VOLUME 115
NO. 6
JUNE 2015
Realising possibilities...
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Re Resource source Evaluation Evaluation
Mineral M ineral Processing Processing
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Tailings Tailings & Waste Waste Management Management
Smelting Smelting ReďŹ ning & ReďŹ ning
M Materials aterials Handling Ha ndling
Environment Environment Approvals & Approvals
T ransport Transport tto oM arket Market
Non-Process Non-Process Infrastructure Infrastructure
WorleyParsons adds v WorleyParsons value alue thr through o ough our full sc scope ope o off services fr om pit tto o port including sstudies, tudies, mine planning, impact from assessments, assessmen ts, p permitting g and appr a approvals, pp ovals, p pr project oject j managemen management, g t, cconstruction onstruction managemen managementt and d global pr procurement. ocurement.
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The Southern African Institute of Mining and Metallurgy OFFICE BEARERS AND COUNCIL FOR THE 2014/2015 SESSION Honorary President Mike Teke President, Chamber of Mines of South Africa Honorary Vice-Presidents Ngoako Ramatlhodi Minister of Mineral Resources, South Africa Rob Davies Minister of Trade and Industry, South Africa Naledi Pando Minister of Science and Technology, South Africa President J.L. Porter President Elect R.T. Jones Vice-Presidents C. Musingwini S. Ndlovu Immediate Past President M. Dworzanowski Honorary Treasurer C. Musingwini Ordinary Members on Council V.G. Duke M.F. Handley A.S. Macfarlane M. Motuku M. Mthenjane D.D. Munro G. Njowa
T. Pegram S. Rupprecht N. Searle A.G. Smith M.H. Solomon D. Tudor D.J. van Niekerk
Past Presidents Serving on Council N.A. Barcza R.D. Beck J.A. Cruise J.R. Dixon F.M.G. Egerton G.V.R. Landman R.P. Mohring
J.C. Ngoma S.J. Ramokgopa M.H. Rogers G.L. Smith J.N. van der Merwe W.H. van Niekerk
Branch Chairmen Botswana
L.E. Dimbungu
DRC
S. Maleba
Johannesburg
I. Ashmole
Namibia
N.M. Namate
Northern Cape
C.A. van Wyk
Pretoria
N. Naude
Western Cape
C. Dorfling
Zambia
D. Muma
Zimbabwe
S. Ndiyamba
Zululand
C.W. Mienie
Corresponding Members of Council Australia: I.J. Corrans, R.J. Dippenaar, A. Croll, C. Workman-Davies Austria: H. Wagner Botswana: S.D. Williams United Kingdom: J.J.L. Cilliers, N.A. Barcza USA: J-M.M. Rendu, P.C. Pistorius
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PAST PRESIDENTS *Deceased * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
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)
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H. Britten (1955–1956) Wm. Bleloch (1956–1957) 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)
Honorary Legal Advisers Van Hulsteyns Attorneys Auditors Messrs R.H. Kitching Secretaries The Southern African Institute of Mining and Metallurgy Fifth Floor, Chamber of Mines 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
The Journal of The Southern African Institute of Mining and Metallurgy
Editorial Board
Editorial Consultant D. Tudor
Typeset and Published by 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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THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS. Copyright© 1978 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 The Copyright Act no. 98, 1978, Section 12, 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. Multiple copying of the contents of the publication without permission is always illegal. 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.
VOLUME 115
NO. 6
JUNE 2015
Contents Journal Comment by G.L. Smith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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President’s Corner by J.L. Porter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Spotlight: SANCOT News by R. Tluczek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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The SAIMM Young Professionals’ Council (SAIMM-YPC) by T. Mmola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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PLATINUM CONFERENCE PAPERS Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) in the Merensky Reef at Lonmin’s Karee Mine by D. Hoffmann and S. Plumb. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
465
Tough choices facing the South African mining industry by A. Lane, J. Guzek, and W. van Antwerpen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
471
Crush pillar support – designing for controlled pillar failure by M. du Plessis and D.F. Malan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
481
The application of pumpable emulsions in narrow-reef stoping by S.P. Pearton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
489
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions exposed to sulphuric acid and sodium chloride media by J. van der Merwe and D. Tharandt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Fire and brimstone: The roasting of a Merensky PGM concentrate by R.I. Rambiyana, P. den Hoed, and A.M. Garbers-Craig. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
507
Strategic and tactical requirements of a mining long-term plan by B.J. Kloppers, C.J. Horn, and J.V.Z. Visser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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GENERAL PAPERS Integration of imprecise and biased data into mineral resource estimates by A. Cornah and E Machaka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
523
Stochastic simulation for budget prediction for large surface mines in the South African mining industry by J. Hager, V.S.S. Yadavalli, and R. Webber-Youngman . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
531
Q-coda estimation in the Kaapvaal Craton by D.J. Birch, A. Cichowicz, and D. Grobbelaar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
541
Geometallurgical model of a copper sulphide mine for long-term planning by G. Compan, E. Pizarro, and A. Videla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
549
Introduction to the production of clean steel by J.D. Steenkamp and L. du Preez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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International Advisory Board VOLUME 115
NO. 6
JUNE 2015
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
The Journal of The Southern African Institute of Mining and Metallurgy
JUNE 2015
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R.D. Beck J. Beukes P. den Hoed M. Dworzanowski M.F. Handley R.T. Jones W.C. Joughin J.A. Luckmann C. Musingwini R.E. Robinson T.R. Stacey R.J. Stewart
Journal Comment
T
he SAIMM biannual platinum conference was first convened in 2004 and has run regularly through to 2014. During this period the industry has moved through a number of challenges – from the global financial crisis to metal pricing spikes and troughs, labour unrest in the form of a crippling five-month strike in South Africa in the first half of 2014, and now sustained oversupply in the face of reduced demand associated with the slow recovery of the European markets and the cooling of the Chinese economy. Despite this eleven-year rollercoaster journey it is still clear that platinum group metals (PGMs) are definitely the metals for the future. Aside from the obvious investment value of platinum and the other PGMs they are an essential part of modern life and can be found in a multitude of applications from autocatalysts to cardiac pacemakers, fertilizer production to food preservation, and fuels cells to jewellery. In fact, it is hard to find areas in which the PGMs have not improved the quality of life as we have come to accept it. The PGMs are crucial to the energy and transportation sectors in establishing environmentally friendly technology, and the long-term outlook for PGM demand is positive. Evolving energy-efficient transport solutions, tightening
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emission regulations, fundamental industrial applications, and sustained demand from the jewellery and investment sectors all indicate continued demand for these metals. Even at the current reduced global economic growth rates, rapidly urbanizing populations will inevitable require even greater quantities of PGMs. Increased recycling efficiency (the ‘urban mine’) will meet a portion of this demand, but demand for freshly mined PGMs will continue to grow, albeit at a slower rate than in previous decades. Within this milieu of opportunity and challenge, the 6th International Platinum Conference: ‘Platinum – Metal for the Future’ held in October 2014 highlighted the market, technical, and social challenges faced by the industry while showcasing the depth of talent in the sector and evolving solutions to the many challenges . For this edition of the Journal, seven papers on topics ranging from geosciences to strategy and mining technology to pyrometallurgy have been selected to tempt you into accessing the full conference proceedings on the SAIMM website and gain a better understanding of the industry that produces the ’metal for the future’ G.L. Smith
The Journal of The Southern African Institute of Mining and Metallurgy
The SAIMM Young Professionals’ Council (SAIMM-YPC)
T ➣ ➣ ➣ ➣
he SAIMM recognizes the value that young professionals can contribute towards the Institute. On 12 September 2014, the Career Guidance and Education (CGE) committee held a workshop with the primary objective of convening an interim council of young professionals to:
Establish and entrench a Youth Council to represent the interests of SAIMM members 35 years of age and younger Set up the rules for the functioning of the Youth Council (similar to the SAIMM Council, but subordinate) Draft suitable terms of reference for the Youth Council for approval Prepare an election process for members to serve on the Youth Council.
