production rates, operating and capital costs, the degree of selective mining, grade control, geotechnical influences, ventilation, use of stope fill, mode of access to underground mines, overall mining sequence.

Metallurgical

Test sample representativeness, product specification, metallurgical recovery and throughput, ore characteristics, operating and capital costs, variable ore characteristics and their potential impact on process performance, overall economics.

Economic

Commodity price forecasts (including the impact of hedging), royalties, operating and capital costs.

Marketing

Sales projections, analysis of demand trends and estimates for off-site treatment terms and costs, product quality and blending abilities (for commodities such as iron ore, coal, and industrial minerals).

Legal

Political risk and security of tenure, environmental factors.

Social

Sustainable development and social license to operate.

Governmental

Government regulatory framework.

interested in the economically extractable portion of the mineral deposit as this is a good indicator of the economic prospects for mining companies in the short to medium term. Securities exchanges worldwide have an overarching principle of protecting investors and so require transparent and consistent reporting of Mineral Resources and Mineral Reserves for easier decision-making by investors. The national reporting code in South Africa is the SAMREC Code. The SAMREC Committee (now SAMCODES Standards Committee or SSC) first published the SAMREC Code in March 2000 and later that year the Johannesburg Stock Exchange (JSE) adopted the Code, requiring compliance with the Code as part of the Listing Requirements for mining companies. The reporting codes are principles-based and therefore not prescriptive. This gives the Competent Person (CP) some discretion in estimating and reporting Mineral Resources and Mineral Reserves, if their approaches and methodologies are reasonable and defendable. The discretion includes assumptions on modifying factors. The 2016 Edition of the SAMREC Code attempted to address the shortcomings associated with the discretion exercised by CPs by introducing the ‘if not, why not’ reporting principle. This principle requires that CPs must report on every aspect as specified in Table I of the SAMREC Code, otherwise they need to provide reasonable justification for not reporting on certain elements (Lomberg and Rupprecht, 2017). This principle makes it more difficult for a CP to be selective when reporting, thus enabling the CP to include all the information that stakeholders, investors, potential investors, and advisors would reasonably expect to find in a public report (Lomberg and Rupprecht, 2017). One of the modifying factors for which a CP needs to exercise reasonable discretion is longterm commodity prices. Long-term commodity prices are one of the most important modifying factors used in Mineral Reserve estimation. Mineral Reserves are the economically mineable portions of Mineral Resources; hence, they provide a better indication of the economic prospects for mining companies in the short to medium term. In order to minimize variations in long-term commodity price assumptions, this research study undertook to estimate an ideal range of longterm commodity prices relative to spot prices. By minimizing variations, it should be possible to improve confidence in the reporting of Mineral Reserves.

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:5B>=AD7@EB,E<BD4 =@>:E7B::B8C='E5>C7@;ECD @;=C:A=CD4E+CD@>A<E"@;@> @; Commodity prices are very important in the Mineral Reserve estimation process. For example, as Appleyard (2001, p. 8) argued, ‘the most sensitive inputs to a mine valuation are those which relate to revenue. While metallurgical recovery directly relates to revenue, the factors which are usually subject to most variation are the price for the commodity’. Conducting a valuation is a critical step in establishing the economic viability of Mineral Reserves. Therefore, the statement by Appleyard (2001) indicates that although all other modifying factors are important in the estimation of Mineral Reserves, commodity price assumptions are the most important modifying factor. However, commodity prices fluctuate over time as dictated by market forces and international reporting codes do not require the revision of Mineral Reserve estimates due to short-term commodity price movements. Another revenue driver, which is critical to the mine valuation process, is grade, as grade (together with production volumes and metallurgical recovery) is a key driver of saleable product volumes to which commodity prices are applied to estimate revenue. The reporting codes provide guidance on the use of long-term price assumptions for Mineral Reserves estimation. The arguments put forward by authors such as Appleyard and Smith (2001), and Baker and Giacomo (2001) are in agreement with these guidelines of the reporting codes. Appleyard and Smith (2001, p. 327) argued that the ‘failure to recognise that a price decrease means that a previous Ore Reserve is now a body of uneconomic mineralisation, can cause a company to continue operating until it is in a position where it can fail’. This argument indicates that in a volatile commodity market, commodity price decreases significantly affect the quantum of Mineral Reserves and can impair the economic viability of a mining operation if they persist in the long term. Baker and Giacomo (2001, p. 669) also argued that commodity prices are critical in that ‘fundamental to the determination of reserves (and to a lesser extent resources) is the underlying metal price assumptions used in the estimation. Contained metal can be highly sensitive to metal price assumptions employed, especially where the margin between cost and revenue is small. At best, a company could consider providing sensitivities of tonnes and grade to commodity price

An empirical long-term commodity price range for Mineral Reserve declarations assumptions. At the very least we are of the view that companies should disclose the price at which the determinations are made (in appropriate currencies)’. This argument indicates that Mineral Reserve estimates are very sensitive to commodity prices. It was therefore, necessary to explore the impact that commodity price assumptions have on Mineral Reserves, but also simultaneously evaluating the impacts of other modifying factors on Mineral Reserves to obtain a balanced view.

+@=#B8B<B4' In order to address the objective set out for this research study, firstly it was necessary to review the annual reports and other publications (available in the public domain) of major South African gold and platinum mining companies to establish their long-term commodity price assumptions. The review focused on the period from 2000 to 2016, which is associated with improved Mineral Resource and Mineral Reserve reporting following the introduction of the SAMREC Code in 2000. The selection of gold and platinum mining was premised on the fact that these two commodities contribute a significant portion of South Africa’s earnings from mining, together making up approximately 44% and 37% of mining income in 2012 and 2015, respectively (Table II). The other major South African commodities, which are coal and iron ore, were not included in this research study since they consist of many different product types and trade differently, usually under contractual agreements. The prices of these other major commodities are thus set in contractual agreements, unlike the more fungible gold and platinum commodities that trade openly in commodity exchange markets where market forces drive commodity spot prices. The second step entailed comparing the long-term commodity price assumptions of the major South African gold and platinum mining companies to actual prices from 2000 to 2016. This comparison provided a basis for establishing the reasonability of the long-term commodity prices used by South Africa’s gold and platinum mining companies in their Mineral Reserve estimations and declarations.

The third step involved evaluating correlations between the long-term commodity price assumptions and the other modifying factors, and the Mineral Reserve declarations of the major South African gold and platinum mining companies from 2000 to 2016 to determine if long-term commodity prices had the most significant impact on the Mineral Reserve estimates. Lastly, it was necessary to make an estimation of the ideal range for the long-term commodity price assumptions for the Mineral Reserve estimates for South African gold and platinum mining companies based on the price range associated with the least number of impairments. The following paragraphs explain the concept of impairment and describe the link between commodity prices and impairments. Commodity price is a primary driver of value for mining companies (MacDiarmid, Tholana, and Musingwini, 2018). Therefore, commodity price fluctuations will affect the company value reported by mining companies in their financial statements. Financial statements typically capture the assets of mining companies as either tangible assets, such as property, plant, and equipment, or intangible assets associated with mining rights. The long-term commodity prices assumed by mining companies have a direct impact on the value of the assets associated with mining rights. The International Accounting Standard 36 (IAS 36) is an accounting standard that provides guidance to companies for the impairment of assets. IAS 36 states that company financial statements should not carry (or record) the assets at a value which is greater than would be recoverable through use or sale. If the carrying value exceeds the recoverable value, then a company should recognize an impairment. IAS 36 does not cover financial assets, inventories, deferred tax, and other assets as other accounting standards provide guidance for their impairment (IFRS Foundation, n.d.). The impairments recorded by mining companies for their non-financial assets are a reliable proxy of the effects of their long-term commodity price assumptions. Where mining companies record the value of their property, plant, and equipment or the value of their mining rights on their financial statements using long-term commodity prices that

Table II

D7B:@E7BD=>C!6=CBDE!'E=#@E A>CB6;E:CDCD4E;@7=B>;ECDE(B6=#E*,>C7AE,B>E9?39EAD8E9?3 E/;B6>7@ E(=A=C;=C7;E (B6=#E*,>C7A2E9?3 . '5@EB,E:CDCD4

9?3

E BD=>C!6=CBD

"E:C<<CBDE

E BD=>C!6=CBD

96 097 66 957 68 061 11 412 10 254 106 555 630 2 398 10 289 8 694 3 330 280 7 822 582 393 361

24.4 17.0 17.3 2.9 2.6 27.1 0.2 0.6 2.6 2.2 0.8 0.1 2.0 0.1 100.0

117 958 63 674 60 699 16 383 17 093 91 099 1 146 2 717 16 584 15 055 5 976 215 10 295 639 419 533

28.1 15.2 14.5 3.9 4.1 21.7 0.3 0.6 4.0 3.6 1.4 0.1 2.5 0.2 100.0

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Coal and lignite Gold and uranium ore Iron ore Chrome ore Manganese ore Platinum group metal ore Dimension stone (granite, marble, slate and sandstone) Limestone and limeworks Other stone quarrying, including stone crushing and clay and sandpits Diamonds (including alluvial diamonds) Other chemical and fertilizer minerals Extraction and evaporation of salt Other mining activities and service activities incidental to mining Other minerals and materials n.e.c. Total

9?39 "E:C<<CBDE

An empirical long-term commodity price range for Mineral Reserve declarations are considerably higher than prevailing spot commodity prices, an impairment may be triggered since the recoverable value of the non-financial assets may be exceeded by the carrying value, in line with the provisions of IAS 36. Impairments can also be recorded when long-term commodity prices are considerably lower than prevailing prices, as the perceived recoverable value (as represented by long-term commodity price assumptions) may be lower than current carrying values. Therefore, impairments can occur under conditions of either excessive optimism or conservatism in long-term commodity price assumptions.

(B6=#E*,>C7ADE4B<8EAD8E5<A=CD6:E:CDCD4E7B:5ADC@; <BD4 =@>:E5>C7@EA;;6:5=CBD; It was necessary to assess the long-term commodity price assumptions relative to spot prices, as this would assist in evaluating how these assumptions influenced Mineral Reserve declarations. An analysis by Barclays Bank plc (2015) concluded that 65% of the South African platinum mining industry was cash flow negative at 2015 spot prices, compared to only 22% of the South African gold mining industry. The better performance by the gold mining sector is probably attributable to a better performing gold price compared to the platinum price pre- and post-2015. However, the quality of investment and operational decisions between the two sectors (based on their long-term commodity price assumptions) could also have partly contributed to differences in their financial performance. Figure 1 indicates that South African gold mining companies adopted more conservative long-term price assumptions for their Mineral Reserve estimations relative to the 2015 gold average spot price, with the exception of Harmony Gold, which assumed a long-term price which was higher than the average spot price (Barclays Bank plc, 2015). As argued earlier, these conservative assumptions would have informed the operational and investment decisions of the South African gold mining companies, which may have contributed to their better cash flow positions compared to platinum mining companies. Figures 2 and 3 illustrate the platinum group metals (PGMs) basket price assumptions relative to average spot prices between 2006 and 2016 for Lonmin plc (Lonmin) and Impala Platinum Holdings Limited (Impala), respectively. A

C46>@E3 $BD4 =@>:E7B::B8C='E5>C7@;E6;@8ECDE+CD@>A<E"@;B6>7@EAD8 +CD@>A<E"@;@> @E@;=C:A=CBD;E,B>E(B6=#E*,>C7ADE4B<8E:CDCD4E7B:5ADC@; CDE9?3 E/;B6>7@ E A>7<A';E AD E5<72E9?3 .

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commodity basket price for a multi-metal deposit is obtained by considering the recoverable proportions of the individual metals found in the deposit and their respective prices, taking into account market considerations. Figure 2 shows that for the period 2008 to 2014, Lonmin consistently assumed long-term PGM basket prices that were considerably higher than the spot prices. This optimistic long-term price outlook would probably have contributed to the poor investment choices made by Lonmin in bringing the K4 shaft into production in 2011. Persistent weak PGM prices rendered the project financially unviable and Lonmin had to place the K4 shaft on care and maintenance in November 2012 (Barclays Bank plc, 2014). Figure 3 indicates that had Impala adopted a long-term PGM basket price closer to spot prices in 2016, its Mineral Reserves would have halved, although the long-term PGM basket price assumed by Impala was 30% above spot prices (Impala, 2016). This shows the significant effect of long-term commodity price assumptions on Impala’s Mineral Reserve estimates. Figure 4 illustrates the 2015 South African gold production cost curve, which explains why South African gold mines were mostly cash flow positive in 2015 (Barclays Bank plc, 2015). It was mostly operations owned by Harmony Gold (indicated as ‘HAR’ in Figure 4) which were in the upper quartile of the cost curve. This is partly the reason why Harmony Gold mines were loss-making in 2015, although this could also have been due to fact that the mines have some of the lowest grades in the South African gold mining sector. The global PGM cost curve in Figure 5

C46>@E9 %&+E!A; @=E5>C7@;E7B:5A>@8E=BE<BD4 =@>:E5>C7@ A;;6:5=CBD;E,B>E$BD:CDE!@= @@DE9??1EAD8E9?3 E/;B6>7@ E A>7<A'; AD E5<72E9?3 .

C46>@E :5A<A ;E+CD@>A<E"@;@> @E%&+E!A; @=E5>C7@;E,B>E9?31E/CD (B6=#E*,>C7ADE>AD8;E5@>EB6D7@.E7B:5A>@8E=BE<BD4 =@>:E!A; @=EAD8 7BD;@D;6;E5>C7@;E/;B6>7@ E :5A<AE%<A=CD6:E B<8CD4;E$C:C=@82E9?31.

An empirical long-term commodity price range for Mineral Reserve declarations

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C46>@E &<B!A<E%&+E7B;=E76> @2ECD7<68CD4E7A5@ 2ECDE9?3 E/;B6>7@ E A>7<A';E AD E5<72E9?3 .

Table III

&B<8EAD8E%&+E5>C7@;E,B>E=#@E5@>CB8E9???E=BE9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E C=7B2ED-8-0E+@=A<A>'2ED-820 6AD8<2ED-8. &B<8E / ( B .

2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000

1 249 1 160 1 266 1 411 1 669 1 572 1 226 973 872 697 604 445 410 364 310 271 279

%<A=CD6:E %A<<A8C6:E / ( B . / ( B . 987 1 052 1 384 1 485 1 549 1 719 1 611 1 205 1 571 1 305 1 142 897 846 692 540 529 545

613 692 803 725 644 733 526 264 352 355 321 202 230 199 337 604 743

"#B8C6:E / ( B .

>C8C6:E / ( E B .

663 919 1 128 1 075 1 270 1 990 2 384 1 442 6 455 6 076 4 442 2 002 904 475 768 1 582 1 993

575 544 556 826 1 070 1 036 642 425 450 447 350 169 186 93 294 413 415

"6=#@DC6:E &B<8E / ( B . / *" B .

indicates that South African platinum mines were mostly cash flow negative in 2014 (Barclays Bank plc, 2014). These observations suggest that there is a relationship between financial performance and long-term commodity price assumptions. Commodity prices fluctuate due to supply and demand dynamics. In the case of gold and PGMs, the period from 2000 to 2008 was marked by a general upward trend in the prices of these precious metals. However, following the 2008 Global Financial Crisis, the precious metal prices that had peaked crashed rapidly in 2009, followed by marginal recoveries in 2010 to 2012. Table III and Figure 6 indicate that precious metal prices continued to experience downward

40 42 58 57 90 110 180 160 110 415 610 87 68 41 40 80 160

18 367 14 798 13 731 13 618 13 699 11 417 8 977 8 249 7 203 4 909 4 093 2 832 2 648 2 753 3 272 2 334 1 937

%<A=CD6:E / *" B .

%A<<A8C6:E / *" B .

14 525 13 421 15 009 14 339 12 720 12 488 11 792 10 211 12 980 9 192 7 733 5 705 5 464 5 233 5 688 4 557 3 780

9 020 8 826 8 710 7 002 5 290 5 328 3 854 2 235 2 909 2 499 2 172 1 282 1 485 1 508 3 548 5 199 5 155

"#B8C6:E >C8C6:E / *" B . / *" B . 9 750 11 731 12 237 10 377 10 425 14 455 17 451 12 218 53 321 42 805 30 086 12 732 5 839 3 596 8 095 13 623 13 832

8 460 6 936 6 027 7 975 8 786 7 527 4 702 3 605 3 718 3 149 2 368 1 078 1 204 704 3 095 3 556 2 880

"6=#@DC6:E / *" B . 588 536 629 550 739 799 1 318 1 356 909 2 924 4 131 553 439 310 422 689 1 110

trends in US dollar terms. The US dollar-denominated gold and PGM prices have shown a general downward trend from 2012 to 2016, while the South African rand- (ZAR)denominated commodity prices have shown a general positive trend from 2000 to 2016. The general depreciation of the rand against the US dollar in periods of weak US dollardenominated commodity prices is the major reason for this . South African gold and platinum mining companies have therefore made their long-term commodity price assumptions in an increasing rand-price environment between 2000 and 2016. South Africa’s major gold mining companies have generally reported on the long-term gold price assumptions VOLUME 119

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@A>

An empirical long-term commodity price range for Mineral Reserve declarations

C46>@E1 &B<8EAD8E%&+E5>C7@;E,B>E=#@E5@>CB8E9???)9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E C=7B2ED-8-0E+@=A<A>'2ED-8-0E 6AD8<2ED-8-.

used in their Mineral Reserve declarations. Table IV shows that by 2001, a year after the introduction of the SAMREC Code, the three major South African gold producers started reporting their long-term gold price assumptions. Table IV and Figure 7 also show that between 2000 and 2016, South African gold mining companies assumed long-term gold prices, in rand per kilogram terms, that were consistently either lower than or in line with prevailing spot prices. This suggests that South African gold mining companies had a lower probability of reporting unprofitable ounces as part of their Mineral Reserves in the period from 2000 to 2016. The abbreviation ‘NR’ means ‘not reported’ and ‘N/A’ means ‘not applicable’. For example, in Table IV, Sibanye Gold did not exist as a gold mining company hence ‘N/A’ is used between 2000 and 2011. The history of South African platinum mining companies as regards reporting long-term price assumptions in Mineral Reserve declaration between 2000 and 2016 has been less detailed than that of the gold mining companies. Implats and Northam began reporting on long-term platinum prices only in 2003, followed by Lonmin in 2005; while Anglo American Platinum (Amplats) did not report its long-term price assumptions in the period under review, as shown in Table V. Figure 8 and Table V show that between 2003 and 2016, South African platinum mining companies generally reported long-term platinum prices that were higher than prevailing spot prices. Implats’ long-term platinum prices have often been the most bullish throughout the period under review, while Northam’s long-term prices have been relatively conservative. Figure 9 shows that for the other PGMs, South African platinum mining companies have generally reported long-term prices higher than spot prices for palladium between 2000 and 2016 and more optimistic long-term prices (relative to spot) for rhodium between 2009 and 2016. However, for gold and iridium, South African platinum mining companies have generally reported long-term prices that were lower than spot prices. The reporting trends of South African platinum mining companies suggest that between 2000 and 2016, they were more likely than gold mining companies to report uneconomic ounces as part of their Mineral Reserves. Persistent reporting of uneconomic ounces can trigger impairment.

B>>@<A=CBD;E!@= @@DE:B8C,'CD4E,A7=B>;EAD8E>@5B>=@8 +CD@>A<E"@;@> @; As discussed earlier, Appleyard (2001) suggested that

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Table IV

* @>A4@E;5B=E4B<8E5>C7@;EAD8E<BD4 =@>:E4B<8E 5>C7@EA;;6:5=CBD;E,>B:E9???E=BE9?31E/;B6>7@; E ( $E+@=A<;E E+CDCD42ED-8-0E*D4<B&B<8E*;#AD=C2 9???)9?310E A>:BD'E&B<82E9???)9?310E&B<8E C@<8;2 9???)9?3120E(C!AD'@E&B<82E9?3 9?31. @A>

(5B=E4B<8E5>C7@

$BD4 =@>:E4B<8E5>C7@EA;;6:5=CBD;E/ *" 4. *D4<B&B<8E A>:BD'E

2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000

&B<8EE

(C!AD'@

/ *" 4.

*;#AD=C

&B<8

C@<8;

&B<8

590 506 475 767 441 465 437 828 440 417 367 051 288 628 265 204 231 573 157 820 131 587 91 065 85 121 88 522 105 194 75 056 62 279

530 000 431 000 398 452 360 252 290 064 269 841 238 028 227 627 200 698 148 536 114 939 86 807 94 764 78 769 94 041 71 262 NR

475 107 450 026 425 064 400 148 339 833 279 888 250 149 224 975 179 883 115 022 104 972 91 996 91 996 92 948 94 845 67 388 60 033

550 000 500 000 400 000 400 000 380 000 310 000 265 000 230 000 150 000 100 000 92 000 92 000 90 000 95 000 95 111 69 992 NR

490 000 430 000 420 000 410 000 380 000 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

NR Not reported N/A Not applicable

C46>@E $BD4 =@>:E4B<8E5>C7@EA;;6:5=CBD;E>@<A=C @E=BE;5B=E5>C7@; ,>B:E9???E=BE9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E*D4<B&B<8 *;#AD=C2E9???)9?310E A>:BD'E&B<82E9???)9?310E&B<8E C@<8;2E9???)9?310 (C!AD'@E&B<82E9?3 )9?31.

An empirical long-term commodity price range for Mineral Reserve declarations Table V

C;=B>C7A<EA @>A4@E;5B=E5<A=CD6:E5>C7@;EAD8E<BD4 =@>:E5<A=CD6:E5>C7@EA;;6:5=CBD;E,>B:E9???E=BE9?31E/;B6>7@; ( $E+@=A<;E E+CDCD42ED-8-0E :5A<AE%<A=CD6:E B<8CD4;2E9???)9?310E B>=#A:E%<A=CD6:2E9???)9?310E$BD:CDE5<72 9???)9?310E*D4<BE*:@>C7ADE%<A=CD6:2E9???)9?310E(C!AD'@2E 9?3 )9?31. @A>

2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000

(5B=E5<A=CD6:E5>C7@E

$BD4 =@>:E5<A=CD6:E5>C7@EA;;6:5=CBD;E/ *" B .

/ *" B .E

:5A<AE%<A=CD6:E B<8CD4;E$C:C=@8

B>=#A:E%<A=CD6:

$BD:CDE%<7

*D4<BE*:@>C7ADE%<A=CD6:

(C!AD'@E%<A=CD6:

14 525 13 421 15 009 14 339 12 720 12 488 11 792 10 211 12 980 9 192 7 733 5 705 5 464 5 233 5 688 4 557 3 780

18 648 17 718 26 760 18 778 16 080 22 560 27 716 20 879 24 083 11 085 7 850 6 700 6 100 6 100 NR NR NR

20 291 19 357 13 455 15 925 11 963 12 866 12 360 9 450 14 391 6 670 5 850 4 950 4 347 5 004 NR NR NR

20 729 18 632 16 785 16 785 14 875 15 200 14 904 14 400 12 000 9 208 7 700 4 940 NR NR NR NR NR

NR NR NR NR NR NR NR NR NR NR NR NR NR NR NR NR NR

15 500 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

NR: Not reported N/A Not applicable

C46>@E $BD4 =@>:E5<A=CD6:E5>C7@EA;;6:5=CBD;E>@<A=C @E=BE;5B=E5>C7@;2E9???)9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E :5A<AE%<A=CD6:E B<8CD4;2E9???) 9?310E B>=#A:E%<A=CD6:2E9???)9?310E$BD:CDE5<72E9???)9?310E*D4<BE*:@>C7ADE%<A=CD6:2E9???)9?310E(C!AD'@2E9?3 )9?31.

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C46>@E $BD4 =@>:E%&+E5>C7@EA;;6:5=CBD;E>@<A=C @E=BE;5B=E5>C7@;E9???)9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E C=7B2ED-8-0E+@=A<A>'2ED-8-0E 6AD8<2ED-8-0 :5A<AE%<A=CD6:E B<8CD4;E/9??? 9?31.0E B>=#A:E%<A=CD6:E/9??? 9?31.0E$BD:CDE5<7E/9??? 9?31.0E*D4<BE*:@>C7ADE%<A=CD6:2E9???)9?310E(C!AD'@2E9?3 )9?31.

An empirical long-term commodity price range for Mineral Reserve declarations Mineral Reserves are most sensitive to commodity prices among the modifying factors. However, other modifying factors are also important for Mineral Reserve estimation. It was therefore necessary to establish the relationship between the Mineral Reserve estimates of South African gold and platinum mining companies and their reported modifying factors. The coefficient of determination (R2) was used to assess the relationship between Mineral Reserve estimates and modifying factors. R2 measures the correlation between two variables by determining how changes in the independent variable affect the dependent variable. R2 uses regression to determine the fit between two sets of variables and indicates the strength of the relationship between the variables. R2 values typically range between 0 and 1, with values closer to 1 indicating a stronger relationship. R2 is measured using the formula:

Regression sum of squares (SSR) R2 = Total sum of squares (SSTO) where SSR is a measure of the sum of the squared differences between the regression line and the mean line of data-points and SSTO is a measure of the squared differences between data-points and their mean line. The process for estimating R2 involves first plotting the two variables whose relationship

is being tested on the y and x axes, with the dependent variable on the y-axis and the independent variable on the xaxis. The best fit or regression line is then fitted to the datapoints, followed by a horizontal line representing the mean of the dependent variable (y). SSR and SSTO are then calculated based on the original data points and points on the mean and regression lines to allow the calculation of R2 (Penn State Eberly College of Science, n.d.).Table VI shows a rating scale for the R2 values for ranking the strengths of the correlations between the Mineral Reserves and modifying factors. Tables VII and VIII provide a summary of R2 values between reported modifying factors and declared Mineral Reserves. Tables VII and VIII show that the reported Mineral Reserves of South African gold and platinum mining companies Table VI

"A=CD4E;7A<@E,B>E"9 A<6@;E,B>E=#@E<CD@A>E>@<A=CBD;#C5 !@= @@DE+CD@>A<E"@;@> @;EAD8E:B8C,'CD4E,A7=B>; /+A;@ B2E9?3 .

B@,,C7C@D=EB,EE8@=@>:CDA=CBDE/"9.

?)E?-

?- )?-1

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Correlation rating Colour

Poor Red

Moderate Orange

Good Green

Table VII 9

" A<6@;E,B>E>@5B>=@8E+CD@>A<E"@;@> @;EAD8E:B8C,'CD4E,A7=B>;E,B>E:A B>E4B<8 :CDCD4E7B:5ADC@;ECDE(B6=#E*,>C7AE!@= @@DE9???EAD8E9?31E/+A;@ B2E9?3 .

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An empirical long-term commodity price range for Mineral Reserve declarations Table VIII 9

" A<6@;E,B>E>@5B>=@8E+CD@>A<E"@;@> @;EAD8E:B8C,'CD4E,A7=B>;E,B>E:A B>E5<A=CD6: :CDCD4E7B:5ADC@;ECDE(B6=#E*,>C7AE!@= @@DE9???EAD8E9?31E/+A;@ B2E9?3 .

:5AC>:@D=;EAD8EC8@A<E>AD4@E,B>E<BD4 =@>: 7B::B8C='E5>C7@; An assessment of the non-financial asset impairments of South African gold and platinum mining companies from 2000 to 2016 gives an indication of the relationship between asset carrying values as represented by long-term commodity prices and recoverable values as represented by prevailing spot prices. The major South African gold mining companies mostly assumed rand-denominated long-term gold prices lower than spot prices between 2000 and 2016. However, these companies recorded significant non-financial asset impairments in this period, as shown in Table IX. This indicates that having a conservative view on long-term prices does not prevent the incurring of impairments. However, Figure 10 shows that when long-term gold price assumptions of the South African gold mining companies were within ¹5% of spot prices, then minimal impairments were recorded. The majority of impairments were recorded outside the ¹5% longterm gold price to spot price range, which suggests that this could be an ideal long-term gold price assumption range in Mineral Reserve declarations for South African gold mining companies. Among South Africa’s major platinum mining companies, Implats and Lonmin have on average assumed long-term platinum prices that were higher than spot prices between

2003 and 2016, as shown in Table X. Northam assumed conservative long-term platinum prices (relative to spot prices) in the earlier part of the period and higher prices in the latter part, resulting in an average variance of zero over the period. Since Amplats did not report its assumed longterm platinum price, a comparison with spot prices could not be undertaken. The major South African platinum mining companies have also recorded significant non-financial asset impairments in the period from 2000 to 2016, with Northam recording the least impairments (Table X). Figure 11 shows that of the several impairments recorded by the major South African platinum mining companies from 2000 to 2016, very few occurred in the Âą5% long-term platinum price to spot price ratio range. The historical long-term commodity prices used for impairment testing purposes would have been in line with the long-term prices used for Mineral Reserve estimation. The use of long-term commodity prices that were within 5% of prevailing spot prices resulted in reporting of ounces that were economically mineable (Mineral Reserves) under the prevailing conditions, as evidenced by the limited number of impairments incurred within this commodity price range. This suggests that this range is ideal for long-term platinum prices to minimize impairments for South African platinum mining companies.

BD7<6;CBD; This study evaluated the impact of modifying factors on the Mineral Reserve estimates reported by the major South African gold and platinum mining companies from 2000 to 2016. Among the modifying factors used in Mineral Reserve VOLUME 119

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between 2000 and 2016 were mostly sensitive to long-term commodity prices as the R2 (on average) values are generally higher than those of the other modifying factors.

An empirical long-term commodity price range for Mineral Reserve declarations Table IX

$BD4 =@>:E4B<8E5>C7@;EAD8EC:5AC>:@D=;E,B>E:A B>E4B<8E:CDCD4E7B:5ADC@;ECDE(B6=#E*,>C7AE!@= @@DE9??? AD8E9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-80E*D4<B&B<8E*;#AD=C2E9???)9?310E A>:BD'E&B<82E9???)9?310 &B<8E C@<8;2E9???)9?310E(C!AD'@E&B<82E9?3 )9?31.

C46>@E3? $BD4 =@>:E4B<8E5>C7@;EAD8E4B<8E:CDCD4E7B:5AD'EC:5AC>:@D=;E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E*D4<B&B<8E*;#AD=C2E9???)9?310E A>:BD' &B<82E9???)9?310E&B<8E C@<8;2E9???)9?310E(C!AD'@E&B<82E9?3 )9?31.

estimation, the Mineral Reserves of South African gold and platinum mining companies appeared to have been most sensitive to changes in long-term gold and platinum prices between 2000 and 2016. The study further noted that South Africa’s major gold mining companies consistently reported the long-term gold prices used in their Mineral Reserves from 2001, a year after the introduction of the SAMREC Code. Mineral Reserve reporting by South Africa’s major platinum mining companies was less detailed than that by gold mining companies. The reporting of long-term platinum prices used in Mineral Reserve estimation by platinum mining companies only started between 2003 and 2005. Amplats did not report its long-term platinum prices over the period between 2000

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and 2016. The South African gold mining companies reported long-term rand-denominated gold prices that were lower than prevailing spot prices over the 2000 to 2016 period. The platinum mining companies, however, reported long-term platinum price assumptions that were mostly higher than prevailing spot prices. Therefore, South African platinum mining companies were most likely to have reported ounces that were uneconomic as part of their Mineral Reserves and would have been more prone to impairments between 2000 and 2016. An assessment of long-term gold and platinum prices relative to historical spot prices and their impact on the number of non-financial asset impairments recorded by South Africa’s gold and platinum mining companies revealed

An empirical long-term commodity price range for Mineral Reserve declarations Table X

$BD4 =@>:E5<A=CD6:E5>C7@;EAD8EC:5AC>:@D=;E,B>E:A B>E5<A=CD6:E:CDCD4E7B:5ADC@;ECDE(B6=#E*,>C7AE!@= @@DE 9???EAD8E9?31E/;B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E :5A<AE%<A=CD6:E B<8CD4;2E9???)9?310E B>=#A:E%<A=CD6:2 9???)9?310E$BD:CDE5<72E9???)9?310E*D4<BE*:@>C7ADE%<A=CD6:2E9???)9?310E(C!AD'@2E9?3 )9?31.

C46>@E33 $BD4 =@>:E5<A=CD6:E5>C7@;EAD8E5<A=CD6:E:CDCD4E7B:5AD'EC:5AC>:@D=;E/B6>7@; E( $E+@=A<;E E+CDCD42ED-8-0E :5A<AE%<A=CD6:E B<8CD4;2E9???) 9?310E B>=#A:E%<A=CD6:2E/9???)9?310E$BD:CDE5<72E9???)9?31.

*7 DB <@84:@D=; The work reported in this paper is part of an MSc research study in the School of Mining Engineering at the University of the Witwatersrand.

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that the least number of impairments were recorded when long-term commodity prices were within ¹5% of prevailing spot prices. This suggests that if South Africa’s gold and platinum mining companies keep their long-term commodity prices within ¹5% of rand-denominated spot prices, it could result in more reliable Mineral Reserve estimates, which could clearly demonstrate economic viability as evidenced by a potentially limited number of impairments incurred in the future.

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http://www.northam.co.za/images/publications/ar/ar_2001/resources_and _reserves.html [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2002. Annual Report 2002. http://www.northam.co.za/downloads/send/39-2002/495-annual-report2002 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2003. Annual Report 2003. http://www.northam.co.za/downloads/send/38-2003/494-annual-report2003 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2004. Annual Report 2004. http://www.northam.co.za/downloads/send/37-ar-2004/493-annualreport-2004 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2005. Annual Report 2005. http://www.northam.co.za/downloads/send/36-2005/492-annual-report2005 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2006. Annual Report 2006. http://www.northam.co.za/downloads/send/35-2006/491-annual-report2006 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2007. Annual Report 2007. http://www.northam.co.za/downloads/send/34-2007/490-annual-report2007 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2008. Annual Report 2008. http//www.northam.co.za/downloads/send/33-2008/489-annual-report2008 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2009. Annual Report 2009. http://www.northam.co.za/downloads/send/32-2009/488-annual-report2009 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2010. Annual Report 2010. http://www.northam.co.za/downloads/send/31-2010/487-annual-report2010 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2011. Annual Integrated Report 2011. http//www.northam.co.za/downloads/send/30-2011/486-annual-report2011 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2012. Annual Integrated Report 2012. http://northam.integrated-report.com/2012/download/NHM-IR2012.pdf [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2013. Annual Integrated Report 2013. http://www.northam.co.za/downloads/send/25-2013/483-annualintegrated-report-2013 [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2014. Annual Integrated Report 2014. http://northam.integrated-report.com/2014/downloads/NHM-IR14.pdf [accessed 14 October 2017]. Northam Platinum Limited. 2015. Annual Integrated Report 2015. http://northam.integrated-report.com/2015/downloads/NHM-IR15.pdf [accessed 14 October 2017]. NORTHAM PLATINUM LIMITED. 2016. Annual Integrated Report 2016. http://northam.integrated-report.com/2016/download/NHM-AIR16.pdf [accessed 14 October 2017]. PENN STATE EBERLY COLLEGE OF SCIENCE, (Not dated). 1.5 - The coefficient of determination, r-squared, https://newonlinecourses.science.psu.edu/ stat501/node/255/ [accessed 14/01/2019]. QUANDL, (Not dated). Ruthenium prices. https://www.quandl.com/data/JOHNMATT/RUTH-Ruthenium-Prices [accessed 10/02/2018]. SIBANYE GOLD LIMITED. 2013. Mineral Resources and Mineral Reserves 2013. http://reports.sibanyegold.co.za/2013/download/SGL-RR13.pdf [accessed 14 October 2017]. SIBANYE GOLD LIMITED. 2014. Mineral Resources and Mineral Reserves 2014. http://reports.sibanyegold.co.za/2014/download/SGL-RR14.pdf [accessed 14 October 2017]. Sibanye Gold Limited. 2015. Mineral Resources and Mineral Reserves 2015. http://reports.sibanyegold.co.za/2015/download/SGL-RR15.pdf [accessed 14 October 2017]. Sibanye Gold Limited. 2016. Mineral Resources and Mineral Reserves Report 2016. http://reports.sibanyegold.co.za/2016/download/SGL-RR16.pdf [Accessed 14 October 2017]. SNL Metals & Mining. (Not dated). Gold price chart. https://www.snl.com/web/client?auth=inherit#industry/priceChart [accessed 10/02/2018]. SNL Metals & Mining. (Not dated). Gold price chart. https://www.snl.com/web/client?auth=inherit#industry/priceChart [accessed 10/02/2018]. STATISTICS SOUTH AFRICA. 2017. Mining Industry 2015 Report no. 20-01-02. http://www.statssa.gov.za/publications/Report-20-01-02/Report-20-01022015.pdf [accessed 25/07/2017]. SAMREC. 2016. South African Mineral Resource Committee. The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (the SAMREC Code). 2016 Edition. http://www.samcode.co.za/codes/category/8-reportingcodes?download=120:samrec N

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a3

Application of geometallurgical modelling to mine planning in a copper-gold mining operation for improving ore quality and mineral processing efficiency by C. Beaumont1 and C. Musingwini2

Kansanshi mine, located in northwestern Zambia, uses conventional openpit mining to produce primarily copper, with gold as a by-product. The ore is extracted from a deposit with highly complex mineralogical suites that are difficult to process, sometimes leading to poor recoveries. The mine was using an ore classification system called ‘Mat-Type’, which classified various ore types into 22 different quality categories, ranging from poor to good quality. Ore with poor recovery was classified as ’poor quality’ ore and directed to long-term stockpiles. Due to a lack of proper integration between the technical and functional areas forming the mine value chain, Mat-Type unintentionally misclassified ore. A geometallurgical analysis was therefore undertaken to review Mat-Type and a new geometallurgical ore classification system, called ‘OXMAT ’, was developed to replace MatType. When implemented, OXMAT eliminated nine of the 22 ore categories and the remaining 13 ore categories could now be more accurately and consistently defined, leading to a significant change in the quantity of ore being mined. OXMAT demonstrated that within a defined test volume of the geological model, waste tonnes could be reduced by 19% while ore tonnes could be increased by 17%. This paper therefore demonstrates the value created through integrated geometallurgical modelling. 9&$8402 acid-soluble copper, total copper, gangue acid consumption, geometallurgical modelling, oxidation ratio, weathering domains.

;35480)*5783 Kansanshi mine is located in northwestern Zambia, approximately 10 km north of the provincial capital Solwezi and about 160 km west of Nchanga mine in the Central African Copperbelt (Broughton and Hitzman, 2002). The location of Kansanshi mine relative to other major copper deposits in the Copperbelt is indicated in Figure 1. Kansanshi mine uses conventional openpit mining methods to exploit copper- and gold-bearing veins hosted in a structurallycontrolled mineral deposit. Mining is carried out in two areas of the deposit, the Northwest and Main pits. A third area of the deposit, called Southeast Dome, is currently not being exploited (Figure 2). Figure 3 illustrates an east-west crosssection of the Kansanshi mine grade control model through the Main Pit area of the deposit, created using assay and logging data

ÂŽ Sulphide ore flotation ÂŽ Mixed ore flotation ÂŽ Oxide ore flotation followed by atmospheric acidic leaching, solvent extraction, and electrowinning (SX-EW).

1 Kansanshi Mining Company PLC, Solwezi, Zambia. 2 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Aug. 2018; revised paper received Oct. 2018.

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to support geostatistical estimations of copper grade continuity. The model has been colourcoded based on total copper (TCu) grade ranges. The model also has mineralogical domains defined by ranges of oxidation intensity and associated mineralogy. The Kansanshi deposit comprises a series of complex mineralogical suites that were formed through the interaction of geological, weathering, and oxidation processes. Geophysical and geochemical processes resulted in the mineralization in the upper portion of the ore deposit being oxidized from a predominantly chalcopyrite, pyrrhotite, and pyrite assemblage to a suite of secondary copper sulphide minerals and primary copper oxide minerals, including bornite, digenite, chalcocite, malachite, tenorite, and chrysocolla. Where large subvertical fault structures cut across the deposit, these processes have affected the host rock and mineralization at much deeper levels. There is a high degree of variability in the composition of the mineral assemblages over short ranges, both laterally across the deposit and vertically within mineralized structures. The mine has a complex metallurgical processing plant which is designed to primarily recover copper from different ore types. In order to treat the different ore types, there are three main metallurgical processes, all of which are served by a dedicated crusher and set of mills. These metallurgical processes are (Beaumont, 2016):

Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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The sulphide flotation plant utilizes a standard flotation method to produce a copper and gold concentrate for smelting. The mixed flotation plant utilizes controlled potential sulphidization (CPS) to improve the recovery of secondary copper sulphide minerals or partially oxidized copper bearing minerals, also to produce a copper- and goldbearing concentrate for smelting. The oxide ore plant utilizes a standard flotation method producing a copper and gold

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concentrate for smelting. Tailings from the oxide ore flotation plant report to an atmospheric leach plant. Copper is leached from the oxide ore using concentrated sulphuric acid, and the pregnant solution is subjected to solvent extraction and electrowinning to produce a final copper cathode product. Gold is also recovered from all metallurgical circuits by centrifugal concentrators. The gold concentrate is upgraded by tabling and smelted to produce dorĂŠ bars.

Application of geometallurgical modelling to mine planning in a copper-gold mining operation

95/80818+& The review of the Mat-Type ore classification system required an understanding of holistically integrating multiple technical and functional areas across the entire mine value chain. It was therefore, important to develop a research methodology that would systematically address each of these areas in a manner that would maintain focus on the relevant data in the context of the overall project goal. To this end, the literature was reviewed for information on how geometallurgical investigations had been undertaken on other mineral deposits. This review demonstrated the need to investigate each aspect of the problem in a systematic manner, starting with the ore deposit characteristics and working forward, through each stage of the mine value chain to establish how each stage impacted consecutive stages. This required mapping out the Mat-Type system as a flow chart to identify each of the decision-making points and the factors or assumptions applied at each of those points. Work could then be carried out to determine if any particular decision point, factor, or assumption was still relevant given the current, more thorough understanding of the deposit and mineral processing system. Through an iterative process, the modified decisions, factors, and assumptions were built into a separate flow chart which was code-named ‘OXMAT ’. This allowed comparisons to be made between the Mat-Type and OXMAT systems to enable the determination of the scale of change caused by each modification as it was applied at each stage of the ore classification system.

98-95611)4+7*61:-8091173+ Bye (2011) described case studies where geometallurgical

modelling has been used in the mining industry to overcome challenges arising from ore variability and to generate significant value-add. Bye (2011) and Chibaya (2013) broadly explained the concept of geometallurgy as a technical approach that integrates geology, mine design and planning, metallurgy, and environmental analysis with the aim of improving the understanding of available mineral resources for better extraction of the minerals of interest. In the case of Kansanshi mine, the Mat-Type system was used for classifying the various ore types, ranging from ‘poor quality’ to ‘good quality’, into 22 different quality categories. It was found that due to poor integration between the technical operating silos that form the mine value chain, the Mat-Type ore classification system did not adequately consider geological, mineralogical, or metallurgical processes in an integrated way.

#94#79$:8.:5/9: 849:*16227.7*65783:2&259The Mat-Type ore classification system was first introduced at Kansanshi mine in 2009. The system was designed with the following objectives (Beaumont, 2016): Ž Separating oxide ore from sulphide ore and categorizing anything that was not sulphide or oxide as mixed float ore Ž Applying a set of cut-off grades to categorize waste, marginal grade, low grade, and high grade for both sulphide ore and oxide ore Ž Improving the ’quality’ of the sulphide ore mill feed product by reducing the acid-soluble copper (ASCu) content to <0.05% Ž Improving the ’quality’ of the mixed float ore product by increasing the quantity of acid-insoluble copper (AICu) in the ore feed Ž Improving the ‘quality’ of the oxide ore product by increasing ASCu grades to >0.6% Ž Managing the consumption of sulphuric acid (H2SO4), which is an important yet costly reagent used in the oxide ore processing circuit Ž Directing any ore that did not conform to the above quality control factors to appropriate long-term stockpiles. Based on the objectives listed above, the Mat-Type ore classification system used a series of ‘IF’ statements to apply fixed grade limits and quality control parameters to determine ore that would be suitable to feed each of the three mineral processing circuits: sulphide flotation, mixed flotation, and oxide leach. Anything that was not suitable for the processing plant would be directed to a long-term stockpile. Figure 4 illustrates the calculation logic of the Mat-Type system, where, each of the 22 hexagonal boxes represents a specific ore type.

/845*8-73+2:8.:5/9: 849:*16227.7*65783 2&259Through a systematic review of the Mat-Type ore classification system a number of key shortcomings were identified (Beaumont, 2016): ÂŽ A lack of ownership and management of the input data, and the absence of a formalized review process to ensure that data inputs remain fit-for-purpose under ever-changing circumstances VOLUME 119

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Some of the mineralogical suites are difficult to process in the metallurgical plant, leading to poor recoveries. A material type ore classification system called ‘Mat-Type’ was used to classify the various ore types based on a number of control parameters that would manage ore quality and ensure efficient and economic recovery of copper and gold. Ore containing mineral assemblages considered to be of ‘poor quality’ were directed to long-term stockpiles, which are scheduled for processing towards the end of the life of mine. In 2015 a smelter was commissioned on the Kansanshi mine site. A by-product of the smelting process is sulphuric acid, which is a reagent that is critical to the oxide ore leaching process. The production of sulphuric acid from the smelter flue gas reduces the impact on local air quality. In addition, the cheap acid produced has a significant impact on the economic viability of some of the ‘poor quality’ oxide ore that was previously considered to be too expensive to process due to its high acid consumption in the leaching process. It was therefore imperative that a review of the Mat-Type ore classification system be undertaken to determine how it could be improved through the application of geometallurgical modelling principles. The ultimate goal of the review was to establish if the value of the ‘poor quality’ ore could be realized sooner through better ore classification so that ore could be fed to the process plant directly rather than being stockpiled and reclaiming it at a later date. Better ore classification could potentially be achieved through an improved geometallurgical understanding that is integrated across the overall mine value chain.

Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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Ž Lack of application of geological data to support the definition of certain ore categories Ž Application of inconsistent ’quality control’ factors at various stages of the ore classification process.