In this regard, the interim council and the CGE committee have been exceedingly successful. The establishment of the Southern African Institute of Mining and Metallurgy Young Professionals’ Council (SAIMM-YPC) and the rules for the functioning of the SAIMM-YPC, ‘By-law I – Young Professionals’ Council’, have been approved by Council (16 January 2015). The election process for the 2015/2016 Young Professionals’ Council commenced in March and will conclude in July prior to ratification at the Annual General Meeting (AGM) in August. The SAIMM-YPC has been involved in activities that were previously organized by the CGE committee. The SAIMMYPC participated in the ‘Engineering Focus Week’ at Sci-Bono from 4 to 8 May, holding informative and inspirational talks to students from several high schools interested in engineering as a career. The SAIMM-YPC has also joined the organizing committee of the Young Professionals’ Conference to be held from 21 to 22 October. A Career Day, to equip final-year students about to start their professional careers with information on what to expect in the first five years of employment, is also being organized. Participation in these activities has been an important process in the transfer of know-how from the CGE committee to the SAIMM-YPC. Going forward the SAIMM-YPC has identified three focus areas to be involved in – Education, Career Guidance and Enterprise (see table). These focus areas have been designated due to the challenges faced by young professionals such as: ➣ ➣ ➣ ➣
Shortage of funding for education Limited opportunities for practical training and vacation work Unemployment and depressing career prospects Obstacles to entry and participation in entrepreneurial activities in the minerals sector
The SAIMM–YPC focus will be to engage with the mining industry to assist in finding support for young professionals in mining and metallurgy.
SAIMM-YPC focus areas Focus Area
Mission
Education
Represent the interests of pre-graduates in basic and higher education on matters of career guidance, academic development and life skills
Career Guidance
Represent the interests of primarily post-graduates in mining and metallurgy on matters of training, professional development and life skills
Enterprise
Undertake industrious initiatives of some scope, complication and risk to serve the interests of young professionals
Overcoming challenges such as getting time off work and travelling long distances to attend meetings, the members of the interim council have shown remarkable commitment, energy, and eagerness to provide active leadership and bring about a positive influence in the mining industry. Their efforts and that of the CGE committee in ensuring sustained long-term success of the younger members of the Institute and ensuring that the SAIMM–YPC becomes an integral part of the Institute is acknowledged.
The Journal of The Southern African Institute of Mining and Metallurgy
JUNE 2015
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T. Mmola Chairman: Young Professionals’ Council (SAIMM)
Spotlight Report back on the ITA 2015 general assembly Forty-first annual meeting held in Dubrovnik, Croatia he International Tunnelling and Underground Space Association (ITA) held its forty-first annual meeting in Dubrovnik, Croatia from 22 to 28 May 2015, in conjunction with the World Tunnel Congress 2015 ‘Promoting Tunnelling in South East European Region’ organized by the ITA and the Croatian Association for Tunnelling and Underground Space. More than 1550 persons participated in the conference. The Association registered two new member nations, Guatemala and Qatar, and 21 new Affiliate Members in the preceding year, which resulted in a total of 73 Member Nations and 282 Affiliate members (taking into account some resignations). 57 of the 73 Member Nations were represented in the General Assembly. Ron Tluczek, chairman of SANCOT, represented South Africa on behalf of the SANCOT Committee. From left to right: Moroke Nteene (Lesotho Tunnelling Association), The Open Session, which took place on 26 May, was Tony Boniface, Monique Walnstein, Veronica Boniface, Lucky Nene dedicated to ‘Underground Space and Natural Resources’ with a (Chairman, SANCOT Young Professional Group), Soren Eskesen (ITA President), Janie Viljoen, Chris Viljoen (SANCOT Vice Chairman), special focus on hydro. A panel of seven experts made Merryn Scott-Tluczek, Ron Tluczek (SANCOT Chairman), Jim McKelvey presentations and a floor discussion was held on three main (past SANCOT Chairman). themes, namely sustainability, constructability, finance, and insurance. At the end of the session it was very clear that hydro power tunnels have proven to be a very sustainable solution, especially when due attention is given to constructability by utilizing advanced technology and contractual practices. Financial models should be based on long-term revenues. With the current market development of renewable energy, hydro tunnels and related structures have proven to provide the most reliable, and most economical long-term solutions for the supply of energy for our planet. Underground space can make hydro power schemes more sustainable with respect to environmental, social, and economic aspects, when used in suitable settings and with a clear understanding of all risks, particularly geotechnical risk. Other risks to be aware of include; financial challenges, construction risk, hydrological risk, off-taker risk, regulatory risk, life-cycle risk, and changes in climate and technology. The ITA has produced a video of the Open Session, which can be seen on the ITA Youtube Channel [https://youtu.be/47fcycz9pyg]. The ITA Young Member Group has been very active during the year, strengthening the international network between young members and participating in two major events in Greece and the UAE. The ITA YM group has also created a new magazine, ‘Breakthrough’. South African representatives participated in four Working Groups. Ron Tluczek participated in WG 2 (Research), Tony Boniface in WG 5 (Health and Safety in Works), Chris Viljoen in WG 12 (Sprayed Concrete Use), and Monica Walnstein participated in WG 21 (Life Cycle Asset Management). Chris Viljoen made a presentation to Working Group 12 on the status of a guideline for sprayed concrete. Eight reports were published in the previous year: three from ITA Working Groups, four from ITAtech Activity Groups, and one from the ITA COSUF Committee, namely: ‘Strategy for Site Investigation on Tunnelling Projects’ ‘Guidelines for Good Working Practice in High Pressure Compressed Air (HPCA)’ ‘An Owners Guide to Immersed Tunnels’ ‘Rebuilt Equipment – Guidelines on Rebuilds of Machinery for Mechanised Tunnel Excavation’ ‘Guideline for Good Practice of Fibre Reinforced Concrete Precast Segments’ ‘Guidelines on Measurement Frequencies’ ‘Remote Measurement’ ‘Survey of Existing Regulations and Recognised Recommendations (on Operation and Safety of Road Tunnels)’. All these documents are available free of charge on the ITA website and available for comments. The next annual meetings of the ITA General Assembly will be held at the following venues: San Francisco, USA, from 22 to 28 April 2016, during the ITA-AITES WTC 2016 ‘Uniting our Industry’. Bergen, Norway, from 9 to 16 June 2017, during the ITA-AITES WTC 2017 ‘Surface Problems – Underground Solutions’. Dubai, UAE, from 20 to 26 April 2018, during the ITA-AITES WTC 2018 ‘Smart Cities: Managing the Use of Underground Space to Enhance Quality of Life’.
T
R. Tluczek
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The Journal of The Southern African Institute of Mining and Metallurgy
A
number of papers in this month’s Journal deal with matters relating to longer-term planning considerations in our hard-rock, deep-level mines (for example, ‘Strategic and tactical requirements of a mining long-term plan’ by B.J. Kloppers, C.J. Horn, and J.V.Z. Visser). It is also really good to see some mining engineering-related topics in this issue, as they have been in short supply for some time now. This may have led to a perception that the mining engineering fraternity were starting to fall behind in the publication of leading work being undertaken in the South African mining industry. Taken in conjunction with the current range of challenges facing the industry, this could also be interpreted as little being done to develop new solutions. It is the SAIMM’s fervent hope that this is not the case. However, it would be remiss of me not to also highlight the fact that local support of SAIMM conferences in 2015 is at the lowest level we have experienced in many years. So there are clearly short-term challenges. A joint meeting of the Technical Programme Committees (TPCs) was held during May with the objective of challenging the historical way that the SAIMM TPCs have functioned and to investigate new, modern options to satisfy a prime objective of the TPCs, as required by our constitution: to ‘Disseminate scientific and technical knowledge to the benefit of the mining and metallurgical industries’. The message here is that the SAIMM needs to be as adaptable and responsive as any other business to the current circumstances. I would not be the first to comment that under difficult circumstances one needs to have strong leadership come to the fore. This is not to say that good quality leadership is not always important, but it is usually adversity that brings out the best in people. In their paper ‘Tough choices facing the South African mining industry’, A. Lane, J. Guzek, and Dr W. van Antwerpen put matters very succinctly in their synopsis: the mining industry in South Africa finds itself in a difficult situation. Operating conditions are tough, the socio-political environment is complex, and financial performance is under pressure. The choices made by all the stakeholders in this industry in the short term will shape the future of the industry.’ The level of energy and investment that is being expended to ‘re-tool’ the platinum sector, for example, is considerable. It is my hope that more papers will be forthcoming in the medium term that tell more of the stories behind the herculean effort to re-position the platinum mining industry. An early indicator is the paper on the implementation of ultra-low profile mechanized equipment at Anglo American platinum mines, by F. Fourie, Dr P. Valicek, G. Krafft, and J. Sevenoaks (to be published at a later date). It is reflecting on these papers and the conditions in the mining industry that brings me back to the matter of leadership. There are many elements to leadership, but the one I want to comment on herein relates to personal discipline and self-control. Let me be clear, I am not referring to the militaristic styles of leadership of the last century and characterized by Robert Malott, CEO of a chemical company in the 1970s and ‘80s, who said ‘Leadership is demonstrated when the ability to inflict pain is confirmed’. I can vouch that I worked for a couple of these guys in my early career! What I AM referring to is the style of leadership that embraces the principal that once a rule or standard is set and agreed upon, then it must be complied with by all. There is not one set of standards of compliance or behaviour for ‘workers’ and another for ‘managers’. At a very fundamental level, most people want to be led and given clear direction, but in today’s technological environment where speed and communication tools are cheap and pervasive, a different level of self-control is required. According to recent research, it turns out that self-control (or willpower) can be depleted. Several hundred studies (Google Professor Roy F. Baumeister) indicate that maintaining the self-control to lead by example, make the hard decisions, to be true to your word, etc. requires a concentration of effort that runs down over time. The good news is that by conscious effort, like a muscle, self-control can be strengthened. I think that many of us can empathize with this scenario? We have all had hard days at the office or with the children, which leave one feeling drained and depleted. It takes real work effort and conscious though to lead in the context that I am using in these personal observations. According to a 2013 study by Wilhelm Hoffman, people with a high degree of self-control are happier than those without. So, for everyone out there under work or domestic pressures, there are five tips that I have picked up that may assist:
t’s iden Pres er Corn
1. Remove temptation: Remove from your environment issues that distract you from your goals. If people are not working according to the accepted standards and norms, do not let it go by without challenge 2. Eat and properly sleep: Probably one of the trickier ones to comply with, but how can you strengthen self-control if you are tired and hungry? 3. Consciously break habits: Of the five, probably one of the hardest with which to comply. There are times when intuitively you know that you are taking the ‘easy option’. That is when your gut feel has to say NO, I am going to do things differently 4. Reward yourself and have fun: Self-discipline does not imply a harsh life – not at all! When you have had a good day or week, when you know that you have made progress, have a glass of wine. Buy the team a cup of decent coffee. Take the family out for a meal. Whatever! 5. Don’t dwell on setbacks: There are always going to be setbacks and things that do not go according to plan. To dwell on failure is unhealthy, to understand the lessons learnt is educational, to start today on the fix is inspirational. My last comment about leadership in difficult times is that no one is alone. I really like this quote from Steve Jobs: ‘It doesn’t make sense to hire smart people and then tell them what to do; we hire smart people so they can tell us what to do.’