9#918,-935:8.:63:7359+46590:6,,486*/:58:849 *16227.7*65783: The Mat-Type ore classification system was developed by a consultancy firm in conjunction with the technical services team at Kansanshi mine. The key objective of the classification system was to direct the best quality and highest grade ore to the process plant in order to increase copper production. Once the system was developed it was handed over to the technical services team on site and implemented in early 2009. Given the simplistic understanding of the ore deposit at that time, there was no process to rigorously validate the Mat-Type ore classification system and fully understand how it was categorizing ore into the different types. From 2010 to 2013, a large-scale mineral resource definition drilling programme was completed and a number of capital projects to upgrade and increase the throughput capacity of the metallurgical plant were also commissioned. During this time, operating costs, mineral royalties, and commodity prices changed. Following the publication of an updated Mineral Resource and Mineral Reserve statement and Life of Mine Optimization (LOM) plan in December 2012, the Kansanshi mine technical services team was asked to review the cut-off grades applied to the grade control model by the Mat-Type system. During this review it emerged that a number of other data inputs to and assumptions in the

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system also needed to be reviewed in the context of the improved understanding of the geology and mineralization of the deposit and of the metallurgical process. A cross-functional team composed of geologists, mining engineers, and metallurgists was tasked to complete a thorough review of the entire Mat-Type ore classification system to ensure that it remained fit-for-purpose. Detailed technical discussions were promoted between stakeholders across the functional areas of the mine value chain, allowing the free flow of ideas and improved understanding of the orebody, the mining process, and the metallurgical process. This integration of knowledge and understanding between the key technical areas allowed for improved decisionmaking based on all of the available data. A cross-functional approach to the systematic review of available data was critical in supporting the development of a geometallurgical ore classification method.

939461:7-,46*57*6175& The Mat-Type system was extremely complicated to use. Only four out of the 22 available ore categories could be used consistently on a daily basis to feed the process plant. Each day the mine geologist on duty would have to manually redirect ore from the digging face to the crusher based on a visual assessment of the characteristics of the material being mined. These manual re-directions were subjective. The intention of the Mat-Type system was to provide greater control over the quality of the ore reporting to the mineral processing circuit. However, the day-to-day reality was that there were so many quality control factors applied to the system that its consistency in application was questionable.

Application of geometallurgical modelling to mine planning in a copper-gold mining operation /9:7-,6*5:8.:$965/9473+:630:8 7065783:83:5/9: 849: The Mat-Type ore classification system used in situ weathering domains as a primary broad-scale criterion for grouping types of ore. The assumption was that each of the following three groups could be linked to a metallurgical process. ÂŽ Mineralization in highly weathered saprolite zones was considered to be suitable for the oxide process plant ÂŽ Mineralization in saprock zones was considered to be suitable for processing in the mixed ore flotation plant ÂŽ Mineralization contained in the fresh rock (unweathered areas) was considered to be suitable for the sulphide ore flotation plant. However, the weathering process was not responsible for the chemical modification of ore minerals from the original chalcopyrite assemblage to secondary sulphide or primary oxide assemblages. This was the result of oxidation. The intensity of weathering and oxidation across the Kansanshi deposit is highly variable. Typically, in areas of intense weathering there is also intense oxidation. However, it was also observed that in deep-seated fault structures, where weathering was limited and the host lithology still remained largely intact, pods of moderately to highly oxidized mineralization could be found. Through statistical analysis of raw sample data gathered from diamond drilling and reverse circulation drilling programmes a series of oxidation domains was defined. An oxidation ratio was calculated using Equation [1] and applied to the raw geological sample data. Assay values below 0.20% TCu were removed from this analysis to aid in interpretation. These results were plotted on a log histogram as presented in Figure 5. [1] This exercise demonstrated that there were four distinct oxidation domains within the Kansanshi mineral deposit.

These domains could be defined as: ÂŽ Ratio range <0.10: Fresh sulphide mineralization composed of chalcopyrite ÂŽ Ratio range between >0.10 and <0.15: Secondary copper sulphide mineralization composed of bornite, with minor amounts of covellite, digenite, and chalcopyrite ÂŽ Ratio range between >0.15 and <0.50: Secondary copper sulphide mineralization composed of chalcocite, with minor covellite, digenite, and copper oxides such as tenorite and chrysocolla ÂŽ Ratio range >0.50: Oxidized mineralization predominantly composed of malachite and chrysocolla with minor chalcocite, tenorite, cuprite, and native copper. A pit wall mapping campaign undertaken in conjunction with re-logging of selected diamond drill core confirmed the spatial presence of these mineral groupings within the ranges of oxidation ratio specified above. This analysis demonstrated that it would be more appropriate to categorize ore types according to oxidation intensity and associated mineralogy rather than by weathering characteristics, as was then the practice in the Mat-Type ore classification system.

8,,94:49*8#94&:-8091173+:6361&272 Metallurgical recovery modelling for each of the process plant routes had typically been done in isolation owing to the view that each ore product and each mineral processing route was separate. The accepted logic was that higher ASCu grades in the sulphide flotation system led to poor recovery. A fixed cut-off grade of 0.05% ASCu was used to manage the quality of the sulphide ore in order to obtain the maximum copper recovery in the sulphide flotation plant. Ore with an ASCu >0.05% would be directed to the mixed float circuit or a longterm stockpile. Analysis was conducted to compare the recovery profiles of the sulphide and mixed float processes. The results of this analysis are presented in Figure 6. The analysis demonstrated that while ASCu values >0.05% reduced

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Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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recovery in the sulphide circuit, the mixed float process still returned a lower recovery for this ore type. Therefore, using a fixed ASCu cut-off grade of 0.05% to direct sulphide ore to the mixed float process was not reliable. From the analysis and understanding of the oxidation domains, discussions between the geology and metallurgical teams revealed that each of the mineral groupings identified was more or less amenable to a specific mineral processing method employed at the Kansanshi metallurgical plant. A hypothesis was presented stating that copper recovery in each mineral processing circuit is a function of the ratio of ASCu to TCu grade. To investigate this hypothesis, head grade and recovery data for the sulphide and mixed float processing circuits spanning a four-year period was analysed to compare the recovery profiles for each circuit as a function of the calculated oxidation ratio (Figure 7). Through the recovery modelling process three key trends were identified: ÂŽ Ore with an oxidation ratio <10% would produce more copper through the sulphide plant compared to the mixed and oxide plants ÂŽ Ore with an oxidation ratio of >10% but <36% would be most effectively treated in the mixed float plant ÂŽ Ore with an oxidation ratio >36% would produce the highest recovery through the oxide plant.

The cross-over points at 10% and 36% oxidation ratio, as indicated in Figure 7, for the metallurgical recovery models were well aligned with the same ratio groupings derived from the analysis of raw geological sample data. It was found that the two separate analyses of separate data sources were aligning well to support the overall hypothesis that oxidation was the key factor in ore classification and metallurgical efficiency. Further analysis of the available metallurgical data demonstrated two significant opportunities to recover additional copper from the mixed float and oxide ore. The first required a modification to the oxide flotation circuit, incorporating CPS technology to increase the recovery of minor secondary sulphide minerals such as chalcocite and covellite. The second required the mixed float ore tailings to be directed through the oxide leach circuit. A comparison of the oxidation ratio of mixed float mill head grade (prior to flotation) with the oxidation ratio of the mixed float tailings (after flotation) is presented in Figure 8. It was found that as copper-bearing sulphide minerals were recovered in the flotation process, the ASCu/TCu ratio of the remaining tailings was increased, indicating that the minerals remaining in the mixed float tailings were more amenable to leaching. By directing the mixed float tailings through the oxide leach circuit the oxidized minerals could be leached and processed through to the SX-EW circuit.

63+)9:6*70:*832)-,5783 Across the industry, managing the consumption of sulphuric acid in an oxide leach plant has always been a serious issue for most metallurgical operations. Typically at Kansanshi the highest grade oxide ore was found in close proximity to a lithological unit termed the ‘upper marble’, composed of calcium carbonate (CaCO3) which is a gangue acidconsuming (GAC) component of the oxide ore. To limit the acid-consuming properties of oxide ore fed to the metallurgical plant, a limit of 50 kg/t GAC was applied by the Mat-Type ore classification system. With an abundance of cheap sulphuric acid being generated from the smelting process, the oxide ore processing strategy could be changed because acid consumption had become less of a constraint. It was therefore important to understand the geological range of

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Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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ÂŽ High-GAC domain, >110 kg/t, dominated by marble ÂŽ Low-GAC domain, <110 kg/t, dominated by clastic rocks composed of schists and phyllites. This analysis demonstrated that the limit of 50 kg/t GAC currently applied to oxide ore in the Mat-Type classification system was suboptimal, as it lay in the middle of one population of the GAC data. By raising the oxide ore GAC limit from 50 kg/t to 110 kg/t the acid consumption could be more appropriately managed through the application of geological controls. The net effect of this was that a significant quantity of oxide ore was now available for processing that would have previously been directed to stockpiles. With this increased quantity of oxide ore at a higher GAC content, a larger quantity of acid produced from the smelting process could be utilized.

*70:58:849:46578 Further investigation into the application of GAC management methods in the Mat-Type ore classification system revealed that it was possible to take advantage of the acid to ore ratio (AOR) factor. The AOR is a metallurgical factor developed to further define and manage the gangue acid consumption in the oxide ore metallurgical process, and is defined by Equation [2]. [2]

Equation [2] assumes that oxide ore with a high ASCu grade and a GAC greater than 50 kg/t could still be fed to the oxide process plant if the AOR was below a nominal value set at five. However, if the AOR was above five the Mat-Type ore classification system indicated that the ore should instead be fed to the mixed float circuit. An analysis of GAC and ASCu grade data demonstrated that there was no statistical relationship between GAC and ASCu grade. Furthermore, the application of the AOR at various stages of the Mat-Type ore classification system was directing oxide ore to either the wrong metallurgical process (mixed float) or to long-term stockpiles. The unintentional effect of this was to artificially increase the ASCu grade of the mixed float ore feed by changing the dominant mineralogical suite from secondary sulphide minerals to oxide minerals. Oxide minerals are less amenable to the CPS process than secondary sulphide minerals, thus while the factor was assisting in reducing the GAC content of the oxide circuit, there was a negative impact on the recovery of copper in the mixed float circuit.

7 90:.1865: ; ):46578 After the implementation of the Mat-Type ore classification system in 2009 a drop in the recovery performance of the mixed float process was noted. Metallurgical test work by Paquot (2009) had demonstrated that the recovery in the mixed float process could be improved by controlling the quantity of AICu in the feed. AICu is defined by Equation [3]: [3] AICu is effectively the portion of a primary or secondary sulphide mineral that has not been fully oxidized to become acid soluble. To improve and maintain recovery of the mixed float ore the ‘mixed float acid insoluble copper ratio’ (MF_AICu_Ratio) was developed as given by Equation [4]. VOLUME 119

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gangue acid-consumers occurring naturally in the deposit and how best to control the feed of high-GAC ore types to the oxide circuit. A review of the available geological assay data showed a highly skewed bimodal population of GAC data across the deposit (Figure 9). There are two distinct domains of GACbearing ore within the Kansanshi deposit. These can be geologically defined as:

Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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[4] The work by Paquot (2009) had demonstrated that a MF_AICu_Ratio of >70% in the mixed float ore feed would improve the recovery. A revision was made to the Mat-Type ore classification system in 2010 to apply the MF_AICu_Ratio to the classification criteria of mixed float ore. Mixed float ore that did not meet the MF_AICu_Ratio criterion was directed to long-term stockpiles. Despite the MF_AICu_Ratio being determined through a logical analysis of available metallurgical data, its application to the Mat-Type ore classification system was done in isolation from other factors applied to the same ore types. Therefore, it did not account for the varied range of mineralogy in the mixed float ore feed, nor the misclassification and redirection of high-GAC oxide ore to the mixed float circuit through the application of the AOR factor. While the MF_AICu_Ratio minimized the impact of poorly classified oxide ore entering the mixed float process, it was a reactive solution that did not prevent oxide ore from being incorrectly classified at source in the pit.

)5 8..:+46092 During the review of the Mat-Type ore classification system it was found that a series of fixed grade limits was applied to each of the 22 ore types as part of the process of determining whether the ore should be processed or stockpiled. These grade limits were linked to the perceived capabilities of the metallurgical process plant at a specific point in time. A mechanism was required to ensure that strategic decisions taken in the long-term planning environment would translate to short-term mine planning and execution. This would allow for economic cut-off grades determined in the Mineral Resource to Mineral Reserve conversion process to be updated as and when they changed, thus gaining alignment between all of the different technical facets of the business.

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9#918,-935:8.:6:39$:849:*16227.7*65783:2&259 6290:83:+98-95611)4+7*61:-8091173+ As a result of the analysis of oxidation ratios, plant recovery models, gangue acid consumption, AOR, MF_AICu_Ratio, and cut-off grades it was necessary to overhaul the Mat-Type ore classification system in order to develop a more optimal ore classification methodology. The ‘OXMAT’ system was developed with the goal of applying it to the ore control process at Kansanshi to supersede the Mat-Type system. A flow chart detailing the newly developed ‘OXMAT’ ore categorization methodology is provided in Figure 10. The first step in revising the ore classification method and creating the OXMAT system was to group ore into representative geological domains. Oxidation ratios were calculated based on the estimated TCu and ASCu block grades of the geological model. Ratio ranges were then applied to the grade control block model, creating three main mineralization categories as follows: Ž Sulphide float ore: oxidation ratio <0.1, fresh, unweathered, un-oxidized chalcopyrite Ž Mixed float ore: oxidation ratio >0.1 and <0.5, secondary sulphides minerals, partial or weak weathering, localized moderate oxidation Ž Oxide leach ore: oxidation ratio >0.5, moderate to intense weathering, moderate to intense oxidation. The second step was to define more appropriate cut-off grades for each of the three categories. These cut-off grades would be required for separating waste from marginal ore, low-grade ore, and high-grade ore in each of the three broad ore categories. The cut-off grades derived from the Mineral Resource to Mineral Reserve conversion process were applied to the new grade-control ore classification system. This brought alignment to the various levels of business planning and assisted in day-to-day ore production and blending requirements. Critical to the management of cut-off grades was the development of a formalized periodic review process

Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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92)152:.48-:6,,1&73+:5/9:49#7290:849:*16227.7*65783 2&259Once all the revisions had been incorporated in the ore classification methodology it looked very different to the original Mat-Type classification system. A total of nine ore categories could be completely removed, making the new OXMAT system far more manageable and easy to understand. The remaining 13 ore categories were redefined to classify ore more optimally at source, for feeding to the metallurgical process plant. A testing process was implemented to compare the ore and waste volumes defined by the two very different systems. The OXMAT ore classification system was applied to a copy of the geological model in conjunction with the MatType system. Using survey data, the volume of material

mined from the deposit between January 2013 and December 2015 was analysed to determine the quantities of ore and waste classified by Mat-Type and by OXMAT. Figures 11 and 12 present the results of this analysis, which indicated the following. Ž Tonnes of waste defined by the OXMAT method were 19% less than the tons of waste defined by ‘Mat-Type’ Ž Within the volume analysed only 8% of the material was considered as ore suitable for direct feed to the process plant using the Mat-Type method. However, when it was classified using the OXMAT method, ore suitable for direct feed to the process plant increased by 25% Ž Despite the substantial increase in the quantity of ore available for direct feed to the process plant using the OXMAT system, the quantity of ore directed to longterm stockpiles remained nearly the same as with classification using the Mat-Type method. Through the application of the OXMAT ore classification method, further possible benefits were identified: Ž Less material would need to be mined to provide the same quantity of ore feed to the process plant, leading to the opportunity to reduce mining costs Ž The ore feed was now consistently classified, thus removing the need for manual re-directions to be made on a daily basis Ž Improved ore quality for all three metallurgical circuits would lead to higher copper recoveries, increasing the value derived from the deposit. VOLUME 119

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that would allow necessary adjustments to the cut-off grades based on changing economic parameters. The final development in the new OXMAT ore classification methodology was to place an appropriate limit on gangue acid consumption in the oxide leach plant by using a geological limit that distinguished ore within the marble lithology from ore hosted in the clastic lithologies. Ore with a GAC >110 kg/t would be directed to the long-term stockpiles and recovered as and when there was sufficient acid available from the on-site smelter, while oxide ore with a GAC <110 kg/t was directed straight to the oxide float/ leach plant and processed. The resulting ore classification method now contained only 13 categories compared to the 22 categories originally created in the Mat-Type system.

Application of geometallurgical modelling to mine planning in a copper-gold mining operation

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mineral processing efficiencies. It is therefore important to have a good understanding of the variability in ore characteristics. Geometallurgical modelling is an integrated approach that assists in overcoming these challenges and generates value-add for an operation. In this paper we presented a case study on geometallurgical modelling for a copper mine producing by-product gold, and provided a review of an ore classification system that was modified through geometallurgical modelling to improve process efficiencies and unlock value. The ore classification system called Mat-Type initially classified the various ore types ranging from ‘poor quality’ to ‘good quality’ into 22 different quality categories. Due to a lack of proper integration between the technical operating silos that formed the mine value chain, the Mat-Type system did not adequately consider geological, mineralogical, or metallurgical processes in an integrated way. The system was modified to produce a new geometallurgical ore classification system called OXMAT. Through the application of geometallurgical modelling, nine of the 22 ore categories in the original Mat-Type ore classification system were eliminated. The remaining 13 ore categories were re-defined in the OXMAT system in order to more appropriately classify the ore. By determining a more accurate and consistent set of ore definitions, it was possible to significantly increase the quantity of ore available for direct feed to the process plant, thus demonstrating the value-creating opportunities that can arise out of proper implementation of geometallurgical modelling.

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Within the volume of ore that was analysed the change in classification method did not directly reduce the quantity of ore going to long-term stockpiles, as was originally intended. It is, however, reasonable to assume that less ore would be directed to long-term stockpiles in the future if the overall annual mining volume is reduced. The implementation of a geometallurgical methodology for ore classification and mine planning promoted the development and integration of knowledge between different technical disciplines across the mine value chain. Problems and challenges that arise during day-to-day operations can now be quickly understood and discussed within a large multi-disciplinary team, leading to effective decision-making which maximizes the flow of value along the mine value chain. Through this more collaborative approach that crosses traditional technical boundaries, the OXMAT system assists in achieving a more effective mine planning process, informed by geometallurgical analysis of the available data and centred on the overall mine value chain from the geological resource through mining, mineral processing, and smelting.

83*1)27832 Variable ore characteristics pose a challenge in the mining industry as they have a direct impact on the mine-to-mill design, planning, and operation by reducing the mining and

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The work reported in this paper is part of an MSc research study by the first author undertaken in the School of Mining Engineering at the University of the Witwatersrand. Permission by the management of First Quantum Minerals Ltd to publish the paper is greatly acknowledged.

9.9493*92 BEAUMONT, C. 2016. A geo-metallurgical strategy for improving ore quality and mineral processing efficiency at Kansanshi mine in Zambia. MSc Research Report, University of the Witwatersrand. BEAUMONT, C. 2017. Analysis of oxidation ratios in RC and diamond drilling data, a revision in the context of the current OXMAT ore classification. Internal company report. First Quantum Minerals Ltd., October 2017. BROUGHTON, D. and HITZMAN, M. 2002. Exploration history and geology of the Kansanshi copper (minor gold) deposit Zambia. Economic Geology Special Publication no. 9. pp. 141–153. Society of Economic Geologists. BYE. A.R., 2011. Case studies demonstrating value from geometallurgy initiatives. Proceedings of GeoMet 2011: The First AusIMM International Geometallurgy Conference, Brisbane, QLD, Australia, 5-11 September, 2011. David, D. (ed.), Australasian Institute of Mining and Metallurgy, Melbourne. pp.9–30. CHIBAYA, A. 2013. Geo-metallurgical analysis - Implications on operating flexibility (A case for geometallurgy for Orapa A/K1 deposit). MSc Research Report, University of the Witwatersrand. DAWSON, P. and BEAUMONT, C. 2014. Defining variable recovery models using oxidation ratios. Internal company report. First Quantum Minerals Ltd. December 2014. DAWSON, P. and BEAUMONT, C. 2017. A review of Mixed Float tails processing and its impact on oxide leach efficiency. Internal company report. First Quantum Minerals Ltd. August 2017. OGILVIE, J. 2014. Spatial analysis of GAC assay data for the Kansanshi deposit. Internal company report. First Quantum Minerals Ltd., November 2014. PAQUOT, F. 2009. The influence of sulphidisation, the addition of dispersant and de-sliming of oxidised ore, on the recovery of the AICu component of mixed float ores. Internal company report. First Quantum Minerals Ltd., May 2009. SILLITOE, R.H., PERELLO, J., CREASER, R.A., WILTON, J., WILSON, A.J., and DAWBORN T. 2017. Age of the Zambian Copperbelt. Mineralium Deposita, vol. 52, no. 8. pp. 1245–1268, WOOD, D. 2011. Geological mapping and interpretation of the Kansanshi Dome, Solwezi, North-western Province, Zambia. Internal company report. First Quantum Minerals Ltd. October 2011. N

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A spatial mine-to-plan compliance approach to improve alignment of shortand long-term mine planning at open pit mines by T.J. Otto1 and C. Musingwini2 term planning processes (McCarthy, 2015). According to Steffen (1997), most open pit mining operations follow a systematic and disciplined mine planning process involving three distinct levels of mine planning in developing the Mineral Reserves. These levels are the:

'% ) The objective of any mining company is to sustainably maximize the net present value (NPV) throughout the life-of-mine (LOM). The expected NPV over the LOM is calculated based on a mine plan, yet the actual value realized by the mining company depends greatly on the level of intertemporal execution against the mine plan. Consequently, a major key performance indicator (KPI) that is often overlooked in open pit mining, due to a greater focus on temporal KPIs, is the degree of spatial compliance to the mine plan. Such focus sometimes leads to short-term actions being detrimental to long-term plans. In this paper we describe the development and implementation of a spatial mine-to-plan compliance reconciliation approach for an open pit mine. Application to a case study open pit iron ore mine in South Africa indicated a significant improvement in the spatial mine-to-plan compliance, from 69% to 94% over a three-year period from 2014 to 2016. These results indicate that implementation of the proposed spatial mine-to-plan compliance reconciliation approach can contribute positively to improving the level of spatial execution and assists in improving the alignment between short-term, medium-term, and LOM plans.

ÂŽ LOM plan ÂŽ Long-term plan (LTP), which follows from the LOM ÂŽ Short-term plan (STP), which in turn follows from the LTP.

,'"$% !")%' The objective of any mining company is to maximize the net present value (NPV) throughout the life of mine (LOM). However, this objective needs to be fulfilled in a sustainable manner that considers the needs of various stakeholders, including employees, shareholders, and local communities where the mines operate. The expected NPV is calculated using a mine plan as a basis. Mine planning broadly involves identifying a strategy to exploit the mineral resource in a way that maximizes value at an acceptable level of risk throughout the LOM (Tholana and Neingo, 2016). The mine planning process is a wellrecognized component in the mining value chain of an open pit mine. Figure 1 reflects the relative position of mine planning within a typical open-pit mining value chain from the mineral resource to the market. The mine planning process begins with a sequence of increasingly detailed feasibility studies and continues throughout the life of the mine through several long-term and short

1 Anglo American Kumba Iron Ore, Corporate Office, Centurion South Africa. 2 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Sep. 2018.

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-( %$ intertemporal compliance, spatial compliance, short-term plan, long-term plan, LOM plan.

Each of these stages of planning represents different levels of risk and has different objectives. The planning horizons for the different stages are also broadly referred to as the strategic, tactical, and operational planning levels (Tholana and Neingo, 2016). Literature on mine planning generally focuses on ways of improving mine plans by incorporating the analysis and evaluation of risk into the planning process, thereby improving the expected NPV and/or riskadjusted NPV of the mine. The literature deals mainly with the strategic level of mine planning. Various studies have been devoted to improvements in the mine planning process with the aim of developing optimal mine plans. These include advances in mine planning software and related systems that are employed in mine planning (Gurgenli, 2011) and the application of orebody flexibility to ensure maximum mineral asset utilization in underground mining applications (Tholana and Neingo, 2016), as well as the progression of stochastic mine planning techniques. Where stochastic approaches have been applied, they have proven to be able to add higher value in production schedules – in the order of 25%,

A spatial mine-to-plan compliance approach to improve alignment

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with consequent improvements of up to 30% in the NPV compared to conventional approaches to mine planning (Dimitrakopoulos, 2011). Despite significant improvements in the mine planning process and the resultant mine plans, open pit mining remains a high-risk business (Sabour and Dimitrakopoulos, 2008). There are numerous sources of risk, and these can be grouped as: ÂŽ External uncertainties such as volatile commodity prices and exchange rates ÂŽ Internal uncertainties such as geology, chemical quality predictions, operational uncertainties, and input cost. Open pit mines are dynamic environments that are characterized by a continuous displacement of the working faces both in time and space (Halatchev and Dimitrakopoulos, 2003). The existence of risk (and opportunity) in open pit mining is evident when considering the differences between the expected and actual outcomes achieved by mining projects and operating mines. This was noted from benchmark studies considering mainly Australian mining operations. A survey of 48 Australian mining projects showed that the actual tonnages extracted for 46% of these mines were more than 20% higher or lower than those expected (Sabour and Dimitrakopoulos, 2008). Benchmark studies of 21 open pit mines by AMC Consultants evaluating the monthly variability between the mine budget and actual production over a 12-month period showed an average variability of 29% for ore tonnes mined (McCarthy, 2015). Ramazan and Dimitrakopoulos (2004) indicated that about 60% of the mines surveyed had an average rate of production in the early years of operation that was less than 70% of the designed production capacity. The failure to have actual outcomes close to or the same as planned targets is widely acknowledged in the mining industry as a top risk (Musingwini, 2016). Ultimately, the actual value realized by the mining company (as opposed to the expected value) is not only dependent on the quality and integrity of the mine plans; it also depends greatly on the level of execution against the mine plan. The effective execution of the agreed-upon mine plan is therefore critical to the successful operation of an open pit mine (Hall and Hall, 2006). In order to sustainably

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operate a large open pit mine two major areas need to be managed; namely the quality and integrity of the mine planning process and the execution of the mine plan. The importance of execution against the mine plan is also highlighted by Musingwini (2016); operations are measured against planned targets in order to evaluate operational performance. The level of execution or compliance against a mine plan can be measured in two ways – time-based or temporal measurements, and area-based or spatial measurements. The measurement of performance against temporal metrics is common practice in the open pit mining industry and these time-based targets are typically short-term focused. The measurement of performance against spatial metrics is equally important in an open pit mine. These area-based metrics provide insight into the long-term aspects of execution against the mine plan. The development and implementation of a spatial mine-to-plan compliance process is important to ensure: Ž Long-term sustainable ore supply Ž That key issues adversely impacting on mine production are identified (e.g. ramp establishment, dewatering, drill-and-blast quality, pre-split and buffer blocks) Ž The achievement of planned mining flexibility. Previous studies on spatial mine-to-plan compliance have been done in mining environments other than open pit mining and have mainly focused on short-term plans. For example, Angelov and Naidoo (2010) described a twodimensional (2D) spatial mine-to-plan compliance approach that determines the degree of deviation from the original short-term mine plans in underground coal mines by comparing actual mined areas to initially planned mining areas. Morely and Arvidson (2017) proposed a spatial mineto-plan compliance approach that determines spatial comparison of volumes measured monthly against the shortterm plan. The purpose of this paper is to describe the development and implementation of a standardized spatial mine-to-plan compliance reconciliation approach at an open pit mine by comparing planned and actual tonnages in three dimensions (3D) instead of just comparing volumes and aligning short-term plan execution to long-term plans.

A spatial mine-to-plan compliance approach to improve alignment To achieve the goal of creating long-term value, mining companies (with operational mines) typically develop shortterm measures of value against which operational and financial performance are tracked. As a result, many timebased or temporal key performance indicators (KPIs) are developed and tracked by mining companies. One grouping of KPIs is concerned with reconciling planned activities with what actually happened. Conducting reconciliation across the mining value chain ensures that the mining process occurs in a progressively more predictable manner (Bester et al., 2016). Reconciliation in the open pit mining industry is a broad subject and is usually associated with comparing the results of planned versus actual activities for temporal KPIs such as the waste production, plant production, and chemical and physical qualities of the product for the specific time period under review. The main reason for undertaking these reconciliations is performance tracking with the purpose of operational improvement. Consequently, a major KPI that is often overlooked in the open pit mining industry is the level of spatial compliance to the mine plan, which measures how well the mine plan is executed spatially. This requires considering not only what is being mined and when, but also where the mining activities are taking place in the open pit. This is important because mining in areas outside of the planned areas can affect future waste stripping or advance into future production areas too early. Hall and Hall (2015) stated that the major focus of highperforming open pit mines should be on delivering the operational and financial targets according to the tactical mine plan, without compromising the mine’s ability to deliver on its future KPIs. The difference (if any) between planned and actual performance should therefore be regularly measured and monitored. The monitoring of compliance with the mine plan should also consider spatial aspects. It is important to identify where material has been mined to ensure that the progress of mine development is adequate to meet long-term strategic targets such as timely access to future ore (Hall and Hall, 2015). An unbalanced focus on short-term temporal KPIs such as productivity improvements or unit cost reduction at the expense of spatial compliance with the mine plan could lead to the prioritization of inappropriate mining activities in the wrong mining areas. The development and implementation of a spatial mineto-plan compliance reconciliation approach is of critical importance in an open pit mine, as it ensures that short-term KPIs are achieved by identifying key factors negatively impacting on mine production while giving adequate consideration to long-term KPIs such as sustainable ore supply and the achievement of planned mining flexibility. An evaluation of the operational performance of an open pit mine that considers only temporal KPIs, such as monthly ore and waste tonnages mined, can provide a false sense of security to mine management as performance against these KPIs could be positive while mining activities are not occurring in the correct spatial areas in the open pit, to the detriment of long-term KPIs such as timely exposure of future ore. Spatial mine-to-plan compliance is therefore a critical KPI as it provides a bridge between the short-term

KPIs that mining companies track on a weekly, monthly, quarterly, and annual basis and the long-term value expected by (and often promised to) stakeholders.

)'( "% #&'*!% #)&'!(* % (# The spatial mine-to-plan compliance at an open pit mine can be measured effectively when the following major components are in place: a quality mine plan, a process of capturing and analysing actual mining progress spatially, and definitions or categories for reconciling the actual areas mined with the planned mining areas. These aspects are explained in the following sections.

As a start, it is important to define ‘the plan’ against which spatial mine-to-plan compliance is measured. The tactical horizon of mine planning normally involves the development of annual tactical plans (also referred to as budget plans, business plans, or medium-term plans). These plans are successors of feasibility studies and LOM plans, and predecessors of operation plans (Phillis and Gumede, 2011). These annual production schedules are a critical factor in the planning of open pit mines. They deal with the effective management of a mine’s production and cash flows in the order of millions of dollars (Ramazan and Dimitrakopoulos, 2004). The reason for focusing on the tactical mine plan is that this plan is typically the basis for annual budgets and, as such, it sets annual operational and financial performance targets. Management is expected to deliver against annual mine plans – these plans are a form of contract between a mine and its stakeholders (Phillis and Gumede, 2011). The tactical plan provides the bridge between strategic mine planning and operational planning and translates the expected long-term value (often expressed as NPV) into operational and financial targets such as monthly production tonnes, product quality, unit cost, and revenue projections. Importantly, it remains a mining plan and therefore also prescribes the spatial development of the open pit mine. For these reasons, the annual tactical plan produced for an open pit mine is used as the basis for tracking the spatial mine-to-plan compliance. The minimum output required from the tactical plan is stage plans for the agreed-upon measuring period (typically monthly) per mining area (or pushback) and mining bench. The stage plans are typically provided in the format of a digital terrain model (DTM) indicating the areas planned for mining per month. In addition, ore and waste tonnages for these areas are calculated from the applicable mining model, which contains the appropriate geological information for these areas. These outputs are typically generated using mine scheduling software and a suitable general mining package (GMP).

In an open pit mine the survey department is typically responsible for capturing and analysing actual spatial mining progress. Best practice in data acquisition involves the use of laser scanners to conduct month-end ex-pit production measuring surveys. Riegl and Maptek scanners exceed the minimum requirements for the spatial accuracy of surveyed data-points and are currently considered industry-leading technologies. Scanners and associated global positioning VOLUME 119

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A spatial mine-to-plan compliance approach to improve alignment envisaged in the tactical plan and the incremental (or monthly) areas within the bigger tactical plan. This introduces the concepts of in-sequence and out-of-sequence mining as subsets of mining within the tactical plan. Actual mining is deemed to be in sequence when it took place within the areas indicated in the tactical plan up to the date of measurement. Actual mining is deemed to be out of sequence when it took place within the areas indicated in the tactical plan but after the date of measurement. For example, for the reconciliation process done at the end of month six of a specific year, all actual mining activities that took place in areas planned for months one to six are deemed in sequence, while actual mining activities that took place in areas planned for months seven to twelve are deemed out of sequence. The categories used for reporting spatial mine-toplan compliance are described in Table I and illustrated in Figure 2. Compliance measurement per category is calculated as a tonnage compliance and expressed as a percentage of planned tonnes for ore, waste, and total material mined.

system (GPS) equipment are checked and calibrated on a weekly basis to ensure their spatial accuracy is within 50 mm. Although Total Stations are also still utilized, laser scanning technology is preferred due to its speed, accuracy, density of survey points captured, and the ability to work effortlessly in a 3D environment. The laser scanning process involves the continuous scanning of open pit working areas during the production month and again at month-end. The working areas are typically scanned in a ‘stop-and-go’ mode from three positions to ensure accurate spatial registration of the scanned data as well as complete coverage of the working areas. These working area scans are then registered and a report is generated which states the spatial accuracy achieved for the scans. A survey DTM of the pit is then generated by combining all the individual scans. The survey DTM represents the actual pit surface at the time of measurement. By comparing the latest survey DTM with the DTM developed at the start of the measuring period (month), areas where mining took place during the month under evaluation, can be identified.

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The components of the mine-to-plan compliance model described above provide the basis from which the reconciliation process can be managed. The detailed steps of the reconciliation process are presented in the following sections.

The spatial data can now be analysed in order to reconcile the actual mined areas with the planned areas in 3D space. Actual and planned mining areas are then divided into three categories: ‘mined in plan’, ‘mined out of plan’, and the resultant ‘planned areas not mined’. The reconciliation is done by considering both the total (or annual) area

The spatial compliance against the tactical mine plan is

Table I

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Areas that were planned to be mined and were mined in sequence. Areas that were planned to be mined but were mined out of sequence. Areas that were mined completely outside of the tactical plan. Areas that were planned to be mined but not mined.

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In addition, the monthly trend provides further insight into month-on-month changes in compliance to the tactical mine plan. The major implication of below-targeted spatial mine-to-plan compliance is the fact that planned areas are not mined (i.e. mining capacity is not applied spatially in line with the tactical plan). This impacts negatively on the open pit mine’s ability to achieve operational (production and product quality) and financial targets. The reporting is further enhanced with plans per mining area illustrating the reconciliation between areas planned and areas where actual mining took place (Figure 4) by using the categories in Figure 2. Although these plans allow for further analysis of the areas actually mined, the real value lies in evaluating the areas that were planned but not mined and

considering how these areas should be prioritized in future operational plans. The plans also assist in understanding the reasons for adverse outcomes with the aim to improve future mine-to-plan compliance.

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reported on a monthly basis using the categories described in Table I. The reporting format consists of graphs in which the spatial compliance is expressed as a percentage of planned tonnes mined. This allows for evaluation of the year-to-date compliance as well as the assessment of trends in the spatial mine-to-plan compliance over a given period of time. Figure 3 illustrates an example of the reporting of spatial mine-to-plan compliance on a cumulative monthly basis. From the information provided in Figure 3 the following interpretation can be made using the mine-to-plan compliance data. 1. The actual tonnes mined for the seven month period is 102% of the planned tonnes 2. 79% of the mining took place in the areas planned to date (mined in sequence) 3. 14% of actual mining took place within the areas planned in the tactical plan but after the date of measurement (mined out of sequence) 4. 9% of mining took place outside of the tactical plan (mined out of plan).

A spatial mine-to-plan compliance approach to improve alignment It is important to note that the mine-to-plan compliance reconciliation results should not be considered in isolation. These results should be included in a consolidated performance dashboard for an open pit mine that provides a holistic view of the mine’s technical and financial performance against temporal and spatial short-term and long-term KPIs.

Target setting is of paramount importance for the successful management of spatial mine-to-plan compliance. It is important to select targets that reflect industry best practice while taking account of historical performance, levels of flexibility in the open pit mine, as well as practical considerations. For the purpose of explaining the process and based on acceptable results achieved, the target for spatial mine-to-plan compliance against the tactical plan is set at a minimum of 85% in-sequence mining and a maximum of 15% mining outside of the tactical plan. The targets should be refined as the mine-to-plan compliance process is embedded.

Spatial deviations from the mine plan (positive and negative) occur when either the quality of the mine plan is not on standard or the execution against the mine plan is not adequate. In an open pit mine, spatial deviations from the mine plan typically occur for two main reasons. Firstly, when the assumptions used as input to the development of the tactical mine plan are not achieved in practice (or are incorrect). Examples include assumptions on the vertical rate of advance, loading and hauling equipment productivity, and assumptions regarding equipment allocated to critical secondary tasks. Actual mining therefore remains spatially on plan but takes place at a different rate from the planned rate. This normally manifests as actual in-sequence mining being higher or lower than planned. If not managed, it could lead to actual mining activities taking place out of sequence and out of plan. Secondly, this occurs when short-term technical or financial objectives are prioritized at the expense of the spatial execution of the tactical mine plan. Examples include cutting back on waste stripping to improve short-term unit cost and targeting an unplanned mining area to reduce short-term hauling distances. Here, actual mining is not spatially honouring the tactical plan. If not managed properly, then out-of-sequence and out-of-plan mining takes place and the areas planned and not mined increase as a result. Adverse outcomes are highlighted as part of the monthly reporting routines and should be viewed as opportunities for improvement of either the quality of the next tactical mine plan or the quality of spatial mining execution against the plan. The operational or short-term planning horizon is utilized to manage improved spatial compliance by directing mining to areas planned and not yet mined.

iron ore Mineral Reserve was approximately 500 Mt and the LOM stripping ratio approximately four times. The Sishen pit is a conventional open pit, truck-and-shovel operation. In 2017, a total of 162 Mt of waste material was mined at Sishen, making it the single biggest open pit mine in southern Africa on a tonnes per annum basis. Both dense medium separation and jig processing plants are employed and the annual product output in 2017 was 31.1 Mt. The spatial mine-to-plan compliance methodology was introduced at Sishen during 2013. The focus of the mine-toplan compliance reconciliation model and process is to track spatial compliance against the annual business plan (tactical plan) agreed upon as part of the annual business planning cycle. Spatial mine-to-plan compliance reconciliation takes place through a well-established monthly management routine using the model and process discussed in this paper. At Sishen, the mine-to-plan compliance results form part of a technical key success factor (TKSF) dashboard that is used to measure and manage the technical health of the mine. The effectiveness of the proposed approach is demonstrated in Figure 5, which shows the Sishen spatial mine-to-plan compliance reconciliation results for 2014, 2015, and 2016. Figure 5 demonstrates that, using the approach discussed in this paper, the spatial mine-to-plan compliance at Sishen improved from 69% to 94% over the three-year period up to 2016. This illustrates the benefits of implementing spatial mine-to-plan compliance reconciliation and incorporating this KPI into the TKSF dashboard of an open pit mine.

%'!# )%' An approach for measuring and managing the spatial mineto-plan compliance against the tactical mine plan has been presented. The approach incorporates a model providing mine-to-plan reconciliation categories as well as a spatial reconciliation process focusing on reporting, target setting, and analysis of the reasons for deviations. The aim of the approach is to improve the spatial execution against the tactical mine plan in an open pit mine. Application of the approach will contribute to the achievement of an open pit mine’s operational and financial targets (derived from the tactical mine plan) while maintaining the mine’s ability to deliver on its future KPIs, thereby contributing towards meeting the ultimate objective of maximizing the NPV throughout the LOM in a sustainable way. A case study at the Sishen iron ore mine, South Africa illustrated how the implementation of the spatial mine-toplan compliance approach contributed positively to improving the quality of mining plans and to an improved understanding of the major reasons for non-compliance. This led to a significant improvement in the spatial mine-to-plan compliance from 69% to 94% over the three-year period from 2014 to 2016, inclusive.

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The spatial mine-to-plan compliance approach discussed above was implemented at the Sishen iron ore mine in the Northern Cape Province of South Africa. Sishen is operated by Anglo American Kumba Iron Ore. At the end of 2017 the

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The work reported in this paper is part of a current PhD research study in the School of Mining Engineering at the University of the Witwatersrand. The permission granted by the management of Anglo American Kumba Iron Ore to publish the paper is greatly appreciated.

A spatial mine-to-plan compliance approach to improve alignment

) $(* &")&#* )'( "% #&'*!% #)&'!(* ($ %$ &'!( * ) ('*)$%'*%$(* )'(* ' &' *

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Strategies (MetPlant 2015), Perth, WA, 7–8 September 2015, pp. 35–45. MORLEY, C. and ARVIDSON, H. 2017. Mine value chain reconciliation demonstrating value through best practice. Proceedings of the Tenth

the mining value chain - mine to design as a critical enabler for optimal

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Kumba. Kumba Iron Ore, internal memo, June 2013. pp. 1-8. GURGENLI, H. 2011. Implementing an efficient mine planning system. Proceedings of the SME Annual Meeting, 27 February - 2 March 2011. pp. 1–3. HALATCHEV, R. and DIMITRAKOPOULOS, R. 2003. On the dynamics of mining operations in open pit mines. International Journal of Surface Mining, Reclamation and Environment, vol. 17, no. 4. pp. 246–263. HALL, A. and HALL, B. 2006. Doing the right things right - Identifying and implementing the mine plan that delivers the corporate goals. Proceedings of the International Mine Management Conference, Melbourne, Victoria, 16-18 October 2006. Australasian Institute of Mining and Metallurgy, Carlton, Victoria. pp. 119–128. HALL, A. and HALL, B. 2015. The role of mine planning in high performance. AusIMM Bulletin, August 2015. pp. 68–71.

PHILLIS, R.C.D. and GUMEDE, H. 2011. Annual mine planning and execution buffering - a safety imperative. Journal of the Southern African Institute of Mining and Metallurgy, vol. 111, no. 8. pp. 565–572. RAMAZAN, S. and DIMITRAKOPOULOS, R. 2004. Uncertainty-based production scheduling in open pit mining. Transactions of the Society for Mining, Metallurgy and Exploration, vol. 316. pp. 106–112. SABOUR, A. and DIMITRAKOPOULOS, R. 2008. Mine design selection under uncertainty. Mining Technology, vol. 117, no. 2. pp. 53–64. SNYMAN, W.J. 2017. Mine-to-plan compliance improvement journey. Internal presentation, Kumba Iron Ore Company, July 2017. pp. 1-6. STEFFEN, O.K.H. 1997. Planning of open pit mines on a risk basis. Journal of the South African Institute of Mining and Metallurgy, vol. 97, March/April 1997. pp. 47–56. THOLANA, T. and NEINGO, P.N. 2016. Extending the application of PAS 55/ISO 55 000 to mineral asset management. Journal of the Southern African

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ESTERHUYSEN, W.D. 2013. Progress update on mine planning reconciliation in

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http://dx.doi.org/10.17159/2411-9717/2019/v119n3a5

Evaluation of government equity participation in the minerals sector of Tanzania from 1996 to 2015 by P.R. Lobe1, A.S. Nhleko1, and H. Mtegha1

Government’s equity role in the minerals sector is one of the nationalist measures implemented in order to ensure greater control and management of a country’s mineral resources. This paper evaluates the Tanzanian government’s equity participation in the minerals sector from 1996 to 2015. The research methodology included determination of the number of mineral rights, minimum allowable exploration expenditures in prospecting licences (PLs), and forms of equity role of the government. Data was collected and analysed for PLs, mining licences (MLs), and special mining licences (SMLs). The study revealed a number of challenges faced by the Tanzanian government as regards its equity strategy in the mineral sector. One of the major challenges was the secrecy surrounding agreements and contracts entered into between the government and private sector investors, which were concluded via various business ownership and mineral development projects. This secrecy resulted in non-transparency and lack of accountability in the mining industry. The financial benefits accruing to the government were inadequately realized, evident through inconsistent payments of corporate income tax and mining royalties by the mining companies. Furthermore, the government does not have solid mechanisms and frameworks for assessing non-financial benefits, thus it is difficult to measure the impact of these factors. It is recommended that the Tanzanian government review the Mining Act and Regulations of 2010 to include the provision of solid mechanisms and frameworks for all forms of government equity role. This paper is based on an MSc research study undertaken at the University of the Witwatersrand. CA0 <=39 Tanzania, minerals sector, government participation, equity role, joint venture.

?:=<367:@<? Governments can grant private companies mining rights to extract mineral resources, or take a stake in the companies as well as establish state companies to oversee their interests in the minerals sector. The establishment of a state company to oversee government interests in the mining sector implies the direct participation of government by way of equity participation. Equity participation or role is defined as the holding of shares in an enterprise, company, or asset by individuals or a body corporate (Otto, n.d.; Brown, 2013; Natural Resource Governance Institute, 2015).

1 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Sep. 2018; revised paper received Mar. 2019.

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It is important to note that the establishment of mines depends on the mineral resources being economically viable. Mines provide local citizens with secure employment and local content opportunities, raising their income and quality of life. Mineral exploitation is also a source of government revenue when mines produce or recover minerals resources through their value chains to the selling points. Production and selling of mineral products enable government to collect revenue through royalties, corporate income taxes, and other legal means (Otto et al., 2006; Wise and Shtylla, 2007). Where there are good government policies, the minerals sector can integrate with other sectors of the economy to establish downstream industries (Highley, Chapman, and Bonel, 2004; Ministry of Energy and Minerals, 2009). Good government policies are defined as implementable plans or courses of action to influence and determine decisions, actions, and other matters in the interest of the country (Businessdictionary, 2017; Free Dictionary, 2017; Bendiola, 2013). Since the inception of the Mining Act of 2010 and the Tanzanian government’s mandate to the State Mining Corporation (STAMICO) to oversee government interests in prospecting, and medium- and large-scale mining, there has been no study to evaluate the equity role of the government in the minerals sector. A mining operation with a capital investment between US$100 000 and US$100 million or the equivalent amount in Tanzanian shillings is classified as a mediumscale operation. Large-scale mining operations are defined those where the capital investment is more than US$100 million or its equivalent in Tanzanian shillings (Mining Act, 2010). This poses a question: ‘How effectively has the

Evaluation of government equity participation in the minerals sector of Tanzania equity role performance of the Tanzanian government in prospecting, and medium- and large-scale mining been since the enactment of Mining Act of 2010?’ In order for this research study to make a meaningful contribution, the period from 1996 to 2015 was investigated.

->? >?@>?B8@?A=>;9BA?3< 8A?:B>?3B8@?A9 This section discusses the nature, quality. and quantity of mineral resources and quantity of known mineral reserves. Tanzania’s minerals endowment includes gold, tanzanite, diamonds, coal, uranium, iron ore, gemstones, and copper. These mineral deposits exist in stratigraphic formations such as Cenozoic volcanics, the Ubendian Belt, greenstone belts, and Archean cratons, to mention a few (Figure 1). According to the Ministry of Energy and Minerals (2010), there are five groups of commodities that exist in Tanzania. These comprise:

Tanzania plays a significant role in the global mining industry, supplying: Ž 1.6% of the world’s gold, making it the 15th largest producer (Yager, 2013; Mineweb, 2015) Ž 166 500 carats of diamond in 2013/2014, making it 13th in the globe (US Geological Survey, 2015). This production was 0.23% of the annual total average production of 72.35 million carats Ž Tanzanite – the only producer in the world (Ihucha, 2014; Yager, 2013).

ÂŽ Metallic minerals such as gold, nickel, tin, rare earth elements, iron ore, copper, lead, and the platinum group metals (PGMs) ÂŽ Gemstones such as diamonds, tanzanite, ruby, emerald, and sapphire ÂŽ Industrial minerals such as phosphate, gypsum, limestone, kaolinite, graphite. and bauxite ÂŽ Building materials such as stone, sand, aggregates, gravel, and fireclay ÂŽ Energy minerals, including uranium and coal. In Tanzania, information pertinent to geology and geophysics useful for assisting investors in selecting areas for prospecting is available from the Geological Survey of Tanzania (GST) Dodoma. Geological mapping has covered 90% of the country (Ministry of Energy and Minerals, 2015). According to the Ministry of Energy and Minerals (2015), mineral prospecting operations carried out between 1990 and 2015 have revealed reserves of approximately 2200 t of gold and 5 Gt of coal (see Figure 2a). Other mineral reserves are depicted in Figure 2b.

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Evaluation of government equity participation in the minerals sector of Tanzania The tanzanite industry has not flourished in Tanzania despite its long history. This is due to the following reasons (Ihucha, 2014; Rimoch and Cherng, 2013; Dodgson, 2016): ÂŽ ÂŽ ÂŽ ÂŽ ÂŽ

The continuing practice of exporting rough tanzanite Inadequate and inefficient jewellery cutting centres High rate of tanzanite smuggling Tax evasion Lack of political will for implementing regulations in the tanzanite sector.

In Tanzania, from 1996 to 2015, there were nine active mines (Mwihava and Masanja, 2015; Tanzania Minerals Audit Agency, 2016a). These included six large-scale mines and three medium-scale mines (Table I).

.<;6:@<?B<5B:1AB->? >?@>?B8@?A=>;B9A7:<= %# (!$ '($ ( $ '!& '&#(%&(# '( %&'!" ( ' #$! ' $!'( In 1992, the Tanzanian government enacted the Public Corporations Act of 1992 to replace the Public Corporations Act of 1969 (Ministry of Finance and Planning, 1992). This action was due to the underperformance of parastatals during socialism and the self-reliance policy between 1967 and the late 1980s. Underperformance of parastatals was attributed to lack of local expertise, lack of managerial skills, embezzlement, bureaucracy, capacity underutilization, lossmaking, reliance on government subsidies, non-payment of taxes, overemployment and monopolistic behaviour of the operation, and huge debts (Muganda, 2004; Ngowi, 2009). In 1993, the Tanzanian government established the Presidential Parastatal Sectoral Reform Commission (PSRC) to be in charge of privatization of state-owned organizations (Twaakyondo, Bhalalusesa, and Ndalichako, 2002; Muganda, 2004). Under the Public Corporations Act of 1992, all companies under government holding corporations were transferred to the Treasury Registrar (TR) for privatization through the PSRC. This action affected all companies under the State Mining Corporation (STAMICO), which were transferred to the TR and were kept under receivership or liquidated (Muganda, 2004; Ngonyani, 2014). Consequently, STAMICO was closed in April 1996.

' % % "#%$&("& ( ' ' % % "#%$&($ ( Specification of a public corporation means that the minister

has declared closure of the corporation, while de-specification refers to the reversal of the order to close a public corporation. After the companies under government holding corporations had been placed in receivership or liquidated, STAMICO was listed in August 1997 as a specified public corporation under PSRC. This was after its closure in April 1996 (Ministry of Finance and Planning, 1992; Ngonyani 2014). As STAMICO became a specified public corporation, all its rights, privileges, powers, duties, or functions were vested in the board of directors. The board of directors then waited for STAMICO’s shares to be allotted or sold by the government (Ministry of Finance and Planning, 1992). However, the government delayed the decision to allot or sell STAMICO, causing it to survive from 1996 until 2008. During this period, STAMICO concentrated on the provision of contract drilling services, consultancy work, property rental income, acquisition of mineral rights, and joint venturing (Ngonyani, 2014). Furthermore, during the same period, there were recommendations in 2008 from the Justice Mark Bomani Commission’s Report favouring restoration of STAMICO rather than its closure. The government then decided to restore STAMICO (Bomani, 2008). De-specification of the corporation took place in April 2009 with the publishing of a de-specification order in the Government Gazette (State Mining Corporation, 2014).

"& ' (%&(# '( "& "&%"&( %&'!" ( ' #$!( !$ ( #$( In 1996, the government realized that the Mining Act of 1979 had failed to attract local and foreign mining investment. This, coupled with economic reforms undertaken by the government between the late 1980s and early 1990s, prompted the government to change the legislation (Tanzania Minerals Audit Agency, 2016b; Muganda, 2004; Ngowi, 2009; Weir, n.d.). The aim was to attract investors and bring into the country capital, technology, and expertise (Tanzania Minerals Audit Agency, 2016b). This resulted in the government endorsing the Minerals Policy of 1997 and Tanzania Mining Act of 1998 (State Mining Corporation, 2016; Ministry of Energy and Minerals, 2009). Other notable changes were the formulation of fiscal incentives aimed at attracting both local and foreign investors, and the opening of six large-scale mines as indicated in Table II. This development brought about an increase in gold production from less than 1 t/a in 1998 to over 45 t/a in

Table I

>=4AB>?3B8A3@68 B97>;AB8@?A9B@?B->? >?@>B>9B<5B A7A8/A=B&,% '<82>?0 2=< A7: Merelani tanzanite mine (MTM) also known as TanzaniteOne tanzanite mine (TTM) Ngaka coal mine (NCM) New Luka gold mine (NLGM) Bulyanhulu gold mine (BGM) Buzwagi gold mine (BZGM) Geita gold mine (GGM) North Mara gold mine (NMGM) Stamigold Biharamulo Mine (SBM) formerly known as Tulawaka Gold Mine (TGM) Williamson diamonds mine (WDM)

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Medium Medium Medium Large Large Large Large Large Large

Tanzanite Coal Gold Gold Gold Gold Gold Gold Diamond

Simanjiro Mbinga Chunya Kahama Kahama Geita Tarime Biharamulo Kishapu

2001 2012 2012 2001 2009 2000 2002 2005 1940

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Source: Mwihava and Masanja (2015); Tanzania Minerals Audit Agency (2016a)

Evaluation of government equity participation in the minerals sector of Tanzania Table II

)@?A9BA9:>/;@91A3B@?B->? >?@>B/A: AA?B% B>?3 &,,

)@?A 2=< A7:

Golden Pride gold mine

A>= 7<88@99@<?A3

(:>:69

1998

Closed in 2012 after reaching its end life.

Geita gold mine (GGM)

2000

Still in operations

Bulyanhulu gold mine (BGM)

2001

Still in operations

North Mara gold mine (NMGM)

2002

Still in operations

Tulawaka gold mine (TGM) currently known as Stamigold Biharamuro Mine (SBM)

2005

Still in operation

Buhemba gold mine

2003

Closed in 2007 due to undertaking of uneconomical large-scale mining

Sources: Tanzania Minerals Audit Agency (2016b); State Mining Corporation (2015b)

2010. Consequently, the minerals sector became the second largest in terms of foreign currency earnings (East African Community, 2011). Other outstanding developments stemming from of Minerals Policy of 1997 and Mining Act of 1998 from 1997 to 2008 were: ÂŽ Increased contribution of the mining sector to the GDP. from 1.4% in 1998 to 3.0% in 2008 (Muganyizi, 2012) ÂŽ Inflation-adjusted foreign direct investment (FDI) in the mineral sector through exploration and mining projects increased from US$1.69 billion in 1997 to US$2.5 billion in 2007 (Ministry of Energy and Minerals, 2009) ÂŽ Increased mineral exports value from US$26 million in 1997 to US$420 million in 2002, as indicated in Figure 3 (Msabaha, 2006). Despite these achievements, Msabaha (2006) and the Ministry of Energy and Minerals (2009) highlighted major challenges as: ÂŽ Low level of integration of the minerals sector with other sectors of the economy

ÂŽ Inadequate capacity to administer the sector ÂŽ Inadequate infrastructure such as roads, reliable power supply, and communications to support the sector ÂŽ Low level of value addition ÂŽ Growing negative public perception of the minerals sector. This is a result of the low contribution in both social and economic development. Some of the impacts of these shortcomings included (Msabaha, 2006; Ministry of Energy and Minerals, 2009): ÂŽ Less contribution to the GDP by other sectors of economy, for example agriculture and manufacturing, due to their low level of integration with the minerals sector ÂŽ Low level of procurement of locally produced goods and services ÂŽ Increased transport costs from mines to the markets and from suppliers of goods and consumables to the mines.

"& ' (%&(# '( "& "&%"&( %&'!" ( ' #$!( !$ ( #$( In order to address challenges that ensued under the Minerals Policy of 1997 regime, changes in the minerals sector were necessary (Msabaha, 2006). Accordingly, the government decided to review the minerals sector through the formulation of several committees in 2004. These committees included the Presidential Committee to advice the government on administering the minerals sector (Bomani, 2008; East African Community, 2011). This initiative resulted in the formulation of the Tanzania Mineral Policy of 2009 and Mining Act of 2010.

)@?A=>;B=@41:9B< ?A3B/0B:1AB->? >?@>?B4<.A=?8A?: The analysis of all mineral rights owned by the government focused on prospecting licences (PLs), mining licences, (MLs) and special mining licences (SMLs) partially and wholly owned by STAMICO and NDC and TR. Figure 4 indicates that STAMICO has 17 partially owned and 20 wholly owned mineral rights from 1996 to 2015. STAMICO’s partially owned mineral rights comprised 13 PLs, 2 MLs, and 2 SMLs while wholly owned rights included 19 PLs and 1 SML. The targeted minerals were gold, tanzanite, phosphate, rare earth elements (REEs), gypsum, kaolinite, feldspar, and coal.

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Evaluation of government equity participation in the minerals sector of Tanzania

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Companies that owned mineral rights from 1996 in relation to the government equity role in the mining industry are presented in Figure 7. Each indicator in Figure 7 comprises the mineral type, type of mineral right, and percentage of ownership by the shareholder(s). It is deduced

$@46=AB !)@?A=>;B=@41:9B2>=:@>;;0B>?3B 1<;;0B< ?A3B/0B '

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Figure 5 shows that NDC had 46 partially owned and 33 wholly owned mineral rights from 1996 to 2015. The partially owned mineral rights consisted of 43 PLs, one ML, and two SMLs and wholly owned rights were 22 PLs. The targeted minerals included coal, iron ore, dolomite, soda ash, and all minerals other than building materials and gemstones (AOBG) and gold. NDC played its role in the minerals industry by entering into joint ventures with Intra Energy Tanzania Limited (IETL) and Sichuan Hongda Group (SHG) of China through Tancoal Energy Limited (TEL) and Tanzania China International Mineral Resources Limited (TCIMRL), respectively. TEL was awarded 10 coal prospecting licences and one coal mining licence. In addition, TCIMRL acquired 10 coal, 17 iron ore, and 7 dolomite prospecting licences and one coal special mining licence (Figure 6). TR partially owned one diamond SML, SML 216/2005. The TR’s percentage of ownership of a mineral right vis-a-vis the private investor was 25%. The private investor that owns the diamond SML with TR is Petra Diamonds Ltd, with a 75% shareholding. Under the government equity role from 1996 to 2015, coal was the most sought-after commodity, followed by gold and iron ore. This was presumably due to high granting of coal mineral rights by the Ministry of Energy and Minerals. From 1996 to 2015, the Ministry of Energy and Minerals granted 36 coal, 28 gold, and 19 iron ore mineral rights in line with the government equity role in the minerals industry.

Evaluation of government equity participation in the minerals sector of Tanzania that, out of 106 mineral rights, 60 were owned through joint ventures (JVs) between the state and private companies; 42 (39.6%) were fully owned by STAMICO and NDC as sole commercial entities, and four (3.8%) were owned through partnerships.

?>;09@9B<5B=A7A@.>/;AB>??6>;B;A.@A9B5<=B;@7A?7A9 During the period under study the government received levies for licences issued under prospecting, mining, and special licence categories. Figure 8 presents annual levies payable by STAMICO, NDC, and TR to the government from 2011 to 2015 for holding licences issued by the government. From Figure 8, the annual payable levies of STAMICO from 2011 to 2015 on its wholly and partially owned mineral rights exceeded those of NDC and TR. This was because STAMICO owned more MLs and SMLs than NDC and TR, with greater ownership shares. It should be emphasized here that the annual levy rates for MLs and SMLs, expressed in US dollars per square kilometre per annum, are much higher than for PLs, which caused higher payable annual levies on mineral rights under STAMICO to the tune of approximately US$0.9 million. Whereas STAMICO had more shares of ownership in mineral rights than NDC and TR, a rationale that mostly necessitated its self-commitment in settling of annual levies, NDC was deemed to have relied on the private sector investors in settling the same. However, it seemed that the parastatal contribution of payable annual levies on partially and wholly government-owned PLs exceeded the private sector investors’ contribution. This was attributed to many partially owned mineral rights issued to NDC having higher shareholding ownerships than private sector investors.

investor’s money spent as government contribution in an investment would be through government-foregone dividends with interest. Free equity is when the company grants a portion of its shares to the government at no cost (Heller, 2011; Cottarelli, 2012). Free-carry equity has the features of both free and carried equity (Kaba, 2017). It is when a percentage of mining company’s shares is offered to the government by the company, and the company also carries the costs and expenses for the government. According to Kaba (2017), the government may contribute in kind by granting the mining licence and/or mining rights. Although the Mining Act, 2010 defines free-carry equity in terms of the free-carried interest (FCI), this equity role approach is not yet practiced in Tanzania. In 2014, TZGT planned to execute free-carry equity in the Nachu graphite project (NGRP) and Mkuju River uranium project (MRUP). Negotiations for free-carried interest (FCI) for each project were conducted between the TZGT and project owners from 2014, but were concluded unsuccessfully in 2015 as the parties could not reach consensus on FCIs. This consequently impeded the signing of MDAs, which also limited execution of the free-carry equity role by the government. However, the government is negotiating with various stakeholders to implement free-carry equity. Table III summarizes the results for forms of government equity role in prospecting and medium- and large-scale mining. Most of the prospecting licence agreements were concluded under carried and paid equity types, comprising 56 and 41 PLs respectively. These agreements may lead to meaningful government participation in the mining industry since the government has paid in one way or another for the shares held in an entity. This participation will allow government to promote the mining industry as an invested party.

(688>=0B<5B:1AB5<=89B<5B:1AB4<.A=?8A?:BA 6@:0B=<;A @?B:1AB8@?A=>;9B9A7:<=

$ '!& '&#(' %# (!$ '(%&( !$ ' #%& (

There are four main types of government equity role, namely paid or full equity, carried equity, free equity, and free-carry equity. Paid equity is the equity capital financing or buying of shares in enterprises by government, as a private investor would do (Heller, 2011; Natural Resource Governance Institute, 2015; Cottarelli, 2012). Carried equity is when the private sector investor meets all capital costs and expenses in an investment without any financial contribution from government (Natural Resource Governance Institute, 2015; Cottarelli, 2012; McPherson, 2008). However, according to Heller (2011) and McPherson (2008), the recovery of an

$ '!& '&#(' %# (!$ '(%&( ' % " '( %&%& % '& '

Table IV depicts the financial benefits of the government equity role in 89 prospecting licences through STAMICO and NDC from 2011 to 2015. The total derived benefits amounted to US$251 839.60.

STAMICO and NDC are involved in carried equity in three medium-scale mines. The government did not earn any dividends from these mines during the period under review. STAMICO could not pay dividends as it was classified as a going concern and later reclassified; however, it was operating at a loss from 2013 to 2014 (Controller and Auditor General, 2015). NDC may not have paid an amount to the government because no dividends were declared for NDC based on the carried equity principle. It is expected that when STAMICO is profitable, dividends will be paid. Furthermore, once NDC has paid its debt of acquiring shareholding in full, dividends may be paid to the State provided that at least the mine is profitable.

$ '!& '&#(' %# (!$ '(%&( "! ' " '( %&%& ( % '& '

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The government was involved in large-scale mining through exercising paid and carried equity roles. The paid equity role is through Stamigold Biharamulo mine and Kiwira coal mine, while carried equity is through Buckreef gold mine,

Evaluation of government equity participation in the minerals sector of Tanzania Table III

$<=89B<5B->? >?@>?B4<.A=?8A?:BA 6@:0B=<;AB@?B69A =A>B<5B2>=:@7@2>:@<?B

$<=8B<5B

=<92A7:@?4B;@7A?7A9B* 9+B>?3B8@?@?4B2=< A7:9B*8@?A9+BA A=7@9@?4B=A92A7:@.AB5<=89B<5BA 6@:0B=<;A

A 6@:0B=<;A

-<:>;

(- ) '

'

-

Prospecting

Carried Paid

13 PLs 19 PLs

43 PLs 22 PLs

None None

56 PLs 41 PLs

Medium scale mining

Carried

Merelani TanzaniteOne Mining Ltd and Kigosi gold mine

Ngaka Coal Mine

None

3 medium scale mines

Large-scale mining

Carried

Buckreef gold mine

Paid

Kiwira coal mine and Stamigold Biharamulo mine

Liganga Iron ore Mine and Mchuchuma Coal Mine None

Williamson Diamonds Mine None

4 large scale mines 2 large scale mines

Free carry

Will apply in Nachu graphite project and Mkuju River uranium project pending meeting first of agreeable FCIs (through negotiations between government and projects owners) and signing of Minerals development agreements. Negotiations deemed to continue after 2015.

Table IV

A=@.>:@<?B<5B5@?>?7@>;B/A?A5@:9B<5B:1AB4<.A=?8A?:BA 6@:0B=<;AB@?B2=<92A7:@?4

STAMICO

NDC

'>:A4<=0B<5B5@?>?7@>;B/A?A5@:B5=<8B&,%%B:<B&,% Payable annual levies from 13 PLs partially owned

33 504.30

Payable annual levies from 37 PLs partially owned

89 365.10

Payable annual levies from 20 PLs wholly owned

107 028.50

Total

251 839.60

"! ($ ( %&"& %" ( '&' %# (#$(# '( $ '!& '&#(

Table V summarizes the financial benefits to the government from 2006 to 2015. Based on the data collected and analysed in this paper, the financial benefits stood at US$53.39 million against exploration costs of US$2.06 million (for all PLs) and payable annual levies of US$1.76 million (for all mineral rights) respectively. In the economics context, minimum allowable exploration expenditures and payable annual levies would be regarded as operating costs. These two costs may be incorporated in the income statements together with mineral sales revenues, cost of sales, other operating costs/expenses, depreciation expenses, etc. Then, the projects’ net profits before and after taxes would be determined for payments of corporate income taxes to the government and dividends to the shareholders (Correia et al., 1993).

21 941.70

Payable annual levies from 19 PLs wholly owned

Mchuchuma coal mine, Liganga iron ore mine, and Williamson diamond mine. The parastatals tasked with heading and protecting the role of government are STAMICO, NDC, and TR (the government agent). The government did not receive any earnings from these roles. This may be explained by the fact that some operations can only begin after certain infrastructure is in place, such as construction of Mchuchuma coal mine, the thermal power station, and a transmission line from Mchuchuma to Liganga.

8<6?:B*#("+

"! ($ (&$& %&"& %" ( '&' %# (#$(# '( $ '!& '&#

The government carried and paid equity roles in prospecting

activities through STAMICO and NDC were analysed to assess the non-financial benefits they generated. Performances were analysed vis-a-vis three set of nonfinancial benefit indicators in prospecting, namely geoknowledge, government confidence in undertaking mining, and national capacity-building. Areas of non-financial benefits include greater control of the minerals sector, employment equity, human resource development, procurement, and enterprise development and community development (see Table VI). There were two major challenges (shortcomings) faced by the Tanzanian government in prospecting, medium-, and large-scale mining. Firstly, STAMICO had financial constraints from 2013 to 2014 after it was reclassified from being a going concern. In this case, as highlighted by the Controller and Auditor General (2015), STAMICO had suffering a recurrence of losses, for instance losses of approximately US$450 293 in 2013 and US$632 452 in 2014. It is important that the government make interventions in STAMICO’s operations with workable strategic solutions to prevent the parastatal from dwindling. Lastly, the secrecy in agreements or contracts in partnerships, private JV companies, and mineral developments between the government and the private sector investors. Secrecy contributed to non-transparency and poor accountability in the prospecting, medium-, and large-scale mining projects under the government equity role. In addition, non-transparency and poor accountability in these VOLUME 119

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>0>/;AB>??6>;B;A.@A9B=A:=@A.A3

Evaluation of government equity participation in the minerals sector of Tanzania Table V

(688>=0B<5B5@?>?7@>;B/A?A5@:9B<5B:1AB4<.A=?8A?:B5=<8B&,, B:<B&,% )@?A=>;B=@41:

'<9:9B@?76==A3B@?B8@?A=>;B=@41:9B >0>/;AB )@?@868 >;;< >/;A >??6>; A 2;<=>:@<?B ;A.@A9 A 2A?3@:6=A9 *#("B8@;;@<?+ *#("B8@;;@<?+

-<:>;B 7<9:9 *#("B8@;;@<?+

$@?>?7@>;B/A?A5@:9B5=<8B8@?A=>;B=@41:9 A7A@.>/;AB '<=2<=>:A >??6>; @?7<8AB ;A.@A9B :> *#("B8@;;@<?+ *#("B8@;;@<?+

)@?@?4 A7A@.>/;A =<0>;:0 2=<5@:9B<= *#("B8@;;@<?+ @?:A=A9:9 *3@.@3A?39+ :1=<641B /A?A5@:9BB =<;A9

:1A=B :> A9 *#("B8@;;@<?+

-<:>;B 5@?>?7@>; /A?A5@:9B *#("B8@;;@<?+

13 PLs partially owned by STAMICO

0.22

0.02

0.24

0.02

-

-

-

-

0.02

19 PLs wholly owned by STAMICO

0.23

0.03

0.26

0.03

-

-

-

-

0.03

37 PLs partially owned by NDC

0.76

0.09

0.85

0.09

-

-

-

-

0.09

20 PLs wholly owned by NDC

0.85

0.11

0.96

0.11

-

-

-

-

0.11

Subtotal US$)

2.06

0.25

2.31

0.25

-

ML 490/2013 of Merelani tanzanite mine (MTM)

-

0.05

0.05

0.05

2.60

-

-

-

1.38

-

8.64

ML 496/2013 of Kigosi gold mine (KGM)

-

0.06

0.06

0.06

ML 439/2011 of Ngaka coal mine (NCM)

-

0.12

0.12

0.25

-

-

-

-

0.06

0.12

-

1.11

-

-

1.23

12.67

Subtotal US$)

-

0.23

0.23

0.23

2.60

2.49

-

8.64

SML 04/92 of Buckreef gold mine (BKGM)

-

0.16

0.16

0.16

-

-

-

-

0.16

SML 533/2014 of Liganga iron ore mine (LIOM)

-

0.15

0.15

0.15

-

-

-

-

0.1

SML 534/2014 of Mchuchuma coal mine (MCM)

-

0.13

0.13

0.13

-

-

-

-

0.13

SML 216/2005 of Mwadui diamond mine (WDM)

-

0.31

0.31

0.31

0.11

9.44

-

25.92

35.78

SML 233/2005 of Kiwira coal mine (KCM)

-

0.23

0.23

0.23

-

-

-

-

0.23

SML 157/ 2003 of Stamigold Biharamulo (SBM)

-

0.30

0.30

0.30

-

0.82

-

1.61

2.73

Subtotal US$) Total (US$)

-

1.28

1.28

1.28

0.11

10.26

-

27.53

39.18

2.06

1.76

3.82

1.76

2.71

12.75

-

36.17

53.39

projects placed the Tanzanian government at risk of entering unfair and/or objectionable agreements or contracts. The legislation states that public single (completely owned) or JV (partially owned) companies are required to release public certified copies of their annual financial statements to the Registrar of Companies (Correia et al., 1993; Marx et al., 1999). In addition, they are obliged to furnish their shareholders with mid-yearly interim reports and audited annual financial statements (Correia et al., 1993; Marx et al., 1999). These two requirements are a reflection of how transparent and accountable public single and JV companies are as compared to businesses such as sole commercial entities, partnerships, and private JV companies.

'<?7;69@<?9B>?3B=A7<88A?3>:@<?9 The Tanzanian government’s equity role from 1996 to 2015 in PLs, medium-, and large-scale mines involving carried and paid forms did not yield the expected outcomes because the government did not receive earnings. This was due to nontransparency and poor accountability in agreements in

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business ownerships that government and private sector investors pursued. In addition, sole commercial entities, partnerships, and private JV companies adopted in the government equity role are secretive in nature and this results in counter-productivity. The Tanzanian government inadequately realized financial benefits through its equity role in prospecting, medium-, and large-scale mining. Inadequacy in financial benefits was characterized by inconsistent payments of corporate income tax, mining royalties, and other taxes by the mining companies. Another reason for this problem was the non-realization of profits and receipt of dividends from mining enterprises in which the government is sole commercial entity (via parastatals) or a shareholder with private-sector investors. The Tanzanian government fairly realized non-financial benefits through its equity role in prospecting, medium-, and large-scale mining. Areas of non-financial benefits were greater control of the minerals sector, employment equity, human resource development, procurement, and enterprise

Evaluation of government equity participation in the minerals sector of Tanzania Table VI

<? 5@?>?7@>;B/A?A5@:9B<5B- -BA 6@:0B=<;AB@?B8A3@68B>?3B;>=4A 97>;AB8@?@?4 (7>;AB<5B 8@?@?4

)@?A

=A>:A=B7<?:=<;B<5BB :1A8@?A=>;B9A7:<=B *5<=B- -B1>.@?4BBB < ?A=91@2B91>=A9B @?B:1AB8@?@?4B91>=A+

82;<08A?:B A 6@:0

68>?B=A9<6=7AB 3A.A;<28A?:B

=<76=A8A?:B>?3B A?:A=2=@9A 3A.A;<28A?:

'<886?@:0B3A.A;<28A?: *)' +

Medium

MTM

Fairly achieved

1,166 locals were cumulative employed from 2009 to 2015 compared with 23 expatriates

Fairly achieved despite lack of monitoring and enforcement mechanisms.

Local procurement at 91.6%from 2012 and 2014 at the value of value of US$11.6 million.

Mine supplied water, constructed school classrooms and roads from 2010 to 2014 at the cost of US$427, 967 to zero respectively.

KGM

Fairly achieved

No data in public domains.

No data in public domains.

No data in public domains.

None.

NCM

Fairly achieved

No data in public public domains.

Fairly achieved despite No data in public lack of monitoring and domains. enforcement mechanisms.

No data in public domains.

BKGM Fairly achieved

No data in public domains

Fairly achieved despite lack of monitoring and enforcement mechanisms.

No data in public domains.

No data in public domains.

LIOM

Fairly achieved

LIOM together with MCM will employ 32 000 locals.

Fairly achieved despite lack of monitoring and enforcement mechanisms.

No data in public domains.

No data in public domains.

MCM

Fairly achieved

MCM together with LIOM will employ 32 000 locals.

Fairly achieved despite lack of monitoring and enforcement mechanisms.

No data in public domains.

No data in public domains.

WDM Fairly achieved

558 locals were cumulative employed from 2009 to 2015 compared with 11 expatriates.

Fairly achieved despite lack of monitoring and enforcement mechanisms.

Local procurement of goods was at 80.5% of total procurement from 2012 to 2014 at a value of US$98.91 million.

Mine supplied water, constructed school classrooms and roads from 2010 to 2014 at the cost of US$381 813 to US$125 323 respectively.

KCM

Fairly achieved

No data n public domains

No data in public domains.

No data in public domains.

No data in public domains.

SBM

Fairly achieved

Only locals, 340 skilled and 43 non-skilled employed at the mine from 2014 to 2015.

Fairly achieved despite lack of monitoring and enforcement mechanisms.

Mine spent US$63 076.92 in local procurement of food stuffs from 2011 to 2015.

From 2011 to 2015, the mine spent a total of US$101 238.47 on CSR activities including supplying of desks to pupils, renovations of water storage facilities, feeder roads as well as classrooms.

development as well as community development. However, in Tanzania there are no solid mechanisms and frameworks for overseeing of non-financial benefits. It is recommended that the following issues be considered in order to improve the government’s effective performance in the equity role strategy. Ž Government to review the Mining Act of 2010 to include solid mechanisms and frameworks for all forms of government equity role, and assessing and measuring performance in equity role Ž Government should develop oversight mechanisms to ensure that the effectiveness of its equity participation is monitored and its entities account to Parliament Ž Government to review the Mining Act of 2010 and Regulations of 2010 to include frameworks for derivation, validation, and auditing of operating and capital costs used in mining projects where the State holds equity interest.

A5A=A?7A9 BENDIOLA, I. 2013. Characteristics of a good policy & overall view of planning and its relationship to the management process. https://www.slideshare.net/IvanBendiola/characteristics-of-a-good-policy [accessed 30 July 2017]. BOMANI, M. 2008. Report of the Tanzanian presidential mining review committee to advise the government of the mining sector. http://www.policyforum-tz.org/sites/default/files/BomaniReportEnglish_0.pdf [accessed 2 February 2016]. BROWN, M. 2013. Mining in Kenya – the start of new era? https://www.mayerbrown.com/files/uploads/Documents/PDFs/Mining_in_ Kenya2.pdf [accessed 9 September 2016] BUSINESSDICTIONARY 2017. Policy. http://www.businessdictionary.com/definition/policy.html [accessed 10 August 2017]. Controller and Auditor General. 2015. Report of the Controller and Auditor General on the financial statements of the state mining corporation for the six months period ended 30th June 2014. National Audit Office, Dar es Salaam, Tanzania. VOLUME 119

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Evaluation of government equity participation in the minerals sector of Tanzania CORREIA, C., FLYNN, D., ULIANA, E., and WORMALD, M. 1993. Financial Management. Par Print, Cape Town, South Africa. COTTARELLI, C. 2012. Fiscal regimes for extractive industries: Design and implementation. http://www.greenfiscalpolicy.org/wpcontent/uploads/2016/11/IMF-2012-Fiscal-Regimes-for-ExtractiveIndustries-Design-and-Implementation.pdf [accessed 5 December 2016]. DODGSON, L. 2016. Tanzanite: How Tanzania can profit from mining its rare stone. http://www.mining-technology.com/features/featuretanzanite-howtanzania-can-profit-from-mining-its-rare-stone-4698401/ [accessed 3 January 2017]. EAST AFRICAN COMMUNITY. 2011. Promotion of extractive and mineral processing industries in EAC- United Republic of Tanzania status. http://industrialization.eac.int/index.php?option=com_docman&task=cat_ view&gid=80&Itemid=70 [accessed 9 February 2016]. FREEDICTIONARY 2017. Policy. https://www.thefreedictionary.com/ Government+policies [accessed 18 May 2017]. HELLER, P. 2011. National participation in oil and mining. http://www.eisourcebook.org/cms/August%202013/National%20Participa tion%20in%20Oil%20&%20Mining.pdf [accessed 19 November 2016]. HIGHLEY, E.D., CHAPMAN, R.G., and BONEL, A.K. 2004. The economic importance of minerals to the UK. https://www.bgs.ac.uk/downloads/ start.cfm?id=1301 [accessed 11 July 2016]. IHUCHA, A. 2014. How Kenya and India plunder tanzania's blue gem. https://empirevoice.blogspot.co.za/2014/12/how-kenya-and-indiaplunder-tanzanias.html [accessed 31 October 2016]. US GEOLOGICAL SURVEY. 2015. Mineral commodity summaries 2015. https://minerals.usgs.gov/minerals/pubs/mcs/2015/mcs2015.pdf [accessed 20 November 2016]. KABA, D. 2017. Free carried interests in Francophone Africa mining legislation Is there such a thing as a free lunch? http://www.fasken.com/en/freecarried-interests-in-francophone-africa-mining-legislation---is-theresuch-a-thing-as-a-free-lunch-01-26-2017/ [accessed 5 February 2017]. MCPHERSON, C. 2008. State participation in the natural resource sector: Evolution, issues and outlook. http://www.imf.org/external/np/seminars/ eng/2008/taxnatural/pdf/mcpherson.pdf [accessed 17 February 2016]. MINEWEB. 2015. Gold’s Top 20 – Mines, miners and countries. http://www.mineweb.com/news/gold/golds-top-20-mines-miners-andcountries/, [Accessed 7 October 2016] MINING ACT. 2010. No. 14 Mining 2010. http://extwprlegs1.fao.org/docs/ pdf/tan97360.pdf [accessed 1 March 2019]. MINISTRY OF ENERGY AND MINERALS. 2009. The Mineral Policy of Tanzania. https://mem.go.tz/wpcontent/uploads/2014/02/0014_11032013_Mineral_ Policy_of_Tanzania_2009.pdf. [accessed 29 March 2016]. MINISTRY OF ENERGY AND MINERALS. 2010. Mining Act, 2010. https://mem.go.tz/wp-content/uploads/2014/02/0013_ 11032013_Mining_Act_2010.pdf [accessed 7 April 2016]. MINISTRY OF ENERGY AND MINERALS. 2015. Tanzania mining industry investor’s guide.http://www.tanzania.go.tz/egov_uploads/documents/TANZANIA_M ining_Industry_Investor_Guide_-_June_2015_-1_sw.pdf [accessed 11 October 2016]. MINISTRY OF FINANCE AND PLANNING. 1992. The Public Corporations Act of 1992. http://faolex.fao.org/docs/pdf/tan142173.pdf. [accessed 16 June 2016].

MUGANYIZI, K.T. 2012. ICTD Research 1: Mining sector taxation in Tanzania. https://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/2311/I CTD%20Research%20Report%201_0.pdf?sequence=1&isAllowed=y [accessed 13 December 2016]. MWIHAVA, C.X.N. and MASANJA, M. P. 2015. Annual Report July 2014 – June 2015.https://mem.go.tz/wp-content/uploads/2015/11/17.11.15annualreport-minerals-division.pdf [accessed 12 May 2016]. NATURAL RESOURCE GOVERNANCE INSTITUTE. 2015. State participation in oil, gas and mining. https://resourcegovernance.org/sites/default/ files/nrgi_StateParticipation_20150311.pdf [accessed 16 February 2016]. NATURAL RESOURCE GOVERNANCE INSTITUTE. 2015. State participation in oil, gas and mining. https://resourcegovernance.org/sites/default/ files/nrgi_StateParticipation_20150311.pdf [accessed 16 February 2016]. NGONYANI, E.A. 2014. Strategic Plan 2014/15–2018/19. http://www.stamico.co.tz/wp-content/uploads/2014/12/STAMICOSTRATEGIC-PLAN-201415-201819.pdf [accessed 9 June 2016]. NGOWI, H.P. 2009. Economic development and change in Tanzania since independence: The political leadership factor. Proceedings of the Conference on Political and Managerial Leadership for Change and Development in Africa, Mbabane, Swaziland, September 2009. pp. 1–23. http://www.academicjournals.org/article/article1379789169_Ngowi.pdf [accessed 19 May 2016]. OTTO, J., ANDREWS, C., CARWOOD, F., DOGGETT, M., GUJ, P., STERMOLE, F., STERMOLE, J., AND TILTON, J. 2006. Mining royalties: A global study of their impact on investors, government, and civil society. http://siteresources.worldbank.org/INTOGMC/Resources/3360991156955107170/miningroyaltiespublication.pdf [accessed 28 September 2016]. OTTO, J.M. Not dated. Mineral policy, legislation and regulation. https://commdev.org/userfiles/files/1217_file_UNCTAD_Otto.pdf [accessed 2 April 2016]. RIMOCH, D. AND CHERNG, S. 2013. A rare and beautiful stone fails to shine: Tanzania’s missed opportunity. http://knowledge.wharton.upenn.edu/article/rare-beautiful-stone-failsshine-tanzanias-missed-opportunity/ [accessed 12 November 2016]. STATE MINING CORPORATION. 2014. State Mining Corporation. http://www.stamico.co.tz/wp-content/uploads/2014/09/CORPORATEPROFILE1.pdf [accessed 18 June 2016]. STATE MINING CORPORATION. 2016. Corporate profile. http://www.stamico.co.tz/corporate-profile/ [accessed 25 December 2016]. TANZANIA MINERALS AUDIT AGENCY. 2016a. Annual Report 2015. http://www.tmaa.go.tz/uploads/TMAA_Annual_Report_2015-4.pdf [accessed 13 June 2016]. TANZANIA MINERALS AUDIT AGENCY. 2016b. History of the Agency. http://www.tmaa.go.tz/tmaa/about/category/history [accessed 29 July 2016]. TWAAKYONDO, M.H., BHALALUSESA, P.E., and NDALICHAKO, L.J. 2002. Factors shaping successful public private investorship in the ICT sector in developing countries the case of Tanzania. http://tanzaniagateway.org/docs/ICT_sector_in_developing_countries_the_ case_of_Tanzania.pdf [accessed 11 June 2016]. WEIR, A. Not dated. Development in post-independence Tanzania. http://www.fastonline.org/CD3WD_40/HDLHTML/EDUCRES/DEP18E/EN/ CH04.HTM [accessed 15 June 2016].

MSABAHA, I.S. 2006. Tanzanian minerals sector- performance, fiscal benefits and challenges. World Bank Fiscal Issues Workshop, Washington DC, USA, October 2006. pp. 1-15. http://siteresources.worldbank.org/INTOGMC/Resources/3360991160575 823247/tanzaniaminister.ppt [accessed 31 January 2016].

WISE, H. AND SHTYLLA, S. 2007. The role of the extractive sector in expanding economic opportunity. https://www.hks.harvard.edu/mrcbg/CSRI/publications/report_18_EO%20Extractives%20Final.pdf [accessed 8 October 2016].

MUGANDA, A. 2004 Tanzania’s economic reforms — and lessons learned. http://www.tanzaniagateway.org/docs/tanzania_country_study_full_case. pdf [accessed 26 May 2016].

YAGER, R.T. 2013. The mineral industry of Tanzania. https://minerals.usgs.gov/minerals/pubs/country/2013/myb3-2013-tz.pdf [accessed 12 October 2016]. N

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http://dx.doi.org/10.17159/2411-9717/2019/v119n3a6

Benefits of including resistivity data in a resource model — an example from the Postmasburg Manganese Field by J. Perold1 and C. Birch1

Due to the challenging geological environment of the Postmasburg Manganese Field (PMF), a study was conducted to determine if any benefits would derive from the inclusion of resistivity data during threedimensional (3D) modelling of the manganese resource. This was achieved by estimating manganese resources from 2011/2012 drilling data and comparing them with manganese resources estimated from the same drilling data and resistivity data collected during 2013 and 2017. Both models were adjusted to limit their extent to the same 3D modelling space. Significant volume and tonnage differences were observed for all lithological units. The greatest differences were noted in the manganiferous zones of alteration – 7.200 Mt for the geological model versus 3.700 Mt for the geoelectric model. This study showed that the inclusion of resistivity data can reduce exploration costs significantly, as a direct consequence of the resistivity data allowing more accurate siting of boreholes. This decreases the number of boreholes, samples, and analyses required due to the 3D electrical delineation of mineralized areas prior to drilling. An additional benefit is the ability to more correctly forecast the net present value of an operation due to more accurate estimation of manganese resources and stripping ratios. This is clearly demonstrated by the estimated gross profit estimation of R409 million for the geological resource model versus R264 million for the geoelectric resource model. The addition of resistivity data can, therefore, reduce exploration costs and can increase confidence in geological and financial modelling. It would be reasonable to conclude that this approach could also be used for karsthosted massive sulphide deposits. ><+ :8/6 resource estimation, manganese, resistivity data, financial modelling.

@798:/219;:7 According to Corathers (2014) and Gajigo, Mutambatsere. and Adjei (2011), South Africa holds between 75–80% of global manganese reserves, followed by Ukraine (10%), Australia (3%), India (3%), and Gabon (2%). The known, land-based, manganese resources located elsewhere are estimated at 2%. The majority of economically important sedimentary manganese ore deposits in South Africa are situated in the Northern Cape Province (Gutzmer, 1996). Manganese ores of the Postmasburg Manganese Field (PMF) were discovered in 1922, and the Kalahari Manganese Field (KMF) in 1940. According to Astrup and Tsikos (1998) and Gutzmer

1 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Sep. 2018; revised paper received Feb. 2019.

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(1996), mining of manganese ore from the PMF ceased during 1989 in favour of the superior quality of manganese ore from the KMF. Gutzmer (1996) also stated that the irregular size and shape of the deposits, shortage of remaining reserves, and ore composition, are the chief factors that stopped the exploitation of the PMF orebodies. The shortage of remaining ore reserves might be ascribed to insufficient geological input, resulting in incorrect geological understanding and modelling of these orebodies. This likelihood is based on the findings of McCarthy (2003), who identified problem areas with associated error frequencies from 105 completed mine feasibility studies analysed (Table I). A significant portion of identifiable errors is directly linked to insufficient geological input, resulting in incorrect geological understanding and modelling of orebodies. Mine design and scheduling using incorrect orebody models will surely contribute to business risks, negatively impacting on potential profitability. The critical role of manganese in the global economy and the fact that mining in the PMF ceased prior to the development of current levels of geophysical prospecting techniques and 3D modelling software are sufficient reason to apply these technologies during prospecting in the PMF. The purpose of this paper is to highlight the impact of resistivity data on the accuracy of PMF mineral resource estimation. This was established by comparing differences in geological interpretation by constructing two 3D resource models; one with and one without resistivity data.

Benefits of including resistivity data in a resource model Table I

;37;,;1579=,<56; ;4;9+=692/+=<88:86=&#1=$5890+)= ( 8: 4<-=58<5

88:8=,8< 2<71+=& (

Geology, resource and reserve estimation Geotechnical analysis Mine design and scheduling Mining equipment selection Metallurgical test work, sampling scale-up Process plant equipment design and selection Cost estimation Hydrology Total

17 9 32 4 15 12 7 4 100

?0<= :69-56 283=#57357<6<=";<4/=& #"( The PMF is situated on the Maremane Dome (Eriksson, Altermann, and Hartzer; 2006), and is located immediately to the north of the town of Postmasburg, extending northwards towards Sishen (Figure 1). The Maremane Dome (Figure 2) is a ‘domed anticline with strata dipping at less than 1:100 in

an arc to the north, east and south’ (Cairncross, Beukes, and Gutzmer 1997). Rocks of the Transvaal Supergroup, Griqualand West Basin, were deposited onto the basement rock of the Maremane Dome. The study area is underlain by manganese ore of the Western Belt (Gutzmer, 1996) of the PMF. The orebody is associated with shale, overlain by quartzite and basaltic lava of the Gamagara Formation, Olifantshoek Supergroup. The irregular size and shape of the orebodies (Gutzmer, 1996) resulted primarily from deposition onto a weathered, Reivilo Formation dolomite paleosurface. The Reivilo Formation is a subdivision of the Ghaap Group, Campbell Rand Subgroup, Transvaal Supergroup. Dolomite that resisted weathering formed unevenly spaced pinnacles when the softer rocks were eroded away. Accurate delineation of the dolomitic floor topography and manganese orebody is of utmost importance for precise geological modelling and mineral resource estimation. A clear understanding of the mineral content and textural relationships within the rocks of the Western Belt of the PMF is also required prior to constructing a 3D geological model as

";328<='! :159;:7=:,=90<= :69-56 283=#57357<6<=";<4/)= :890<87=$5.<= 8:*;71<)= :290= ,8;15

";328<= ! :890 6:290=18:66 6<19;:7=908:230=90<=#58<-57<= :-<=&$5;8718:66)= <2 <6)=57/=%29 -<8)=' (

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Benefits of including resistivity data in a resource model shown in Figure 3. Gutzmer (1996) and Gutzmer and Beukes (1997) suggest that the ferruginous manganese ores formed through the following sequence of events: 1.

2. 3.

4.

Deposition of a mixture of aluminous clay and ferruginous manganiferous wad in depressions in the karst topography of the paleodolomite surface of the Reivilo Formation. The deposition of haematite pebbles, shale, and quartzite of the Gamagara Formation. Low-angle thrusting causing lower level greenschist facies metamorphism, resulting in the formation of braunite (Mn2+,Mn63+SiO12) and partridgeite (Mn2O3), as well as coarse crystalline bixbyite-rich (Mn23+O3) ferruginous ores (Mindat, 2017). Exposure and erosion producing: (a) Supergene mineralization resulting from secondary karstification, slumping, and brecciation, leading to the formation of romanechite ((Ba, H2O)2(Mn4+,Mn3+)5O10) (Mindat, 2017) crusts and wad (b) Placer (canga) deposits resulting from accumulation.