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J.L. Porter President, SAIMM
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PAPERS IN THIS EDITION These papers have been refereed and edited according to internationally accepted standards and are accredited for rating purposes by the South African Department of Higher Education and Training
Platinum Conference Papers Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) in the Merensky Reef at Lonmin’s Karee Mine by D. Hoffmann and S. Plumb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
465
This study focuses on the estimation of the probability of iron-rich ultramafic pegmatite (IRUP) occurrence in the Merensky Reef at Lonmin’s Marikana Karee Mine, using block model kriged estimates of IRUP probability derived from mapping and surface borehole data. Tough choices facing the South African mining industry by A. Lane, J. Guzek, and W. van Antwerpen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
471
This paper characterizes some of the difficult choices that the South African mining industry must face to ensure long-term sustainability, and discusses how these decisions could be approached in a fact-based and robust way. Crush pillar support – designing for controlled pillar failure by M. du Plessis and D.F. Malan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
481
An overview of in-stope crush pillars is provided, including the application, behaviour, function, mechanism, impact, and design of a crush pillar system. An idealized crush pillar layout was simulated using a limit equilibrium model to predict the potential residual state of crush pillars. The results indicate that there are many factors affecting the initial stress state of the pillar, which determines whether failure will occur in a stable manner. The application of pumpable emulsions in narrow-reef stoping by S.P. Pearton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
489
The viability of using pumpable emulsion explosives in South African narrow-reef mining operations is evaluated from multiple perspectives. The study concludes that pumpable emulsions are able to provide narrow-reef operations with increased levels of flexibility, efficiency, and control that are unavailable or limited through the use of alternative commercially available explosives. Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions exposed to sulphuric acid and sodium chloride media by J. van der Merwe and D. Tharandt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
499
The corrosion behaviour of 304L stainless steel samples laser-cladded with various amounts of ruthenium was evaluated by open circuit potential and cyclic potentiodynamic polarization tests. The results show that for each environment there is an optimal ruthenium concentration for the best corrosion protection, beyond which further ruthenium additions do not confer increased protection. Fire and brimstone: The roasting of a Merensky PGM concentrate by R.I. Rambiyana, P. den Hoed, and A.M. Garbers-Craig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
507
This paper discusses the roasting of Merensky concentrate in air before smelting, with the purpose of reducing the matte load to the converter. A brief description is given of the mechanisms by which pyrrhotite, chalcopyrite, and pentlandite are oxidized during roasting, and these mechanisms are explored in relation to chemical thermodynamics and microstructures. Strategic and tactical requirements of a mining long-term plan by B.J. Kloppers, C.J. Horn, and J.V.Z. Visser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Lonmin process of linking company strategy with long-term planning, tactical planning, and the execution of the plan through an annual planning cycle to maximize organizational flexibility is described. This flexibility enables a mining company to respond to the many internal and external forces that impact on both strategy formulation and delivery of results.
These papers will be available on the SAIMM website
http://www.saimm.co.za
515
PAPERS IN THIS EDITION These papers have been refereed and edited according to internationally accepted standards and are accredited for rating purposes by the South African Department of Higher Education and Training
General Papers Integration of imprecise and biased data into mineral resource estimates by A. Cornah and E Machaka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
523
The exclusion of imprecise or biased data from mineral resource estimations is often wasteful. This paper evaluates a number of specialized geostatistical tools that are available to extract maximum value from such ‘secondary data’. Stochastic simulation for budget prediction for large surface mines in the South African mining industry by J. Hager, V.S.S. Yadavalli, and R. Webber-Youngman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
531
This paper investigates the complex problem of a large mining operation’s budgeting process. The use of stochastic simulation is examined and a model enabling its application to the budgeting process is proposed.. Q-coda estimation in the Kaapvaal Craton by D.J. Birch, A. Cichowicz, and D. Grobbelaar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
541
The Q-coda method was used to characterize seismic wave attenuation in a region of the Kaapvaal Craton that includes the mining areas of the Bushveld Complex and Witwatersrand Basin. An accurate understanding of the attenuation is important since it affects not only the results of day-to-day monitoring such as magnitude calculations, but also advanced seismological studies such as determining the characteristics of the seismic source. Geometallurgical model of a copper sulphide mine for long-term planning by G. Compan, E. Pizarro, and A. Videla. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
549
A multivariate regression model is used to predict metallurgical recovery in a copper sulphide milling-flotation plant as a function of geo-mining-metallurgical data and ore characteristics, including feed grades, ore hardness, particle size, mineralogy, pH, and flotation reagents. The model is able to predict, with acceptable accuracy, the actual copper recovery, and allows for an improvement in the investment decision process by forecasting performance and risk. Introduction to the production of clean steel by J.D. Steenkamp and L. du Preez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This paper describes clean steel production from a pyrometallurgist’s perspective with the aim of introducing these concepts to the broader metallurgical community
These papers will be available on the SAIMM website
http://www.saimm.co.za
557
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a1 ISSN:2411-9717/2015/v115/n6/a1
Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) in the Merensky Reef at Lonmin’s Karee Mine by D. Hoffmann* and S. Plumb*
IRUP is an iron-rich ultramafic pegmatite rock that formed due to hot ironrich fluids and gases replacing local stratigraphic zones of the Bushveld Complex. This study focuses on the estimation of the probability of IRUP occurrence on the Merensky Reef at the Marikana Karee Mine. A 2.15 million centare IRUP-rich domain at the Karee Mine was initially defined from the interpretation of a surface aeromagnetic anomaly, and exposures in the mine workings. Surface boreholes (spaced 250–500 m apart) within this IRUP domain contain approximately equal numbers of IRUP-replaced and IRUP-free intersections. Owing to the uncertainty in the continuity of the IRUP alteration (a result of the wide borehole spacing), the risk associated with the development of mining infrastructure within this domain is unquantifiable. Semi-quantitative data comprising visual estimates of IRUP replacement from reef development mapping data and surface borehole reef intersections was interpolated into blocks using indicator kriging estimation. Comparative analyses of the estimate of IRUP occurrences were made by changing the block size and declustering the data systematically. Reconciliations of the probability of IRUP predicted from the block models derived from the different data-sets and sequential indicator simulation models were analysed for four mining study blocks. A quantitative approach to modelling the occurrence of IRUP can be an additional tool for refining the estimate of the geological losses that inform the mine plan in such high-risk zones. Keywords geological losses, Merensky Reef, IRUP replacement, indication kriging.