..4;159;:7=:,=8<6;69;*;9+=628*<+=/595 Research results related to the findings of resistivity studies conducted on the rocks of the PMF are not available in the public domain. Research by Ramazi and Mostafaic (2012) on manganese deposits associated with the Uremich-Dokhtar volcanic belt in the northwest of Iran showed that resistivity results can be used to accurately differentiate between manganese interbeds and limestone. The manganese mineralization underlying their study area correlated well with areas of low resistivity. Moreira et al. (2014) used low resistivity values to accurately differentiate between manganese mineralized zones, soil, and saprolite associated with supergene manganese deposits in southern Brazil. Both

publications concluded that the inclusion of resistivity data can result in much greater detail than is usually assimilated from drilling and sampling alone. To construct a 3D geoelectric model, the relationship between identified geological domains and georeferenced resistivity values has to be defined. Identification of consistent apparent resistivity ranges depends entirely on the order of magnitude of the variability in electrical conductivity for a specific rock domain (Oldenburg and Jones, 2007). Guidelines used to determine apparent resistivity ranges were based primarily on their research. Oldenburg and Jones’s (2007) generalized comparison for different rock types (Figure 4) clearly indicates that the expected resistivity ranges for metallic minerals (0.01 to 1 .m) should differ significantly from that for shale (7 to 50 .m), sandstone (60 to 10 000 .m) and dolomite (1000 to 100 000 .m). Braunite (Mn2+Mn3+6[O8|SiO4]), bixbyite (Mn,Fe)2O3, partridgeite (Mn2O3), haematite (Fe2O3), and specularite (Fe2O3) are the economically important ore minerals associated with the PMF (Gutzmer, 1996; Gutzmer and Beukes, 1997). Smith (2002) records the resistivity of haematite as 3.5 × 10-3 – 107 .m. and that of specularite averaging 6 × 10-3 .m. Srigutomo, Trimadona, and Prihandhanu (2016) concluded that manganese ore of a mudrock-hosted manganese deposit is associated with resistivity values less than 5 .m and that the resistivity of the mudrock rarely exceeds 100 .m. The 2D resistivity surveyed conducted comprised 70 lines, each 550 m in length. Differences between project-specific selected resistivity ranges and the published resistivity ranges cited are mainly ascribed to differences in conductivity. Oldenburg and Jones (2007) stated that conductivity is mainly dependent on clay content, moisture content, hydraulic permeability, porosity, temperature and phase of pore fluid, and the concentration of dissolved electrolytes.

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";328<= !#:/<4=,:8=90<=:8;3;7=:,=90<=,<8823;7:26=-57357<6<=:8<6=:,=90<= <69<87= <49=&%29 -<8)=' =%29 -<8=57/= <2 <6)=' (

Benefits of including resistivity data in a resource model

";328<= !%<7<854; </=1:-.58;6:7=:,=90<=< .<19</=8<6;69;*;9+=*542<6=:,=;-.:89579=8:1 =38:2.6=& 4/<7 283=57/= :7<6)= (

";328<= !%<:8<,<8<71</=-<5628</=5..58<79=8<6;69;*;9+=.6<2/: 6<19;:7)=4;7<=''

@79<8.8<959;:7=57/=-:/<44;73 The impact of resistivity data on the accuracy of PMF mineral resource estimation was tested by constructing two 3D resource models, for comparison in Geovia Surpac 6.7. A primary geological model was created from data gathered during geological mapping, drilling, and sampling within the study area. A second 3D geoelectrical model was created by using resistivity data to guide the interpretation of the data used during construction of the primary model.

Construction of the geological model commenced (Turner and Gable, 2006), by creating 15 west-east geological crosssections perpendicular to the strike of the orebody. Interpretation of the geology was limited to the sectional areas between boreholes drilled. The range of influence of data from individual holes was limited to half the distance between holes unless geological contacts could be accurately recognized on the surface. Delineation of manganiferous zones of alteration (manganore) was restricted to areas with reported Mn values equal to or greater than 11% and/or combined Mn and Fe values of 21% or greater. This resulted in the inclusion of

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areas with low manganese concentrations when iron concentrations were close to or higher than 21%. Additional zones of higher grade Mn mineralization, [Mn] 24% and [Mn] 28%, were then delineated within the manganore zone.

The resistivity data was imported into Geovia Surpac 6.7. Care was taken to ensure that the resultant dispersion patterns compared favourably with the inverse model resistivity sections, which accompanied the data. This is evident when comparing the georeferenced, measured apparent resistivity pseudo-section for line 11 (Figure 5) with the graphical resistivity section presented for line 11 (Figure 6). Georeferencing of the data resulted in the actual 3D orientation of each data-point, thus ensuring that it represents its physical location. When constructing the 3D geoelectrical model, relationships between identified geological domains underlying the project area and the georeferenced resistivity values had to be defined (Loke, 2000). Table II summarizes the resistivity ranges selected for identified lithological units underlying the project area. This was achieved by draping the

Benefits of including resistivity data in a resource model

";328<= ! ;,,<8<79=385.0;154=8<6;69;*;9+=6<19;:76)=4;7<=''=& :7 )= ' (

<6;69;*;9+=8573<6=6<4<19</=,:8=;/<79;,;</=4;90:4:3;154 27;96 ;90:4:3;154=27;9

#<954=1:79<79 & (

<6;69;*;9+= & -(

Manganiferous and ferruginous zone Shale Quartzite Surface dumps Dolomite Dolomite

[MnFe] ≼ 21% [Mn] < 11% [Mn] < 1.7% Unknown [Mn] < 11% [MnFe] ≼ 21%

0 - 100 100 - 170 > 170 > 370 > 170 > 370

georeferenced resistivity data over borehole geological logs and analytical data contained in the project database. Resistivity ranges were found to be fairly constant but showed some variation between lines. Two lines showed reduced resistance, approximately 50 .m, for mineralized areas compared to the other lines surveyed. The resolution of the resistivity values is unfortunately not high enough to conclusively deduce whether ferruginous shale with low concentrations of manganese is associated with areas of low resistivity (< 50 .m). As Mn and Fe mineralization are uncorrelated, zones of alteration (Table II) were subdivided into mostly manganiferous (50–100 .m) and mostly ferruginous (< 50 .m). Solid models were constructed from the created geoelectrical sections to demarcate geoelectric lithological units inside the model.

<62496=57/=/;61266;:7

geological model. The differences observed are significant and relate to: ÂŽ The shape, position, and orientation of the dolomite floor and associated pinnacles ÂŽ Areas of mineralization and differentiation of areas with ferruginous and manganiferous alteration created for the geoelectrical model ÂŽ The inability to delineate a separate ferruginous zone of the alteration domain in the geological model, due to insufficient geological and chemical data ÂŽ The shape, position, and orientation of the overlying quartzite ÂŽ The shape, position, and orientation of the unaltered shale. The principal reason for the volumetric differences is the high resolution of the sectional resistivity data, which allows detailed modelling on a very small scale. Results obtained from the resistivity data and associated geoelectrical model resemble the model of origin proposed by Gutzmer (1996) and Gutzmer and Beukes (1997) more closely than the geological model derived from drilling results alone.

Table III

6<8*</=3<:4:3;154=/;,,<8<71<6=,8:-=90< -:/<44;73=9<107; 2<6=<-.4:+</ ;90:4:3+

#:/<44;73=<88:86 %<:4:3;154=

Table III compare volumetric differences for lithological domains that resulted from the modelling regimes employed, based on geological interpretation as shown in Figure 7. The differences are expressed as percentage change, based on the modelled volume of individual lithological units of the

%<:<4<198;154=

$0573<

:42-<=&- ( Quartzite Dolomite Shale Ferruginous zone Manganiferous zone

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0.438 1.368 2.189 1.869

0.566 1.425 1.333 1.586 0.954

26.9 4.2 39.1 Undefined 49.0

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Table II

Benefits of including resistivity data in a resource model

";328<= ! <19;:754=1:-.58;6:7=:,=3<:4:3;154=;79<8.8<959;:7= ;90=57/= ;90:29=8<6;69;*;9+=/595

Applying a cut-off grade (Pittuck, 2015) to the created geological and geoelectric models resulted in mineral resource statements suitable for comparison. This was achieved by applying financial constraints to determine if ‘a reasonable and realistic prospect for eventual economic extraction’ exists (SAMREC, 2016). Estimated differences are shown in Table IV, expressed as percentage changes, based on the modelled tonnage of individual lithological units related to the geological resource model. The impact of the different modelling techniques on manganese resources and stripping ratios is drastic. The reduction in economically extractable ore and subsequent increase in total waste tonnage in the geoelectrical model resulted in a 208.9% increase in stripping ratio. The incorporation of the resistivity results survey undoubtedly increased the geoscientific confidence and level of knowledge regarding the challenging spatial orientation and extent of the drilled portion of the mineral deposit. The following parameters are recommended for classification of manganese mineral resources associated with the PMF. ÂŽ Inferred Resource—A resistivity line spacing of 100 m and a core drilling grid of 100 Ă— 200 m located on resistivity lows ÂŽ Indicated Resources—A resistivity line spacing of 50 m and a core drilling grid of 50 Ă— 50 mters located on resistivity lows ÂŽ Measured Resources—A resistivity line spacing of 25 m and a core drilling grid of 25 Ă—x 25 m located on resistivity lows.

The magnitudes of financial risks and rewards stemming from resistivity modelling of PMF exploration were defined by comparing exploration expenditure and the impact of stripping ratios on gross profit. These financial indicators were chosen as they directly impact on affordability, cash flow, and the return on capital invested.

During exploration, non-invasive resistivity surveys are critical prior to siting boreholes. Areas of low resistivity

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Table IV

$:-.58;6:7=:,=698;..;73=859;:6=566:1;59</= ;90 -:/<44;73=9<107; 2<6=<-.4:+</ ;90:4:3+

#:/<44;73=<88:86 %<:4:3;154=

%<:<4<198;154=

$0573<

#9 Quartzite Shale Ferruginous zone Manganiferous zone Total waste Manganese resource Stripping ratio

0.830 3.920 0.180 4.930 5.500 1:0.90

1.190 1.940 3.700 0.150 6.980 2.510 1:2.78

43.4 50.5 Undefined 16.7 41.5 54.4 208.9

(< 150 .m) showing horizontal continuity in excess of 100 m should be chosen as drilling targets. The unfortunate fact that exploration drilling was conducted before resistivity surveys were undertaken presented a unique opportunity for cost comparison. This was done by using the results of the resistivity survey to determine any errors made during the initial drilling programme. The following was observed from a total of 32 boreholes drilled in the study area: Ž Drilling too deep – 81.6 m Ž Percentage of boreholes correctly sited: 56% Ž Percentage correct sampling: 48%. Exploration activities were costed by using actual costs from a geophysicist and drilling contractor on site during 2017. Table V clearly shows that results from a resistivity programme conducted prior to drilling would have reduced associated drilling expenditure by approximately 46% and expenditure on chemical analyses by approximately 52%. The total cost estimate for prospecting with resistivity data is 47% lower than without resistivity data. Costs associated with quality control and quality assurance, core logging, and sampling will most probably be incrementally lower due to the reduced workload. Costs associated with 3D modelling will be fairly similar, as the additional time spent on georeferencing of resistivity data is offset by the reduced time spent on deducing geological and

Benefits of including resistivity data in a resource model Table V

$:-.58;6:7=:,=< .4:859;:7=< .<7/;928< .4:859;:7=< .<7/;928<

;90:29=8<6;69;*;9+

;90=8<6;69;*;9+

$0573<

0.151 0.515 0.102 0.767

Undefined 45.7 51.7 46.8

=-;44;:7 Resistivity survey Core drilling Chemical analysis Total cost estimate

0.948 0.211 1.159

Table VI

,,<19=:,=698;..;73=859;:=:7=-:7904+=.8:/219;:7=1:696=57/=38:66=.8:,;9=&42-.=:8<=.8:/219( ;90:29=8<6;69;*;9+

;90=8<6;69;*;9+

$0573< '

=-;44;:7 RoM ore feed Waste mining Crushing and screening Secondary breaking (20% of feed) Lump ore cost of production (LCOP) Gross sales revenue (DAP) Gross profit after LCOP (DAP)

1.488 1.269 1.129 0.031 3.917 11.070 7.153

geochemical contacts during 3D modelling of the mineral deposit.

The effect of different stripping ratios, from the created resource models, on monthly gross profit is illustrated in Table VI. Stripping ratio is the only variable considered when the cost of production (lump ore product) is calculated. If the stripping ratios for the created models were the same, the estimated costs would have been equal. This caused the cost of waste mining associated with the geoelectrical model to be 209% higher than that estimated for the geological model. Gross profit for the geoelectrical model reduces by 41% compared to the geological model based on drilling data alone. Assuming that the geoelectrical model is more accurate, the monthly estimated gross profit of the geological model is overestimated by R3 million. This equates to R409 million for the declared mineral resources.

54;/59;:7=:,=90<=0+.:90<6;6 To confirm the conclusions drawn and endorse the recommendations made, 16 core boreholes were drilled in the study area during November/December 2017, the first drilling since completion of the initial prospecting phase (without resistivity data) during 2011/2012. Boreholes were sited on an approximate 50 Ă— 50 m grid covering an area 150 m by 175 m in extent. Boreholes were deliberately placed on resistivity lines surveyed earlier during 2017 or 2013. The area chosen showed continuity of resistance levels <150 .m over distances exceeding 120 m. The results of the 2017 drilling campaign were very encouraging. All 16 boreholes drilled intersected manganese mineralization. The ore intersections in 15 of the boreholes

1.488 3.919 1.129 0.031 6.568 11.070 4.202

0.0 208.8 0.0 0.0 67.7 0.0 41.3

averaged above 28% Mn. The average in situ manganese grade of the area drilled during 2017 is 8% higher than the average grade of the project estimated from the 2011/2012 drilling data. The average estimated iron grade is 6.5% higher than in 2011/2012, while the total average metal concentration (MnFe) increased by 14.5%. A similar grade comparison confined to the area drilled during 2017 showed that the average in situ manganese grade estimated from the 2017 data is 2.54 % higher than that estimated from the 2011/2012 drilling data. The average estimated iron grade is 4.49% higher than in 2011/2012, while total average metal concentration (MnFe) increased by 7.03%.

$:71426;:76 The aim of this study was to determine if any benefits will derive from the inclusion of resistivity data in a manganese resource model for the PMF. This was achieved by estimating manganese resources from 2011/2012 drilling data for comparison with resources estimated from the same drilling data plus resistivity data collected during 2013 and 2017. It was established that the addition of resistivity data can reduce exploration costs significantly in this challenging geological environment. The reduction in costs is a direct consequence of the application of resistivity data allowing more accurate placing of boreholes. This lessens the number of boreholes, samples, and analyses required due to the 3D electric delineation of mineralized areas prior to drilling. An additional benefit is the ability to more correctly forecast the net present value of an operation due to more accurate estimation of manganese resources and stripping ratios. This is clearly demonstrated by the estimated gross profit difference of R409 million for the geological resource model and R264 million for the geoelectrical resource model. VOLUME 119

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Benefits of including resistivity data in a resource model The addition of resistivity data can, therefore, reduce exploration costs and can increase confidence in geological and financial modelling. It would be reasonable to conclude that this approach could also be used for karst-hosted massive sulphide deposits.

This paper is based on the MSc research project by Perold (2018), supervised by C. Birch from the School of Mining Engineering, University of the Witwatersrand.

ASTRUP, J. and TSIKOS, H. 1998. Manganese. The Mineral Resources of South Africa. Wilson, M.G.C. and Anhaeusser, C.R. (eds). Handbook 16. Council for Geoscience, Pretoria. pp. 450–460. CAIRNCROSS, B., BEUKES, N.J., and GUTZMER, J. 1997: The Manganese Adventure: The South African Manganese Fields. Associated Ore & Metal Corporation Limited, Johannesburg. 236 pp. CORATHERS, L.A. 2014. Manganese. US Geological Survey Mineral Commodity Summaries. February 2014. http://minerals.usgs.gov/minerals/pubs/commodity/manganese/ ERIKSSON, P.G., ALTERMAN, W., and HARTZER, F.J. 2006. The Transvaal Supergroup and its precursors. The Geology of South Africa. Johnson, M.R., Anhaeusser, C.R., and Thomas, R.J. (eds.). Geological Society of South Africa, Johannesburg and Council for Geoscience, Pretoria. pp. 237–260. GAJIGO, O., MUTAMBATSERE, E., and ADJEI, E. 2011. Manganese industry analysis: Implications for project finance. Working Paper Series no. 132. African Development Bank Group. https://www.afdb.org/en/documents/ document/working-paper-132-manganese-industry-analysisimplications-for-project-finance-24162/ GUTZMER, J. 1996. Genesis and alteration of the Kalahari and Postmasburg manganese deposits, Griqualand West, South Africa. PhD thesis, Faculty of Science, University of Johannesburg. GUTZMER, J. and BEUKES, N.J. 1997. Mineralogy and mineral chemistry of oxide facies manganese ores of the Postmasburg manganese field, South Africa. Mineralogical Magazine no. 61. pp. 213–230.

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LOKE, M.H. 2000. Electrical imaging surveys for environmental and engineering studies. A practical guide to 2-D and 3-D surveys. http://www.heritagegeophysics.com/images/lokenote.pdf MC CARTHY, P. 2003. Managing technical risk for mine feasibility studies. Proceedings of the Mining Risk Management Conference. Australasian Institute of Mining and Metallurgy, Melbourne, Australia. Mindat.org. 2017. https://www.mindat.org/ MOREIRA, C.A., BORGES, M.R., VIEIRA, G.M.L., FILHO, W.M., and MONTANHEIRO, M.A.F. 2014. Geological and geophysical data integration for delimitation of mineralized areas in a supergene manganese deposit. Geofisica Internacional, vol. 53, no. 1. pp. 199–210. OLDENBURG, D.W. and JONES, F.H.M. 2007. Inversion for applied geophysics; Learning resources about geophysical inversion. Geophysical Inversion Facility, University of British Colombia. https://www.eoas.ubc.ca/ ubcgif/iag/foundations/properties/resistivity.htm PITTUCK, M. 2015. Cut-off grades for mineral resource reporting: going the extra rare earth mile. SRK Consulting International Newsletter, no. 51. http://www.srk.com/files/File/newsletters/srknews51-MRE_A4-LR.pdf RAMAZI, H. and MOSTAFAIC, K. 2012. Application of integrated geoelectrical methods in Marand (Iran) manganese deposit exploration. Arabian Journal of Geosciences, vol. 6. pp. 2961–2970. doi 10.1007/s12517-0120537-2. SAMREC. 2016. South African Mineral Resource Committee. The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (the SAMREC Code. 2016 Edition. http://www.samcode.co.za/codes/category/8-reportingcodes?download=120:samrec SMITH, R.J. 2002. Geophysics of iron oxide copper-gold deposits. Hydrothermal Iron Oxide Copper-Gold & Related Deposits: A Global Perspective. Volume 2. Porter, T.M. (ed.). PGC Publishing, Adelaide. pp 357–367. SRIGUTOMO, W. TRIMADONA, and PRIHANDHANU, M.P. 2016. 2D resistivity and induced polarization measurement for manganese ore exploration. Proceedings of the 6th Asian Physics Symposium, Bandung, Indonesia, 19–20 August 2015. Journal of Physics: Conference Series, vol. 739, no. 1. 0121385. IOP Publishing. https://iopscience.iop.org/article/10.1088/17426596/739/1/012138/pdf TURNER, A.K. and GABLE, C.W. 2006. A Review of Geological Modeling. Colorado School of Mines, Golden, CO. https://pdfs.semanticscholar.org/5777/ c2b949527397ac42ac9be64e5bb4fe7e8c7e.pdf N

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a7

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat by M.A. Mahboob1, B. Genc1, T. Celik2, S. Ali3, and I. Atif3

Mapping of hydrothermally altered areas, which are usually associated with mineralization, is essential in mineral exploration. In this research, open source reflectance spectroscopy data from the multispectral moderate-resolution Landsat 8 satellite was used to map altered rocks in the Gauteng and Mpumalanga provinces of South Africa. The unique spectral reflectance and absorption characteristics of remotely sensed Landsat data in the visible, near-infrared (NIR), shortwave-infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum were used in different digital image processing techniques. The band ratios (red/blue, SWIR 2/NIR, SWIR 1/NIR), spectral band combinations (Kaufmann ratio, Sabins ratio) and principal component analysis (Crosta technique) were applied to efficiently and successfully map hydrothermal alteration minerals. The results showed that the combination of spectral bands and the principal component analysis method is effective in delineating mineral alteration through remotely sensed satellite data. The validation of results by using the published mineral maps of the Council for Geoscience South Africa showed a good relationship with the identified zones of mineralization. The methodology developed in this study is cost-effective and time-saving, and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up-to-date mineral information. <*$6945 remote sensing, mineral mapping, reflectance spectroscopy, Landsat, mineral exploration, hydrothermal alteration.

:7964,2786: Many developing nations depend on exploration and exploitation of mineral resources to sustain their economic growth. Usually, the traditional mineral exploration techniques require enormous finances, prolonged time, and tremendous manpower, particularly in areas that are not easily reachable (Maduaka, 2014). Furthermore, mineral exploration required state-of-the-art techniques and expertise along with geological, geochemical, and geophysical datasets, which may not be easily available or may be lacking where access is problematic (Kaiser et al., 2002; Bemis et al., 2014). Modern remote sensing technology has proved to be one of the highly efficient and robust techniques used for mineral exploration. The use of remote sensing satellite images for geological mapping and mineral exploration usually involves studying the physicochemical

1 School of Mining Engineering, University of the Witwatersrand, South Africa. 2 School of Computer Science and Applied Mathematics, University of the Witwatersrand, South Africa. 3 School of Advanced Geomechanical Engineering, National University of Sciences and Technology (NUST), Pakistan. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Sepc. 2018; revised paper received Mar. 2019.

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properties of rocks and weathering soils, such as mineralogy, landforms, geochemical signatures, and the spatiall distribution of lineaments (Bhattacharya et al., 2012). A fundamental principle of mineral exploration is that it is quite possible that undiscovered deposits will be located in the close vicinity of discovered ones. For example, if mining is taking place in a particulat area, then similar minerals will be more likely found nearer to the discovered deposit, and as the distance increases, the likelihood of new discoveries will decrease. In that situation, before drilling exploratory boreholes at new locations, remote sensing can be used effectively to identify regions with higher chances of mineralization, mainly through multi- or hyperspectral remote sensing images (Gholami, Moradzadeh, and Yousef, 2012; Ciampalini et al., 2013). The use of reflectance spectroscopic information derived from remote sensing data allows effective localization of mineral exploration and reduces the cost and time spent on fieldwork for geological, geophysical, and geochemical studies (Short and Lowman Jr, 1973; Tedesco, 2012; Marjoribanks, 2010). Several remote sensing studies for mineral exploration and lithological mapping have been done in arid and semi-arid regions. In areas with good geological exposure, satellites in orbit are capable of acquiring spectral reflectance data directly from rock or/and soils (Sabins, 1999; Di Tommaso and Rubinstein, 2007; Zhang et al., 2007; Pour and Hashim, 2012; Mahboob, Iqbal, and Atif, 2015).

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data Hydrothermal alteration minerals with diagnostic spectral absorption properties in the visible and near-infrared through the shortwave length infrared regions can be identified by multispectral and hyperspectral remote sensing data as a tool for the initial stages of porphyry copper and epithermal gold exploration (Di Tommaso and Rubinstein, 2007; Zhang, Pazner, and Duke, 2007; Ramakrishnan and Bharti, 2015). In general, porphyry copper deposits are formed by hydrothermal alteration of fluids. These altered rocks can be identified through their spectral characteristics in the visible and infrared wavelengths (Pour and Hashim, 2012). Many minerals have unique and characteristic spectral properties, with a specific amount of electromagnetic (EM) energy reflected and/or absorbed at a particular wavelength, which can be used to identify them with a high degree of confidence. The portion of the electromagnetic spectrum from 0.4 to about 2.5 Οm (the visible, near-infrared, and shortwave-infrared region) is useful to sense the geological features with moderate and low-temperature properties because most of the sunlight is reflected in this portion of the spectrum (Mahboob, Iqbal, and Atif., 2015a). Usually, Iron oxides, oxyhydroxides, and ligands can be mapped very well in this range of the electromagnetic spectrum because of their high- or low-temperature alteration characteristics (Clark et al., 1990). This portion of the spectrum can also be used for differentiation between silicate (clay) minerals and other features. This spectral differentiation of minerals has been the basis for the use of this technique in mineral exploration (Calvin, Littlefield, and Kratt, 2015). The thermal infrared (TIR) portion of the spectrum, usually from 7 to 14 Οm, senses the energy emitted from the Earth's surface. In addition to water, carbonates, and sulphates, this region of the electromagnetic spectrum is also sensitive to Si-O bonds in silicates (Repacholi, 2012; Udvardi et al., 2017; Manley, 2014). The spectral signatures of some typical minerals (calcite, orthoclase feldspar, kaolinite, montmorillonite, and haematite) that can be clearly and confidently mapped using reflectance spectroscopy data (i.e. hyperspectral data) are shown in Figure 1. In this study, the identification of hydrothermally altered rocks and features associated with hydrothermal mineralization in South Africa’s Gauteng and Mpumalanga provinces is examined using Landsat 8 (originally known as Landsat Data Continuity Mission (LDCM)) remote sensing reflectance spectroscopy.

(80,9<='& <-3<27;:2<=5.<279;=6-=56/<=7*.82;3=/8:<9;35=8:=71<=!858+3<) :<;9 8:-9;9<4)=;:4=51697$;!< 8:-9;9<4=9<086:5=6-=71<=<3<2796/;0:<782 5.<279,/= ;3<)=" '

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;7<98;3=;:4=/<71645 The study areas for this research were Roodepoort and Westonaria in Gauteng Province and Witbank and Kriel in Mpumalanga Province, as shown in Figure 2. Gauteng's name is derived from the Sotho, ‘Gauta’ which means ‘gold’, with the local suffix ‘-eng’. Gauta has been taken from the Dutch word for gold, ‘goud’. The main economic sectors are financial services, business services, logistics, communications, and mining. The most significant geological formation in Gauteng is the Witwatersrand Supergroup. Gold in this region was derived from granitegreenstone terranes and transported to and concentrated in the Witwatersrand Basin by fluvial activity. Gauteng was built upon the wealth of gold found deep underground, i.e. almost 40% of the world’s reserves (Durand, 2012). Mpumalanga is another mineral-rich province of South Africa. The general geology of of this area consists of mudrock, siltstones, sandstones, conglomerate, and several coal seams. Mpumalanga accounts for 83% of South Africa's coal production. Ninety per cent of South Africa's coal consumption is used for electricity generation and the synthetic fuel industry (Dabrowski et al., 2008).

Usually, ASTER is the most commonly used satellite data for hydrothermal mineral mapping and exploration. However, since 2008, ASTER’s six SWIR sensors have not been operational because of malfunctioning (Wessels et al., 2013). Landsat data is also free and is used in mapping and exploration of hydrothermally altered minerals and rocks. In this research, the cloud-free level 1T (L1T) data from the Landsat 8 satellite (path 170 / row 78) recorded in August 2017 was used. Landsat images are processed in units of absolute radiance using 32-bit floating-point calculations. These values are then converted to 16-bit integer values in the finished Level 1 product (Chander and Markham, 2003). Landsat 8 is equipped with the Operational Land Imager

(80,9<="& ;.=6-= ;,7<:0=;:4= .,/;3;:0;=.96!8:2<5=6-=%6,71= -982;=

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data (OLI) and the Thermal Infrared Sensor (TIRS); their spatial and spectral characteristics are shown in Table I. A single Landsat 8 image covers an area of 170 km (north-south) by 183 km (east-west).

The pre-processing of raw satellite images is necessary to obtain geometrically and atmospherically corrected images so that the spectral information can be extracted and analysed. The raw satellite images of the study areas are shown in Figure 3. The geometric correction entails the georeferencing of satellite images with respect to ground control points in order to obtain pixels with the same dimensions. For atmospheric corrections there are two different approaches: relative normalization (Schroeder et al., 2006) and absolute correction (Chavez, 1996; Song and Woodcock, 2003). Relative normalization includes radiometric correction of the Landsat data-sets with respect to a reference image based on the correlation between pseudo-invariant objects from multidate images (Song et al., 2001). Absolute correction can be subdivided into two more categories: empirical and physical-

based methods. In the empirical approach, the spectral properties of the ground features are used for transforming the spectral data from the sensor’s radiance to ground reflectance. Empirical methods are simple but do not incorporate the pixel-to-pixel variation in atmospheric effects, whereas the physical-based methods, such as Atmospheric/Topographic CORrection or ATCOR (Richter, 1997), MODerate resolution atmospheric TRANsmission or MODTRAN (Berk et al., 1998), and the Satellite Signal in the Solar Spectrum (6S) code (Vermote et al., 1997), incorporate the heterogeneity of the atmosphere but require several complicated and manual procedures, which make it difficult to process large amounts of satellite data. On the other hand, the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software (Masek et al., 2006), which has implemented the 6S code, made atmospheric correction for Landsats 4–7 fully automated. Recently, Vermote et al. (2016) derived an improved atmospheric correction algorithm for Landsat 8 (L8SR), which has shown an improvement over the ad-hoc Landsat 5–7 LEDAPS product. The modified LEDAPS product was applied in this study for atmospheric corrections as shown in Figure 4.

;:45;7= =5;7<3387<=5.<279;3=;:4=5.;78;3=4<7;835 %.<279;3== +;:4= :,/+<9 1 2 3 4 5 6 7 8 9 10 11

%.<279;3= +;:4= :;/<

;!<3<:071= /

%.;78;3= 9<563,786: /

Coastal aerosol Blue Green Red Near-Infrared (NIR) Shortwave-Infrared (SWIR) 1 Shortwave-Infrared (SWIR) 2 Panchromatic Cirrus Thermal-Infrared (TIRS) 1 Thermal-Infrared (TIRS) 2

0.43 - 0.45 0.45 - 0.51 0.53 - 0.59 0.64 - 0.67 0.85 - 0.88 1.57 - 1.65 2.11 - 2.29 0.50 - 0.68 1.36 - 1.38 10.60 - 11.19 11.50 - 12.51

30 30 30 30 30 30 30 15 30 100 100

(80,9<= & ;$)=,:.962<55<4= ;:45;7=5;7<3387<=8/;0<5=6-=71<=57,4* ;9<;5=

As OLI band 1 (coastal aerosol) is useful for imaging shallow waters and band 9 (cirrus) for detecting high-altitude clouds and tracking fine particles like dust and smoke, these two bands were not included in further analysis. Moreover, according to the literature, for mineral exploration mapping the most appropriate bands are located in the visible, NIR, and SWIR regions. In this research study, OLI bands 2–7 and TIRS bands 10–11 were used for advanced processing. All these bands were stacked as a single image using the layer stacking digital image processing technique (Mahboob, Atif, and Iqbal, 2015).

Usually, the L1T images of Landsat 8 consist of the digital numbers (DNs), which cannot be used due to lack of physically meaningful information and should be converted

(80,9<= & 7/65.1<982;33*=2699<27<4=;:4=<:1;:2<4= ;:45;7=5;7<3387< 8/;0<5=6-=71<=57,4*=;9<;5=,58:0=:<;9 8:-9;9<4)=9<4)=;:4=09<<:=5.<279;3 +;:45=26/+8:;786: VOLUME 119

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Table I

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data to surface reflectance. This conversion is required for quantification of different features in remote sensing data as it incorporates solar conditions (geometry, illumination, and intensity) when the images were captured. In this study, the data was converted to Top of Atmosphere (TOA) reflectance using radiometric coefficients as recommended by Roy et al. (2016), whereby DNs were converted to reflectance representing the ratio of the radiation reflected from a surface to the radiation striking it (Han and Nelson, 2015), as shown in Figure 5. Equation [1] was used to convert DN values to TOA reflectance (Zanter, 2016): [1] where = M

=

A Qcal

= = =

Top of Atmosphere Planetary Reflectance (dimensionless) Reflectance multiplicative scaling factor for the band Reflectance additive scaling factor for the band Level 1 pixel value in DN Solar elevation angle (degrees).

The data captured by Landsat 8 represents the reflected and/or emitted spectral energy, which can be further used based on the absorption characteristics of spectra to detect different materials and features of the Earth’s surface. Some minerals and mineral groups in hydrothermally altered rocks have unique absorption characteristics in the EM spectrum. For example, some alunite and clay ores have unique absorption features at approximately 2.1 Οm, and their spectral responses are much higher at approximately 1.7 Οm

(Sabins, 1999). Usually, minerals like iron oxide and sulphate have low and high reflectances in the ultraviolet/blue and near-infrared portion of the EM spectrum respectively (Johnson et al., 2016), hence these minerals have a rusty shade in a natural colour image.

Band-ratioing is a digital image-processing technique that enhances the contrast between features by dividing a measure of reflectance for the pixels in one band by that of the pixels in another band of the same satellite image. This technique has been widely used for visualizing and mapping hydrothermally altered rocks. For example, Han and Nelson (2015) efficiently used the image ratios of Landsat Thematic Mapper (TM) band 5 (1.55–1.75 Οm) over band 7 (2.09– 2.35 Οm) to differentiate areas with high concentrations of alunite and clay, where pixels in the satellite image appear bright. Another study, conducted by van der Meer (2004), used the ratio image of band 3 (0.63–0.69 Οm) over band 1 (0.45–0.515 Οm) to highlight areas with rich iron ores. In the current research work different band ratios, as shown in Table II, were developed and applied in order to enhance hydrothermally altered rocks and lithological units. The selection of bands is related to the spectral reflectance and position of the absorption bands of the mineral or assemblage of minerals to be mapped. Even though the band-ratioing technique works very well for visualization, it is not capable of mapping or quantification of the land features. One of the reasons for this limitation is that most of the optical multispectral remote sensing instruments use the bandwidths > 0.05 Οm, which is too wide to explicitly differentiate the unique spectral absorptions related to specific alteration minerals (van der Meer, 2004). Furthermore, many band-ratioing techniques use only two or (sometimes) three bands, whereas multispectral remote sensing instruments offer many more bands than that. Based on these limitations, there is a need to adopt another advanced digital satellite image analysis approach which can utilize all available satellite bands together, as described in the following sections.

The spectral band combination technique, also known as red-

Table II

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(80,9<= &%.<279;3=+;:45=6-= ;:45;7= =5;7<3387<=

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%.<279;3= +;:4=:;/<

;!<3<:071 :/

*.<=6-=/8:<9;3 <:1;:2<4

1

Red Blue

640–670 450–510

Iron oxide

2

Shortwave-Infrared (SWIR) 1 Shortwave-Infrared (SWIR) 2

1570–1650 2110–2290

Hydroxylbearing rock

3

Shortwave-Infrared (SWIR) 2 Near-Infrared (NIR)

2110–2290 850–880

Clay minerals

4

Shortwave-Infrared (SWIR) 1 Near-Infrared (NIR)

1570–1650 850–880

Ferrous mineral

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data green-blue (RGB) combination, is a very useful image enhancement technique which offered powerful means to visually interpret multispectral satellite data (Novak and Soulakellis, 2000). The band combinations of satellite data can be true (natural) or false-colour composites (FCC) using individual bands or band ratios. For this purpose, several band ratios and bands combinations have been developed over time to differentiate lithologies in a satellite image. A few examples are given in Table III. In this research, the Kaufmann and Sabins ratios were applied to highlight the hydrothermally altered rocks associated with the minerals present in the study areas. Most research studies have applied these two ratios for the identification of altered areas and their associated minerals. For example, Mia and Fujimitsu (2012) and Abhary et al. (2016) applied the Kaufmann ratio for the mapping of minerals containing hydroxyl and iron ions using Landsat satellite data. Similarly, da Cunha Frutuoso (2015) used the Sabins ratio for the identification of sulphide deposits associated with the alteration areas of iron oxides.

Principal component analysis (PCA) is an advanced information extraction technique and is frequently used in the Earth sciences (Cheng et al., 2011, El-Makky, 2011). PCA is a well-known multivariate statistical method and has been generally used to study associations between variables. By orthogonal transformation, several correlated variables can be transformed into uncorrelated combinations (eigenvector loadings) of principal components (PCs) based on their covariance or correlation matrix (Horel, 1984; Loughlin, 1991). Generally, the first few PCs highlight the most variability in the original data-set (Panahi, Cheng, and Bonham-Carter, 2004). Therefore, PCA reduces the dimensionality and redundancy of data-sets and is commonly applied to enhance information interpretability (Cheng et al., 2011; Horel, 1984; Jolliffe, 2002). According to the algorithm for PCA, PCs are linear combinations of the original variables, whereas each PC incorporates the input variable uniquely and signifies only limited information within the complete dataset (Abdi and Williams, 2010). Due to its handling of multivariate data-sets, PCA has been extensively used in remote sensing for geological mapping of ores, igneous rocks,

strata, etc. (Grunsky. Mueller, and Corrigan, 2014). Generally, the spectral properties of different features present in the area, i.e. vegetation, rocks, and soils, are responsible for the statistical variance mapped into each PC, which becomes the basis of the Crosta technique (Tangestani and Moore, 2001). In this study, the same technique has also been applied based on highly variable non-correlated satellite bands for hydrothermally altered rocks.

<5,375=;:4=4852,5586: The effectiveness of remote sensing-based hydrothermal minerals identification and mapping depends on the clear differentiation of the reflected spectra of altered bedrock from those of the other objects. The true colour composite of bands 3, 2, and 1 as red, green, and blue respectively highlighted the textural characteristics of the igneous rocks, which could be separated from those of sedimentary rocks. Pournamdari, Hashim, and Pour (2014) tested the same satellite band combinations and found them to be effective for differentiating igneous rocks in south Iran. The false-colour composite was assigned to bands 4, 3, and 2 as red, green, and blue respectively as shown in Figure 6 to analyse the reflected satellite spectroscopy. The false-colour composite is important to enhance the regional geological and geomorphological features, as also reported by Bedini (2009). Vegetation appeared in red shades because of the near-infrared (0.7–1.2 Οm) band, which was highlighted with a red colour and vegetation reflects the maximum in this band. Hydrothermally altered clay and carbonate minerals are recognizable as yellow areas in crystalline igneous rocks in the Gauteng and Mpumalanga districts as shown in Figure 7. This may be due to clay and carbonate minerals having absorption in the 2.1–2.4 Οm range (band 7 of Landsat 8) and reflectance at 1.55–1.75 Οm (band 6 of Landsat 8) properties. Van der Meer (2004) also reported the same absorption and reflectance bands for the clay minerals using NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Another study, conducted by Zaini et al. (2016), concluded that clay and carbonates have the same absorption and reflectance bands and these can be effectively used to map them using reflectance spectroscopy.

Table III

;:45;7= =5.<279;3=+;:4=26/+8:;786:5)= =26/+8:;786:5=-69=<:1;:2</<:7=6-=1*49671<9/;33*=;37<9<4=962 5 ;:4=69<+648<5

5

:;3*585=7*.<

%.<279;3=+;:45

*.<=6-=/8:<9;3=<:1;:2<4=

<-<9<:2<5

1

Kaufmann ratio

7 : 4 : 5 4 3 7

Red represents minerals containing iron ions; green represents vegetated zones, and blue represent hydroxyl-bearing minerals.

(Kaufmann, 1988)

2

Chica–Olma ratio

5 : 5 : 3 7 4 1

Red depicts clay; green represents iron ions; blue represents ferr in colour.

(Mia and Fujimitsu, 2012b)

3

Sabins ratio

5 : 3 : 3 7 1 5

Yellow represents hydrothermal alteration areas; black identifies water; dark green indicates vegetation, lighter green signifies clay-rich rocks; blue shows sand; red, pink or magenta indicates iron oxides.

(Sabins, 2007)

4

Sultan’s ratio

5 : 5 : 5 Ă— 3 7 1 4 4

Deep violet represents the hydroxyl minerals; green ferric ions, and blue the ferrous oxides.

(Gad and Kusky, 2006)

Abrams ratio

5 : 3 : 4 7 1 5

Hydrothermally altered iron- oxide represented as green and clay minerals as red. (Pour and Hashim, 2012)

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Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data

(80,9<= & =26/+8:;786:=-69=+;:45= )= )="#= :1;:2<4=8/;0<5=$1<9< 6,7296.=85=9<.9<5<:7<4=8:=51;4<5=6-=09<<:=;:4=!<0<7;786:=8:=4;9 =9<4=

in dark blue represent the iron deposits in Gauteng and Mpumalanga. In addition, the research conducted by Holmes and Lu (2015) supported the results of this study and highlights the potential of iron ore deposits in Gauteng and Mpumalanga. Gold cannot be ‘seen’ directly in any remotely sensed satellite image. However, the presence of this precious metal can be mapped through its association with several other minerals based on their spectral reflectances (Kotnise and Chennabasappa, 2015). The group of minerals present in the alteration zones related to gold deposits generally includes the clay minerals illite-1M and illite-2M1, dioctahedral smectite, and kaolinite (Drews-Armitage, Romberger, and Whitney, 1996). These minerals have characteristic spectral signatures mostly in the shortwave infrared portion of the electromagnetic spectrum. These spectral signatures can be used to map the sites that are most favourable for the occurrence of gold deposits, which is very cost- and timeeffective for mineral exploration programmes. The band combination of shortwave infrared 2 with spectral band 2.11– 2.29 Îźm and near infrared with spectral band 0.85–0.88 Îźm

(80,9<= & *.82;3=5.<279;3=9<-3<27;:2<=2,9!<=6-=896:=6 84<=8:=36;/=56835 ;7164= #)=" ' =

(80,9<= & =26/+8:;786:=-69=+;:45= )= )='#= :1;:2<4=8/;0<5=$1<9< 6,7296.=85=9<.9<5<:7<4=8:=51;4<5=6-=*<336$=

The iron oxides present within the study areas were highlighted by the spectral band ratio of red and blue bands as discussed in Table II. The soils rich in iron oxides reflect more in the red band of the spectrum, i.e. 0.64–0.67 Οm and have absorption characteristics in the blue band i.e. 0.45– 0.51 Οm (Schwertmann, 1993). The typical reflectance curve of iron oxide-rich soils is shown in Figure 8. Pour and Hashim (2015) have also shown the importance of the red and blue bands of Landsat data for mapping of iron oxides in Iran. In 2017, Pour et al., also identified the iron-rich mineralized zones of remote Antarctica from Landsat imagery by using spectral band ratio techniques. The map of potential iron oxides present in the study areas derived from the band ratio of the red and blue wavelengths of the spectrum is shown in Figure 9. This map showed a good agreement with the Council for Geoscience (CGS) map of iron deposits of South Africa, as indicated in Figure 10. The areas highlighted

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(80,9<= & ;:4=9;786=6-=9<4 =;:4=+3,< +;:4=<:1;:2<4=56835=$871 .67<:78;3=896:=6 84<5=9<.9<5<:7<4=8:=51;4<5=6-=.,9.3<=

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data

(80,9<=' & 96:=4<.65875=6-=%6,71= -982;= 6957<9)=" '"+ =

(80,9<=''& ;:4=9;786=-69=<:1;:2</<:7=6-=.67<:78;3=23;*=/8:<9;35 9<.9<5<:7<4=8:=51;4<5=6-=9<4=76=*<336$=

The other important band ratio comprising shortwave infrared and near infrared was applied for identification of ferrous minerals. The results showed that this band ratio technique produced good results for ferrous minerals in Gauteng but overestimated their occurrence in Mpumalanga. The soils with ferrous minerals reflect mostly in shortwave infrared 1 with spectral band 1.57–1.65 Οm and nearinfrared with spectral band 0.85–0.88 Οm (Ducart et al., 2016) and are shown in Figure 13 in shades of orange to yellow. The Kaufmann and Sabins ratios were developed by combining spectral reflections of satellite bands ratios, and the results proved to be promising for mapping of hydrothermal minerals (Mahandani, 2018). Da Cunha Frutuoso (2015) applied the same ratios to map hydrothermal gold mineralization in Portugal and found them to be very effective and accurate. Figures 14 and 15 VOLUME 119

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in Figure 11 shows the areas which have the potential to host clay minerals in shades of red to yellow. Liu et al. (2018) applied various image-processing techniques, including false-colour composite, band ratios, and matched filtering, to process ASTER satellite data and map the distribution of hydrothermal minerals associated with gold deposits, and concluded that the SWIR and NIR bands are the most effective for this purpose. In general, clay minerals, which are associated with gold mineralization potential, are more widespread in Gauteng Province compared to Mpumalanga Province (Durand, 2012; Sutton et al., 2006; Sutton, 2013). The approach that was applied to map potential gold mineralization in this study has also been applied by several other researchers. Safari, Maghsoudi, and Pour (2017) used a similar approach to identify gold mineralization and concluded that the integration of remote sensing data with other information led to the definition of locations possibly suitable for hosting SnW and Au-Ag occurrences. Another study, conducted by Gabr, Ghulam, and Kusky (2010), utilized the shortwave infrared and near-infrared bands of ASTER to map highpotential gold mineralization in Egypt. However, through an optical sensor satellite, it is quite difficult to directly map any mineral located at depths of 100 m or more in the ground, as in the case of Gauteng. However, there are always indirect measurements from the surroundings, such as altered rocks or soils on the surface, which give an indirect indication of the presence of these minerals. In this study, the identified potential gold areas (Figure 11) can be associated with gold mining tailings, because no direct or indirect measurement can be used to map surface gold potential in Gauteng due to the geological setting of the region. The map of gold deposits published by the CGS (Figure 12) was used in order to verify/support the potential gold (tailings) identified in the area. The map shows that there are several gold deposits in Gauteng Province but none or very few in Mpumalanga, which supports the results of the gold (probably gold tailings) map (Figure 11) generated during this study.

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data

(80,9<='"& 634=4<.65875=6-=%6,71= -982;= 6957<9)=" '"; =

(80,9<=' & ;:4=9;786=-69=<:1;:2</<:7=6-=.67<:78;3=-<996,5=/8:<9;35=8: 56835)=9<.9<5<:7<4=8:=51;4<5=6-=69;:0<=76=*<336$=

show the results obtained using these ratios for Gauteng and Mpumalanga respectively. In the Kaufmann ratio map, the red colour represents minerals containing iron; green represents vegetated zones, and yellow represents hydroxylbearing minerals. In the Sabins ratio map the shades of green represent hydrothermal alteration areas, black identifies water, the dark tones of green indicate vegetation, the lighter tones of greenish-yellow signify clay-rich rocks; blue shows sand, red, pink, or magenta indicates iron oxides. These colour shades for several Earth features and minerals are in accordance with the research done by Sabins (2007), and show the effectiveness of the Sabins ratio in mineral mapping. Similarly, the outputs of PCA also revealed very good results in terms of hydrothermal mapping of minerals present in the study areas. In our study, the principal component

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(80,9<=' & ;,-/;::=;:4=%;+8:5=9;7865=9<.9<5<:78:0=1*49671<9/;3 /8:<9;35=8:= ;,7<:0#= :=71<= ;,-/;::=9;786=/;.)=9<4=9<.9<5<:75 /8:<9;35=26:7;8:8:0=896:)=09<<:=!<0<7;7<4= 6:<5)=;:4=*<336$=1*496 *3 +<;98:0=/8:<9;35#= :=71<=%;+8:5=9;786=/;.=51;4<5=6-=09<<:=9<.9<5<:7 1*49671<9/;3=;37<9;786:=;9<;5)=+3;2 =84<:78-8<5=$;7<9)=4;9 =09<<: 8:482;7<5=!<0<7;786:)=$1<9<;5=71<=38017<9=76:<5=6-=09<<:851 *<336$ 580:8-*=71<=23;* 9821=962 5)=+3,<=516$5=5;:4)=9<4)=.8: )=69=/;0<:7; 8:482;7<5=896:=6 84<5

transformation specifies that the first principal component (PC1) is composed of a negative weighing of all total bands, as shown in Table IV. The PC1 is about 94.53% of the eigenvalue of the total variance for unstretched data of PCA. The eigenvector loadings for PC3 indicates that PC3 is dominated by vegetation, which is highly reflective in band 4; the positive loading of band 4 in this PC (0.602391) also indicates that strongly vegetated pixels will be bright in this PC image. Similar results were found by Cheng et al. (2011), as they have vegetation-dominated pixels in band 4 loadings for PC3. The eigenvalues for bands 2 and 4 in PC6 of Table IV are also opposite in sign, which indicates that iron oxides will be

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data

(80,9<=' & ;,-/;::=;:4=%;+8:5=9;7865=9<.9<5<:78:0=1*49671<9/;3 /8:<9;35=8:= .,/;3;:0;#= 636,9=2648:0=;5=-69=(80,9<=' =

distinguished by bright pixels in PC6. PC5 can be used to map the hydroxyl-bearing minerals because of the positive and negative eigenvalues of bands 7 and 5 respectively, and these minerals appeared as dark. These selected PCs were further used in the Crosta technique; the hydroxyl (H) and iron oxide (F) images are combined with selected PCs to produce a map revealing the pixels indicating abnormal concentrations of both hydroxyls and iron oxides. The PCs having positive eigenvalues from both input bands are selected for the analysis. Figure 16 shows the Crosta images of hydroxyl (H), hydroxyl plus iron oxide (H+F), and iron oxide (F) as an RGB composite. This band combination returns a dark bluish composite image on which alteration zones are highlighted as bright regions, as also discussed by Ciampalini et al. (2013).

The most common method to evaluate the results of this sort of mineral mapping is by spectrometer and/or spectral testing of samples in the laboratory. According to Clark (1999) there are two types of method for authentication of information extracted from remotely sensed imagery: virtual and in situ. If the spatial and/or spectral resolution of remotely sensed satellite data is fine and accurate then virtual verification can be done by inspecting the remote sensing data directly and comparing it with already published reports or data. In this

(80,9<=' = = *49671<9/;3=/8:<9;35=/;.=0<:<9;794=+*=71<= 9657; 7<21:8 ,<#= 37<9;786:= 6:<5=;9<=180138017<4=;5=+98017=9<086:5=

paper, virtual verification, i.e. visual interpretation of absorption of spectral bands and comparison with the published maps by the CGS was used to evaluate the results of hydrothermal mineral exploration, along with the several similar research studies done by different researchers. Good qualitative agreement was observed for the results of PCA and the spectral reflectance of the satellite data.

6:23,586:5 This study confirms that Landsat reflectance spectroscopy data can be used easily and efficiently to map hydrothermal minerals. Different digital image processing algorithms and techniques were applied to assess their significance for hydrothermal mineral exploration. The principal component analysis (PCA)-based Crosta technique and band ratio techniques like the Kaufmann and Sabins ratios proved to be more significant and efficient for hydrothermal mineral exploration. Several researchers have also concluded that advanced image processing techniques like those applied and tested in the current study are quite efficient in terms of mineral mapping through remote sensing data (Manuel et al., 2017; Liu et al., 2018). The maps produced in this study are not only appropriate for any spatial queries and analysis, but also for environmental modelling such as assessing the impacts of mining activities on environmental features such

Table IV

:.,7=+;:45 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Eigenvalues (%)

'

"

0.360613 0.346576 -0.352093 -0.489315 0.484542 -0.389437 94.53

-0.149047 0.127976 -0.273256 0.846570 0.143013 0.386786 7.93

-0.473757 0.409188 0.602391 -0.302391 0.471431 -0.256332 3.67

0.642169 0.052900 0.608503 -0.155955 0.321111 0.295141 0.08

-0.156241 -0.093199 0.463980 -0.127694 0.435239 0.738839 0.04

-0.431860 0.827397 -0.352654 0.055763 -0.037414 0.005991 0.001

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98:28.;3=26/.6:<:7=;:;3*585=6-= ;:45;7=5;7<3387<=+;:45=-69= ;,7<:0=;:4= .,/;3;:0;=

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data as the forest, farmland, and urban areas. The hydrothermal minerals identified in this study are only the estimation based on literature-cited, proven algorithms for remotely sensed data, and should be treated as a preliminary assessment of the study areas. The other important point to consider is the resolution of the satellite data used in this study; Landsat 8 has a 30–100 m spatial and moderate spectral resolution. Data-sets with this kind of resolution may suffice for regional work but not for detailed mineral mapping. Using satellite data with higher spatial and spectral resolutions, such as that from Worldview 3 satellite (Kruse, 2015) or aerial drones, may be more suitable for detailed minerals mapping. Nevertheless, the maps developed in this research are a valuable data source for comprehensive studies to be conducted in the future. The methodology developed in this study is cost-effective and time-saving, and can be applied to inaccessible and/or new areas with limited groundbased knowledge where reliable and up-to-date minerals information is desired.

2 :6$3<40/<:75 The work presented in this paper is part of a PhD research study in the School of Mining Engineering at the University of the Witwatersrand. The authors would like to acknowledge the administrative and financial support provided by School of Mining Engineering, as well as the School of Advanced Geomechanical Engineering (SAGE), at the National University of Sciences and Technology (NUST) Risalpur Campus, Pakistan.

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KRUSE, F.A. 2015. Comparative analysis of airborne visible/infrared imaging spectrometer (AVIRIS), and hyperspectral thermal emission spectrometer (HyTES) longwave infrared (LWIR) hyperspectral data for geologic mapping. Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. International Society for Optics and Photonics. 94721F. doi: 10.1117/12.2176646 LIU, L., LI, Y., ZHOU, J., HAN, L., and XU, X. 2018. Gold-copper deposits in Wushitala, Southern Tianshan, Northwest China: Application of ASTER data for mineral exploration. Geological Journal, vol. 53. pp. 362–371. LOUGHLIN, W. 1991. Principal component analysis for alteration mapping. Photogrammetric Engineering and Remote Sensing, vol. 57. pp. 1163–1169. Maduaka, A.C. 2014. Contributions of solid mineral sectors to Nigeria’s economic development. Eastern Mediterranean University (EMU) and DoÄ&#x;u Akdeniz Ăœniversitesi (DAĂœ). MAHANDANI, S., PUTA, I., HIRAWAN, A., ABBAS, R., and TITISARI, A. 2018. Integrated remote sensing and geological mapping to identify landslide prone zone in Loano, Purworejo, Central Java. Proceedings of EAGE-HAGI 1st Asia Pacific Meeting on Near Surface Geoscience and Engineering. doi: 10.3997/2214-4609.201800372 MAHBOOB, M.A., ATIF, I., and IQBAL, J. 2015. Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, vol. 34. pp. 179–188. MAHBOOB, M.A., IQBAL, J., and ATIF, I. 2015. Modeling and simulation of glacier avalanche: A case study of gayari sector glaciers hazards assessment. IEEE Transactions on Geoscience and Remote Sensing, vol. 53. pp. 5824–5834. MANLEY, M. 2014. Near-infrared spectroscopy and hyperspectral imaging: nondestructive analysis of biological materials. Chemical Society Reviews, vol. 43. pp. 8200–8214. MANUEL, R., BRITO, M.D.G., CHICHORRO, M., and ROSA, C. 2017. Remote sensing for mineral exploration in Central Portugal. Minerals, vol. 7. p. 184. https://pdfs.semanticscholar.org/9b8a/8073aadc6d19c5e4f14f517fe4450 96b8465.pdf MARJORIBANKS, R. 2010. Geological mapping in exploration. Geological Methods in Mineral Exploration and Mining. Springer. MASEK, J.G., VERMOTE, E.F., SALEOUS, N.E., WOLFE, R., HALL, F.G., HUEMMRICH, K.F., GAO, F., KUTLER, J., and LIM, T.-K. 2006. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters, vol. 3. pp. 68–72. MIA, B. and FUJIMITSU, Y. 2012. Mapping hydrothermal altered mineral deposits using Landsat 7 ETM+ image in and around Kuju volcano, Kyushu, Japan. Journal of Earth System Science, vol. 121. pp. 1049–1057. NOVAK, I. and SOULAKELLIS, N. 2000. Identifying geomorphic features using LANDSAT-5/TM data processing techniques on Lesvos, Greece. Geomorphology, vol. 34. pp. 101–109. PANAHI, A., CHENG, Q., and BONHAM-CARTER, G.F. 2004. Modelling lake sediment geochemical distribution using principal component, indicator kriging and multifractal power-spectrum analysis: A case study from Gowganda, Ontario. Geochemistry: Exploration, Environment, Analysis, vol. 4. pp. 59–70. POUR, A.B. and HASHIM, M. 2012. The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geology Reviews, vol. 44. pp. 1–9. POUR, A.B. and HASHIM, M. 2015. Regional hydrothermal alteration mapping using Landsat-8 data. Proceedings of Space Science and Communication (IconSpace) 2015. IEEE. pp. 199–202. POURNAMDARI, M., HASHIM, M. and POUR, A.B. 2014.Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex, south Iran. Advances in Space Research, vol. 54, no. 4. pp. 694–709. RAMAKRISHNAN, D. AND BHARTI, R. 2015. Hyperspectral remote sensing and geological applications. Current Science, vol. 108. pp. 879–891. RATHOD, P.H., MĂœLLER, I., VAN DER MEER, F.D., and DE SMETH, B. 2016. Analysis of visible and near infrared spectral reflectance for assessing metals in soil. Environmental Monitoring and Assessment, vol. 188. pp. 558. REPACHOLI, M. 2012. Clay Mineralogy: Spectroscopic and Chemical Determinative Methods. Springer Science & Business Media. RICHTER, R. 1997. Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. International Journal of Remote Sensing, vol. 18. pp. 1099–1111. ROY, D.P., KOVALSKYY, V., ZHANG, H., VERMOTE, E.F., YAN, L., KUMAR, S., and EGOROV, A. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, vol. 185. pp. 57–70.

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THE ORCID INITIATIVE – AUTHOR REGISTRATION The SAIMM has joined the ORCiD initiative, and from 2019 authors (and co-authors) of papers submitted to the Journal or conferences will need to include their ORCiD identifiers (ORCiD iDs). An ORCiD is a persistent digital identifier that distinguishes authors from every other researcher, using a 16-digit code, e.g. https://orcid.org/0000-0002-3843-3472. ORCiD (Open Researcher and Contributor ID) is a is an open, not-for-profit organisation with the aim of providing a persistent and unique digital identifier – an ORCiD (pronounced ORC-iD) – to any individual who is involved in research, scholarship, and innovation activities, similar to the way in which the digital object identifier (DOI) constitutes a persistent identity for documents on digital networks. The ORCID record can be enhanced with the registrant’s professional information and linked to other identifiers (such as Scopus, ResearcherID, or LinkedIn). The National Research Foundation (NRF) in South Africa requires that all researchers and students applying for funding and rating at the NRF have an ORCiD identifier. This allows them to uniquely identify themselves as the author of their work across all systems integrated with the ORCID registry. Unique identification is particularly import when a researcher’s citations are assessed. If there is any confusion about an author’s details then the citations are separated, e.g. JA Smith, John Smith, J Smith. Citation counters will interpret these formats as three different people. Using an ORCiD will automatically group all this individuals research together. There are currently 1025 ORCiD member organisations, including publishers, research institutes, universities, professional associations, and funding agencies, and around 6 million registrants. To obtain your unique ORCiD identifier, register now at https://ORCiD.org/register The process is simple and takes less than a minute.

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a8

The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery, South Africa by M. Katuruza1 and C. Birch2

the absence of a short-term geological model that could be used for short-term mine planning. Short-term models are built by mine geologists, when they take the latest release of the resource geological model and add in data as it becomes available from progressive mining. The data includes surveyed highwall seam-roof and/or seam-floor contacts, faults, dykes, blast-hole data, grade control hole data, and any other measurements deemed necessary for modelling. Highwall mapping with a survey total station is standard practice at Isibonelo Colliery. Recent technological advances have made new digital photogrammetry techniques available for mapping, with a camera being carried by a UAS which does highwall mapping. The process of short-term geological modelling allows for the incorporation of small-scale features, such as seam rolls and previously unidentified faults, which may not be obvious from broadly spaced exploration drill-hole data.

&%.5+/1/ The purpose of this study was to demonstrate unmanned aircraft system (UAS) technology in opencast highwall mapping using the experience at Isibonelo Colliery in Mpumalanga Province of South Africa. In opencast mines, geological mapping has evolved over time and there have been challenges in extracting valuable information from exposed highwalls due to restricted access for safety reasons. A UAS was used at Isibonelo Colliery to conduct highwall mapping, using drone-based digital photogrammetry techniques. A demonstration survey was undertaken during February 2018. The highwall flyover demonstration was planned for two areas (North and South pits) where field control points were placed by the mine survey department. A contractor used a DJI Phantom 4 (Pro) for the highwall mapping. Raw data was processed within 48 hours and a 3D model of the mapped pit area was produced. Further data extraction included obtaining updated weathering and lithological contact elevations and thicknesses for use in short-term planning. The results showed a good correlation between the resource model and the UAS data model. The project was considered a success and highwall flyover mapping and is now standard practice at Isibonelo Colliery. 6% 54*/ geological model, highwall mapping, coal mining, unmanned aircraft systems, UAS, drone, photogrammetry.

'67 &726!'.5-5)%73.* +'525)43,,6241!7+45!6//1.) Detailed geological mapping is crucial to the understanding of the structure of any mineral deposit. Several mapping methods may be applied depending on the available exposure/outcrop and nature of the orebody; for example, coal deposits typically suboutcrop only, therefore an opencut highwall provides the best exposure for mapping the geology. Mapped information, when incorporated into a resource model (generated from exploration borehole data), improves the understanding of the orebody or coal seam. This research paper focused on the use of unmanned aircraft system (UAS) technology for pit highwall mapping to generate data for updating geological models and avail the latest information to mine planning to improve the short-term plans. The Isibonelo Colliery’s geological model is updated annually and is termed a resource geological model, from which the mining model is built. This research was motivated by

UAS technology has developed rapidly in South Africa in the last 6 years, following the regularization of its use by the country’s controlling body, the South African Civil Aviation Authority (SACAA). These aircraft are also referred to as unmanned aerial vehicles (UAVs) or remotely piloted aircraft (RPAs). (Bucalo et al., 2015). The latest data from Business Insider indicates that there are 686 operators with remote pilot licences (RPLs) in South Africa. (Caboz, 2018).

1 Geology Manager–Production, Isibonelo Colliery, Anglo American Coal, South Africa. 2 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Oct. 2018; revised paper received Feb. 2019.

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The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery UASs are used for geological and topographic mapping, coastal control, landslide inspections, and are capable of integrating geophysical instrumentation like magnetic, electromagnetic, infrared, radar, and natural gamma ray sensors (Bucalo et al., 2015). Therefore, UASs have become a very important tool in the field of geoscience. The UAS discussed here uses a high-resolution camera that provides excellent ground coverage. The aircraft are generally inexpensive, with small commercial systems like the DJI Phantom 4 costing a few thousand rands. UASs are good at focusing on specific areas and do not require excessive flight lines to cover an area of interest. UASs are a useful tool for extracting geological information from inaccessible areas. Data is generated rapidly; an area that takes three days of physical mapping may take only an hour or less to capture with a UAS. On completion of the UAS survey, the captured data is processed to produce a 3D surface model. In an open pit setting. The lithological contacts can then be mapped from the 3D model. Figure 1 shows one of the most modern UAS platforms used in South Africa, the DJI Phantom 4. The DJI Phantom 4 has a camera with the capability of recording in RAW image format, which makes editing even easier. (Caboz, 2018). It is fitted with a 1-inch 20-megapixel sensor capable of shooting 4K/60fps video. Previously, the perspective gained from the UAS could only be attained from a manned aircraft, such as a helicopter. With the UAS it is possible to obtain digital images of the highwall as well as a multitude of digital terrain points, all in 3D space. ‘Structure from Motion or SfM is a photogrammetric method for creating three-dimensional models of a feature or topography from overlapping two-dimensional photographs taken from many locations and orientations to reconstruct the photographed scene.’ (Ullman, 1979). Thus, the software processes digital photographs, forming a 3D surface image or point cloud that can be compared against the 3D geological model. Software packages like Agisoft, 3-D Rippler, and Geo Arc use algorithms (mathematical descriptors of each feature on the images) to create a 3D image through triangulation of

matched image pairs. The result of the triangulation process is initially a sparse point cloud. Finally, a dense point cloud was produced for the highwall map. The greater the amount of collected high-resolution images, the more detailed the model produced, a dense 3D image of the pit. The generated drone data model was validated against the resource model and actual survey data. CloudCompare, a 3D point cloud manipulation software package, was used to extract geological mapping data from the drone data model, which was then exported to Stratmodel (the geological modelling software used at Isibonelo) for update of the shortterm geological model.

'67 /1 5.6-57 &7'1)' 3--7,3++1.)769+6416.!677 Isibonelo Colliery is situated near the northern margin of the Highveld Coalfield of Mpumalanga Province (Figure 2). The area is underlain by sedimentary strata of the Karoo Supergroup, which was deposited in the Permian period (248–290 Ma) (Anhaeusser and Maske, 1986). The Karoo sequence is made up predominately of sediments and is capped by a sequence of volcanic strata. Three stratigraphic units define the sedimentary succession of the Karoo Supergroup. From oldest to youngest, these are the Dwyka, Ecca, and Beaufort groups (Cadle, 1995). Coal seams of the

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The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery Highveld Coalfield are hosted within the Vryheid Formation of the Middle Ecca Group (270 Ma). Sediments of the Vryheid Formation represent coal-capped upward-fining cycles of clastic sediments deposited in a fluviodeltaic/shallow marine environment. After considering several options for highwall mapping (e.g. total station survey) at Isibonelo, so that geological data could be produced for the short-term geological modelling, a UAS flyover was eventually given a trial in February 2018. The demonstration was carried out by Vidblast (Pvt) Ltd and Premier Mapping. Permissions and flight planning were needed before the survey could be undertaken. The planning included delineation of the mapping area and determining the position of the surveyed marker pegs, which serve as ground control points (GCPs) for the survey. The coordinates (in .csv format) of the control pegs were sent to the service provider for flight planning purposes. The demonstration was planned to cover selected areas in both the North and South pits. Figure 3 is an overview reference map of the survey areas. Highwall mapping focused on delineation of the weathering horizons, namely the base horizon of ‘softs’, which include soils and sands (BHSO), the base horizon of friable weathering (BHFW), the base horizon of weathering (BHWE), and finally the interburden and the coal seams (no. 5 and no. 4 seams). Figure 4 illustrates typical weathered horizons of a highwall where the UAS flyover mapping project was conducted at Isibonelo Colliery. The South pit survey covered the furthest southern area between the S5 and S6 ramps, through to the end of the cut. For the North pit, the survey was between the N1 and N2 ramps (strip 31 and strip 32). Figure 5 illustrates the UAS flyover focus area in the North pit, as planned in the survey office in conjunction with the author The UAS demonstration flight was planned for both pits on the same day. The area delineated in yellow in Figure 5 was defined by control points pegged on the ground by the surveyor prior to the flight. Since this was the maiden flight, the process required a surveyor on site on the day of the flight to provide additional

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control points if required. Additional control points were pegged on the day of the flight to ensure optimal georeferencing of the final 3D model.

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The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery The demonstration flight was conducted by a licenced UAS pilot using the DJI Phantom 4. The UAS was flown from a mini-pad at the back of a utility vehicle parked on the dragline walkway bench on the low wall (spoils) side of the pit. The flight height was 50 m above the pit, with six flight lines being flown, each 500 m in length with a spacing of 60 m between lines.

&7*3237+45!6//1.) The data produced during the highwall mapping consisted of 3D point clouds comprising 13 million data-points over the South pit and 11 million data-points over the North pit. These were produced from digital photographs with a 60% overlap between images in the series. The UAS captured finer structures along the highwall with its high-resolution camera. The data was processed using Agisoft, a 3D photogrammetry software package for drone data processing. Agisoft builds a 3D point cloud using SfM. The data points were first cleaned out to remove any anomalies, especially the machinery that was working in the pit during the flyover, as this can distort the model. The GCPs assisted in georeferencing of the model within the surveyed area. The control points pegged on the ground are visible in the image of the model (Figure 6). The software also processed the digital photos to form a 3D surface image, or textured mesh. CloudCompare was used to compare the data provided by this survey to 3D data from the resource model. The UAS model was further validated by a surveyor and geologist who mapped the no. 4 and no. 5 seam top and bottom contacts in selected places, using a total station. The total station observations are shown on the 3D mesh model in Figure 7.

and is shown in Figure 9. There is a good correlation between the model contact and the results from the UAS data. The highwall UAS mapping technique is quick in generating geological data for updating geological models (short-term modelling) compared to the total station highwall mapping technique, which is time-consuming and prone to human error. The data resolution is exceptionally high, with a point cloud density of 13 million over an area of 120 000 m2 for the South pit demo area.

5.!-0/15./73.*746!5,,6.*3215./77 The spatial accuracy of the UAS data is high and eliminates human error when mapping the lithological contacts. The geological contacts are well defined in the 3D point cloud data, with the contacts already in a 3D coordinate system compatible with the geological model at Isibonelo Colliery. A UAS flyover generates data within minutes (e.g. 35 minutes over an area 500 m Ă— 240 m) over a very large area of the open pit. Furthermore, the digital photographs provide a usefull reference tool during the 3D modelling process. The turnaround time, from survey to completion of data processing, including a 3D model of the flown highwall area, can be as little as 48 hours, which is needed for decisionmaking in mining. There is also the scope for zooming into specific problem areas and extracting additional information without

.3-%/1/75 746/0-2/77 Figure 8 illustrates a comparison of the bottom and top contacts of the sandstone (as derived from drone data) to the BHFW from the resource model. The sandstone contacts are currently not modelled in the resource model (using the defined weathering surfaces only from borehole data). This is the reason for the short-term model intervention. The no. 4 seam top contact from the UAS data was compared to the geological model at the same point in space

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The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery

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86 646.!6/ ANHAEUSSER, C.R. and MASKE, S. 1986. Mineral Deposits of South Africa (Vols. 1-2). Geological Society of South Africa, Johannesburg. BIRCH, J. 2004. 3DM Analyst 2.1 trial at BMA Coal’s Goonyella Mine. ADAM

Technology. http:// www.adamtech.com.au/3dm/goonyella% 20report%20(abridged).pdf BUCALO, F., D'ALLESANDO, A., COLTELI, M., and MARTORANA, R. 2015. Drones: new technologies for geophysics? Near Surface Geoscience 2015. Proceedings of the 21st European Meeting of Environmental and Engineering Geophysics, Turin, Italy. European Association of Geoscientists & Engineers, The Netherlands. pp. 6–10. CABOZ, J. 2018. Business Insider South Africa. https://www.businessinsider.co.za/sas-most-popular-drone-remains-wayahead-of-its-competitors-here-are-the-5-reasons-why-2018-3 CADLE, A.B. 1995. Depositional systems of the Permian Vryheid Formation, Highveld Coalfield, South Africa, Their relationship to coal seam occurence and distribution. PhD thesis, University of the Wistwatersrand, Johannesburg DE BRUYN, D. 2018. Drone hole identification. Johannesburg. Unpublished report. KATURUZA, M. 2017. Competent Person Report, Resources Isibonelo Colliery. Unpublished meport KATURUZA, M. 2017. Competent Person's Report for Isibonelo Colliery. Unpublished, mine internal document. KATURUZA, M. and LEVINGS, S. 2017. Isibonelo Colliery CPR presentation. Unpublished internal presentation. KATURUZA, M. and PRETORIUS, D. 2018. Improving short term modelling using drone data at Isibonelo Colliery. Unpublished internal document. PRETORIUS, D.J. 2018. Isi drone data. Unpublished internal report. ULLMAN, S. 1979. Structure from motion (SfM) photogrammetry. Proceedings the Royal Society B, vol. 203, no. 1153. The Royal Society, London: pp. 405–426. : https://royalsocietypublishing.org/doi/abs/10.1098/ rspb.1979.0006 N VOLUME 119

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compromising the safety of man and machinery. This is not possible with conventional highwall mapping, which is limited by access. The UAS highwall mapping technique proved it can generate large volumes of useful geological data over a short period of time, something not achievable through physical mapping. The availability of new UAS technologies coupled with photogrammetry software to process data has improved the speed and accuracy of geological data acquisition and interpretation at Isibonelo Colliery. Geologists should be equipped with this new technology to update short-term geological models timeously. Training on software for building 3D models and extrapolation of data is also recommended for geologists using UAS for highwall mapping. In addition to highwall mapping, UAS technology is exceptionally useful in a mining environment, having numerous other applications which include blast monitoring, stockpile reconciliations, in-pit coal reconciliation, and inspection of mine infrastructure.

Hydrometallurgy Colloquium Impurity removal in hydrometallurgy 21–22 May 2019 Glenhove Conference Centre, Melrose Estate, Johannesburg

BACKGROUND

WHO SHOULD ATTEND

Removal of impurity metals plays a very important role in the final grade and quality of the target metals recovered in hydrometallurgical processes. From base to precious metals, a proper consideration of a metal separation and impurity removal circuit can have a huge impact on the success of an operation. This course aims to enhance knowledge in the separation and removal of metal ions in solution, with focus being on solvent extraction, ion exchange, precipitation cementation, membrane separation. Selected current practice and new technologies applicable to base metals, precious metals, uranium etc. will be considered.

> Process engineers (chemical, metallurgical) who would like a refresher course or enhance their knowledge on techniques used for impurity removal in hydrometallurgy. > Graduates from other disciplines such as chemistry or environmental science, who are involved in related work. > Plant operators who wish to enhance their skills and knowledge. > University postgraduate students.

TOPICS Day 1 — Fundamentals > Precipitation > Cementation > Membrane separation > Solvent extraction > Ion exchange

Day 2— Case Studies > Practical — Case studies

FOR FURTHER INFORMATION CONTACT: Yolanda Ndimande, Conference Co-ordinator, SAIMM P O Box 61127, Marshalltown 2107 · Tel: +27 (0) 11 834-1273/7 E-mail: yolanda@saimm.co.za · Website: http://www.saimm.co.za

Conference Announcement

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a9

Optimization of conditions for the preparation of activated carbon from olive stones for application in gold recovery by M. Louarrat1, G. Enaime1, A. Baçaoui1, A. Yaacoubi1, J. Blin2, and L. Martin2

The purpose of this study is to prepare a new activated carbon from olive stones for use in gold recovery by carbon-in-leach (CIL) and carbon-inpulp (CIP). The preparation method chosen was physical activation using steam. The effect of four process parameters: the residence time for carbonization, the activation temperature, the residence time for activation, and steam flow, were studied by the mean of response surface method (RSM) in order to optimize the yield, iodine index, and attrition characteristics. These two last responses were used as primary indicators of gold recovery capacity and mechanical strength. The results obtained show that optimal activated carbon can be prepared under the following conditions: a carbonization time of 157 minutes, activation at 921°C for 53 minutes, and a water vapour flow of 0.18 mL/min. This optimum carbon has an iodine value greater than 1100 mg/g and an attrition index in the order of 0.74%. These values reflect the quality of the precursor (olive stones) as a raw material for the development of an effective new activated carbon for the gold mining industry. E.&CA>? olive stones, activated carbon, response surface method (RSM), physical activation, gold recovery.

$BDAC>;@DFCB Activated carbon is an effective adsorbent widely used in industry due to its high surface area, well-developed reproducible microporous structure, and high degree of surface reactivity and adsorption capacity. World consumption of activated carbon is steadily increasing. It is primarily used in industrial wastewater and gas treatment. and also for silver and gold recovery from cyanide solutions (Syna and Valix, 2003; Soleimani and Kaghazchi, 2008; Buah and Williams, 2010; Eddy et al., 2011). However, activated carbon is expensive and it needs to be regenerated after each adsorption cycle. In order to decrease the cost of manufacturing activated carbon, low-cost forest and agricultural wastes are considered promising new materials. In recent years considerable research has been reported on activated carbon from agricultural wastes, such as olive stones (Yavu et al., 2010), acorn shells (Sahin and Saka, 2013), peanut shells (Wu, Guo, and Fu, 2013), grape seeds (Jimenez-Cordero, 2014), coconut shells

ÂŽ High adsorption capacity ÂŽ High adsorption rate ÂŽ Good resistance to abrasion.

1 Laboratory of Applied Chemistry, Department of Chemistry, Morocco. 2 Centre for International Cooperation in Agronomic Research for Development (Cirad), France. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Jun. 2018; revised paper received Aug. 2018.

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(Yalcin and Arol, 2002; Gratuito, 2008), palm shells (Sumathi, 2009), cherry stones (Jaramilloa, Gomez-Serrano, and A´lvareza, 2009), macadamia nut shells (Eddy, 2011), apricot stones (Soleimani and Kaghazchi, 2008), and bagass (Syna and Valix, 2003). There are basically two methods of preparing activated carbon: physical or chemical activation. Physical activation is most commonly used. This thermal gas process is a two-step process. The production of activated carbon by the thermal route starts with carbonization, followed by activation. During activation the carbonized product develops an extended surface area and a porous structure of molecular dimensions. Activation is generally conducted at temperatures between 800 and 1100°C in the presence of a suitable oxidizing agent such as steam, air, or carbon dioxide. Activated carbon has had a tremendous impact on the technology and economics of gold recovery. The main advantages of activated carbon are: its high selectivity towards gold relative to base metals, its ease of elution, and its large particle size (Arriagad and Garcia, 1997). It should be noted that the physical and chemical properties of the activated carbon used can strongly affect the amount of gold adsorption. The primary criteria for activated carbon intended for use in a gold adsorption process are as follows (Yalcin and Arol, 2002; Gergova, Petrov, and Minkova, 1993):

Optimization of conditions for the preparation of activated carbon from olive stones In gold recovery plants, activated coconut-shell carbons have been the preferred adsorbent for many years, due to their resistance to abrasion and selectivity for gold. However, increasing gold production necessitates the exploitation of other sources. Many research investigations published recently used low-cost precursors such as apricot stones, peach stones, hazelnut shells, bagasse, and macadamia nut shells (Yalcin and Arol, 2002; Syna and Valix, 2003; Souza et al., 2004; Mansooreh Soleimani et al., 2008; Gerrard Eddy et al., 2011; Raminez-Muniz, 2010; Mpinga et al., 2014) to produce an adequate activated carbon. The aim of this study was to evaluate olive stones as an alternative raw material to coconut shells for the production of activated carbon used in gold metallurgy. The characteristics of the activated carbon prepared under optimal conditions were compared with those of the commercial carbon (GoldSorb). To our knowledge, this is the first time that olive stones have been used as a precursor for the preparation of activated carbon for gold applications.

The thermogravimetric analyses of olive stones were conducted by a simultaneous TGA-DTA instrument (Setaram Instrumentation) in an air atmosphere at a heating rate of 10°C/min to attain a maximum temperature of 930°C. FTIR spectroscopy provides information about chemical characterization of the functional groups in olive stones and the resulting activated carbon. The FTIR spectra were recorded between 400 and 4000 cm−1 using a PerkinElmer spectrometer.

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The iodine number of the prepared activated carbon was measured by titration based on the standard method ASTM D 4607-94 (ASTM. 2006).

Olive stones from Marrakech Province in Morocco were used. The material was crushed to between 3.15 and 4 mm, rinsed with water to remove traces of olive pulp, and dried in the drying oven overnight at 105°C.

! # ! # "! "! # ! # " Carbonization was carried out in a Carbolite type CTF 12/65/650 programmable electrical furnace. A mass of 30 g of olive stones was introduced into the reactor, which was heated to the desired temperature of 500°C and maintained for different residence times in a nitrogen flow. The activation process was carried out in the same furnace. The activation time was between 30 to 120 minutes, and the activation temperature was between 800 and 950°C in a steam flow of 0.1 to 0.2 mL/min. The resulting activated carbon was rinsed for 30 minutes with distilled water and dried. A portion was ground and sifted to obtain a powder with a particle size smaller than 45 Οm, and the powder was then dried and kept in a hermitic bottle for iodine testing. The rest of granular activated carbon was used for attrition, bulk density, and kinetic activity testing. The activated carbon mass yield was determined from the following equation:

(Yield = (Activated carbon weight)â „(Raw material weight)) Ă— 100 ! ! # ! # "

"$ $)('"*')'" %+% The determination of the total content of carbon, hydrogen, and nitrogen for the raw material (in our case olive stones) is based on the standard method (XP CEN/TS 15104, Afnor Group, 2005), but for activated carbon the determination is based on the standard method ASTM D5373 (ASTM, 2016).

)+(+'"*')'" %+% The initial analysis (moisture, ash content, volatile matter,

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and fixed carbon) allows prediction of certain behaviours in the pyrolysis processes based on a standard method (Norme AFNOR XP CEN/TS 14 774-3).

*')#* *

&! &"& *')'" %+% The morphology of the activated carbon prepared under the optimal conditions was observed using scanning electron microscopy (VEGA3 TESCAN instrument).

((!+(+&) Activated carbon hardness was determined using a wet attrition test as described by Toles et al. (2000). The fraction of granular activated carbon between 0.5 and 2 mm was used for the attrition tests. One gram of granular activated carbon was added to 100 mL of acetate buffer in a 150 mL beaker. The solution was stirred for 24 hours at 25°C on an IKA magnetic stirrer at 500 r/min using 0.500 stir bars. The samples were then poured onto a 0.3 mm screen and the retained carbon was washed with 250 mL distilled water. After washing, the retained carbon was dried at 110°C for 2 hours. The sample was removed and allowed to cool in a desiccator and weighed. The percentage attrition was calculated as:

% Attrition=([(initial wt (g)–final wt (g)))⠄ (initial wt (g)]) × 100

" *#$)%+( Apparent or bulk density is a measure of the weight of the material that is contained in a given volume under specified conditions. A 10 mL cylinder was filled to a specified volume with activated carbon that had been dried in an oven at 80°C for 24 hours. The cylinder was weighed. The bulk density was then calculated according to the method of Snell and Ettre (1968):

Bulk density = (weight of dry material(g))â „ (volume of packed of dry material(ml)) #%&! (+&)*($%( Essentially, all procedures used to determine the kinetic activity involve contacting a known mass of carbon with a solution of known initial gold concentration, collecting and analysing samples of the solution over a period of time, and then calculating the activity of the carbon sample based on the data obtained.

Optimization of conditions for the preparation of activated carbon from olive stones In this study, the activated carbon activity was usually determined based on the Mintek method (Staunton, 2016). The activated carbon was separated from the solution by filtering, and the gold concentration of the solution was measured by atomic absorption spectrophotometry (AAS) (Unicam model 939 instrument) using an air-acetylene flame and absorbance peak at a wavelength of 242.8 nm. The quantity of gold absorbed onto the activated carbon was calculated from the difference between the initial and final gold concentration in the solution.

* ')+#'(+&)*($%(% The cyanidation tests using the CIL technique were carried out according to the experimental methodology used by various authors (Staunton, 2016). The gold ore used in this study was a residue from cobalt extraction, containing 24% moisture. The samples were pulped with water. Cyanidation was carried out in beakers at a pulp density of 1.24, stirring for 12 hours in the presence of a solution of 0.8 g/l free cyanide at a pH of 10.5 adjusted with quicklime. Periodic checks of free cyanide and pH were carried out and the desired values were maintained by the addition of cyanide and quicklime. For adsorption of the gold cyanide complex, activated carbon of particle size between 1.4 and 2 mm was added at a dosage of 20 g per litre of solution. The pulp was then screened at 1 mm to recover the activated carbon. The solution was filtered and the solids dried. Samples of the solution, solids, and activated carbon were taken to determine the gold concentration by AAS.

individual and interaction effects of various parameters. Four variables were investigated in this study: residence time for carbonization (X1), temperature of activation (X2), residence time for activation (X3), and steam flow (X4). Table I shows the ranges and levels of the four independent variables with actual coded values of each parameter. The independent variables are coded to two levels, namely low (–1) and high (+1). The yield of activated carbon (Y1), iodine number (Y2), and attrition (Y3) were analysed as responses. For this study, response surface methodology based on empirical mathematical modeling was used. More precisely, a second-order polynomial model was postulated to capture the possible nonlinear effects and curvature in the domain:

where Y is predicted response, Xi ( i = 1,2‌n) the unidimensional variables. The Doehlert experimental design and the corresponding experimental conditions and responses are given in Table II.

Table I

!)6EAF=EBDG<HAGB7E?HGB>H<E8E<?HC:HFB>E6EB>EBD 8GAFG9<E?H

" " # ! " #

%C>E

The carbonization and activation optimization were studied using response surface methodology and multi-criteria optimization with a Doehlert design and desirability function (Doehlert, 1970). This method helps to optimize the

X1 X2 X3 X4

+GAFG9<E

BFD

2

2

Carbonization time Activation temperature Activation time Steam flow

min °C min mL/min

0 800 30 0.10

120 875 75 0.15

240 950 120 0.20

Table II

!)6EAF=EBDHBC, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

2

0

*

5(4

-2 5=7#74

-0 5(4

-*

1.0000 -1.0000 0.5000 -0.5000 0.5000 -0.5000 0.5000 -0.5000 0.5000 0.0000 -0.5000 0.0000 0.5000 -0.5000 0.5000 0.0000 -0.5000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.8660 -0.8660 -0.8660 0.8660 0.2887 -0.2887 -0.2887 0.5774 0.2887 -0.5774 0.2887 -0.2887 -0.2887 0;5774 0.0000 0.2887 -0.5774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.8165 -0.8165 -0.8165 -0.8165 0.8165 0.8165 0.2041 -0.2041 -0.2041 -0.2041 0.6124 0.2041 0.2041 -0.6124 0.0000 0.0000 0.0000 0.0000 0.0000

0.15 0.15 0.15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.7906 -0.7906 -0.7906 -0.7906 -0.7906 0.7906 0.7906 0.7906 0.0000 0.0000 0.0000 0.0000 0.0000

15.19 19.75 15.26 25.33 19.83 17.05 12.16 23.21 23.03 20.50 13.25 17.70 15.11 19.31 22.50 17.57 18.06 13.98 20.08 20.04 19.36 18.26 18.80 19.03 17.86

922.00 823.60 992.90 599.52 666.37 976.25 922.00 747.10 743.09 951.59 1097.62 958.62 1042.79 867.70 733.12 939.40 915.24 1090.28 866.02 889.80 800.97 889.79 818.72 755.62 874.98

1.72 0.11 1.59 0.02 2.01 2.12 2.94 0.17 0.11 0.19 2.65 1.28 1.93 1.86 0.51 2.39 2.27 2.53 0.92 0.34 1.12 0.43 0.66 0.77 0.32

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CE1<EAD ?HE)6EAF=EBDG<H>E?F7B"HE)6EAF=EBDG<H@CB>FDFCB?"HGB>HAE?6CB?E?,H-2 H.FE<>"H-0 HFC>FBEHG>?CA6DFCB @G6G@FD."H-* HGDDAFDFCB

Optimization of conditions for the preparation of activated carbon from olive stones The centre points are repeated five times in order to calculate the variance of experimental error and to test the reproducibility of the data. The experiments were carried out in a random order to minimize the effect of systematic errors. The models were validated using analysis of variance (ANOVA). They were also checked by the correlation coefficient (R2) and the adjusted determination coefficient (R2A) in order to measure the proportion of the total observed variability described by the model (Bacaoui et al., 1998, 2001; Hameed, Tan, and Ahmad, 2009).

E?;<D?HGB>H>F?@;??FCBH ! ! # ! # " " ! " ! #! " The TG/DTA curves for olive stones are shown in Figure 1. The first endothermic event was due to water loss, and the second to loss of mass. The mass loss curves make it possible to visualize and decouple different phenomena distinguished as a function of the temperature level in the reactor. Two losses of mass are shown: the first is a loss of 5.59% in the drying phase from 43.9°C to 161°C, during which time the residual moisture is driven off. The second is a loss of 67.4% in the range from 162°C to 801.4°C. In this phase several zones may be associated with the degradation of the principal constituents of olive stones (hemicelluloses, cellulose, lignin) (Guinesi and Cavalheiro, 2006; Aigner et al., 2012). This process begins with the most thermally unstable compound. Hemicelluloses degrade between 162°C and 325°C, followed by celluloses between 325°C and 375°C. The change in the slope of the curve reflects a change in chemical kinetics. Lignin degrades between 375°C and 500°C; its kinetics of degradation are slower than those of other compounds. Finally, the end of degradation is located at 500°C and above. The FT-IR spectrum of olive stones is shown in Figure 2. The broad band observed at 3425 cm–1 corresponds to the stretch vibration of hydroxyl groups involved in hydrogen bonding (O-H), possibly due to absorbed water. A small band located at 2920 cm–1could be assigned to the (C-H) stretch vibration in methyl and methylene groups, and the band at 1739 cm–1 is ascribed to the vibration of (C=O), mainly from the esters, carboxylic acids, or aldehydes derived from cellulose and lignin (Saidatul, Jamari, and Howse, 2012, Sevilla and Fuertes, 2009). The vibration stretching (C=C) from the aromatic rings present in lignin caused the emergence of the bands at about 1639 and 1508 cm–1. The

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vibration at 1463, 1425, 1380, 1330 cm–1 can be allocated to the asymmetric and symmetric bending (C-H) due to the bands of methylene and methyl groups (Sundqvist, Karlsson, and Westermark, 2006). The band at 1253 cm–1 can be attributed to (C-O) asymmetric stretching of aromatic ethers, esters, and phenols. The stretch vibration from acids, alcohols, phenols, and esters are observed in the relativity intense bands at 1157 and 1041 cm–1. The deformation of the (C-H) bond in aromatic compound gives rise to the last band at 603 cm–1. The FT-IR and TG/DTA spectra of olive stones are similar to those of other lignocellulosic materials such as coconut shell (Sahin and Saka, 2013). The high fixed carbon, volatile content, and carbon concentration (Table IV) indicate that this material a good precursor for preparing a hard activated carbon to be used in gold adsorption.

! ! # " " " " (+ '($#* '! &)* +$"#* The carbon yield varied between 12.16 and 25.33% (Table II). These high yields are due to the nature of olive stones, which are very rich in carbon and volatiles materials as noted in Table IV, which in turn favours the production of pores during carbonization by the volatilization of volatile matter in the form of gas and tar. The total yield in carbon can be described as follows:

Y1 = 18.57 – 1.931 X1 – 3.786 X2 – 4.275 X3 – 0.361 X4

The coefficients of correlation (R2= 0.939, R2A = 0.832) between the theoretical and experimental response, calculated by the model, are satisfactory. Analysis of the results shows, as expected, that the activation temperature (X2) and the activation time (X3) have a very negative influence on yield.