Introduction IRUP is an iron-rich ultramafic pegmatite rock that occurs as discordant pipe-, vein-, or sheet-like bodies that formed subsequent to cumulate crystallization within the Bushveld Complex (Viljoen and Scoon, 1985). The occurrence of large IRUP zones in platinum mining operations result in changing reef conditions that adversely affect mining layouts and efficiency due to (i) variable strike of the reef associated with slumping of the strata, thus influencing haulage positioning, (ii) poor stope extraction where iron replacement on the reef horizon has obliterated the economic zone and redistributed part of the platinum group metal mineralization, and (iii) more complex processing conditions related to harder mill feed and poorer concentrator recoveries arising from the increased petrological variability of IRUP ore. At the Marikana Karee Mine, the current mine planning practice assigns a 100% geological loss to IRUP-affected Merensky Reef The Journal of The Southern African Institute of Mining and Metallurgy
Geological setting At Marikana, the IRUP-rich alteration zones on the reef horizons have diameters ranging from tens to hundreds of metres, and are often observed as conformable sheets below the UG2 Reef footwall contact, where replacement of the plagioclase-rich pegmatoidal pyroxenite unit occurs, or as a discordant replacement of
* Lonmin Geology, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Platinum Conference 2014, 20–24 October 2014, Sun City South Africa. VOLUME 115
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Synopsis
where there is no on-reef development, which effectively discounts approximately 1.4 million centares of mineral resources. A quantitative predictive model for the occurrence of IRUP ahead of mining would be useful since the geological loss discount value could be factored more proportionally to the risk associated with the estimated probability of IRUP occurrence. A conceptual study is presented in which geological information from three sources (aeromagnetic survey, surface borehole core logging, and underground mapping) is examined and used to predict the probability of IRUP occurrence ahead of mining at Karee (Figure 1). The approach was to establish a geostatistical estimate of the IRUP probability using ordinary kriging. The borehole and mapping source data, converted to categorical indicators, was interpolated into block models and compared to outcomes from multiple sets of declustered data. The objective being to reconcile models derived from widely-spaced data with a reference model based on all the data, and to comment on the change in the probability of IRUP occurrence in selected mining study blocks. A sequential indicator simulation model using only the surface borehole intersection data was also investigated to examine its suitability for predicting the probability of IRUP occurrence.
Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP)
Figure 1 – Plan showing the location of the study area at the Marikana Karee Mine in relation to the shaft blocks (right) and the surface magnetic low anomaly on the aeromagnetic image (left). The Marikana Karee Mine is comprised of three shafts: namely 4 Belt Shaft, K3 Shaft, and K4 Shaft. Scale defined from coordinates in metres
the Merensky Reef pyroxenite unit. Due to the irregular nature of the IRUP, exposures seen in the footwall excavations do not always continue directly to the overlying reef, typically 20 m above. In the study area at the western part of the Karee Mine (Figure 1), the IRUP domain has an area of 2.15 million centares and occurs as an irregular north-south elongate zone that forms part of the Brakspruit Pipe described by Viljoen and Scoon (1985). Within the Lonmin mining right, the zone has dimensions of 2.7 km north-south and 1.7 km east-west based on the interpretation of an aeromagnetic anomaly. Along the north and northeastern margin of the aeromagnetic anomaly in the K3 Shaft block the occurrence of IRUP is closely correlated with the mining limit (Figure 1). Towards the east and southeast of the anomaly, mining has not advanced into the IRUP domain, and the distribution of IRUP will be tested as mining progresses within the 4 Belt Shaft block. Surface borehole geological logging has revealed that
within the aeromagnetic anomaly, five of the 16 boreholes on the reef horizon define a large core of IRUP towards the northeast. Detailed mining exposure in this area, however, reveals that this core is fragmented and consists of erratic lenses of iron-replaced pyroxenite (Figure 2) with larger areas of replacement towards the central zone. The areas along the remaining periphery of the aeromagnetic anomaly towards the north, west, and southwest were found to be mostly IRUP-free on the Merensky Reef horizon, based on intersections from ten boreholes. It is within this zone of the aeromagnetic anomaly that the conundrum arises. Since in the exposures towards the east there is a good correlation of IRUP with the aeromagnetic anomaly, a similar high probability of IRUP occurrence would be expected towards the west; however, the surface borehole intersection data indicates a low probability of IRUP occurrence in these unmined areas.
Data preparation In the underground developments, the location and continuity of the IRUP occurrence is well defined from mapping information. For the on-reef development, mapped IRUP alteration, where visible in significant proportions, has been used to define categorical indicators of IRUP occurrence. A pseudo-borehole data-set was compiled using the mapping data. The on-reef development mapping was divided into 10 m intervals and used to compile a database for the occurrence of IRUP (Figure 2). The actual percentage of IRUP exposed in the development was not determined, but a visual estimate was defined consisting of three categories, viz.: (1) no replacement with no visible IRUP, (2) partial replacement, <50% IRUP, and (3) strong replacement, 51–100% IRUP. The same categories were assigned to the surface boreholes for the mother hole only. These were then assigned values of 0, 50, and 100 respectively. In the variography data analyses and estimation block models, the 50 and 100 values were combined into a single category to reflect ‘some’ or ‘full’ occurrence of IRUP and assigned a value of one. The resulting categorical data-set represents two rock types, one
Figure 2 – Location of mapping and surface borehole data relative to the surface magnetic anomaly and mined out area (left), and plan and cross-section of raise mapping within the mine’s mapping system (top right). Mapping data was used to define hypothetical boreholes points to reflect IRUP occurrence intersections at 10 m intervals. Plan showing erratic IRUP occurrences in underground development as magenta polygons (bottom right). Scale defined from coordinates in metres
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The Journal of The Southern African Institute of Mining and Metallurgy
Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) with no observed IRUP and the other where IRUP may be present in varying proportions. A total of 3617 measured mapping points and 60 surface borehole points were used in the analysis.