#%&! (+&)* ' ' +( * &!*+&#+)$* It is known that the iodine number is a reliable measurement of the microporosity of activated carbons, with higher iodine number defining a higher microporosity and higher internal surface area (Eddy et al., 2011). The adsorption capacity of iodine (Y2) can be described by the model:

Y2 = 846.116 – 77.348 X1 + 164.326 X2 + 109.336 X3 + 2 2 2 76.581 X4 – 100.107 X1 – 72.962 X2 + 129.363 X3 + 2 129.009 X4 – 127.206 X1X2 – 110.279 X2X3 + 147.090 X1X4 + 86.287 X2X4 + 131.671 X3X4

3F7;AEH0/3 $ H?6E@DA;=HC:HC<F8EH?DCBEH6AE@;A?CA

Optimization of conditions for the preparation of activated carbon from olive stones The coefficients of correlation (R2 = 0.976, R2A = 0.950) between the maximum capacities of adsorption calculated by the model and those determined experimentally are satisfactory. Analysis of this response (Y2) shows that the activation temperature (X2) and the time of activation (X3) have a strong impact on the development of the porous texture during activation. These factors therefore influence the adsorption behaviour of the activated carbon. Moreover, temperature seems to be more influential during a long activation time. It seems that more micropores are opened with increased activation temperature and activation time. The iodine adsorption test is normally used as a performance indicator for gold adsorption, with the minimum recommended iodine number required for activated carbons for use in the gold industry being 1000 mg/g (Buah and Williams, 2010). Figures 3–6 illustrate the interaction between the variables in contour plots. Figure 3 shows the combined effect of time of carbonization (X1) and temperature of activation (X2) on the iodine number. The highest iodine number (>1000 mg/g) was achieved at approximately 940°C and 60 minutes of carbonization time. The figure shows that the iodine number increased with increasing temperature and decreasing time of carbonization. The increased temperature causes an increase in the release of low molecular weight volatiles from the matrix structure, which in turn causes pore development. The numbers of pores and porosity will increase indirectly by this volatilization process (Bansal, Donnet, and Stoeckli, 1988; Suhas, Carrott, and RibieroCarrott, 1988; Lua and Guo, 2000). The effects of steam flow and carbonization residence time are shown in Figure 4. The highest iodine number determined in this case was of 1052 mg/g. An increase in steam flow and an average time of carbonization (approx. 2 hours) improved the capacity of iodine adsorption. However, when the carbonization time was too long, the iodine number decreased. This is related to the excessive burn-off the carbon content, which affects the quality of pores in the activated carbon and causes enlargement of the micropores (Bansal, Donnet, and Stoeckli,. 1988; Lua and Guo, 2000; Gratuito et al., 2008). Figure 5 shows the interaction between the temperature and the activation time. The highest iodine numbers were obtained at high temperatures with short activation times,

and lower temperatures for longer activation times. An increase in the activation time increases the porosity and modifies the pore structure, and also promotes the formation of active sites within the activated carbon (Lua and Guo, 2000). Figure 6 shows the combined effects of activation time and steam flow on the iodine adsorption capacity. It seems that the maximum iodine number was achieved when the time of activation and the steam flow were increased. Physical activation is a process in which the carbonized product develops a porous structure of molecular dimensions

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Optimization of conditions for the preparation of activated carbon from olive stones and an extended surface area. At high temperature in the presence of a steam activating agent, the loss of mass increases proportionally to the reactivity of the carbon atoms with the activating agent. This causes the unblocking of the pores, which makes the activated carbon structure more open. Subsequently, the carbon of the aromatic ring structures starts burning, producing active sites and wider pores (Sahin and Saka, 2013)

# # " Another parameter that is important for assessing the suitability of the activated carbon for gold adsorption is resistance to attrition. This response is described by the following equation:

Y3 = 0.633 + 0.574 X1 + 0.601 X2 + 1.193 X3 – 0.647 X4 2 2 + 0.976 X2 + 1.729 X4 – 1.455 X1X2 + 1.281 X2X3 + 1.285 X3X4. The coefficients of correlation (R2= 0.945, R2A = 0.848) between the attrition responses calculated by the model and those determined experimentally are satisfactory. The activation residence time (X3) has a strong positive correlation with attrition resistance, the temperature of activation (X2) also has a positive effect on this parameter, and the steam flow (X4) has a negative one. Figsures 7, 8, and 9 demonstrate the relationships between the variables in contour plots. Figure 7 shows the combined effects of carbonization time and activation temperature on attrition resistance of the activated carbon, the lowest attrition value (0.5%) being achieved at approximately 850°C and 60 minutes. A brittle activated carbon was obtained when both variables were increased. When the activation temperature is average and the carbonization residence time is minimal, a rigid activated carbon was obtained. The effect of activation temperature and activation time on attrition of the activated carbon is shown in Figure 8. The lowest point of attrition (0.2%) was determined. It seems that at the average activation temperature and the minimal activation time, the resistance to attrition increases. Furthermore, Figure 9 shows that the lower attrition value is obtained when the steam flow is increased and the activation time decreased. The attrition test is a critical performance indicator for activated carbon, with resistance to attrition depending mainly on preparation conditions. Resistance to attrition can

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be improved by reducing the activation time. Indeed, a long activation time leads to the weakening of the carbon structure and increases the surface area by opening up the micropores, as indicated by a higher iodine number.

# # #!" # # ! # " # " # ! # # " # The aim of this study was to identify the optimal conditions for preparation of activated carbon with good characteristics for use in gold adsorption. The comparison of optimal conditions for responses Y1, Y2, and Y3 shows that the maximization of each response was not obtained at the same conditions. To determine an acceptable compromise zone, responses were simultaneously optimized by using the desirability functions approach (Derringer and Suich, 1980) included in the NEMROD software (Mathieu et al., 2000).

$%+!' +"+( * ) (+&)*' !&' This method first converts each estimated response Yi into an individual scale-free desirability function di that ranges from zero (outside of the desired limits, if Yi ≤ Yi,min) to unity, the target (desired) value (if Yi ≼ Yi,max), where Yi,min and Yi,max are the lower and upper acceptability bounds for response i, respectively. The lower and upper acceptability bounds were set at the extreme values, which were selected according to quality criteria for the activated carbon used in the gold industry. Since the response surface had been reasonably explored in the range of studied variables, the weights for the first two

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Optimization of conditions for the preparation of activated carbon from olive stones responses, Y1 (yield) and Y2 (iodine number), were set to unity. The third response, Y3 (attrition), was set to two because the friability of activated carbon is the most important criterion to monitor during preparation. Once the function di is defined for each response of interest, an overall objective function (D), representing the global desirability function, is calculated by determining the geometric mean of individual desirabilities. Therefore, the function D over the experimental domain is calculated using the relationship D = (d1.d2.31)1/3. The maximum of the function D gives the best global compromise in the studied domain and corresponds to optimal experimental conditions.

It was necessary to establish domains of variation of each response in relation to the material characteristics required. The activated carbon should have a yield mass between 12 and 30%, the iodine number should be greater than 1000 mg/g, and the attrition should be lower than 2%. To optimize all responses under the same conditions was difficult, because most of the regions of interest are different. For example, if the iodine number (Y2) increases the attrition number (Y3) also increases, which is unsatisfactory. Therefore, in order to find a compromise, we have resorted to the function of desirability. The compromise domain for the preparation of activated carbons with a high yield level, high iodine number, and low attrition number is depicted in Figures 10, 11, and 12. As can be observed in Figure 10, the maximum value of the desirability function was obtained when the activation time was less than 60 minutes and the activation temperature greater than 900°C. Figure 11 shows the variation of the desirability as a function of the activation time and steam flow. It seems that, the maximum desirability was obtained at a higher value of steam flow and an activation time less than 60 minutes. Figure 12 shows the combined effect of carbonization time and activation time. The maximum value of the desirability function was obtained when the carbonization time increased and the activation time decreased. The optimal point indicated by the model corresponds to a carbonization residence time of 157 minutes, an activation temperature of 921°C, an activation time of 53 minutes, and a steam flow of 0.18 mL/min. In order to test the validity of this method, three samples of activated carbon were prepared under the optimal experimental conditions. The characteristics of are samples are shown in Table III, together with those calculated from the model. The experimental values agree well with those calculated from the model.

# ! "! " # ! " # " " " # ! The immediate and elemental analyses of the olive stones and activated carbon prepared under the optimum conditions are given in Table IV. As can be seen, the fixed carbon content was greater in activated carbon (97.3%) than in the raw material (20.2%), due to the release of volatile components during the activation step, as shown by the decrease in volatile content in the activated carbon sample (1.9%). After activation, a

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significant reduction was observed for many bands in the FTIR spectrum. For example, in the spectrum of activated carbon (Figure 13), a significant decrease in the intensity of the band located at 3425 cm-1, assigned to the reduction of hydrogen bonding, was observed. Due to the dehydrating effect of activation, the decrease of the bands at about 2374 and 2923 cm-1, ascribed to asymmetric (C-H) stretching, indicated that the hydrogen content decreases, which is confirmed by the elemental analysis given in Table IV. The decrease of the band at 1700 cm-1 corresponding to C=O may be due to the decomposition of these groups during VOLUME 119

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Optimization of conditions for the preparation of activated carbon from olive stones Table III

C&EAH<F=FD?HGB>HDGA7EDH8G<;E?HGB>HD1EH6GADFG<H>E?FAG9F<FDFE?HG??C@FGDE>H&FD1HEG@1HAE?6CB?E E?6CB?E

C&EAH<F=FD

GA7EDH8G<;E

EF71D

>F 5(4

>F =FB,H5(4

>F =G),H5(4

Y1 Y2 Y3 Desirability

12 1000 0

30 1500 2

1 1 2

36.51 5.39 81.76 33.86

29.18 0 62.94 0

43.84 16.39 100 51.78

%G<@,H8G<;E

!)6,H8G<;E

18.57 1026.93 0.36

17.67 1009.23 0.74

di: partial desirability of response Yi, di min: minimal partial desirability of response Yi, di max: maximal partial desirability of response Yi calc. value: calculated value, exp. value: experimental value

Table IV

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Olive stones Activated carbon

'?1

+C<GDF<E?

3F)E>H@GA9CB

7.3 3.3

0.5 0.7

79.3 1.9

20.2 97.3

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activation. The disappearance of the band located at 1639 cm-1 corresponding to olefines, and the appearance of the band at 1569 cm-1 attributed to the aromatic C=C bond, can be explained by the fact that the structure of activated carbon becomes richer in aromatics. The vibration at 1433.79 cm-1 is assigned to the asymmetric and symmetric bending of (C-H). A significant reduction was observed for the bands located between 850 and 1300 cm-1, which include (C-O) in carboxylic acids, alcohols, and esters. The intense band at 592 cm-1 was ascribed to the stretching vibration (C-C). FT-IR and chemical analysis demonstrate that during this study, an activated carbon with good properties was prepared from olive stone. The porous nature and surface structure of the activated carbon were examined using scanning electron microscopy (SEM). The specific surface area (SBET) was determined from the adsorption-desorption isotherm of N2 at 77K (Brunauer, Emmett, and Teller, 1951). The activated carbon produced under the optimal conditions had a specific surface area of 1073 m2/g with an average pore size of 1.4 nm. The specific surface area of our activated carbon is similar to that of commercial activated carbon (1100 m²/g). The SEM images

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%HHHHHHHHHHHHHHHHHHHHHHHHHHH 47.8 90.8

6.4 0.93

0.19 0.67

presented in Figures 14 and 15 show the morphologies of the two activated carbons (GoldSorb and the optimal activated experimental carbon). A porous surface can be seen, with mainly a microporous and mesoporous structure, which will provide the maximum number of possible gold loading sites for both activated carbons. The textural characteristics of the activated carbon obtained from olive stones are similar to those of the commercial activated carbon GoldSorb, which is used extensiveily in the gold industry (Table V). It was found that the prepared activated carbon has a high iodine number, very low ash content, low attrition, and a strong affinity for gold. From Table VI it can be seen that the particle size and the size distribution of the activated carbon prepared from olive stones were lower than those of the commercial activated carbon. Consequently, the size of granules of the raw material during the crushing stage must be increased. Figure 16 illustrates the percentage gold recovery in the cyanidation test (CIL) after having recycled the activated carbon six times. It can be seen that the prepared activated carbon based on olive stones performs comparably to the commercial carbon (GoldSorb). The percentage gold recovery with commercial activated carbon decreases from the third cycle and reaches 79.2% at the last cycle. The activated carbon based on olive stones achieved a 100% gold recovery in cycles 1 to 5, and 96% in the sixth. This result was confirmed by analysis of the carbon after each cycle. Figure 17 shows the gold loading per gram of activated carbon. It is concluded that the adsorption capacity of the activated carbon prepared from olive stones is superior to that of the commercial carbon.

%CB@<;?FCBH The response surface method (RSM) was used to optimize the process conditions for the preparation of activated carbon based on olive stones and its affinity for gold. ÂŽ Olive stones are a good precursor for producing efficient activated carbon with high performance for use in the gold industry.

Optimization of conditions for the preparation of activated carbon from olive stones

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Table V

1.?F@G<HGB>H@1E=F@G<H6AC6EADFE?HC:HD1EHC6DF=;=HE)6EAF=EBDG<HGB>HD1EH@C==EA@FG<HG@DF8GDE>H@GA9CB $C>FBEHB;=9EAH5=7#74

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H5(4

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;< H>EB?FD.H5 7#=*4

1009.23 1090.45

1073 1100

0.74 0.28

0.7 1.6

57.25 59.48

28.72 30.75

495 500

Optimum Commecial (GoldSorb)

Table VI

GADF@<EH?F EHGB>H?F EH>F?DAF9;DFCBHC:HD1EHC6DF=;= E)6EAF=EBDG<HGB>HD1EH@C==EA@FG<HG@DF8GDE>H@GA9CB '@DF8GDE>H@GA9CB Optimum Commecial (GoldSorb)

H2, H== 2, 0H== 5% 3%

35% 15%

0 *,2 H== H*,2 H== 55% 74.48%

5% 7.5%

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® Experimental design and the response surface methods were used to determine the acceptable compromise zone of preparation conditions. Analysis of the effects of carbonization time, activation temperature, activation time, and steam flow showed that the most important factors affecting the iodine number and attrition are the activation temperature and the activation time. ® Doehlert design, response surface methodology, and multi-criteria optimization using desirability functions

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Optimization of conditions for the preparation of activated carbon from olive stones were used to determine the acceptable compromise zone for yield, iodine number, and attrition. The optimal conditions were identified as a carbonization residence time of 157 minutes, an activation temperature of 921°C, an activation time of 53 minutes, and a steam flow of 0.18 mL/min. Ž The activated carbon produced under the optimal conditions had a high iodine number and a low attrition rate. The gold adsorption capacity was similar to that of the commercial activated carbon and the carbon performed well in CIL tests. The studies suggest that olive stones can serve as a precursor for activated carbon to be used as an alternative to the imported commercial activated carbon (coconut-shell based) used in the Moroccan gold extraction industry.

'@ BC&<E>7=EBD? The authors gratefully acknowledge the Higher Education Ministry of the Kingdom of Morocco, the Center for International Cooperation in Agronomic Research for Development (CIRAD), and the Department of Chemistry, Science Faculty, Semlalia Marrakech, Cadi Ayyad Marrakech University.

E:EAEB@E? AIGNER, Z., BERKESI, O., FARKAS, G., and SZABO-REVESZ, P. 2012. DSC, X-ray and FTIR studies of a gemfibrozil/dimethyl-beta-cyclodextrin inclusion complex produced by cogrinding. Journal of Pharmaceutical and Biomedical Analysis, vol. 57. pp. 62–67. AFNOR GROUP. 2005. Biocombustibles solides - DĂŠtermination de la teneur totale en carbone, hydrogène et azote - MĂŠthodes instrumentals. Paris. ARRIAGAD, R. and GARCIA, R. 1997. Retention of aurocyanide and thiourea–gold complexes on activated carbons obtained from lignocellulosic materials. Hydrometallurgy, vol. 46. pp. 171–180. doi: 1016/S0304386X(97)00010-8 ASTM. 2006. D 4607-94. Standard test method for determination of iodine number of activated carbon. ASTM International, West Conshohocken, PA. ASTM. 2016. D5373-16. Standard test methods for determination of carbon, hydrogen and nitrogen in analysis samples of coal and carbon in analysis samples of coal and coke. ASTM International, West Conshohocken, PA, BAÇAOUI, A., YAACOUBI, A., DAHBI, A., BENNOUNA, C.. AYELE, J., and MAZET, M. 1998. Characterization and utilization of a new activated carbon obtained from Moroccan olive wastes. Journal of Water Supply: Research and Technology, vol. 47, no. 2. pp. 68–75 BAÇAOUI, A., YAACOUBI, A., DAHBI, A., BENNOUNA, C., PHAN-TAN-LUU, R., MODANOHODAR, F.J., RIVER-UTRILLA, J., and MOREO-CASTILLA, C. 2001. Optimization of conditions for the preparation of activated carbons from olive-waste cakes. Carbon, vol. 39, no. 3. pp. 425–432. BANSAL, R.C., DONNET, J.B., and STOECKLI, F. 1988. Active Carbon. Marcel Dekker, New York. BRUNAUER, S., EMMETT, P.H., and TELLER, E. 1938. Journal of the American Chemical Society, vol. 60. p. 309 . BUAH, W.K. and WILLIAMS, P.T. 2010. Activated carbons prepared from refuse derived fuel and their gold adsorption characteristics. Environmental Technology, vol. 31, no. 2. pp. 125 137. doi: 10.1080/09593330903386741 DERRINGER, G. and SUICH, R. 1980. Simultaneous optimization of several response variables. Journal of Quality Technology, vol. 12. pp. 214–219. DOEHLERT, D.H. 1970. Uniform shell design. Applied Statistics, vol. 19, no. 3, pp. 231–239. doi: 10.1155/2103/549729. GERGOVA, K., PETROV, N., and MINKOVA, V. 1993. A comparison of adsorption characteristics of various activated carbon. Journal of Chemical Technology & Biotechnology, vol. 56. pp. 77–82. doi: 10.1002/jctb.280560114 GRATUITO, M.K.B., PANYATHANMAPORN, T., CHUMNANKLANG, R.-A., SIRINUNTAWITTAYA, N., and DUTTA, A. 2008. Production of activated carbon from coconut shell: Optimization using response surface methodology. Bioresource Technology, vol. 99. pp. 4887–4895. doi: 10.1016/j.biortech.2007.09.042 GUINESI, L.S. and CAVALHEIRO, E.T.G. 2006. The use of DSC curves to determine the acetylation degree of chitin/chitosan samples. Thermochimica. Acta, vol. 444. pp. 128–133. doi: 10.1016/j.tca.2006.03.003 GUINESI, L.S. and CAVALHEIRO, E.T.G. 2006. The use of DSC curves to determine the acetylation degree of chitin/chitosan samples. Thermochimica. Acta, vol. 444. pp. 128–133. doi:10.1016/j.tca.2006.03.003 HAMEED, B.H., TAN, I.A.W., and AHMAD, A.L. 2009. Preparation of oil palm empty fruit bunch-based activated carbon for removal of 2,4,6trichlorophenol: Optimization using response surface methodology.

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Journal of Hazardous Materials, vol. 164. pp. 1316–1324. doi: 101016/j.hazmat.2008.09.042 JAMARI, S.S. and HOWSE, J.R. 2012. The effect of the hydrothermal carbonization process on palm oil empty fruit bunch. Biomass & Bioenergy, vol. 47. pp. 82–90. doi: 10.1016/j.biombioe.2016.09.061 JARAMILLIO, J., GOMEZ-SERRANO, V., AND A´LVAREZA, P.M. 2009. Enhanced adsorption of metal ions onto functionalized granular activated carbons prepared from cherry stones. Journal of Hazardous Materials, vol. 161. pp. 670–676. doi: 10/1016/j.jhazmat2008.04.009 https://www.sciencedirect.com/science/article/pii/S030438940800530X JIMENEZ-CORDERO, D., HERAS, F., ALONSO-MORALES, N., GILARRANZ, M.A., and RODRĂ?GUEZ, J.J. 2014. Preparation of granular activated carbons from grape seeds by cycles of liquid phase oxidation and thermal desorption. Fuel Processing Technology, vol. 118. pp. 148–155. doi: 10.1016/j.fuproc.2013.08.019 LUA, A.C. and GUO, J. 2000. Activated carbon prepared from oil palm stone by one-step CO2 activation for gaseous pollutant removal. Carbon, vol. 38. pp. 1089–1097. doi: 10.1016/S0008-6223(99)00231-6 MATHIEU, D., NONY, J., and PHAN-TAN-LUU, R. 2000. NEMROD-W software. SociĂŠtĂŠ LPRAI, Marseille, France. MPINGA, C.N., BRADSHAW, S.M., AKDOGAN, G., SNYDERS, C.A., and EKSTEEN, J.J. 2014. The extraction of Pt, Pd, and Au from an alkaline cyanide simulated heap leachate by granular activated carbon. Minerals Engineering, vol. 55. pp. 11–17. doi: 10.1016/j.mineng.2013.09.001 NORME. AFNOR XP CEN/TS 14 774-3. POINERN, G.E.J., SENANAYAKE, G., SHAH, N., THI-LE, X.N., PARKINSON, G.M., AND FAWCETT, D. 2011. Adsorption of the aurocyanide, Au(CN)2- complex on granular activated carbons derived from macadamia nut shells – A preliminary study. Minerals Engineering, vol. 24. pp. 1694–1702. doi: 10.1016/j.mineng2011.09.011 RAMINEZ-MUNIZ, K., SONG, S., BERBER-MENDOZA, S., and TONG, S. 2010. Adsorption of the complex ion Au(CN)2- onto sulfur-impregnated activated carbon in aqueous solutions. Journal of Colloid and Interface Science, vol. 349. pp. 602–606. doi: 10.1016/j.cis.2010.05.056 SAHIN, O. and SAKA, C. 2013. Preparation and characterization of activated carbon from acorn shell by physical activation with H2O–CO2 in two-step pretreatment. Bioresource Technology, vol. 136. pp. 163–168. doi: 10.1016/j.biortech.2013.02.074 SEVILLA, M, and FUERTES, AB. 2009. The production of carbon materials by hydrothermal carbonization of cellulose. Carbon, vol. 47. pp. 2281–2289. doi: 10.1016/j.carbon.2009.04.026 SNELL, F.D. and.ETTRE, L.S. 1968. Encyclopedia of Industrial Chemical Analysis. vol. 8. Interscience, New York. p. 179. SOLEIMANI, M. and KAGHAZCHI, T. 2008. Adsorption of gold ions from industrial wastewater using activated carbon derived from hard shell of apricot stones – An agricultural waste. Bioresource Technology, vol. 99. pp. 5374–5383. doi: 10.1016/j.biortech.2007.11.021 SOUZA, C., MAJUSTE, D., DANTAS, M.S.S., and CIMINELLI, V.S.T. 2014. Selective adsorption of gold over copper cyanocomplexes on activated carbon. Hydrometallurgy, vol. 147–148. pp. 188–195. doi: 10.1016/j.hydromet.2014.05.017 STAUNTON, W.P. 2016. Gold recovery (carbon-in-pulp). Development in Mineral Processing, vol 15, chapter 30. Elsevier. SUHAS, G.V.K. CARROTT, P.J.M., and RIBEIRO CARROTT, M.M.L. 2007. Lignin - from natural adsorbent to activated carbon: a review. Bioresource Technology, vol. 98. pp. 2301–2312. doi: 10.1016/j.biortech.2006.08.008 SUMATHI, S., BHATIA, S., LEE, K.T., and MOHAMED, A.R. 2009. Optimization of microporous palm shell activated carbon production for flue gas desulphurization: Experimental and statistical studies. Bioresource Technology, vol. 100. pp. 1614–1621. doi: 10.1016/j.biortech.2008.09.020 SUNDQVIST, B., KARLSSON, O., and WESTERMARK, U. 2006. Determination of formic-acid and acetic acid concentrations formed during hydrothermal treatment of birch wood and its relation to colour, strength and hardness. Wood Science and Technology, vol. 40. pp. 549–561. doi: 10.1007/S00226-006-0071-z SYNA, N., and VALIX, M. 2003. Modelling of gold (I) cyanide adsorption based on the properties of activated bagasse. Minerals Engineering, vol. 16. pp. 421–427. doi: 10.1016/S0892-6875(03)00053-0 TOLES, C.A., MARSHALL, W.E., JOHNS, M.M., WARTELLE, L.H., and ALOON, A.M. 2000. Acid-activated carbons from almond shells: physical, chemical and adsorptive properties and estimated cost of production. Bioresource Technology, vol. 71. pp. 87–92. doi: 10.1016/S0960-8524(99)00029-2 WU, M., GUO, Q.J., and FU, G.J. 2013. Preparation and characteristics ofmedicinal activated carbon powders by CO2 activation of peanut shells. Powder Technology, vol. 247. pp. 188–196. doi: 10.1016/j.powtec.2013.07.013 YALCIN, M. and AROL, A.I. 2002. Gold cyanide adsorption characteristics of activated carbon of non-coconut shell origin. Hydrometallurgy, vol. 63. pp. 201–206. doi: 10.1016/S0304-386X(01)00203-1 YAVUZ, R., AKYILDIZ, H., KARATEPE, N., and ÇETINKAYA, E. 2010. Influence of preparation conditions on porous structures of olive stone activated by H3PO4. Fuel Processing Technology, vol. 91. pp. 80–87. doi: 10.1016/j.fuproc.2009.08.018 N

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a10

Improved ultrafine coal dewatering using different layering configurations and particle size combinations by M.E.C. Snyman1 and N. NaudĂŠ1

of finer and coarser materials has been proposed to deliver better dewatering efficiency and improved the quality of the recycled water (Kenney, 1994). This project aimed to solve belt filter problems associated with ultrafine materials at a South African coal mine. The plant operates two belt filter plants: the thickener underflow solids feed to Plant 1 is relatively coarse, having a particle size of approximately 75 Îźm, while that of Plant 2 is fine, approximately 34 Îźm. The optimum blend proportions and layering arrangements of the two materials on a belt filter were assessed to compare dewatering efficiency and the quality of the filter cake and filtrate produced. Factors influencing the final moisture content of a filter cake generated by layering super-fine and coarser material on a belt filter to maximize water removal efficiency were experimentally determined using different layering configurations. The optimum fines content of the blend was then determined.

!31'040 Coal fines produced during processing are difficult to dewater and result in a lower quality product and consequent lower value. A South African coal mine experiences severe difficulties with belt filter dewatering operations due to the presence of fines reporting from the thickener underflows. Plant 2 currently handles super-fine particles of size −34 Îźm and has low belt filter efficiency: excessive moisture retention lowers the product quality and strains downstream processing. It was necessary to determine an alternative method for dewatering these fines. Blending of fine material with coarser material was proposed as a solution. The effect of coal particle size and layering during ultrafines belt filter dewatering was evaluated using various blends of the fine Plant 2 material with coarser Plant 1 material. The best layering arrangement of the two materials and its optimum blend required to achieve reduced filter cake moisture content was determined in practise using a vacuum filter to simulate belt filtration. A blend of the two materials gave improved dewatering efficiency for the belt filters compared with that of the Plant 2 material alone. The best layering configuration was with Plant 2 material at the bottom and Plant 1 material on top. The optimum blend for industrial applications comprised 48% fines from Plant 2. :7! 12+0 ultrafine coal, dewatering, belt filter, vacuum filter, moisture content.

86* ,21-3+ Fines are produced during the processing of coal. It is estimated that 2 to 3% of run-ofmine coal reports to the ultrafine portion (−100 Îźm) (le Roux, 2005). The particle size of coal is inversely proportional to the amount of water adsorption, so smaller particles adsorb more moisture. Moisture removal requires either mechanical processes or thermal drying: mechanical processes are less expensive, but the fines still have high moisture retention. Discarding of fines leads to economic losses, so it is essential that they be dewatered so that they can then be sold as a product: fines need to be treated for both resource management and conservation reasons (Kenney, 1994). Removal of free surface moisture from ultrafine coal particles using a belt filter therefore remains a process that needs to be continuously improved. Ultrafine coal originating from a thickener underflow is known to be difficult to dewater owing to factors such as filter cloth blockage and moisture retention due to clogging. Mixing

" # !%$ #% # Insufficient dewatering influences the quality of the final coal product, especially in the thermal coal industry. The moisture content of coal is strongly governed by the quality specifications and economic possibilities for a plant. A lower moisture content results in a higher quality product and hence a higher sales price, so it is essential that continuous improvement of dewatering technologies is undertaken (de Korte, 2008). A previous study showed that the proportion of −75 Îźm material in the belt filter feed exhibited a linear

1 Department of Materials Science and Metallurgy Engineering, University of Pretoria, South Africa. 2 School of Mining Engineering, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Jun. 2018; revised paper received Aug. 2018.

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Improved ultrafine coal dewatering using different layering configurations relationship with the product surface moisture content (Arnold, 1999). The final filter cake moisture is influenced by modification of the coal surface, type of surfactant, type of dewatering device, conditioning time, and the pH value of the system (Nkolele, 2004). Modelling of water and coal particle interactions is continually evolving to enable a better understanding of the interactions that occur during dewatering. Tests have proven that the densest packing of spherical particles results in a total void volume of 26%, due to the comparable densities of coal and water (Nkolele, 2004). For smaller coal particle sizes, the voids form capillary cavities filled by water. An increased attraction between the water and the coal surface results, leading to mechanical dewatering mechanisms not being able to adequately remove this water (Nkolele, 2004). Decreasing particle size causes an increased water-exposed surface area. The particle size of the coal is inversely proportional to the final moisture content of the filter cake: finer coal sizes have larger moisture retention (i.e., are more difficult to dewater) than coarser particle sizes (Basim, 1997).

" # !%$!% #" $# ! $ % " The coal industry often reduces the moisture content of the filter cake by blending high-moisture fine coal with lowmoisture coarse coal. The blending proportions differ depending on the materials used in the colliery. The optimum blend must therefore be individually determined for each plant. Blending increases the permeability of the cake, leading to reduced blinding of the belt filter cloth.

! #%!" #" $ $ % #$ " #%!$ #% Belt filter systems are specifically designed for a high solids capacity. The concentration of solids for the system is determined by the concentration of the primary solids in the feed and further solids that may precipitate during treatment, hence a varying feed solids concentration is experienced. For most sludges fed to belt filter presses, the feed dry solids concentration falls in the range of 1–10% and the resulting moisture content of the filter cake is the range of 12–50%. The input solids loading depends on the sludge type and the filter media used. This input to the belt filter press is generally measured as the mass of dry solids per unit time per unit belt width. For lower ranges of solids, the feed is normally 40–230 kg/h/m belt width; for higher solids’ ranges, the feed increases to 300–910 kg/h/m belt width. Dilute feed solids concentrations result in a cake of higher moisture content, while a higher feed solids concentration yields an improved solids filtration rate and a drier end product. The thickness of the cake formed should also be considered when selecting the percentage of solids in the feed. Cake thickness affects the permeability of the filtration mix and thus the filtration rate. Generally, the minimum design discharge cake thickness is 3–5 mm. This size ensures that the cake is thick enough to discharge and easy to remove from the belt (Schonstein, 2008).

specified by ASTM 2234 (ASTM, 2017a) for gross sample collection from the coal mine Plant 1 and Plant 2 thickener underflow materials. Plant 1 provided the coarse material and Plant 2 the fine material. The sampling position was selected to avoid any mixing of added reagents (such as flocculants) that would be present if samples were taken from the belt filter. The collected coal samples were dried at 105°C for 24 hours to remove all moisture. The samples were then broken down using a roller pin and divided into the appropriate sample masses required for testing by means of a rotary splitter.

!#" %$ " %$ " #!" #" The particle size distribution (PSD) of each material sample was determined. Wet sieving, using the ASTM D4749 (ASTM, 2017b) procedure, was employed to obtain an accurate representation of size. Each test employed three 500 g samples from each plant. The PSD analyses were carried out in triplicate to obtain representability. The size distribution ranged from −1000 to −53 Îźm.

%!" $ " ! #" Figure 1 shows the layering configurations and their descriptors used in this study. The samples from Plants 1 and 2 had different material size distributions and the drainage would differ for different layering configurations. For each of the five layering possibilities, three tests were conducted to determine the relative error. Different layering set-ups were tested to determine the configuration that gave the best dewatering results. This optimum layering set-up was then used for further test work. Each test required a combined mass of 200 g. The appropriate amount of water was then added to obtain the typical underflow solids density of the industrial thickeners of 44%; thus, for 200 g solids, 454 mL water was added. Industrial AP520 C flocculant (Pathlochem) was mixed with the water at 0.05 mass percentage, as used in industry. Filter paper (Econofilt, 240 mm diameter) was placed on a Buchner vacuum filter, after which the slurries of the various materials were layered in the filter according to the required configuration in such a way as to not disturb the layering. The vacuum filter was switched on and the mass of water removed from the slurries was measured every second for a period of 300 seconds using a load cell (ZEMIC model H3-C3100kg-3B).

#" $ % $ %#%! " #" Once the most efficient layering configuration was established, this was used to determine the optimum blend for the most efficient moisture removal. The slurries comprised 0, 10, 25, 50, 75, and 100% Plant 2 material. All tests were carried out in triplicate

75 1+1/1,! %$ !% ! #" Sampling was carried out in accordance with the procedure

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4,-27 # 6!7243, *13.4,-2654130 57057+ 727 )/635 # (657246/ 27'2707350 5 7 *16207 '6254*/70 63+ )/635 & (657246/ 5 7 .437 '6254*/70

Improved ultrafine coal dewatering using different layering configurations " # !%$ #% #$ " $ $ " #%!$ % In addition to using load cell measurements to determine the mass of water removed by filtering, the moisture retention of the filter cake was measured by removing the surface moisture by drying, according to the procedure described by ASTM D3302 (ASTM, 2017c). This entailed spreading the sample into tared pans and weighing the pans, after which the sample was dried in an oven at 30ÂşC with intermittent stirring until the sample appeared dry. The pan was then removed and the new mass recorded before it was placed back in the oven. This procedure was repeated at 2-hour intervals until the mass change between the intervals was less than 0.1%. The sample was then cooled to ambient temperature, the final mass measured, and the moisture change calculated.

4,-27 )6254*/7 04 7 +40524 -54130 1. )/635 # 63+ )/635 & 5 4* 7372 -3+72./1 (657246/0 60 06('/7+

%!" % # $ %#

Table I

A stand was designed and built, in which the load cell could be placed and connected to the ceramic head of the vacuum filter. A water distribution system was designed to distribute water evenly over the entire layered sample at a low enough rate to prevent mixing of the layers. The experimental equipment and configuration are shown in Figure 2.

"26! +4..26*5413 270-/50

70-/50 63+ +40*-00413 !#" %$ " %$ " #!" #" The PSD was used to characterize the materials used for the experiments. The wet sieving results are shown in Figure 3. The D50 values (particle size at which 50% of the material passed through the sieve) for Plant 1 (coarse) and Plant 2 (fine) materials were 75 Îźm and 34 Îźm, respectively.

Calcite Dolomite Kaolinite Magnetite Pyrite Quartz Siderite

)/635 # 600

72212

2.04 7.81 38.17 3.76 3.79 39.89 4.54

0.51 1.17 1.35 0.42 0.39 1.29 0.63

% " $ $% %!" % # To best illustrate the layering set-up for the experiments. labels were used that referred to the order in which the samples from Plant 1 (D50 of 75 Îźm) and Plant 2 (D50 of 34 Îźm) were configured. Table III shows an explanation of the labelling system used.

72212

2.21 8.91 43.09 10.49 2.75 27.44 5.13

0.57 1.2 1.38 0.57 0.39 1.11 0.66

Table II

"26! ./-1270*73*7 270-/50

%$ ! %!#"% Samples were also split out, ground, and subjected to X-ray diffraction (XRD) and X-ray fluorescence (XRF) analysis. XRD indicated that the higher percentage clay phase in the Plant 2 material was kaolinite. The XRD and XRF results are given in Tables I and II, respectively.

Calcite Dolomite Kaolinite Magnetite Pyrite Quartz Siderite

)/635 & 600

SiO2 TiO2 Al2O3 Fe2O3 MnO MgO CaO Na2O K2O P2O5 Cr2O3 NiO V2O5 ZrO2 SO3 BaO CuO ZnO SrO LOI Total

7254.47+

36/!07+

99.6 0.01 0.05 0.05 0.01 0.05 0.01 0.05 0.01 0 0 0 0 0 0 0 0 0 0 0 100

99.70 0.00 0.01 0.01 0.00 0.01 0.01 0.02 0.01 0.03 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.10 99.93

)/635 #

17.90 0.40 7.01 2.54 0.06 0.53 1.36 0.04 0.39 0.09 0.01 0.01 0.01 0.02 0.08 0.04 <0.01 <0.01 0.02 69.50 100.00

)/635 &

18.40 0.60 8.69 5.97 0.08 0.65 1.74 <0.01 0.28 0.10 0.02 0.03 0.02 0.03 0.06 <0.01 <0.01 0.01 0.02 63.20 99.91

" # !%$!%#% #" $ $ ! " %$! #%

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The results for the amount of water removed for the different layering configurations are shown in Figure 4. Some noise in the data occurred due to vibration of the vacuum filter motor on the laboratory bench. For the specified filtration time of 300 seconds, higher drainage values were achieved when the Plant 2 (fine) material was placed first on the filter paper. The

Improved ultrafine coal dewatering using different layering configurations Table III

'/6365413 1. 7 '724(735 /6 7/0 6 7/ Plant 1 Plant 2 Plant 1-2 Plant 2-1 Plant 1-2-1 Plant 2-1-2

'/6365413 Only Plant 1 sample used Only Plant 2 sample used Plant 1 sample on top with Plant 2 sample layered at the bottom Plant 2 sample on top with Plant 1 sample layered at the bottom Plant 1 sample on top with Plant 2 sample in the middle and Plant 1 sample layered at the bottom Plant 2 sample on top with Plant 1 sample in the middle and Plant 2 sample layered at the bottom

4,-27 16+ *7// (760-27(7350 0 1 43, 2657 1. (600 /100 1. 6572 -3+72 +4..72735 .4/5265413 /6!7243, *13.4,-2654130

Plant 2-2 configuration gave the highest drainage rate. The drainage rates decreased when Plant 1 (coarse) material was layered at the bottom on the filter. As expected, using only Plant 2 (fine) material resulted in a very slow drainage rate. The data shown in Figure 4 followed an exponential decay trend for the drainage rates. The Plant 1-2-1 configuration showed an initial increase in moisture drainage. It is theorized that the water drained through the upper Plant 1 (coarse) layer and was then trapped in the middle Plant 2 (fine) layer, which prevented further draining until the force from the vacuum became sufficient to drain the water through the bottom Plant 1 coarse layer; the drainage rate then started increasing. Table IV details the final results. The Plant 2-1 and Plant 2-1-2 layering configurations gave the best drainage rates, while those in which the Plant 1 material was placed at the bottom gave the least-desired results. Layering of Plant 2 (fine) material at the bottom of the filter cake resulted in 22% moisture retention; layering Plant 1 (coarse) material at the bottom gave 30% moisture retention. This large difference can be explained by the size differences between the two materials. When Plant 2 material was at the top, it started to settle in the gaps formed by the coarser Plant 1 material. The finer material then trapped the water and formed a layer of fine wet material. This layer did not settle to the bottom of the filter cake and the suction power of the vacuum filter was not adequate to extract this trapped water.

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When Plant 2 (fine) material was layered at the bottom, most water seeped quickly through the gaps formed in the Plant 1 material to the Plant 2 layer. The Plant 1 material did not settle into the Plant 2 layer because the gaps between the latter particles were too small. The water then became trapped in the bottom Plant 2 layer. The fine material did not trap the water in the Plant 2 layer that was placed directly on the filter paper, and the force of the vacuum was strong enough to extract more of this water. The best layering configuration was therefore to use Plant 2 (fine) material at the bottom. Both the Plant 2-1 and Plant 2-1-2 configurations offered improvement; however, to minimize complications in industrial application, it would be easier to have a single layer of each particle size range. The Plant 2-1 configuration was therefore selected as optimum.

Table IV

70-/50 1. /6!7243, 7 '724(7350 6!7243, 156/ (600 1. *13.4,-265413 6572 27(1 7+ $,% Plant 2 Plant 1 Plant 2-1 Plant 1-2 Plant 2-1-2 Plant 1-2-1

337.83 339.3 356.08 317.07 341.13 323.61

26436,7 2657 1405-27 2756437+ $( 0% $ % 1.13 1.13 1.19 1.06 1.14 1.08

26 25 22 30 25 29

Improved ultrafine coal dewatering using different layering configurations It is worthy of note that the 25% and 26% moisture retentions reported for the individual Plant 1 and Plant 2 materials, respectively (Table I), were close to the values currently measured on an industrial scale. The experimental results are therefore considered to be comparable to projected industry performance.

equation was obtained and the Microsoft Excel Solver function was used to determine the optimum percentage of Plant 2 fines that should be employed in this layering configuration. Using the best-fit quadratic relationship indicated in Figure 7, it was determined that the optimum Plant 2-1

" #%!$ %$ # " "# The quality of the filter cake after dewatering is very important with regard to its further processing. If the cake is too wet, it will be heavy and have the characteristics of sludge, making it difficult for the dewatered material to be transported on the mine conveyor systems. Figures 5a to 5c show considerable differences in the final filter cakes: the cake from the Plant 1-2 configuration still had water lying on the top; the cake resulting from the Plant 2 configuration did show moisture retention and a tendency to be sludgy; that resulting from the Plant 2-1 configuration was much sturdier and had a visually drier appearance.

#%!$ "# $ $ %! Water extracted from the belt filter is recycled to the process plant so its quality is important. Figures 6a to 6c show large differences in the water qualities produced from the Plant 2, Plant 1-2, and Plant 2-1 configurations. For the Plant 1-2 configuration, a large amount of fines washed through the filter paper with the water, which resulted in poor water quality and would cause a build-up of ultrafine material in processes where the recycled water is utilized.

4,-27 156/ 6572 27(1 6/ 60 6 .-3*5413 1. '72*7356,7 .4370 73 -043, 5 7 )/635 &"& .4/5265413 *13.4,-265413

Table V

70-/50 .12 '72*7356,7 .4370 6++45413 -043, )/635 &" & *13.4,-265413

% %$ $ ! !#" $ $ " % $ $ #%!$!%

)21'125413 1. )/635 & $.437% (657246/ $ %

To determine the optimum blend of the two materials, the Plant 2-1 layering configuration was employed with different proportions of fines in the bottom layer. The amount of water removed was plotted against the percentage fines, as shown in Figure 7. The data is given in Table V. A fitted linear

0 20 50 60 100

156/ (600 1. 6572 27(1 7+ $,%

26436,7 2657 $( 0%

1405-27 2756437+ $ %

339.3 353.75 356.08 357.44 337.83

1.13 1.18 1.19 1.19 1.13

25 22 22 21 26

4,-27 ) 151,26' 0 1. .436/ .4/572 *6 70 '21+-*7+ -043, $6% )/635 & $ % )/635 #"& 63+ $*% )/635 &"# .4/5265413 *13.4,-2654130

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4,-27 -6/45! 1. .4/5727+ 6572 '21+-*7+ ! $6% )/635 & $ % )/635 #"& 63+ $*% )/635 &"# .4/5265413 *13.4,-2654130

Improved ultrafine coal dewatering using different layering configurations layering required that 48% by mass of Plant 2 material should be used for the bottom layer. This is expected to give 21% water retention in the final cake, as proven in this test work. However, as shown in Table II, there is a range of percentage fines – from 20% to 60% – for which moisture removal would be efficient. Industrial applications will therefore have some leeway if the process parameters fall within this range. A comparison of the PSD for the optimum blend with those of Plants 1 and 2 is presented in Figure 8. The optimum Plant 2-1 blend has a D50 of 53 Οm. 4,-27 1('624013 1. '6254*/7 04 7 +40524 -54130 1. .77+0 51 )/635 # )/635 & 63+ 1'54(4 7+ )/635 &"# /6!72 *13.4,-2654130

13*/-04130 A South African coal mine suffers from poor belt filtration performance when treating fine material originating from the Plant 2 thickener underflow. This study evaluated the filtration characteristics and water quality produced using various blends and layering configurations of coarser Plant 1 material with the fine Plant 2 material. The experimental results showed that mixing Plant 1 and Plant 2 materials gave improved dewatering efficiency compared with Plant 2 material alone. A layering configuration with the fine material placed directly on the filter cloth and the coarse material layered above gave the best filtration characteristics. The optimum blend for this industrial application contained 48% of the Plant 2 fines. The existing Plant 1 and Plant 2 arrangements can be retained, but the materials currently fed to the two plants should be appropriately split to ensure sufficient dewatering and plant capacity.

* 31 /7+,7(7350 This work was financially supported by EXXARO. This manuscript was edited for publication by Dr Kathryn Sole (University of Pretoria). Mrs J. Dykstra carried out the XRF analyses and Mrs W. Grote the XRD at Stoneman (University of Pretoria).

7.7273*70 ARNOLD, B. 1999. Simulation of dewatering devices for predicting moisture content of coal. Coal Preparation, vol. 20. pp. 35-54. ASTM. 2017a. D2234. A. Standard practice for collection of a gross sample of coal. ASTM International, West Conshohocken, PA. ASTM. 2017b. D4749. Standard test method for performing the sieve analysis of coal and designating coal size. ASTM International, West Conshohocken, PA. ASTM. 2017c. D3302. A. Standard test method for total moisture in coal. ASTM International, West Conshohocken, PA. BASI, G. 1997. Fine coal dewatering. Master's thesis, Virginia Tech State University. https://vtechworks.lib.vt.edu/handle/10919/35680 DE KORTE, G. 2008. Dewatering of ultra-fine coal with filter presses. Coaltech, Pretoria. KENNEY, M.E. 1994. Coal dewatering. US patent 5.346.630. Laboratories, E.G. 2016. PSD of GG8 and GG2. Exxaro GG, Lephalale, South Africa.. LE ROUX, M.E.A. 2005. The optimization of an improved method of fine coal dewatering. Minerals Engineering, vol. 18, no. 9. pp. 931–934. NKOLELE, A. 2004. Investigations into the reduction of moisture in final coal by plant tests with surfactants. Journal of the South African Institute of Mining and Metallurgy, vol. 104, no. 3. pp. 171–176. SCHONSTEIN, P. 2008. Horizontal vacuum belt filters in the mining and chemical industries. Filtration and Separation, vol. 28, no. 2. pp. 121–122. N

4–5 July, 2019

SMART MINING, SMART ENVIRONMENT, SMART SOCIETY Implementing change now, for the mine of the future

For further information contact: Yolanda Ndimande ¡ Conference Co-ordinator ¡ SAIMM Tel: +27 (0) 11 834-1273/7 ¡ E-mail: yolanda@saimm.co.za ¡ Website: http://www.saimm.co.za Background Society in general, and the economy in particular, is evolving at breakneck speed. Old jobs are being lost and new ones are constantly being created. It is likely that mines developed in the next ten years will have a very different staff complement to those currently in production. It is also clear that we are at tipping points for climate systems and for biodiversity: the challenges facing the extractive sector in the coming decades are likely to be much greater than anything in the past century. What does this mean for environmental management? How should we be planning mines to reduce environmental footprints? What skills do we need to do this? Which energy sources will be available and will they have an impact on production? Can we close mines in a way that ensures that viable post-mining economies can be established? This conference bridges the gap between practitioners and decision-makers and managers in both the public and private sectors. The intention is to transfer knowledge and to debate the big issues facing regulators, mining companies, labour, and communities in a way that identifies solutions. The conference will consist of a number of invited keynote speakers who will focus on strategic issues, with the possibility of a workshop included.

Accolades Boutique Venue, Midrand

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http://dx.doi.org/10.17159/2411-9717/2019/v119n3a11

Contributors to fatigue at a platinum smelter in South Africa by J. Pelders1,2 and G. Nelson2

Fatigue is a significant concern in the mining industry as it is a causal or contributing factor in many incidents and accidents. Fatigue can be caused by work- and non-work-related factors. There is a lack of information about associations between demographic and other non-work factors, and fatigue. This study aimed to assess associations between demographic, work, living, and socioeconomic conditions, and lifestyle characteristics, and fatigue at a platinum smelter in South Africa. Eight interviews with management and two focus groups with production workers were conducted, and 75 questionnaires were completed by production and other workers. Both work- and non-work-related factors were considered to be causes of fatigue. These factors included overtime, shift work, high workloads, activities performed outside of work, age, race, housing tenure, diet, sleep disorders, stress, and job satisfaction. In general, higher levels of fatigue were reported by younger participants, those who rented accommodation, ate less healthily, had a sleep disorder, and those with high levels of stress and low job satisfaction. As various demographic, lifestyle, and wellness-related factors were associated with fatigue, both work and non-work contributors should be addressed in fatigue management plans. -' #" $ Sleepiness, living conditions, lifestyle, fatigue management, mining industry.

,) "# % Fatigue may be defined as ‘a state of impaired mental and/or physical performance and lowered alertness arising as a result of, or a combination of, hard physical and mental work, health and psychosocial factors or inadequate restorative sleep’ (BHP Billiton, 2005). Williamson et al. (2011) define fatigue as a biological drive for recuperative rest. Sleepiness and fatigue are related, and the terms are often used interchangeably. However, sleepiness, more specifically, can be defined as sleep propensity, or a person’s tendency to fall asleep (Hossain et al., 2003; Shen, Barbera, and Shapiro, 2006). Increasing levels of fatigue and sleepiness are associated with lapses in attention, errors, response slowing, fluctuations in alertness and effort, and impaired attention, working memory, long-term memory, decision-making, and creativity (Alhola and Polo-Kantola, 2007; van Dongen and Dinges, 2000). Fatigue is also associated with adverse effects on mood and impulsive behaviours (Alhola and Polo

1 Mining and Mineral Resources, Natural Resources and the Environment, CSIR, South Africa. 2 School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Oct. 2017; revised paper received Aug. 2018.

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. %# $($

Kantola, 2007; Minkel et al., 2011; Schwarz et al., 2013). It is a significant concern in the mining industry as it is commonly identified as a causal or contributing factor in many accidents and injuries (Schutte, 2009). Fatigue can also lead to long-term health problems, including digestive problems, heart disease, stress, and mental illness (Shaw, 2003). Fatigue can be caused by both workrelated and non-work-related factors. Workrelated causes include work schedules and design, task requirements at work, and the work environment conditions. Work scheduling factors include the number and pattern of hours worked, time of day, shift work, and night work (Di Milia et al., 2011; NSW, 2009; Schutte and Maldonado, 2003; Shaw, 2003). Task requirements associated with fatigue include high mental and physical demands, especially monotonous tasks or tasks requiring sustained attention (Ă…kerstedt et al., 2014; Jing-Gang and Lei, 2013; NSW, 2009; Schutte and Maldonado, 2003; Shaw, 2003; Williamson et al., 2011). Work environment factors include noise, temperature extremes, or the psychosocial environment (Jing-Gang and Lei, 2013; NSW, 2009; Shaw, 2003). Individual and non-work contributors to fatigue include aspects such as sleep, health, fitness for work, lifestyle factors, and commuting time (Ă…kerstedt et al., 2014; Di Milia et al., 2011; Horrey et al., 2011; NSW, 2009). Age, diet, drug and alcohol use, medical conditions, quality and quantity of rest before a work period, circadian rhythm (‘body clock’), sleep disorders, strenuous activities outside of work (e.g. a second job), family commitments, and psychological issues such

Contributors to fatigue at a platinum smelter in South Africa as stress also play a role in the levels of fatigue experienced (Ă…kerstedt et al., 2014; Molarius et al., 2006; ORR, 2012; Schutte and Maldonado, 2003; Shaw, 2003). In addition, living conditions are likely to influence the levels of fatigue experienced by mineworkers due to poor conditions in which to sleep, long commuting times between work and home, and poor health arising from a lack of access to services and amenities (Ă…kerstedt et al., 2014; Galobardes et al., 2006). Horrey et al. (2011) reported that many demographic, personality, and workplace factors can impact the levels of fatigue experienced and their consequences. De Milia et al. (2011) noted that the interaction between demographic factors and fatigue is a neglected area of research and that, apart from age and sex, the influence of many demographic factors is largely unknown. There is a lack of information about associations between demographics, living conditions, and various other non-work factors and fatigue (Di Milia et al., 2011; Horrey et al., 2011). Living and socioeconomic conditions have been highlighted as a particular concern in the South African mining industry. Further study on these factors is warranted to assist in broadening the understanding of their effects on fatigue so that they might be considered in fatigue management programmes in order to implement appropriate interventions. The aim of this study was to assess associations between demographic, work, living and socioeconomic conditions, and lifestyle characteristics, and fatigue in the South African Mining Industry.

'& # #!# The study design was cross-sectional. Although most data collected was quantitative, qualitative data was also gathered, and methodological triangulation was used to verify the results. Participants were recruited from a platinum smelter located in Limpopo Province in South Africa, in 2016. Semi-structured interviews were held with eight individuals in management positions or their representatives. These individuals were selected based on their positions at the smelter, namely those from human resources, finance, health and safety, laboratory, production, engineering, and protection services. Two focus group discussions were held with groups of 11 (9 male and 2 female) and 13 (7 male and 6 female) production workers, respectively. Convenience sampling was used to select these participants. The interview and focus group participants were asked about fatigue experienced at the smelter and its potential causes, both at work and outside of work, and were requested to make recommendations for reducing it. The interviews and focus group discussions were held in the language most readily understood by the participants which, in this case, was English. The interviews and discussions were voice-recorded. Quantitative data was collected using questionnaires that were completed by workers employed at the smelter. Participants were selected using convenience sampling. The smelter employed 188 permanent employees in the workgroups included in the study, and around one-third of these were sampled. The study focused on production workers, such as those working at the furnace, as it was assumed that they would be at a high risk of fatigue and its effects. However, other groups of workers, such as those working in engineering departments and in laboratories, were also included.

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Closed-ended questions were used to gather information about participant demographics, work, living and socioeconomic conditions, lifestyles, health and wellness, and fatigue. Four questions were used to assess levels of fatigue, namely (1) whether participants thought they received enough sleep, (2) general sleepiness using the Karolinska Sleepiness Scale (KSS) (Ă…kerstedt and Gillberg, 1990), (3) a subjective rating of fatigue at work, adapted from the SamnPerelli fatigue scale (Samn and Perelli, 1982), and (4) whether the participant had unintentionally fallen asleep at work in the past 12 months. For the KSS, participants were asked to rate how sleepy they generally felt on a scale from 1 (‘extremely alert’) to 9 (‘very sleepy, great effort to stay awake’). Subjective fatigue at work was recorded on a fivepoint Likert scale from ‘fully alert, wide awake’, to ‘completely exhausted.’ The participants completed the questionnaires under the guidance of researchers who were fluent in local languages. Data from the voice recordings was downloaded and transcribed. Thematic content analysis was used to summarize responses from the interviews and focus groups. Data from the questionnaires was captured electronically using Excel software. A binomial fatigue variable was created using data from the four questions in the questionnaire that were designed to assess fatigue. If data from two or more of these questions indicated high levels of fatigue (i.e. not receiving enough sleep, a KSS score of over 5, a fatigue rating of more than ‘a little tired’, and/or falling asleep at work unintentionally in the past year), a positive fatigue score was indicated for the participant. In addition to descriptive analyses, Pearson chi-squared and Fischer’s exact tests were conducted using Stata 13 software to determine whether there were significant associations between each of the demographic, work, living and socioeconomic condition, and lifestyle variables, and the summary fatigue variable. To improve the integrity of the inferential statistics, data was grouped or omitted, where appropriate, to increase the number of responses per category. Fisher’s exact test was used in cases where the number of participants per cell was less than five. Statistical significance was set at 95% (p < 0.05). Ethical approval to conduct this study was obtained prior to data collection from the CSIR Research Ethics Committee (reference number: 158/2015) and the University of the Witwatersrand Human Research Ethics Committee (certificate number: M140222). Necessary permission to conduct the study was obtained from the company.

/'$ !&$

Participants in the interviews and focus groups generally felt that fatigue was a problem at the smelter, especially for those working in hazardous occupations. However, it was considered to be relatively well managed. Those in management identified various work-related causes of fatigue, including overtime, shift work, repetitive and monotonous work, and work that required high levels of concentration. Overtime work was a particular concern for engineering personnel as they worked on ‘standby’ in

Contributors to fatigue at a platinum smelter in South Africa

A total of 75 questionnaires were completed. The average response rate to each of the questions was 98%, ranging from 95% to 97%. Table I shows the demographic profile of the study participants. The majority of the participants were male (68.5%) and the average age was 37 (¹7) years, with a range of 22–60 years. Most of the participants were Black Africans (87.7%). All were South African, and most (79.5%) came from Limpopo, the province where the smelter was located. The participants were relatively well educated: 56.9% had a post-Grade 12 qualification and only 5.6% had not completed secondary school. Selected work-related variables are shown in Table II. The majority of the participants (53.4%) worked in the furnace section, 17.8% in the engineering department, 15.1% in the regional laboratory, and 13.7% in the process laboratory. Most (86.3%) were permanent employees, and 67.1% worked shifts. In general, those working in the furnace section and in the process laboratory worked shifts, while those in the engineering department and the regional laboratory did not. The shifts were 12 hours long, and included night shifts. About one-third (34.2%) of the participants reported that they often worked overtime. Findings relating to the living and socioeconomic conditions of the sample are included in Table III. Access to services was relatively high, and most (97.2%) had electricity in their dwellings. Most of the participants lived within a 50 km radius of the smelter, and the majority (84.7%) had commuting times of less than 30 minutes. Most used their own vehicles to travel to and from work. The median number of people that relied on the participants’ incomes was four. Over a quarter (27.4%) reported always or often finding it difficult to pay for all the things they needed, and 39.7% reported owing money to someone. With regard to lifestyle, 32.9% reported exercising in their leisure time more than once a week, 15.7% smoked, and 41.1% drank alcohol more than once a month (Table IV). In terms of nutrition, 76.7% considered the food they ate to be healthy or very healthy; none reported it to be unhealthy or very unhealthy. Over a quarter of the participants (26.0%) reported usually having fewer than 6 hours of sleep in the 24 hours before a work shift; 27.4% slept for 6–7 hours, and 46.6% slept for 7 or more hours. These values compare to

Table I

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%*

'$*%* #*%*

Sex Male Female

50 (68.5) 23 (31.5)

21 (42.0) 29 (58.0) 11 (47.8) 12 (52.2)

Age ≤35 years >35 years

36 (51.4) 34 (48.6)

21 (58.3) 15 (41.7) 0.015* (P) 10 (29.4) 24 (70.6)

Race Black Other

64 (87.7) 9 (12.3)

32 (50.0) 32 (50.0) 0.004* (F) 0 (0.0) 9 (100.0)

Marital status Married Not married

37 (50.7) 36 (49.3)

16 (43.2) 21 (56.8) 16 (44.4) 20 (55.6)

0.918 (P)

Home province Limpopo Elsewhere in South Africa

58 (79.5) 15 (20.5)

25 (43.1) 33 (56.9) 7 (46.7) 8 (53.3)

0.804 (P)

Highest level of education Grade 12 or less Higher than Grade 12

31 (43.1) 41 (56.9)

10 (32.3) 21 (67.7) 22 (53.7) 19 (46.3)

0.070 (P)

0.641 (P)

*Indicates significance at p < 0.05. (P) Pearson’s chi-square test performed. (F) Fisher’s exact test performed.

Table II

#" * )"() !'$*)% *& '*)$$# ()&(#%$* (& * )&( ' )"() !'

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Work area Furnace Process laboratory Regional laboratory Engineering

39 (53.4) 10 (13.7) 11 (15.1) 13 (17.8)

20 (51.3) 5 (50.0) 4 (36.4) 3 (23.1)

19 (48.7) 5 (50.0) 7 (63.6) 10 (76.9)

0.318 (F)

Work status Permanent Contract

63 (86.3) 10 (13.7)

29 (46.0) 3 (30.0)

34 (54.0) 7 (70.0)

0.497 (F)

Shift work Yes No

49 (67.1) 24 (32.9)

25 (51.0) 7 (29.2)

24 (49.0) 17 (70.8)

0.077 (P)

Overtime Yes No

25 (34.2) 48 (65.8)

12 (48.0) 20 (41.7)

13 (52.0) 28 (58.3)

0.605 (P)

*Indicates significance at p < 0.05. (P) Pearson’s chi-square test performed. (F) Fisher’s exact test performed.

11.1%, 16.7%, and 72.2%, respectively, prior to days off work. Participants also generally reported having better quality sleep before a day off work than before a work day. Table V shows that 21.4% of the participants reported having a medical condition and 23.3% reported taking medication. Most (86.3%) considered their health to be good or very good, and 34.7% had not taken any sick leave in the previous year. While some of the participants were unsure if they had a sleep disorder or not, 15.9% reported having one. Around 4.2% of the sample reported having a work-related injury, and 2.8% had been involved in a work-related VOLUME 119

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addition to working normal day shifts. Production and laboratory workers, on the other hand, worked 12 hour shifts that included night shift work. The production workers noted short staffing, including that resulting from absenteeism, as the main concern relating to fatigue, as this led to increased workloads. Activities outside of work were also considered to contribute to fatigue by both those in management positions and the production workers. These included family responsibilities, part-time studies, and social activities. Personal differences such as age, intrinsic sleeping rhythms, and stress were also linked to fatigue. The socioeconomic conditions (e.g. income, education, transport, and housing) of the smelter workers were seen to be relatively good, and the daily commuting times were not generally excessive; as such, these factors were not of particular concern for fatigue.

Contributors to fatigue at a platinum smelter in South Africa Table III

( (% *)% *$# (#' #%# ( * #% (&(#%* )"() !'$*)% *& '*)$$# ()&(#%$* (& * )&( ' )"() !'

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#*%*

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Location of dwelling Mine (smelter) property Suburban area Township Rural area

8 (11.3) 34 (47.9) 18 (25.3) 11 (15.5)

5 (62.5) 15 (44.1) 9 (50.0) 2 (18.2)

3 (37.5) 19 (55.9) 9 (50.0) 9 (81.8)

0.230 (F)

Housing tenure status Owned and fully paid off Owned but not yet paid off Rented Occupied rent-free

6 (8.7) 25 (36.2) 33 (47.8) 5 (7.3)

1 (16.7) 10 (40.0) 19 (57.6) 0 (0.0)

5 (83.3) 15 (60.0) 14 (42.4) 5 (100.0)

0.037* (F)

Level of crowding (number in room) One Two More than two

17 (23.6) 40 (55.6) 15 (20.8)

9 (52.9) 15 (37.5) 8 (53.3)

8 (47.1) 25 (62.5) 7 (46.7)

0.415 (P)

Piped water in dwelling Yes No

60 (87.0) 9 (13.0)

26 (43.3) 3 (33.3)

34 (56.7) 6 (66.7)

0.724 (F)

Flush toilet Yes No

64 (88.9) 8 (11.1)

31 (48.4) 1 (12.5)

33 (51.6) 7 (87.5)

0.068 (F)

Commuting time Less than 15 minutes 15 to less than 30 minutes 30 minutes to less than an hour

9 (12.5) 52 (72.2) 11 (15.3)

3 (33.3) 25 (48.1) 4 (36.4)

6 (66.7) 27 (51.9) 7 (63.6)

0.598 (F)

Difficultly affording necessities Always/often Sometimes Occasionally/never

20 (27.4) 24 (32.9) 29 (39.7)

11 (55.0) 13 (54.2) 8 (27.6)

9 (45.0) 11 (45.8) 21 (72.4)

0.076 (P)

Indebtedness Yes No

29 (39.7) 44 (60.3)

15 (51.7) 17 (38.6)

14 (48.3) 27 (61.4)

0.270 (P)

*Indicates significance at p < 0.05. (P) Pearson’s chi-square test performed. (F) Fisher’s exact test performed.

Table IV

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49 (67.1) 24 (32.9)

24 (49.0) 8 (33.3)

25 (51.0) 16 (66.7)

0.206 (P)

Smoke Yes No

11 (15.7) 59 (84.3)

6 (54.5) 24 (40.7)

5 (45.5) 35 (59.3)

0.394 (P)

Drink alcohol Less than once a month More than once a month

43 (68.9) 30 (41.1)

15 (34.9) 17 (56.7)

28 (65.1) 13 (43.3)

0.065 (P)

Healthiness of diet Healthy or very healthy Neither healthy nor unhealthy

56 (76.7) 17 (23.3)

21 (37.5) 11 (64.7)

35 (62.5) 6 (35.3)

0.048* (P)

Average hours of sleep – before work <7 hours ≼7 hours

39 (53.4) 34 (46.6)

20 (51.3) 12 (35.3)

19 (48.7) 22 (64.7)

0.170 (P)

Average hours of sleep – before off-days <7 hours ≼7 hours

20 (27.8) 52 (72.2)

8 (40.0) 23 (44.2)

12 (60.0) 29(55.8)

0.745 (P)

Exercise Once a week or less More than once a week

*Indicates significance at p < 0.05. (P) Pearson’s chi-square test performed. (F) Fisher’s exact test performed.

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Contributors to fatigue at a platinum smelter in South Africa Table V

')!& *)% * '!!%'$$* )"() !'$*)% *& '*)$$# ()&(#%$* (& * )&( ' )"() !'

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Medical condition Yes No

15 (21.4) 55 (78.6)

6 (40.0) 24 (43.6)

9 (60.0) 31 (56.4)

0.801 (P)

Sleep disorder Yes No

11 (15.9) 58 (84.1)

8 (72.7) 21 (36.2)

3 (27.3) 37 (63.8)

0.024* (P)

Medication Yes No

17 (23.3) 56 (76.7)

10 (58.8) 22 (39.3)

7 (41.2) 34 (60.7)

0.155 (P)

Self-reported health Good or very good Not good

63 (86.3) 10 (13.7)

27 (42.9) 5 (50.0)

36 (57.1) 5 (50.0)

0.740 (F)

Sick leave in previous year 0 days 1–9 days More than 10 days

25 (34.7) 34 (47.2) 13 (18.1)

8 (32.0) 16 (47.1) 8 (61.5)

17 (68.0) 18 (52.9) 5 (38.5)

Stress Not at all / a little stressed Very stressed

48 (67.6) 23 (32.4)

13 (27.1) 18 (78.3)

35 (72.9) 5 (21.7)

0.000* (P)

Job satisfaction Not satisfied Satisfied

39 (54.9) 32 (45.1)

26 (66.7) 5 (15.6)

13 (33.3) 27 (84.4)

0.000* (F)

Quality of life Good Not good

57 (79.2) 15 (20.8)

23 (40.4) 8 (53.3)

34 (59.7) 7 (46.7)

0.366 (P)

0.202 (P)

*Indicates significance at p < 0.05. (P) Pearson’s chi-square test performed. (F) Fisher’s exact test performed.

Table VI

)&( '*")&(% $* )"() !'

)"&( ( )%&$

)&( '

(

%*

'$*%*

#*%*

)! '

Enough sleep Yes No

49 (67.1) 24 (32.9)

11 (22.5) 21 (87.5)

38 (77.6) 3 (12.5)

0.000* (P)

KSS ratings of general sleepiness 1 – 5: Not sleepy 6 – 9: Sleepy

38 (53.5) 33 (46.5)

8 (21.0) 24 (72.7)

30 (79.0) 9 (27.3)

0.000* (P)

Fatigue rating – in general while at work Fully alert, wide awake Somewhat fresh, lively A little tired Moderately or very tired Completely exhausted

9 (12.7) 18 (25.4) 29 (40.8) 9 (12.7) 6 (8.5)

0 (0.0) 5 (27.8) 11 (37.9) 8 (88.9) 6 (100.0)

9 (100.0) 13 (72.2) 18 (62.1) 1 (11.1) 0 (0.0)

Fallen asleep unintentionally at work in past year Yes No

38 (52.1) 35 (47.9)

30 (79.0) 2 (5.7)

8 (21.1) 33 (94.3)

0.000* (F)

32 (43.8% 41 (56.2)

32 (100.0) 0 (0.0)

0 (0.0) 41 (100.0)

N/A

Summary fatigue variable Fatigued Not fatigued

*

*Indicates significance at p < 0.05. (P) Pearson’s chi-square test performed. (F) Fisher’s exact test performed.

A summary of the fatigue ratings of the participants can be found in Table VI. Two-thirds (67.1%) thought that they received enough sleep. The average level of sleepiness recorded in the KSS was 5 (‘neither alert nor sleepy’); 46.5% reported some degree of sleepiness, as indicated by a score of 6 or higher. When the participants were asked to rate how VOLUME 119

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accident in the previous 12 months. Almost half of the participants (45.1%) reported being satisfied with their jobs, 23.9% were neither satisfied nor dissatisfied, and 31.0% were dissatisfied. Most (79.2%) considered their quality of life to be good. One-third (32.4%) reported being very stressed.

Contributors to fatigue at a platinum smelter in South Africa fatigued they usually felt while at work, 21.2% reported feeling moderately tired or completely exhausted. Over half (52.1%) reported having unintentionally fallen asleep while at work in the past year. From the summary variable based on the results for these four fatigue-related items, 43.8% of the participants were classified as fatigued. A summary variable could not be calculated for two participants, as a result of missing data, reducing the sample size for the chisquare and Fisher’s exact tests to 73.

Results of the Pearson chi-square and the Fisher’s exact tests are shown in Tables I to VI, together with the percentage of participants in each category that were identified as fatigued. Both age and race were associated with fatigue (p<0.05, Table I). A higher proportion of those aged 35 years or younger reported to be fatigued than those older than 35. Black participants reported fatigue more frequently than those of other race groups. The proportion of participants that was not black was small, which could influence the validity of this result. Most of the non-black participants (89.9%) were over 35 years and only 22.2% of this group did shift work. No statistically significant associations with fatigue and sex, marital status, home province, or education were seen. None of the recorded work-related variables were significantly associated with fatigue (Table II). However, some trends were evident, such as higher levels of fatigue in those working at the furnace and the process laboratory than in those working in the regional laboratory and the engineering department. Higher levels of fatigue were also reported by those that did shift work, those that worked overtime, and permanent employees, although the differences were not statistically significant. Housing tenure was associated with fatigue (p<0.05, Table III). Those who owned and had fully paid off their homes and those who occupied dwellings rent-free reporting lower levels of fatigue than those who rented or owned but had not yet paid off their dwellings. Area of dwelling, level of crowding, access to piped water and flush toilets, commuting time, difficulty affording necessities, and indebtedness were not significantly associated with reported fatigue. Those who considered their diets to be healthy or very healthy reported less fatigue (p<0.05), but exercise, alcohol use, smoking, and sleep received were not significantly associated with fatigue (Table IV). Sleep disorders, stress, and job dissatisfaction were positively associated with fatigue (p<0.05) (Table V). Those reporting as having a sleep disorder, high stress levels, and poor job satisfaction reported higher levels of fatigue. Having a medical condition, taking medication, self-reported health conditions, sick leave, and quality of life were not statistically associated with the fatigue variable in this study. The four variables that were used to predict fatigue were significantly associated with the summary fatigue variable (p<0.001, Table VI).

($ $$(#% Fatigue was identified in 44% of the study participants, as indicated by subjective responses and reports of falling asleep unintentionally while at work. From the interviews and focus groups conducted, both work- and non-work-related factors

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contributed to fatigue. The factors that were significantly associated with fatigue were age, race, housing tenure status, healthiness of diet, sleep disorders, stress and job satisfaction. The higher levels of fatigue reported by the younger workers could be a result of the ‘healthy worker effect’, as those that can best handle the work demands are most likely to remain in the job. It might also be a result of social activities that the younger participants are involved in outside of work. This was mentioned in the focus groups. Reasons for the non-black group reporting lower levels of fatigue could be because they were, on average, older than the black participants, and because a lower percentage of them did shift work. However, only nine (12.3%) of the participants were non-black, which could have biased the results. It was unexpected that none of the work-related variables were significantly associated with fatigue. It was anticipated that factors such as working shifts and working overtime would be associated with higher levels of fatigue. However, it is noteworthy that the participant groups generally either worked shifts (i.e. production and laboratory workers) or commonly worked overtime (e.g. those in engineering). As such, it is possible that these factors counteracted the associations, as shift work and overtime would both affect fatigue in the different groups. Those who rented accommodation or were paying housing debts reported higher levels of fatigue than those who lived rent-free or had paid off their housing. The latter group might be less stressed and under less pressure to work harder or longer hours to earn production bonuses. As the living conditions, commuting times, and socioeconomic conditions of the participants were generally not seen to be problematic, this could explain the lack of association of these factors with fatigue in this sample. Those who considered their diets to be healthy reported lower levels of fatigue. Healthier diets might lead to better or more sustained energy levels. Reports of healthy eating might also be linked to personality or psychosocial states or traits, with those who are more optimistic reporting better health and lower fatigue. As might be expected, those who reported having a sleeping disorder experienced higher levels of fatigue, likely due to inadequate restorative sleep. It was unexpected that hours of sleep obtained were not significantly associated with fatigue. This could point to the importance of sleep quality in relation to fatigue, including the ability to adapt to shift work, rather than sleep length alone. It could also be that the 7hour cut-off used in the study as an indicator of insufficient sleep was too high. Higher stress levels and poorer job satisfaction were both strongly associated with fatigue. Stress and job dissatisfaction could be either causes or consequences of fatigue. Workers might be stressed and dissatisfied because they are fatigued, or fatigue may result from high levels of stress and dissatisfaction (e.g. because of impaired sleep quality). Stress and fatigue have commonly been considered to be associated, and fatigue has been associated with impairments in mood (Ă…kerstedt et al., 2014; Republic of South Africa, 2014; Shaw, 2003). Stress and job dissatisfaction have also been linked (e.g. Coomber and

Contributors to fatigue at a platinum smelter in South Africa

#% ! $(#%*)% *"' # '% )&(#%$ Several demographic, living condition, lifestyle, health, and wellness variables were associated with fatigue in this group of smelter workers. Generally, factors within the work environment receive most attention when it comes to fatigue management. It is recommended that mining workplaces also take factors outside of the workplace into consideration in their fatigue management programmes. Further study in different settings and with larger sample sizes would be worthwhile in order to confirm these findings, and to generate recommendations relating to non-work contributors to fatigue that can be applied more broadly.

+ %# !' ' '%&$ This paper arises from a project conducted by the CSIR. We would like to thank Sophi Lestoalo, who was a research assistant for this study, and Sizwe Phakathi and Schu Schutte for acting as co-supervisors. We are also grateful to the participating smelter and each of the participants involved in the study.

/' '"'% '$

ALHOLA, P. and POLO-KANTOLA, P. 2007. Sleep deprivation: Impact on cognitive performance. Neuropsychiatric Disease and Treatment, vol. 3, no. 5. pp. 553–567. BHP Billiton. 2005. Fit for Work/Fit for Life. Issue 1, May 2005. COOMBER, B. and BARRIBALL, K.L. 2007. Impact of job satisfaction components on intent to leave and turnover for hospital-based nurses: A review of the research literature. International Journal of Nursing Studies, vol. 44, no. 2. pp. 297–314. DI MILIA, L., SMOLENSKY, M.H., COSTA, G., HOWARTH, H.D., OHAYON, M.M., and PHILIP, P. 2011. Demographic factors, fatigue, and driving accidents: An examination of the published literature. Accident Analysis and Prevention, vol. 43, no. 2. pp. 516–532. GALOBARDES, B., SHAW, M., LAWLOR, D.A., LYNCH, J.W., and SMITH, G.D. 2006. Indicators of socioeconomic position (Part 1). Journal of Epidemiology and Community Health, vol. 60, no. 1. pp. 7–12. HORREY, W.J., NOY, Y.I., FOLKARD, S., POPKIN, S.M., HOWARTH, H.D., and COURTNEY, T.K. 2011. Research needs and opportunities for reducing the safety consequences of fatigue. Accident Analysis and Prevention, vol. 43, no. 2. pp. 591–594. HOSSAIN, J., REINISH, L., KAYUMOV, L., BHUIYA, P., and SHAPIRO, C. 2003. Underlying sleep pathology may cause chronic high fatigue in shift workers. Journal of Sleep Research, vol. 12, no. 3. pp. 223-230. Minkel, J., Htaik, O., Banks, S., and Dinges, D. 2011. Emotional expressiveness in sleep-deprived healthy adults. Behavioral Sleep Medicine, vol. 9, no. 1. pp. 5–14. MOLARIUS, A., BERGLUND, K., ERIKSSON, C., LAMBE, M., NORDSTRÖM, E., ERIKSSON, H.G., and FELDMAN, I. 2006. Socioeconomic conditions, lifestyle factors, and self-rated health among men and women in Sweden. European Journal of Public Health, vol. 17, no. 2. pp. 125–133. NSW (New South Wales, Australia). 2009. Department of Primary Industries, Fatigue Management Plan: A practical guide to developing and implementing a fatigue management plan for the NSW mining and extractives industry. Version 1.0. www.dpi.nsw.gov.au/__data/assets/ pdf_file/0017/302804/Guide-to-the-Development-of-a-FatigueManagement-Plan-Amended-17-6-10.pdf ORR (Office of Rail and Road). 2012. Managing rail staff fatigue. January 2012. London, UK.http://orr.gov.uk/__data/assets/pdf_file/0005/ 2867/managing_rail_fatigue.pdf REPUBLIC OF SOUTH AFRICA. 2014. Guideline for the Compilation of a Mandatory Code of Practice for Risk-Based Fatigue Management at Mines. Department of Mineral Resources. Government Gazette, vol. 32, no. 38339, 19 December 2014. SCHUTTE, P.C. 2009. Fatigue risk management: Charting a path to a safer workplace. Proceedings of the Hard Rock Safety Conference. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 245–250. SCHUTTE, P.C. and MALDONADO, C.C. 2003. Factors affecting driver alertness during the operations of haul trucks in the South African mining industry. Report no. SIM 02 05 02. CSIR Mining Technology, Pretoria, South Africa. SCHWARZ, J.F., POPP, R., HAAS, J., ZULLEY, J., GEISLER, P., ALPERS, G.W., OSTERHEIDER, M., and EISENBARTH, H. 2013. Shortened night sleep impairs facial responsiveness to emotional stimuli. Biological Psychology, vol. 93, no. 1. pp. 41–44. SHAW, A. 2003. Guideline on fatigue management. Shaw Idea Pty Ltd,. Mt Egerton, Victoria, Australia. SHEN, J., BARBERA, J., and SHAPIRO, C.M. 2006. Distinguishing sleepiness and fatigue: Focus on definition and measurement. Sleep Medicine Reviews, vol. 10, no. 1. pp. 63–76.

ÅKERSTEDT, T., AXELSSON, J., LEKANDER, M., ORSINI, N., and KECKLUND, G. 2014. Do sleep, stress, and illness explain daily variations in fatigue? Journal of Psychosomatic Research, vol. 76, no. 4. pp. 280–285.

VAN DONGEN, H.P. and DINGES, D.F. 2000. Circadian rhythms in fatigue, alertness, and performance. Principles and Practice of Sleep Medicine, vol. 20. pp. 391–399.

ÅKERSTEDT, T. and GILLBERG, M. 1990. Subjective and objective sleepiness in active individuals. International Journal of Neuroscience, vol. 52, no. 1–2. pp. 29–37.

WILLIAMSON, A., LOMBARDI, D.A., FOLKARD, S., STUTTS, J., COURTNEY, T.K., and

CONNOR, J.L. 2011. The link between fatigue and safety. Accident Analysis and Prevention, vol. 43, no. 2. pp. 498–515. N VOLUME 119

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Barriball, 2007). Extraneous variables, such as high workloads or challenging work conditions, might contribute to the experience of stress, job dissatisfaction, and fatigue, simultaneously. Although some research has shown fatigue to be associated with increased sick leave or absenteeism and poor health (e.g. Ă…kerstedt et al., 2014), the current study failed to demonstrate these associations. Nevertheless, some trends were evident in the data, such as correlation of increased fatigue with increased sick leave, poorer subjective health, and poorer quality of life A limitation of the study is the small sample size, which might have reduced the statistical significance of the findings. The use of convenience sampling was a further limitation, as it could result in sampling bias. The study participants might not have represented the workforce, being healthier and less fatigued workers, as they were not absent from work or on sick leave at the time of the study. Another limitation is that the study made use of self-reported data. Factors such as personality, mood, and understanding could have affected the responses. Furthermore, as the focus of the study was to gather more information about non-work contributors to fatigue, only basic data about work-related factors was gathered. The inclusion of more detailed workrelated information, such as work design, scheduling, and environmental factors, would have allowed for a more complete understanding of contributors to fatigue at these operations. It should also be noted that as the study was conducted at a single workplace, the findings may not be generalizable. Notwithstanding these limitations, we believe that the results provide an indication of levels of fatigue and contributors to them.

Skills for the future – yours and your mine’s Glenhove Conference Centre, Melrose Estate, Johannesburg

BACKGROUND Previous SAIMM Mine Planning forums have clearly highlighted deficiencies in mine planning skills. The 2012, 2014, and 2017 colloquia all illustrated developing skill-sets with a variety of mine planning tools in a context of multiple mining methods. Newer tools and newer skills for the future of mining will feature in 2019.

OBJECTIVES

WHO SHOULD ATTEND

Using a backdrop of a generic description of the multidisciplinary mine planning process, the forum provides a platform for the mine planning fraternity to share mining business-relevant experiences amongst peers. While different mining environments have their specific information requirements, all require the integration of inputs from different technical experts, each with their own toolsets. The forum’s presentations will highlight contributions from a series of technical experts on current best practice, and will be augmented by displays of state-of-the-art mine planning tools in order to create a learning experience for increased planning competencies.

­ ­ ­ ­ ­ ­ ­ ­ ­

Mine planners and practitioners Mine designers Technical support staff Cost and financial modellers Chief planners Mineral resource managers Mine managers responsible for planning Consultants Independent mine planning consultants

FOR FURTHER INFORMATION CONTACT: Camielah Jardine • Head of Conferencing • SAIMM Tel: +27 11 834-1273/7 • Fax: +27 11 833-8156 E-mail:camielah@saimm.co.za •Website: http://www.saimm.co.za

http://dx.doi.org/10.17159/2411-9717/2019/v119n3a12

South Africa’s options for mineimpacted water re-use: A review by T. Grewar1

South Africa is a water-scarce country, rated as one of the 30 driest countries in the world. Worryingly, government expects water demand to outstrip supply as early as 2025. The South African government believes that substantial volumes of water can be made available through the reuse of treated mining-impacted water. Currently, however, large volumes of treated mining-impacted water are produced, often with no end use in mind other than discharge or disposal, when these treated effluents may have further uses in applications which are currently and unnecessarily using high-quality water. Treating the wastewaters to levels that meet ’fitness-for-use’ guidelines for alternative applications such as agriculture, sanitation, or industrial processes will reduce the treatment cost burden compared to potable water production. The primary conclusion of this review is that the most suitable option for re-using treated mine-impacted water is in agriculture, for irrigation of crops (food, forage, or energy crops), which currently and unnecessarily makes use of the majority of South Africa’s high-quality resources or potable water. Other findings include the realization that although guidelines and legislation for water re-use in South Africa exist and are readily available, they tend to be contradictory and confusing in many cases. It is imperative that these ambiguities and incongruities in the available guidelines and legislation for water re-use be clarified and updated swiftly. =*'98.6 water re-use, mining-impacted water (MIW), agriculture, acid mine drainage (AMD).

7;89.-2;:97 South Africa is a water-scarce country and is currently rated as one of the 30 driest countries in the world, with an average rainfall of 490 mm/a, approximately half of the global average. As an indicator of the degree of regional variability in South Africa’s water supply, it has been shown that 70% of all runoff is from approximately 20% of the land area. Regardless of the water scarcity faced in South Africa, our water conservation track record is poor, with an average consumption of 280 L/d per person, almost 60% more than the global average of 175 L/d per person. Around 40% of this allocation is utilized in watering lawns and gardens (Zhuwakinyu, 2017). The South African government predicts water demand to outstrip supply as early as 2025. On an international scale, the situation appears just as dire, with the United Nations High Level Panel on Water (HLPW) expecting

1 Biotechnology Division, Mintek South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Jan. 2018; revised paper received Oct. 2018.

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*7916:6

a 40% water shortfall by 2030, which may affect up to 1.8 billion people based on current water demand trajectories (Zhuwakinyu, 2017). Currently, South Africa is experiencing the worst drought since 1904, which has triggered severe water shortages, negatively affecting agricultural output in all sectors (News24, 2016). The potential exists for nation-wide ‘water-shedding’ initiatives, similar to the electrical load-shedding programme initiated by Eskom, being implemented for homes and businesses in the near future if the situation continues to decline. Currently, water restrictions are in place country-wide with the Western Cape being the worst affected province. In 2016, eight of the nine provinces, with the exception of Gauteng, were declared drought disaster areas. The country’s total water supply is currently estimated at 14.6 km3/a, of which surface water is the main source. The current demand is estimated to be between 15 km3/a and 16 km3/a, and it is expected that South Africa will experience a 17% water supply and demand gap by 2030 (Webb, 2015; News24, 2016; Zhuwakinyu, 2017). The UN World Water Development Report (2017) argues that improved wastewater management could facilitate the achievement of the UN’s 2030 Sustainable Development Goals (SDGs). SDG-6 specifically has a target to reduce the proportion of untreated wastewater by half by 2030. while sustainably increasing water recycling and safe re-use (WWAP, 2017). The report also suggests that wastewater which is traditionally discarded could be treated to provide a non-potable water resource for use in agriculture and energy production. According to the Water 2017 Report, more than 50 countries

South Africa’s options for mine-impacted water re-use: A review worldwide are already making use of treated wastewater for irrigation, accounting for approximately 10% of all irrigation water world-wide. A number of proposals have been put forward by the South African government to increase water supply. These include; rainwater harvesting; desalination; increasing the yield from surface water; and promoting the use of groundwater on a larger scale (Webb, 2015). Currently, surface water comprises about 79% of the total water supply of the country while groundwater, mainly used in rural areas, comprises only about 14%, with the balance (7%) comprising re-used water. Government believes that substantial volumes of water can be made available through the increased re-use of return flows, especially in some coastal cities where potentially re-usable wastewater is currently discharged into the sea, as well as the re-use of treated acid mine drainage (AMD) in particular or mine-impacted water (MIW) in general (Webb, 2015). In the context of this review, AMD is defined as an acidic effluent with high sulphate and metal concentrations emanating from abandoned or ownerless mines, and MIW is defined as any wastewater of varying pH that has in some way been affected by mining or a mining-related process, and hence can encompass AMD among other effluent types. For this reason, these two terms will be used interchangeably. The goal of this review was to perform an overview of the literature to determine the current state of MIW re-use in South Africa and to provide a central resource of information regarding legislation, available guidelines, and potential options for re-using treated MIW.

treating minewater to potable standards, with the treatment costs estimated to be between R12 and R18 per cubic metre (Annandale et al., 2007; Webb, 2015). The question, however, remains whether it is necessary to treat the minewater to potable levels, thereby incurring high treatment costs, when activities like irrigation of crops, flushing of toilets, and washing of clothes do not require potable water. Does it not make sense to reduce the treatment burden and subsequent cost by treating the water only to levels that are suitable for these or similar activities? Minister Mokonyane reaffirmed the South African government’s stance on water re-use and recycling while speaking at the launch of the United Nations World Water Development Report 2017 in March 2017. She stated that ’recycling water for industrial and agricultural purposes would go a long way towards ensuring the country’s water security’ (Zhuwakinyu, 2017). To help incentivize water reuse, the DWS announced in May 2016 that government would provide R600 million every year towards treating MIW, with the funds allocated directly by the National Treasury. Some of these funds will be recovered from users of the treated water (33%) while the balance of 67% will be recovered from the polluting mining operations through a proposed environmental levy (Zhuwakinyu, 2016). Government’s short-term intervention for MIW treatment in the Witwatersrand Basin in Gauteng have included the installation of three high-density sludge (HDS) treatment plants in Krugersdorp, Germiston, and the most recent commissioned in Springs during February 2017. These plants can treat 50 ML/d, 82 ML/d, and 110 ML/d of decant respectively, after which it is released into nearby water resources (Zhuwakinyu, 2017). The major problem with this solution, however, lies in the fact that water treated by HDS has sulphate levels that are well above those allowable for discharge, in addition to a number of issues surrounding storage and disposal of the sludge produced.

":7:70 :51<2;=.>'<;=8>:7> 9-;)>(38:2<> The impacts of mining range from initial exploration through the life-cycle of the mine to beyond mine closure. Miningimpacted water disposal poses serious problems globally, and the quality of the impacted water depends greatly on the chemical and mineralogical characteristics of the orebody. Owing to its salinity, minewater generally cannot be discharged into river systems unless diluted with good quality water to reduce the salinity to within acceptable limits. For this reason, the Department of Water and Sanitation (DWS) is assessing the installation of desalination plants to treat MIW to levels that will enable safe discharge; however, the long-term view on MIW treatment includes desalination to potable standards to augment fresh water supply. A number of coal mines in Mpumalanga are already

&-88=7;>;8=<;5=7;>;=2)79490:=6 Since this reviews’ focus is not on MIW treatment technologies but rather on uses for the treated water, this section will merely highlight the more mainstream treatment options currently in use, along with some of the newer technologies that are becoming available. These include Mintek’s passive biological sulphate reduction (BSR) process and SAVMINTM, both of which address salinity issues. These technologies are summarized in Table I.

Table I

-55<8*>93>< <:4< 4=>" >;8=<;5=7;>;=2)79490:=6 =2)79490* 1Reverse

osmosis (RO)

2High-density

sludge (HDS)

4SavminTM

8=<;5=7;>5=;)9.

>296;

Membrane

High (R12-18/m3)

Precipitation Precipitation

6Passive

biological sulphate reduction (BSR)

et al. (2009) (2017) 3Van Zyl et al. (2001) 4Van Rooyen (2017)

5Potable

2Zhuwakinyu

6Grewar

(R1-2/m3)

Med/high (R10/m3 for 2 g/L SO4)

Biological dissimilatory sulphate reduction

1Greenlee

L

322

3Low

<6;=>:66-=6>

7Low/med

(R3-5/m3)

9;< 4= 797 19;< 4=

Brines

Potable

Sludges

Non-potable/high-sulphate

Metal hydroxides, gypsum 8Sulphide

5Potable

and non-potable

Non-potable/fit-for-use

water is possible depending on inlet feed composition (2017) 7Mintek (2018) 8Potential to include a polishing step where sulphide can be recovered as biosulphur (value-added product)

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South Africa’s options for mine-impacted water re-use: A review The Department of Water and Sanitation is the custodian of South Africa's water resources and is responsible for ensuring that water resources remain fit for recognized uses and are maintained and protected (DWAF, 1996). Four broad categories of water use are recognized in the South African National Water Act (Act 36 of 1998), namely domestic, industrial, agricultural, and recreational use. These categories can each be subdivided into a number of subcategories, all of which may have vastly different water quality requirements. The minimum contaminant limits (MCLs) for a variety of constituents have been defined for the recognized water use categories in the South African Water Quality Guidelines (volumes 1 through 8) produced by the Department of Water Affairs and Forestry (1996). ’Water use’ characterization involves investigating various characteristics of a specific water use (i.e. costs, socio-economic benefits, volume of use), which assists in determining the consequence thereof. In addition, various legislated guidelines are considered in conjunction with risk-based factors to determine the water quality required for a particular use. The water quality requirements for water use are usually determined on a site- or case-specific basis (DWAF, 1996). The traditional ‘once-through’ centralized water management system is being re-thought due to growing water scarcity owing to population growth, urbanization, and climate change. This has become a global problem that has triggered renewed interest in water recycling and re-use. An array of ecological and financial benefits can be generated by decentralized water re-use systems, in addition to the supplementing of existing stressed water supplies (Stoakley, 2013).

The National Water Resources Strategy (NWRS) first edition (2011) identified water re-use as one of a number of important strategies to balance water availability and requirements in the future. One of the commitments of the NWRS is, ‘DWA [now the DWS], with sector partners will explore the use of new technologies for re-using waste water and for using treated mine water’. With the 2013 NWRS2 an annexure (D) was released specific to the national strategy for water re-use. It stated that water re-use can be classified as direct or indirect, planned or unplanned, on a large or a small scale irrespective of location. It may involve a variety of treatment options or none at all, with the reclaimed water being used for a number of activities, each with its own reuse strategy. There is, however, an associated risk that water re-use may be unplanned, unregulated, and/or result in unintended or undesirable consequences. Water quality and security of supply, water treatment technology, cost relative to other water supply alternatives, social and cultural perceptions, and environmental considerations are the five key considerations that affect choices related to water re-use as an option for water supply augmentation (South Africa, 2013). The inclusion of planned water reclamation, recycling, and re-use schemes in water resource systems reflects the increasing scarcity of water sources as a whole. Some of the categories of potential wastewater re-use are agricultural, landscape irrigation, industrial recycling and re-use (i.e.

cooling, washing), non-potable urban uses such as fire protection, air conditioning, sanitation (toilet flushing), and potable re-use. Re-use and recycling within industrial processes is fast becoming the norm as regulations and laws regarding environmental protection and water security are becoming increasingly stringent. However, there are still large volumes of treated effluent being produced with no end use in mind other than discharge or disposal. These treated effluents could be re-purposed for other applications that are unnecessarily using high-quality water resources. By simply adopting the ‘water quality cascade’ approach (Stoakley, 2013), which refers to the linking of the quality of a wastewater source to an appropriate subsequent activity, all available water would be put to its most beneficial use even as the quality decreases after each re-use. For example, minewater may be suitable for a number of alternative applications (agriculture, sanitation, industry) provided that it is treated to acceptable levels for various contaminants. Advantages of this approach include the resultant capex and opex cost savings from a reduced treatment burden as well as the identification of an alternative water resource. The aim of this re-use ‘philosophy’ would ultimately be to reduce potable water consumption and subsequently increase water conservation. The practice of using reclaimed wastewater is, however, not risk-free, hence proper planning and risk analysis are essential to mitigate the risks applicable to the re-use application identified.