Block model process Block models reflecting an estimate of the probability of IRUP occurrence were constructed by interpolating the binary data of categorical values of 0 (no IRUP) and 1 (some IRUP) using ordinary kriging estimation. Deutsch (2006) suggests that the ordinary kriging method for indicators is a reasonable approach where local data is plentiful, and there is ‘some evidence of non-stationarity areas’. Generally, stationarity was assumed within each categorical variable. The spatial analysis revealed non-anisotropy for the IRUP categorical data, and a spherical model was applied to the semivariogram, which had a range of 480 m at the normalized sill. The first search distance of 500 m for data selection was
derived from the semivariogram range, and a second search distance of 1000 m was permitted to complete filling of the model. The sample numbers were set to a minimum of 5 and a maximum of 20. Block models using the total data-set were constructed for 50, 100, 250, and 500 m block sizes in the X and Y directions. The estimate of IRUP for the study domains was stable for block sizes ≤250 m; however, an increased divergence in the probability of IRUP occurrence was found for the 500 m blocks. A block size of 100 m was selected as the preferred size because of the closeness to the dimensions of the planned selective mining unit. Additional block models with 100 m block sizes were derived for the declustered datasets with centres at intervals of 50, 100, 250, and 500 m. The data points in the declustering process were selected closest to the centre of the cell. Similarly, additional block models were constructed using only the surface borehole intersections. All the above block modelling was conducted using the Datamine software (Figure 3). Finally, a sequential
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Figure 3 – Block models showing the estimated percentage probability of IRUP as interpolated into 100 m blocks for (A) Reference Model (all data), (B) 100 m declustered data model, (C) 250 m declustered data model, and (D) surface borehole data model. Compare with Figure 2 for scale, individual blocks are 100 m
Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) indicator simulation block model using the ‘BLOCKSIS’ module in the GSLIB software suite (Deutsch, 2006) was used by applying the same estimation parameters for the surface borehole data. Here, 100 realizations were composited into a single model (Figure 4).
IRUP block models The IRUP block model using the combined mapping and surface borehole data (called the Reference Model) honours the IRUP aeromagnetic outline within the K3 Shaft block for estimated IRUP probability values >25%. It conflicts, however, with a portion of the aeromagnetic anomaly towards the south within the 4 Belt Shaft block (Figure 3A). Here, the higher IRUP probability was influenced by sparse borehole spacing within the 4 Belt Shaft block, particularly between boreholes spaced 1000 m apart. The block model also reveals a core of higher IRUP probability for the K3 Shaft block, which lies within a NE-SW trending ellipsoid of lower IRUP probability. Where the IRUP probability is estimated at >62%, the block boundary coincides with the termination of mining where panel faces have been stopped due to excessive IRUP. By declustering the data to 100 m and 250 m centres, the NE-SW trend of the IRUP block model is maintained (Figure 3B and 3C). The higher IRUP probability core for 100 m declustered data continues to honour the aeromagnetic footprint. The declustered data block models have increased smoothing, which results in a more diffuse distribution of IRUP probability. In particular, the 250 m declustered data block model no longer exhibits the higher IRUP probability core. An interesting pattern emerges for the surface borehole data block model, which reveals a well-developed concentric core of high IRUP probability with decreasing IRUP probability trending outwards (Figure 3D). Furthermore, the NE-SW trend is no longer developed. Where the estimated IRUP probability is >75% in the core, a close correlation with the aeromagnetic anomaly exists; however, it extends partially into the mined-out area. Comparison of this model with the simulated IRUP block model (Figure 4) reveals an expansion of the core and erroneous extension well beyond the mined-out contact towards the east beyond the aeromagnetic limit, thus overestimating the IRUP probability. A distinctive low IRUP probability trend (NW-SE direction) evident in the Reference Model and 100 m declustered data block model (Figure 3A and 3B) has the effect of enclosing the IRUP core zone towards the west. This is due to the effect of four boreholes in this area exhibiting IRUP-free intersections and the absence of IRUP intersections on the reef horizon. This low-probability IRUP area becomes increasingly prominent in the block models based on surface borehole data only (Figure 3D and Figure 4), and predicts that the remaining part of the K3 Shaft block towards the mining right boundary will have a low probability of IRUP occurrence.
Reconciliation The success of the IRUP prediction model rests on the closeness of the probability estimates using the surface borehole and declustered data models compared with the Reference Model (total data model). This was examined for
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Figure 4 – Block model showing the estimated percentage probability of IRUP as simulated using surface borehole data only. Compare with Figure 2 for scale, individual blocks are 100 m
Figure 5 – Plan showing the four study blocks (SB1 to SB4) with dimensions 500 × 500 m along the western mining levels of K3 Shaft. *Study blocks shifted 500 m towards the west. Hypothetical boreholes (red open circles) are assumed to have intersected IRUP replacement on the Merensky Reef. Scale defined from coordinates in metres
four study blocks with dimensions 500 × 500 m along the western mining levels of the K3 Shaft block (Figure 5). These mining study blocks were selected such that there was overlap into the areas of extensive on-reef development where there would be greatest confidence in the model. Each study block had varying proportions of stope extraction The Journal of The Southern African Institute of Mining and Metallurgy
Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) Table I
Probability of IRUP occurrence estimated for the 500 m study blocks
Reference Model (all data) 100 m (50 m declust.) 100 m (100 m declust.) 100 m (250 m declust.) 100 m (500 m declust.) 100 m (SBH) 100 m (SBH simulation) Approx. % stope depletion
SB 1
SB 2
SB 3
SB 4
0.80 2.34 5.43 13.31 20.26 19.44 9.46 >90
29.10 25.83 32.51 31.31 54.76 67.01 73.74 50
54.08 49.52 53.81 31.48 41.11 54.50 58.18 <10
6.49 6.53 11.18 9.92 13.17 25.63 9.18 >60
(Table I). Study blocks 1 and 4 (SB1, SB4) have low IRUP occurrence and a high stope extraction, whereas study block 2 (SB2) is divided into zones of high and low stope extraction due to the IRUP distribution, and study block 3 (SB3) has high IRUP occurrence and very low stope extraction. There is a close correspondence between the IRUP probabilities of the Reference Model and the models derived using 50 and 100 m declustered data (Table I). In contrast, the IRUP probability for the 250 m declustered data model has close estimates for SB2 and SB4, but grossly underestimates SB3 where there is a high probability of IRUP occurrence and concomitant poor stope extraction. Similarly, the 500 m declustered data significantly overestimates the
IRUP probability in SB1 and SB2, where IRUP occurrence is low and medium, respectively, in the Reference Model. The surface borehole model consistently overestimates the probability of IRUP in SB1, SB2, and SB4 compared to the Reference Model, where the IRUP probability is low to medium. In contrast, it has a very close comparison for SB3, where IRUP probability is high. The surface borehole simulation model generally reported higher IRUP probabilities compared to the Reference Model, in particular for SB2, but has reasonable comparisons for SB1, SB3, and SB4. These 500 m study blocks reveal a reasonable reconciliation for data spaced 100 m apart, and thus longer term mine planning could reasonably apply the IRUP probability as a discount factor. However, caution should be exercised when considering the IRUP probabilities for 250 m and 500 m spaced data, due to the variable and erratic nature of the alteration. It is of interest to see if the 100 m declustered data IRUP block model can be used to predict a reliable local estimate of the IRUP occurrence. SB2 was selected for this exercise as the northeast part of the block has low IRUP occurrence and a high stope extraction, whereas the southwest part has high IRUP occurrence and the prospect of stope extraction is poor (Figure 6). SB2 was divided into 100 m block sizes, because this size coincides with the mining crosscut layout, where a decision could be made to suspend development pending the outcome of further drilling information. A good correlation for the estimated IRUP probability in the Reference Model and the 100 m declustered data model (Figure 6) is obtained for the subdivided 100 m blocks in SB2.