There are a number of South African Acts that are applicable when water is used and effluent is discharged. Although the Water Services Act (No. 108 of 1997) (WSA) and the National Water Act (Act 36 of 1998) (NWA) form the foundation for the legislative framework within the sector, other Acts which are also important include the Environment Conservation Act (Act 73 of 1989) (ECA) and the National Environment Management Act (Act 107 of 1998) (NEMA), where recent amendments would also be applicable. In some instances specific municipal bylaws may also be relevant (South Africa, 1989, 1997, 1998a, 1998b). The NWA (Act 361 of 1998), however, is the primary legislation governing the use and discharge of ‘wastewater’. The Act aims to ensure that the nation’s water resources are protected and managed to reduce and prevent pollution and degradation of water resources. The Act also promotes the ‘polluter pays’ principle, and the stringent Wastewater Discharge Standards Guidelines proposed by the then Department of Water Affairs in 2010 will force industries to develop their own cleaner production systems. The Act requires that any person using wastewater for irrigation purposes, discharge, or disposal must register with the responsible authority as a registered water user and must ensure that the wastewater does not impact other water sources, property, or land and that the wastewater is not detrimental to the health of the public. In South Africa, the WSA (Act 108 of 1997) relates more to the management of human drinking water and directs bulk water suppliers to the compulsory national standards in the form of SANS 241-1:2015. The SANS 241 standard is specifically designed with bulk water suppliers in mind and is VOLUME 119

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South Africa’s options for mine-impacted water re-use: A review aimed at safe water provision for domestic drinking use, and thus focuses on life-long safety for all types of users and aesthetic acceptability (South Africa, 1997). Currently the re-use of effluent streams requires environmental authorization in terms of the NEMA (107:1998), and in some cases requires water use licences (WULs) in terms of the NWA (36:1998). Specifically, all new and existing mines are required, in terms of the NWA (36:1998), NEMA (107:1998), and the Mineral and Petroleum Resources Development Act (Act No. 28 of 2002) (MPRDA), to optimize water re-use and reclamation (DWA, 2013). Other regulations relevant to the use of water for mining and related activities in South Africa, aimed at the protection of water resources, are published in terms of the NWA (36:1998) in the Government Notice 704 of 4 June 1999, known as GN704, which states that mines must collect, confine, and take reasonable measures to prevent water resource contamination as well as ensure that water used in any process at a mine or activity is recycled as far as practicable (Munnik and Pulles, 2009). Although it is unfortunate that much of South Africa’s water-quality woes stem from a lack of coordination in addressing the water contamination issue, this concern is expected to be addressed when the Mine Water Management Policy (MWMP) comes into effect. The draft proposal was approved by Cabinet in 2017 and later gazetted by the DWS in July 2017, allowing a 60-day period for public comment. The MWMP policy is aimed at (a) ensuring improved water quality management and reduction of water pollution, including through AMD treatment, (b) strengthening the protection of water resources from mine water contamination from short to long term, and (c) providing a basis for holding parties potentially liable for negative effects and damages through AMD-related pollution (DWS media statement, 14 July 2017; Zhuwakinyu, 2017).

Bruvold and Ward (1972) conducted one of the earliest studies on public perceptions of recycled water usage. They identified ‘psychological repugnance’, also known as the ‘yuck factor’, as the main reason for opposition to the use of treated water originating from municipal wastewater and

which will most likely also be applicable to the re-use of treated minewater. Several surveys completed more recently have revealed numerous additional factors that appear to influence the perceptions, acceptability, and overall successful implementation of recycled water regardless of the source within a community. Some of these factors include the sources of the water to be re-used, trust in and knowledge of the treatment processes producing the water for re-use, the specific activities related to water re-use, and the cost of water reuse (Bruvold and Ward, 1972; Stoakley, 2013). Stoakley (2013) conducted surveys among South African university students to determine their perceptions around water re-use. The results showed a high degree of acceptability for non-potable uses such as watering gardens and toilet flushing, which increased with the premise that either the individual would personally not have access to clean water without re-use or it would benefit the environment. Reluctance appeared greater in the potable use category, with concerns surrounding health and safety being paramount. Water re-use projects in the past have, however, shown that the level of community acceptance and perceptions around elements such as cost, risk, and necessity are vital indicators of a planned project’s eventual success or failure (Stoakley, 2013). For these reasons, the benefits of proactively providing opportunities for public participation in the development process should be carefully considered and will far outweigh the perceived administrative burdens of such a task by ensuring a smoother, more successful implementation phase.

:;7=66>398>-6= The South African Water Quality Guidelines, volume 3 (DWAF, 1996), sums up ‘fitness for use’ as a judgement of how suitable the quality of water is for its intended purpose or for protecting aquatic ecosystems. However, with modern technology water of nearly any quality can be produced for a specific purpose provided it can be treated to the required specifications. Therefore, how fit a particular water source is for use depends also on the design specifications for the process and how much the user is prepared to invest in treating the water to comply with these specifications. Hence, ‘fitness for use’ is largely defined by the use of water quality guidelines. Table II provides a summary of a number of different categories of water re-use.

Table II

&<;=098:=6>93>19;=7;:<4>8= -6=>398>;8=<;=.>(" >,<.<1;=.>3895> ( >$/#%+ &<;=098*>93>8= -6= Urban use Unrestricted irrigation

=628:1;:97>93>2<;=098*

Restricted irrigation Other uses

Landscape irrigation of parks, playgrounds, school yards, golf courses, cemeteries, residential areas, green belts Irrigation of areas with infrequent and controlled access Fire protection, disaster preparedness, construction

Agricultural use Food crops Non-food crops and crops consumed after processing

Irrigation for crops grown for human consumption Irrigation for fodder, fibre, flowers, seeds, pastures, commercial nurseries, instant lawn

Other use Industrial re-use Residential re-use Potable re-use

Cooling system water, process water, toilets, laundries, air-conditioning, wash-down water Cleaning, laundries, toilets, air-conditioning Blending with municipal water supply, pipe-to-pipe supply

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South Africa’s options for mine-impacted water re-use: A review Both locally and internationally, a number of limits and guidelines for different contaminants are available in the literature. However, many of these deviate significantly from one another in terms of approaches and methodologies used to arrive at the specified criteria. For this reason, the DWAF (now DWS) South African Water Quality Guidelines (SAWQG) volumes 1 to 8 (1996) were developed to provide a single set of guidelines and criteria for water quality and fitness for use that is appropriate for recognized water uses in a South African context. Based on the definitions, concepts, and guidelines above, an assessment was performed in order to determine the most suitable uses for treated MIW. Using available data, which in some cases was limited, we directly compared the maximum contaminant limits (MCLs) of the more relevant elements in MIW for potentially feasible re-use activities, including crop irrigation (DWAF Volume 4; 1996), discharge to a sewer for sanitation (City of Johannesburg Metropolitan Municipality Water Services By-laws, 2008), discharge to a watercourse (South Africa, 1998b), and drinking water (SANS 241:2015). These are compared in Table III. It should be noted here that although industrial re-use is an option and is being considered in some respects, it was not considered in this review to be a viable option. This was primarily due to the results from a study performed by the DWA (2013) that determined that although there are a large number of industries located within the Rand Water supply area affected by AMD decant, 67% of the industrial water demand is required by only three individual super-factories, all of which are distant from the AMD generation points and require very high-quality water (above potable water standards), which is not provided by neutralized or minimally treated AMD. Interestingly, this direct comparison in Table III highlighted a number of incongruities in the available guidelines and legislation used to compare the MCLs:

ÂŽ MCL levels for Ca and Mg have not been set for the select group of activities where re-use is possible, not even for human consumption ÂŽ SO4 MCL levels are more stringent for discharge to a sewer than for human consumption, with none set at all for discharge to a watercourse ÂŽ MCL for SO4 for irrigation is not set in any of the formal guidelines or legislation ÂŽ Allowable EC levels for irrigation differ in the NWA guidelines (South Africa, 2013) compared to the DWAF irrigation guidelines (1996) ÂŽ Fe MCL for discharge to a water resource is more stringent than that for human consumption, and the same applies in the case of Mn ÂŽ Allowable pH levels differ in the DWAF irrigation guidelines (1996) from those in the NWA guidelines (South Africa, 2013). These incongruities and the absence of set limits in some cases within the available guidelines and legislation for water re-use pose a potential hindrance to the technology and R&D framework, which requires clear and unambiguous guidelines upon which new technology is designed and developed. These guidelines therefore need to be clarified and updated timeously so as to not adversely affect technology development in this sector. The various options for treated MIW reuse are discussed in more detail below.

The success of the eMalahleni Water Reclamation Plant (EWRP) in Witbank has demonstrated the viability of using MIW treated to potable levels for residential human consumption. Potable water, however, comes at a hefty treatment price and depending on the treatment process, significant amounts of wastes (brines) are produced that present a costly disposal dilemma.

Table III

&951<8:697>93>;)=>5< :5-5>297;<5:7<7;>4:5:;>,"&!+> <4-=6>398><> <8:=;*>93>8= -6=><2;: :;:=6>':;)>8=61=2;>;9 18:5<8*>" >295197=7;6> "< :5-5>297;<5:7<7;>4:5:;6>,"&!6+>,50 !+ &97;<5:7<7; SO4 ClMg Al Ca Mn Fe Na SAR (sodium adsorption ratio) EC (mS/m) pH

&891>:88:0<;:97>>

(a)700 (b)(c)20

(d)10

20 (e)460 (f)15 (g)540

(13150–200) 6.5-8.4 (135.5–9.5)

#/

:62)<80=> >6='=8>

##

:62)<80=> >'<;=829-86=>,0=7=8<4>4:5:;6+>

250 1000 50 200 500 -

#$

0.1 0.3 <150 5.5-9.5

8:7 :70> <;=8 500 300 0.3 0.4 2 200 170 5.0-9.7

Notes: (a) May exceed, depending on crop tolerance, target 100 mg/L; (b) Limits not set or not available; (c) May exceed, only acceptable over short term, target 5 mg/L; (d) May exceed, only acceptable over short term, target 0.02 mg/L; (e) May exceed, depending on crop tolerance, target 70 mg/L; (f) May exceed, some economically important crops can be irrigated without developing sodium toxicity, target 2.0 (g) May exceed, requires sound irrigation management, target 40 mS/m. 12SANS

10City

1996. South African Water Quality Guidelines for Agriculture: Irrigation (Volume 4) of Johannesburg. 2008. Metropolitan Municipality Water Services By-laws 11South Africa (2013)

13South

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241:2015 Drinking water specification African National Water Act (No. 36 of 1998)

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9DWAF

South Africa’s options for mine-impacted water re-use: A review As mentioned previously, potable water is not necessary for a number of domestic activities. Hence MIW treated to the lowest requirements for such activities could provide significant savings in terms of treatment costs while simultaneously having a positive impact on the environment and our highly stressed water resources. This partial treatment should, however, not only focus on neutralization and the removal of metals, which in the past have been the primary focus of treatments options, but also on reducing the sulphate content as the salt load of the partially treated MIW would still present a contamination risk to resources via domestic sewerage systems.

Agriculture accounts for around 70% of fresh water withdrawals from rivers, lakes, and aquifers worldwide with approximately 1000 L of water required to produce 1 kg of cereal grain, and 43 000 L to produce 1 kg of beef (Pimentel et al., 2004; Webb, 2015). Irrigated agriculture is the largest consumer of available water in South Africa. However, the abstraction rate related to irrigation has decreased dramatically from 80% of available water resources 45 years ago to around 63% currently (Zhuwakinyu, 2017). This downward trend has continued, with irrigators experiencing increasing pressure to reduce their water use. Since many irrigation schemes are situated at the lower end of drainage basins they often receive poor quality water due to the influence of upstream activities (DWAF: Volume 4, 1996). Irrigators may experience a range of adverse effects as a result of variable water quality that may include reduced crop yield and quality, and soil degradation and damage to equipment from scaling or corrosion. These effects can be mitigated by switching to a more tolerant crop or simply changing the irrigation method (DWAF, 1996, vol.). The South African government is planning a 33% increase in irrigated land by 2030 due to anticipated increasing demand from the agricultural sector. This is being driven by the National Development Plan (NDP), which has identified agriculture as a key element in food security, job creation, and social capital in rural communities as it has been recognized as having significant employment-creation potential due to the labour-intensive nature of the industry. Other factors such as climate change, drought, and population growth are also expected to contribute heavily to

the increased demand in this sector, which is expected to grow from 8.9 km3/a currently to 9.7 km3/a by 2035 (Webb, 2015; Zhuwakinyu, 2017). The South African National Water Act (No. 36 of 1998), specifically the revision of general authorization in terms of Section 39 of the National Water Act (Act no. 36 of 1998), published under Government Notice 665 in Government Gazette 36820 dated 6 September 2013, classifies irrigation, specifically irrigation with biodegradable industrial wastewater and domestic wastewater, as a controlled activity in terms of Section 21(e) that can be practiced only under license. Biodegradable industrial wastewater is defined in the Act as wastewater that contains predominantly organic waste arising from industrial activities including, but not limited to, milk processing, abattoirs, manufacture of animal feed, and production of alcohol or alcoholic beverages in breweries, wineries, or malthouses. Although the Act specifies biodegradeable wastewater, all wastewater regardless of source may be used for irrigation provided the water quality remains within the set guidelines. Permission is granted only if the location of the water use complies with the guidelines that aim at preventing pollution of other water sources such as aquifers, boreholes, watercourses, or wetlands. The Act also details precautionary practices to ensure that the water user follows acceptable construction, maintenance, and operational activities. In addition, sampling and monitoring is also required to be performed on a regular basis and the results reported to the relevant authority. Wastewater limit values for a variety, albeit limited list, of contaminants are described in the Act (36:1998) for the irrigation of any land or property specific to areas of (a) up to 2000 m3, (b) up to 500 m3, and (c) up to 50 m3 with domestic or biodegradable waste water (Table IV). This list is unfortunately far from comprehensive and does not cover contaminants such as metals and sulphates. However, guidelines for some of these are available in the South African Water Quality Guidelines - Volume 4: Agricultural Use: Irrigation (1996), produced by the DWAF (summarized in Table III), and these need to be used in conjunction with the Act to determine the quality of water required for irrigation purposes.

Pulles (2006) describes management options for saline MIW in South Africa as (1) pollution prevention at source, (2) re-

Table IV

<6;='<;=8>4:5:;> <4-=6>398>:88:0<;:97>93><7*>1891=8;*>98>4<7.>-1>;9>$///>5% ,<.<1;=.>3895> 9-;)>(38:2< >$/#%+> <8:< 4=6

!:5:;6>>>>>>>, $///>5%+

!:5:;6>, //>5%+

!:5:;6>, />5%+

5.5-9.5 150 25 0.25 1 75 3 15 10

6-9 200 400 -

6-9 200 5000 -

pH Electrical conductivity (mS/m) Suspended solids (mg/L) Chloride as free Cl (mg/L) Fluoride (mg/L) COD (mg/L) Ammonia (ionized and un-ionized) as N (mg/L) Nitrate, nitrite as N (mg/L) Orthophosphate as P (mg/L)

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South Africa’s options for mine-impacted water re-use: A review

In summary, it is clear that using treated MIW for crop irrigation confers a number of notable advantages, including the facts that (a) MIW requires a low level of treatment prior to re-use in irrigation, resulting in substantial treatment cost savings, (b) large portions of irrigated land are located near MIW sources, which would limit the collection and distribution costs if MIW was utilized, (c) the treatment and re-use of MIW could operate as a single financial initiative, with income from the MIW-irrigated crops funding the MIW treatment and/or creating jobs and food security for the local community, (d) treatment and re-use of MIW in crop irrigation would relieve the environmental and health liabilities of the mines producing the MIW, while simultaneously stimulating agriculture in the vicinity, and (e) would make more high-quality or potable water available for more pressing needs (van Zyl et al., 2001). The potential for linking mining with agriculture has been recognized by some in the industry, although it will need widespread adoption to make a significant impact on the status quo. A recent article in Mining Weekly Online (14 September 2016) quoted Exxaro CEO Mxolisi Mgojo as saying that ’I really think the land around our coal mines should be developed into agricultural hubs’. The same article reports that Sibanye Gold is rolling out a modern approach to ensure community development in the areas in which it operates, with the aim to leave an agricultural economy in its place when operations cease in 30–40 years’ time. It was reported that 640 people are already employed and the community has been benefitting from the sale of produce to the Spar retail group.

Over the last 30 years various studies have been performed in South Africa on the potential of using minewater for irrigation, with varying levels of success (du Plessis, 1983, Jovanovic et al., 1998; Annandale et al., 1999, 2001, 2002, 2006, 2007, 2009; Pretorius et al., 1999; Jovanovich et al., 2002, 2004; van der Laan et al., 2014). A few examples are discussed below. In 1983 Du Plessis (1983) was the first to investigate the use of gypsiferous minewater for irrigation purposes. He found that lower soil and percolate salinity was achieved by irrigating with gypsum-rich water instead of chloride-rich water of similar composition and attributed this to the precipitation of gypsum in the soil. Furthermore, the increase in sodium caused by gypsum precipitation did not significantly affect the physical properties of the soil or crop yield. Lime-treated MIW has been used successfully in the irrigation of crops at the Landau Colliery Kromdraai Opencast Section (Witbank, Mpumalanga). A field screening trial of 20 agronomic and pasture crops, including maize, soybean, rye, wheat, lucerne, and kikuyu was investigated for irrigation by lime-treated MIW on a sandy acidic soil. The results indicated that considerable yield increases of irrigated crops could be realized compared with rain-fed cropping, provided that irrigation and fertilization practices were managed correctly (Jovanovic et al., 1998). In 1997–1998, Annandale et al. (2001) undertook a field trial at KleinkopjÊ Colliery in Witbank (Mpumalanga) to determine the effect of using gypsiferous minewater for VOLUME 119

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use and recycling of water to minimize the volume of polluted water that could also be treated, (3) treatment of effluents if the problem cannot be solved through prevention, re-use, and recycling, and (4) discharge of treated effluent, which is considered the last resort. In addition, he also mentions the potential for using gypsiferous or lime-treated minewater for agricultural irrigation as a re-use strategy, particularly post mine closure. A number of opportunities are provided by using gypsiferous mine wastewater in irrigation, including the potential to enable dry season production as well as stabilizing dryland crop production. Simultaneously, it would provide a relatively cheap method of reducing the volumes of minewater decant being produced unchecked. By irrigating with gypsiferous wastewater, a large percentage of the salts present can be removed from the mine effluent through gypsum precipitation into the soil profile (Annandale et al., 2007). Several studies have been published around the treatment of MIW within the upper Olifants River catchment area which identified a number of advantages to irrigation with treated MIW. While the quantities, MIW treatment technologies, flow sheets, and the corresponding costs would have changed since these studies were done, it can be expected that the advantages identified would still be relevant today, and these are explored in more detail below. Grobbelaar et al. (2004) reported estimates of the volumes of minewater stored and generated for various mines in the central Witbank coalfields in Mpumalanga. They state that after closure of the entire Mpumalanga coalfields, approximately 360 ML/d of mine-impacted water may be generated, while for the Olifants catchment area a volume of 170 ML/d was estimated. These enormous volumes, acting as an alternative water resource, could potentially support over 6 000 ha of irrigated land. On a more site-specific scale, the KleinkopjÊ Colliery in Witbank, which has an estimated daily water make of around 14 ML with a further 120 000 ML of water stored underground could, depending on the crop system chosen, sustain irrigation of 500–700 ha, alone. In addition, van Zyl et al. (2001) reported that simply treating minewater decant to irrigation standards, rather than for urban or industrial applications, would reduce capital and running costs by 87% and 78%, respectively. Similarly, Annandale et al. (2007) compared the capital costs of several treatment options, and showed treatment for irrigation to be lower in cost by an order of magnitude. In addition, and of particular importance in the post-closure period of a mine, the income generated from the sale of the water could be offset against the running costs. Further benefits include job creation and protection of water resources, which are aligned to the government’s NDP. Currently, mine land post mine closure is rehabilitated, but usually not to the advantage of the local communities. Mines in South Africa tend to be located in water-scarce areas, and use of the land for agricultural purposes would require fresh water to be provided from further afield, which is not economically viable. However, as explained above, the treatment of MIW provides a water source on site or nearby, which then allows agriculture on the mine land to become a realistic opportunity for the surrounding community on a year-round basis (Jovanovich et al., 2002).

South Africa’s options for mine-impacted water re-use: A review irrigation of crops of sugar bean and wheat on rehabilitated opencast mine land. They found considerable increases in yields of sugar beans and wheat under irrigation with gypsiferous minewater compared with rain-fed cropping. Specifically, yields of sugar beans on virgin land were higher compared to those on the rehabilitated land. They attributed this to a number of reasons, including late planting date, soil compaction, low soil pH, and nutrient deficiencies, and not necessarily the quality of the irrigation water. Excellent wheat yields were obtained on both virgin and rehabilitated land, which was attributed to the fact that wheat is more tolerant to salinity than beans. In addition, no symptoms of foliar injury due to overhead irrigation with gypsiferous water were observed. Annandale et al. (2007, 2009) carried out a long-term study at three mines, namely KleinkopjÊ Colliery near Witbank, New Vaal Colliery near Vereeniging, and Syferfontein near Secunda. The average water qualities used are summarized in Table V. All sites were centre-pivot irrigated with gypsiferous minewater, and some had begun irrigation with minewater as far back as 1997. They found that the maize yield from irrigation with minewater was lower than dryland-produced crops, particularly on rehabilitated land, but attributed this to the poor drainage of the site and the rapid rise in the electrical conductivity (EC) observed over the irrigation period. However, wheat yield was unaffected by similar water quality. They also determined that potatoes were successfully produced using gypsiferous minewater, and that pasture crops like lucerne and fescue were unaffected by irrigation with Na2SO4-rich minewater at Syferfontein. No symptoms of foliar injury due to centre-pivot sprinkler irrigation with gypsiferous minewater were observed for all crops. They did, however, note that potassium (K) uptake was suppressed by the presence of high calcium (Ca) and magnesium (Mg) in the water, and suggested that this could be corrected by regular application of K-containing fertilizer and following proper irrigation management. Some crop failures were noted but these were attributed to poor site selection, resulting in waterlogging of crops, rather than the water quality.

Table V

( =8<0=>:88:0<;:97>'<;=8> -<4:;:=6><;>;)=>.:33=8=7; 5:7=6>,<.<1;=.>3895>(77<7.<4=>et al >$// >$// + (7<4*;=

Al Ca Mg K Na Fe Mn SO4 ClTDS pH EC (mS/m)

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(7<4*6=6>,50 !+ 4=:7 91 >

4=:7 91 >

,"< 98+

, '==397;=:7+

0.3 513 158 51 0.3 6 2027 18 2917 6.4 294

0.01 405 196 47 0.08 0.01 1464 32 2212 7.0 205

*3=8397;=:7

='> <<4

0.1 32 88 16 796 0.1 0.03 1647 17.8 2435 9.2 372

93 31 5 132 0.32 0.08 430 42 1120 7.5 132

VOLUME 119

In general, crop production under irrigation with minewater rich in Ca and sulphate was found to be feasible and sustainable, if properly managed. The same team also investigated the sustainability of irrigation with gypsiferous coalfield minewater on a commercial scale and found that crops like sugar beans, wheat, maize, potatoes, and pastures were very successfully produced with no significant impact on the soil or surface and groundwater resources being observed, at least in the short to medium term (eight years). In 2014, van der Laan et al. reported on the potential for goldfield minewater from the Witwatersrand area to be used as an alternative irrigation water resource. The minewater was either pre-neutralized, as was the case for prior studies performed on coalfield minewater described above, or raw minewater was applied to soils or mine tailings that had been preconditioned with slaked lime or limestone. The preconditioning of the soils was expected to facilitate in situ neutralization and sequestration of many of the contaminants present in the raw minewater. The results illustrated that goldfield minewater could be used as a cost-effective solution for the irrigation of salt-tolerant crops on agricultural land or vegetation on mine tailings. The researchers calculated that 60% of the salts from the neutralized minewater would be retained within the soil profile, compared to 75–90% salt removal achieved from the raw water when irrigating pretreated mine tailings or clay soils. The work described above is significant as it highlights the potential for the use of coal or gold minewater to be used for the production of various types of crops ranging from food for human consumption to forage and pasture crops for livestock consumption and even for energy crops for biofuel production. This is where the re-use strategy for MIW within agriculture specifically fits within the framework of the Water-Energy-Food (WEF) nexus, which is rapidly becoming a strategic area of importance, globally.

<7:;<;:97 About 40% of the water consumed by South African households is used to flush toilets, and about 60% of the total water and sanitation costs are used to fund the treatment of this contaminated wastewater. On average, 200 g/d of human waste per person is flushed down the toilet, while 6–9 L of drinking quality water is used for each flush (Webb, 2015). Potable water is, however, not necessary for sanitation purposes and this represents a major mismanagement of our limited water resources. Unfortunately in South Africa, unlike other countries such as Japan and Australia, dual reticulation systems which allow for the use of treated wastewater for sanitation are rare. These would be required in order to make use of wastewater for sanitation purposes in residential homes. The lack of these dual reticulation systems therefore currently prevents the use of wastewater for any purpose, be it sanitation or otherwise, in the majority of residential homes in South Africa. However, Dr Jo Burgess from the WRC (26 May 2016) explained in a personal communication that wastewater reuse may be permitted in a commercial building such as a business or separate ablution facilities as in schools or camping grounds, provided adequate signage is put up to alert users to the fact that wastewater is in use for sanitation

South Africa’s options for mine-impacted water re-use: A review and should not be consumed. The wastewater would also need to meet various limits for discharge to the sewer system in order to prevent scaling of the pipelines and not overburden the treatment facilities. The sanitation system would also need to be situated nearby a decant point or MIW source in order to make logical and economic sense, since transporting ‘dirty’ water large distances is not economically viable. These limitations unfortunately reduce the volume of treated MIW that could be feasibly utilized, thereby making the overall impact of sanitation as an alternative activity for MIW re-use somewhat insignificant under the current circumstances. Going forward, however, re-use of MIW in sanitation should be revisited once legislation is amended and infrastructure improved to allow for this option to be more widely utilized.

Although the re-use of MIW in industry was not considered during this comparative study, the option is still available and was therefore included in the overall review discussion surrounding options for MIW re-use. Significant amounts of water are used (both consumed and non-consumed) in almost all aspects of mining, although the most intensive areas of water use are for cooling of drilling machinery, dust suppression, and minerals processing. However, these are case-specific and can vary greatly depending on factors such as water chemistry, climate, water availability, mine management and practices, geology, ore mineralogy, and the commodity being mined. Generally, lower grade ores require more water for processing. This water is abstracted either directly (from boreholes, dams, streams, or rivers) or indirectly (via a water services provider) from water resources (surface water and groundwater). Due to water scarcity becoming more widespread, governments around the world are becoming increasingly aware of the importance of safeguarding water resources, which is forcing mining companies to revisit their processes with a view of reducing their water footprint. Many mining companies are investing heavily in new water infrastructure and management systems to reuse/recycle water as well as improving metal recovery processes and treating effluent prior to discharge. Over 90% of minewater can be re-used if treatments such as reverse osmosis and microfiltration are employed. An oftenoverlooked aspect of mining is that potable water is often not

required for the majority of processes, especially mineral processing and cooling, where some mining operations are making use of treated residential waste/sewage in an effort to reduce their water footprint. With proper water management procedures in place, the mining industry would be able to save up to 40% of its fresh water intake. However, it is recognized that an acceptable water quality is not the only consideration when determining the re-use of MIW (treated or otherwise). The primary concern is the removal of salts from the system so that there would be no need to use dilution water on eventual discharge (DWA, 2013; Toledano and Roorda, 2014). Four different categories are defined in the DWAF Volume 3: Industrial Use (1996) for industrial processes according to the purity of water required, and these are summarized in Table VI. From the DWA (2013) report, it was concluded that only Category 4 industrial processes might be capable of using neutralized MIW, and the options for this are limited. Raw MIW cannot be considered for any of the industrial use categories listed in Table VI. Desalinated MIW, if treated to a high enough standard to be considered domestic quality, will be suitable for Category 3 processes while Categories 1 and 2 require a higher water quality than domestic (potable) water. The extra costs associated with attaining the higher quality water required for Categories 1, 2, and 3 will outweigh the benefits of supplying users in these categories, in which case only select Category 4 users could benefit depending on their proximity to an AMD decant point. A few case studies of water re-use strategies by various mines are discussed in more detail below.

In South Africa, the Witbank coalfields, located in the eMalahleni Municipality, contain many mines, some of which are closed or abandoned and contain significant volumes of polluted groundwater which contaminates groundwater and surface water sources. The eMalahleni Municipality is struggling to meet the water demand of its burgeoning population and was coping with this by removing 60% more than it was licensed to (75 ML/d) from the Witbank Dam by abstracting approximately 120 ML/d, with predictions of this increasing to 180 ML/d by 2030 (ICMM, 2012). In 2007, Anglo American's AngloCoal division partnered with BHP Billiton and the eMalahleni Local Municipality in an effort to

Table VI

&<;=098:=6>93>1892=66=6>;)<;>)< => ==7>.=3:7=.><2298.:70>;9>8= -:8=.>'<;=8> -<4:;*>, ( ># > 94 >%+ Processes that require a high-quality water with relatively tight to stringent specification of limits for most or all of the relevant water quality constituents. Standard or specialized technology is essential to provide water conforming to the required quality specifications. Consequently, costs of in-house treatment to provide such water are a major consideration in the economics of the process.

Category 2

Processes that require water of a quality intermediate between the high quality required for Category 1 processes and domestic water quality (Category 3 processes). Specifications for some water quality constituents are somewhat tighter or more stringent than for domestic water quality. Standard technology is usually sufficient to reach the required water quality criteria. Cost for such additional water treatment begins to be significant in the economics of the process.

Category 3

Processes for which domestic water quality is the baseline minimum standard. Water of this quality may be used in the process without further treatment, or minimum treatment using low to standard technology may be necessary to reach the specifications laid down for a desired water quality. Costs for further in-house treatment are not significant in the economics of the process.

Category 4

Processes that within certain limitations can use water of more or less any quality without creating any problems. No additional treatment is usually required and there is therefore no further cost.

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Category 1

South Africa’s options for mine-impacted water re-use: A review overcome the problems of AMD pollution and water scarcity in the city. They built the flagship eMalahleni Water Reclamation Project (EWRP), mentioned previously in this review, which utilizes treatment technologies including ultrafiltration and reverse osmosis, at a cost of US$100 million. The EWRP produces potable water from the mine effluent and currently supplies around 12% of eMalahleni’s water. Pipelines were constructed from the participating mines (KleinkopjÊ, Greenside Colliery, and South Witbank Colliery) to a central water storage facility, a water treatment plant, and two reservoirs. Part of the plant is financed by the selling of potable water back to the municipality at the operating costs (Toledano and Roorda, 2014). Anglo Platinum’s Modikwa Mine, located in the mineralrich Bushveld Complex region in South Africa, originally extracted all its process water from the Olifants River, much to the ire of the Kruger National Park and surrounding communities. In an effort to reduce its overall water usage and the subsequent costs involved in transportation and handling, the mine installed two separate dewatering plants, including high-rate clarifiers and a filter press, on the north and south shafts. The plants recover over 98% of the process water, and the clarified water is stored for re-use during mining operations (Talbot and Talbot, 2012).

The National Water Act (Act 36 of 1998) also governs the discharge of domestic and industrial wastewater to a water resource through a pipe, canal, sewer, or other conduit. The user must register as a water user with the relevant authority. The Act provides a list of wastewater limit values that must be adhered to during discharge. These limits are compared, among others, to the limits set by the City of Johannesburg (2008) bylaws for discharge to sewers in Table III. The Act governs the discharge of up to 2 000 m3 of wastewater on any given day provided that the discharged water complies with the general wastewater limit values set out in Table III, does not alter the natural ambient water temperature of the receiving water resource by more than 2 or 3°C, depending on the water source as listed in the Act, and is not a complex industrial wastewater. Similarly to irrigation with wastewater, the Act details precautionary practices in addition to the requirements for sampling and monitoring on a regular basis and the reporting of results to the relevant authority.

&9724-6:976><7.>8=2955=7.<;:976 The re-use of treated mine-impacted water is not currently a priority in South Africa. Although the discharge of effluent to water resources should be the option of last resort, it is often the first choice of many in the mining industry and industry as a whole due to its simplicity and low cost. Unfortunately, the discharge option in many instances requires the use of high-quality water to dilute the treated effluent to within allowable discharge limits for TDS in particular (600 mg/L) and salinity in general. In addition, many activities are using high-quality or potable water unnecessarily (e.g. sanitation, crop irrigation). Throughout this report, various options for re-using treated MIW were investigated. The primary conclusion of

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this review is that the most suitable option for re-using treated mine-impacted water is in agriculture, namely irrigation of crops (food, forage, or energy crops), which currently and unnecessarily makes use of the majority of South Africa’s high-quality resources or potable water. This is due to a number of facts. Ž Using MIW for irrigation would make substantial volumes of high-quality or potable water, that was previously reserved for irrigation, available for more pressing needs (e.g. drinking water). Ž Treating minewater decant to irrigation levels, rather than for urban/industrial use, would lead to a 87% and 78% reduction in capital and running costs, respectively. Ž Irrigated farm areas are often located near MIW sources, limiting the need for collection and distribution of treated MIW for crop production. Ž The treatment and use of the MIW could operate as a single financial initiative, with income from the MIWirrigated crops (energy/forage/food crops) funding the MIW treatment and/or creating jobs and providing food/energy security for the local community. Ž This option would relieve the environmental and health liabilities of the mines producing the MIW, while stimulating agriculture in the vicinity. Ž This option fits well within the WEF nexus, which is becoming increasingly prominent on the international agenda that continues to focus on collaborative initiatives on solving the numerous limitations surrounding water, energy, and food provision to a growing global population. Another important finding of this review includes the realization that although guidelines and legislation relating to water re-use in South Africa exist and are readily accessible, they tend to be contradictory and confusing in many cases, which will have the unintended consequence of negatively affecting technology development in the sector. For this reason, it is imperative that these ambiguities and incongruities within the available guidelines and legislation for water re-use be clarified and updated swiftly.

(2 79'4=.05=7;6 The author would like to acknowledge Mintek for permission to publish this work.

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MINTEK. 2018. Biological sulfate reduction - Value proposition. Mintek Technology Demonstration, Randfontein, South Africa

African Institute of Mining and Metallurgy, Johannesburg.

Embracing the Fourth Industrial Revolution in the Minerals Industry Driving competitiveness through people, processes and technology in a modernised environment 5–6 JUNE 2019

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T H E C A N VA S R I V E R S A N D S

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F O U R WAY S

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SOUTH AFRICA

BACKGROUND The SAIMM ran a very successful conference in 2017, which was called ’New Technology and Innovation in Mining’. Now, two years on, it is time to update ourselves on the latest developments, both in the digital world as well as the physical world of the minerals industry. 2XU LQGXVWU\ VWHHSHG LQ JUHDW WUDGLWLRQ DQG LQQRYDWLRQ QRZ ¿ QGV LWVHOI IDFLQJ QHZ FKDOOHQJHV DQG PDQ\ RSHUDWLRQV have already embraced these challenges and introduced people-centric technologies that are truly at the cutting edge of innovation. Besides showcasing many exciting innovations and case studies, the conference will be supplemented with a trade show where many of these technologies will be featured.

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NATIONAL & INTERNATIONAL ACTIVITIES 2019

31 July–1 August 2019 — Entrepreneurship in the Minerals Industry Conference 2019 ‘Bringing ideas to life’ The Canvas Riversands, Fourways, Johannesburg, South Africa Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 5–7 August 2019 — The Southern African Institute of Mining and Metallurgy in collaboration with the Zululand Branch is organising The Eleventh International Heavy Minerals Conference ‘Renewed focus on Process and Optimization’ The Vineyard, Cape Town, South Africa Contact: Yolanda Ndimande Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 18–21 August 2019 — Copper 2019 Vancouver Convention Centre, Canada Contact: Brigitte Farah Tel: +1 514-939-2710 (ext. 1329), E-mail: metsoc@cim.org Website: http://com.metsoc.org 19–22 Augusts 2019 — Southern African Coal Processing Society 2019 Conference and Networking Opportunity Graceland Hotel Casino and Country Club, Secunda Contact: Johan de Korte Tel: 079 872-6403 E-mail: dekorte.johan@gmail.com Website: http://www.sacoalprep.co.za 28–29 August 2019 — International Mine Health and Safety Conference 2019 ‘Beyond Zero Harm’ Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 28–30 August 2019 — IFAC MMM 2019 Symposium Stellenbosch Institute for Advanced Studies Conference Centre, Stellenbosch, Cape Town, South Africa Email: info@ifacmmm2019.org Website: https://www.ifacmmm2019.org/ 4–5 September 2019 — Surface Mining Masterclass 2019 Birchwood Hotel & OR Tambo Conference Centre, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 16–17 October 2019 — Tailing Storage Conference 2019 ‘Investing in a Sustainable Future’ Birchwood Hotel & OR Tambo Conference Centre, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 13–15 November 2019 — XIX International Coal Preparation Congress & Expo 2019 New Delhi, India Contact: Coal Preparation Society of India Tel/Fax: +91-11-26136416, 4166 1820 E-mail: cpsidelhi. india@gmail.com, president@cpsi.org. inrksachdevO1@gmail.com, hi.sapru@monnetgroup.com

20 25–29 May 2020 — The 11th International Conference on Molten Slags, Fluxes and Salts The Westin Chosun Seoul Hotel, Seoul, Korea Tel: +82-2-565-3571, Email: secretary@molten2020.org http://www.molten2020.org VOLUME 119

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2–3 April 2019 — Global Mining Guidelines Group 2019 Wits Club, University of the Witwatersrand, Johannesburg, South Africa Contact: Yolanda Ndimande Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 8–10 May 2019 — 22nd International Conference on Paste, Thickened and Filtered Tailings The Westin, Cape Town, South Africa Email: paste2019@patersoncooke.com Website: www.paste2019.co.za 18–25 May 2019 — ALTA 2019 Nickel-Cobalt-Copper Uranium-REE, Gold-PM, In Situ Recovery, Lithium Processing Conference & Exhibition, Perth, Australia Contact: Allison Taylor Tel: +61 411 692 442, E-mail: allisontaylor@altamet.com.au Website: https://www.altamet.com.au/conferences/alta-2019/ 21–22 May 2019 — Hydrometallurgy Colloquium 2019 ‘Impurity removal in hydrometallurgy’ Glenhove Conference Centre, Melrose Estate, Johannesburg Contact: Yolanda Ndimande Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 22–23 May 2019 — SA Mining Supply Chain Conference and Strategy Workshop Bateleur Conference Venue, Nasrec, Johannesburg Contact: Yolanda Ndimande Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 22–23 May 2019 — Mine Planner’s Colloquium 2019 ‘Skills for the future – yours and your mine’s’ Glenhove Conference Centre, Melrose Estate, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 27–29 May 2019 — The 9th International Conference on Sustainable Development in the Minerals Industry Park Royal Darling Harbour, Sydney , Australia Contact: Georgios Barakos Tel: +49(0)3731 39 3602, Fax: +49(0)3731 39 2087 E-mail: Georgios.Barakos@mabb.tu-freiberg.de Website: http://sdimi.ausimm.com/ 5–6 June 2019 — New Technology Conference and Trade Show ‘Embracing the Fourth Industrial Revolution in the Minerals Industry’ Driving competitiveness through people, processes and technology in a modernised environment The Canvas Riversands, Fourways, Johannesburg, South Africa Contact: Yolanda Ndimande Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 24–27 June 2019 — Ninth International Conference on Deep and High Stress Mining 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 4–5 July 2019 — Smart Mining, Smart Environment, Smart Society ‘Implementing change now, for the mine of the future’ Accolades Boutique Venue, Midrand Contact: Yolanda Ndimande Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za

Company Affiliates The following organizations have been admitted to the Institute as Company Affiliates

3M South Africa (Pty) Limited

Expectra 2004 (Pty) Ltd

Murray and Roberts Cementation

AECOM SA (Pty) Ltd

Exxaro Coal (Pty) Ltd

Nalco Africa (Pty) Ltd

AEL Mining Services Limited

Exxaro Resources Limited

Namakwa Sands(Pty) Ltd

Air Liquide (Pty) Ltd

Filtaquip (Pty) Ltd

Ncamiso Trading (Pty) Ltd

Alexander Proudfoot Africa (Pty) Ltd

FLSmidth Minerals (Pty) Ltd

New Concept Mining (Pty) Limited

AMEC Foster Wheeler

Fluor Daniel SA (Pty) Ltd

Northam Platinum Ltd - Zondereinde

AMIRA International Africa (Pty) Ltd

Franki Africa (Pty) Ltd-JHB

OPTRON (Pty) Ltd

ANDRITZ Delkor(Pty) Ltd

Fraser Alexander (Pty) Ltd

PANalytical (Pty) Ltd

Anglo Operations Proprietary Limited

G H H Mining Machines (Pty) Ltd

Anglogold Ashanti Ltd

Geobrugg Southern Africa (Pty) Ltd

Paterson & Cooke Consulting Engineers (Pty) Ltd

Arcus Gibb (Pty) Ltd

Glencore

Perkinelmer

ASPASA

Hall Core Drilling (Pty) Ltd

Atlas Copco Holdings South Africa (Pty) Limited

Hatch (Pty) Ltd

Polysius A Division Of Thyssenkrupp Industrial Sol

Herrenknecht AG

Precious Metals Refiners

Aurecon South Africa (Pty) Ltd

HPE Hydro Power Equipment (Pty) Ltd

Rand Refinery Limited

Aveng Engineering

Immersive Technologies

Redpath Mining (South Africa) (Pty) Ltd

Aveng Mining Shafts and Underground

IMS Engineering (Pty) Ltd

Rocbolt Technologies

Axis House Pty Ltd

Ingwenya Mineral Processing

Rosond (Pty) Ltd

Bafokeng Rasimone Platinum Mine

Ivanhoe Mines SA

Royal Bafokeng Platinum

Barloworld Equipment -Mining

Joy Global Inc.(Africa)

Roytec Global (Pty) Ltd

BASF Holdings SA (Pty) Ltd

Kudumane Manganese Resources

RungePincockMinarco Limited

BCL Limited

Leco Africa (Pty) Limited

Rustenburg Platinum Mines Limited

Becker Mining (Pty) Ltd

Longyear South Africa (Pty) Ltd

Salene Mining (Pty) Ltd

BedRock Mining Support Pty Ltd

Lonmin Plc

BHP Billiton Energy Coal SA Ltd

Lull Storm Trading (Pty) Ltd

Sandvik Mining and Construction Delmas (Pty) Ltd

Blue Cube Systems (Pty) Ltd

Maccaferri SA (Pty) Ltd

Bluhm Burton Engineering Pty Ltd

Magnetech (Pty) Ltd

Bouygues Travaux Publics

MAGOTTEAUX (PTY) LTD

CDM Group

Maptek (Pty) Ltd

CGG Services SA

MBE Minerals SA Pty Ltd

Coalmin Process Technologies CC

MCC Contracts (Pty) Ltd

Concor Opencast Mining

MD Mineral Technologies SA (Pty) Ltd

Concor Technicrete

MDM Technical Africa (Pty) Ltd

Council for Geoscience Library

Metalock Engineering RSA (Pty)Ltd

CRONIMET Mining Processing SA Pty Ltd

Metorex Limited

SRK Consulting SA (Pty) Ltd

CSIR Natural Resources and the Environment (NRE)

Metso Minerals (South Africa) Pty Ltd

Technology Innovation Agency

Minerals Council of South Africa

Time Mining and Processing (Pty) Ltd

Data Mine SA

Minerals Operations Executive (Pty) Ltd

Timrite Pty Ltd

Digby Wells and Associates

MineRP Holding (Pty) Ltd

Tomra (Pty) Ltd

DRA Mineral Projects (Pty) Ltd

Mintek

Ukwazi Mining Solutions (Pty) Ltd

DTP Mining - Bouygues Construction

MIP Process Technologies (Pty) Limited

Umgeni Water

Duraset

Modular Mining Systems Africa (Pty) Ltd

Webber Wentzel

Elbroc Mining Products (Pty) Ltd

MSA Group (Pty) Ltd

Weir Minerals Africa

eThekwini Municipality

Multotec (Pty) Ltd

Worley Parsons RSA (Pty) Ltd

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VOLUME 119

Sandvik Mining and Construction RSA(Pty) Ltd SANIRE Schauenburg (Pty) Ltd Sebilo Resources (Pty) Ltd SENET (Pty) Ltd Senmin International (Pty) Ltd Smec South Africa Sound Mining Solution (Pty) Ltd

Size It matters! Our high-level academic staff offers specialist higher degrees and produces the largest number of graduates in the English-speaking world.

Substance Africa, Africa and the World. The School is student-centred

bring the environment to our students.

Style Our multi-disciplinary and specialist postgraduate programme leads to sustained research output, fascilitating regular step changes in industry.

School of Mining Engineering University of the Witwatersrand, Johannesburg

www.wits.ac.za/miningeng/

The Journal of the Southern African Institute of Mining and Metallurgy SPECIAL 125TH ANNIVERSARY ISSUE

A SPECIAL COMMEMORATIVE PUBLICATION The SAIMM will be celebrating its 125th Anniversary in July 2019. This is an occasion worthy of the attention of the minerals industry in particular and of South Africa as a whole. This souvenir issue will showcase the SAIMM and the mineral industry’s challenges and achievements since 1994. It will be distributed to members and company affiliates of the SAIMM, Government Departments, kindred organisations locally and internationally, as well as to delegates attending the SAIMM conferences in 2019. SAIMM Affiliates, key industry players, equipment suppliers and major companies in mining and metallurgy can equally demonstrate their support of this commemorative edition with congratulatory messages and profiles of their own developments and successes over the last 25 years. Organisations wanting to align their message with a particular Past President, or next to an identified topic, will need to secure this positioning urgently.

To advertise in this Special Publication contact: Barbara Spence, Avenue Advertising, Telephone (011) 463-7940, E-mail: barbara@avenue.co.za

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