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â&#x2013;˛
Figure 6 â&#x20AC;&#x201C; Plan showing stoping extraction within mining study block 2 (SB2) with 100 m subdivisions (top). Grids of estimated IRUP probability percentage for Reference and 100 m declustered data models in SB2. Scale defined from coordinates in metres
Predicting the probability of Iron-Rich Ultramafic Pegmatite (IRUP) Table II
Probability of IRUP occurrence estimated for the 500 m study blocks. * Study blocks shifted 500 m west
Reference Model (original study block positions) Reference Model tested 500 m further west Reference Model tested 500 m further west with 5 hypothetical surface boreholes
SB 1*
SB 2*
SB 3*
SB 4*
0.80
29.10
54.08
6.49
0.00
18.11
32.50
31.41
0.00
30.60
42.72
36.30
probability value of 43% and would likely be considered a target for further drilling, the outcome of which would confirm the decision to exclude it from future stoping. This demonstrates that the predicative model is sensitive to data volumes. Limited attention was dedicated to the borehole block simulation model due to the poor correlation with the reference model. An approach to improve on the block simulation would be to use the aeromagnetic outline as a spatial reference. This would constrain the simulation to a known domain, thus assessing the IRUP probability within the aeromagnetic area.
Conclusions Discussion The practice of assigning a 100% discount to the mineral resource for blocks outside the on-reef development in Merensky Reef IRUP domains requires discussion. This cautious approach is founded on the close correlation of IRUP occurrence with the aeromagnetic anomaly, and is further supported by the high rate of stope termination along its perimeter. However, the significant number of surface boreholes that are free of IRUP within the aeromagnetic anomaly suggests that there may be large continuous areas that are minimally affected by IRUP. Thus blocks for which the IRUP probability is estimated to be below a certain threshold may have reasonable prospects for extraction. In consideration of the mining study blocks, it would be an option to differentiate blocks that have an estimated probability of <50% IRUP from those with >50%. Blocks in the aeromagnetic anomaly with an estimated probability of <50%, such as SB2, were found to have a high stope extraction. Similar blocks could be considered to have a realistic prospect of extraction, albeit with an additional nominal geological loss, for example +5% or +10%. Blocks that have >50% probability of IRUP occurrence would continue to be fully discounted from the mineral resource as the prospects of stoping extraction remain poor. This approach is reasonable for model estimates based on closely spaced data, typically ≤100 m. In contrast, the results for widely spaced data reveal a different outcome. A similar examination of the surface boreholes with data spaced 250–500 m apart reveals that the models are unsuitable for local estimation of blocks 100 m in size. Testing the areas ahead of the mining study blocks (SB1–SB4) by querying the model 500 m further west of each block generally revealed a lower probability of IRUP occurrence. The study blocks SB2* and SB3* have significantly lower IRUP probability (Table II) due to their closer proximity to IRUP-free boreholes ahead of mining, whereas SB4* has a significantly higher IRUP probability due to a similar effect of a single IRUP-bearing borehole. To test the robustness of the model, five additional hypothetical surface boreholes were assumed to have intersected IRUP within the aeromagnetic anomaly. These boreholes were placed 100 m east of the IRUP-free borehole intersections and then remodelled. The new model reveals that the IRUP probability increases materially for the shifted study blocks in SB2*, SB3*, and SB4* (Table II). In particular, SB3* has a
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➤ The aeromagnetic low anomaly associated with the occurrence of IRUP has been shown to be a useful tool to delineate the macro IRUP domain, which is associated with low stope extraction rates in the K3 Shaft block. ➤ The application of categorical indicators to estimate the occurrence of IRUP has revealed useful trends in the distribution of the IRUP probability. Block model kriged estimates of IRUP probability derived from mapping and surface borehole data at 50 m and 100 m intervals correlate well with the actual IRUP occurrence, which in turn can be correlated to the stoping extraction. In contrast, the IRUP probability for 250 m and 500 m spaced data may prove to be less reliable, due to the variable and erratic nature of the alteration. ➤ Estimated blocks in the aeromagnetic anomaly with a probability <50% of IRUP occurrence could be considered to have a realistic prospect of extraction. This threshold should be further tested. These blocks would likely carry an additional nominal geological loss for contingency. Blocks that have >50% probability of IRUP would continue to be fully discounted from the mineral resource, as the prospects of stoping extraction remain poor. ➤ A quantitative approach to modelling the occurrence of IRUP can provide an additional tool to refine the estimate of the geological losses that inform the longterm mine plan in such high-risk zones. This is work in progress and will be considered in the next planning cycle. Further testing of the simulation model by constraining the estimate to the aeromagnetic domain is a potential enhancement that could prove to be a more reliable predictor of IRUP occurrence.
Acknowledgements The management of Lonmin Platinum is thanked for the opportunity and permission to publish this paper.
References DEUTSCH, C.V. 2006. A sequential indicator simulation program for categorical variables with point and block data: BlockSIS. Computers and Geosciences, vol. 32. pp. 1669–1681. VILJOEN, M.J. and SCOON, R. 1985. The distribution and main geologic features of discordant bodies of iron-rich ultramafic pegmatite in the Bushveld Complex. Economic Geology, vol. 80. pp. 1109–1128. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a2 ISSN:2411-9717/2015/v115/n6/a2
Tough choices facing the South African mining industry by A. Lane*, J. Guzek† and W. van Antwerpen†
of economic, financial, and operational challenges. South African mining companies1 must also account for uniquely local issues with profound operational implications. Some of the pressing issues are shown in Figure 1.
Synopsis Strategy is about making choices. Mining companies choose to do certain things and not to do other things. Mining is a long-term business, and the choices made typically have large investments attached to them, long payback periods, and significant socio-economic consequences. In today’s uncertain world, it is important to make the right choices. The mining industry in South Africa finds itself in a difficult situation. Operating conditions are tough, the socio-political environment is complex, and financial performance is under pressure. The choices made by all the stakeholders in this industry in the short term will shape the future of the industry. This paper characterizes some of the big, difficult decisions faced by the mining industry in the South African context, and discusses how these decisions could be approached in a fact-based and robust way.
The global situation
Throughout this paper, the term ‘South African mining companies’ is used interchangeably to refer to international mining companies with South African mining operations, as well as mining companies registered in (and with primary operations in) South Africa.
* Monitor Deloitte. † Deloitte Consulting. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Platinum Conference 2014, 20–24 October 2014, Sun City South Africa.
Keywords Strategy, choices, community, social impact, scenarios, portfolio optimization, adaptive cost management, stakeholders, innovation.
Introduction Mining companies in South Africa face significant challenges, putting the industry at a crossroads. Local mining companies manage unique South African operational complexities while still operating in the context of global pressures. Monitor Deloitte has identified five tough choices that mining executives must face to ensure long-term sustainability. The answers to these questions are not obvious, and require an analytical approach. This paper proposes five tools that can assist mining executives in understanding the issues underlying these questions, and how mining companies can develop integrative strategies to drive sustainable growth.
The current mining situation
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Globally, mining companies are facing a series
Mining companies are inevitably influenced by global developments, with macro-economic growth and international markets strongly influencing both the demand for resources and profitability. Historically, there has been a strong correlation between the performance of commodity markets and mining stocks; however, this relationship appears to have broken down. Mining stocks (including those of global diversified mining players such as BHP Billiton and Rio Tinto) continue to underperform broad commodity price benchmarks. This gap between stock performance and commodity indices may be due to investors attaching a higher risk premium to mining stocks owing to a poor track record of project delivery and a lack of new discoveries, resulting in sub-optimal shareholder returns. Globally important economies such as the USA, Europe, and China are slowly recovering from the recession; however, there are mixed signals for future growth. While the USA, the world’s largest economy, has been recovering slowly, Europe continues to face a sovereign debt crisis. In response to this, the European Union has undertaken deep structural reforms, including various financial support mechanisms (such as bailouts and austerity programmes) for countries with troubled economies. While this may have temporarily
Tough choices facing the South African mining industry
Figure 1 – Global and local influences on mining companies with South African operations
appeased markets, the memory of the Eurozone crisis is likely to remain fresh in investors’ minds in years to come. With limited post-recession growth prospects in the USA and Europe, companies have looked to Asia to drive global demand. China’s expected growth rate of 8.4% in 2013 (Deloitte Market Intelligence, 2013) falls short of its prerecession growth rate, which averaged 10.3% between 1999 and 2009 (McNitt, 2013); however, the year-on-year increase from 7.5% in 2012 is positive news for mining companies
that rely on China’s continued appetite for resources. While the global economic outlook for these key economies remains constrained, the ongoing trend towards industrialization and urbanization is likely to sustain long-term demand for resources. In addition to the current decline in demand, mining companies face further challenges to profitability in the form of unfavourable commodity prices and tougher mining conditions. While commodity prices have improved since their 2008 lows, prices remain stagnant or falling, limiting revenue potential. Declining ore grades at current depths also mean that mining companies have to mine deeper to reach new deposits, significantly increasing the cost of extraction. Since the start of 2000, over 75% of new base metal discoveries have been at depths greater than 300 m (Deloitte Market Intelligence, 2013). Mining at these depths also introduces additional safety issues due to the high risk of rockfalls, flooding, gas discharges, seismic events, and ventilation problems. Compounding these economic and operational factors, mining companies also face regulatory uncertainty following a global trend of resource nationalism. Governments throughout the world are looking to increase their share of mining profits as a means to bolster slow economies and drive socio-economic development. State interventions in the mining industry vary from the introduction of new resourcebased taxes to transferring of mining rights to state-owned companies, as shown in Figure 2. This regulatory uncertainty poses a significant challenge to mining companies’ long-term strategic planning. Despite the particularly uncertain regulatory environment in Africa, global mining companies cannot ignore the
Figure 2 – Resource nationalism across the world
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The South African situation In addition to the complex factors affecting mining companies at a global level, companies with South African operations face further complexities. Mining has historically been a very important sector to the South African economy. Like many other African countries, South Africa has vast mineral wealth with immense value generation potential. With more than 52 commodities under its surface, South Africa has the world’s largest reserves of platinum, manganese, chrome, vanadium, and gold, as well as major reserves of coal, iron ore, zirconium, and titanium minerals (Monitor Deloitte analysis). The combined value of these resources is estimated at US$2.5 trillion. The industry’s substantial wealth has supported the country’s growth with strong resource exports and job creation. However, the mining industry’s relative contribution to the economy has declined due to growth in the financial and real estate sectors. To an even greater extent than their global counterparts, South African mining companies’ margins are under pressure. The combination of stagnant or falling global commodity prices and rising input costs is forcing mining companies to make difficult decisions in an attempt to sustain short-term operations, while still aligning these decisions with long-term objectives. In particular, increases in labour and energy costs have exceeded inflation. The annual ‘strike season’ is characterized by ever-increasing demands by unions and mineworkers who may not have a full appreciation of the challenging operating environment that mining companies face. In addition to the requirements by workers, there are rising demands by government as to the role mines should play in society. The government increasingly expects mining companies to fulfil social needs typically addressed by government in developed countries, such as the provision of basic services, education, and health care. These expectations are often not clearly defined, and are compounded by local communities’ demands for employment opportunities, skills development opportunities, education, and modern healthcare facilities. ‘Gone are the days when mining contribution is measured only its contribution to the gross domestic product, or royalties that it pays to the fiscus. Communities expect mining companies to become engines of socio-economic development of their areas’ - Susan Shabangu, Minister of Minerals The perception of a lack of (or inadequate) progress in these key areas is often met with vocal opposition, strikes, and unrest. This can have a significant impact on project development through costly operational delays and reputational damage to mining companies. This puts mining companies in a tenuous position, with corporate social responsibility (CSR) today extending well beyond the minimum legal requirements. South African mining companies require a deep understanding of shifting community and government expectations and a commitment to a high level of transparency and operational sustainability to address the demands of relevant stakeholder groups. Government’s requirements are further obscured by a local environment loaded with rhetoric. Some government officials have criticized the country’s inability to translate its mineral wealth into sustainable economic development at VOLUME 115
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substantial growth prospects that the continent offers. Africa has vast mineral riches, with significant reserves of more than 60 metals and mineral products, estimated at 30% of the world’s entire mineral reserves (Deloitte Mining Intelligence, 2013). Despite this resource base, Africa’s production represents only 8% of global mineral production, and is mostly exported in raw form. The relatively low exploration spend (at US$5 per square kilometre across Africa compared with US$65 per square kilometre in Canada, Australia, and Latin America) (McNitt, 2013) further highlights the opportunity for mining companies to take advantage of this new frontier for expansion, especially for those companies looking to expand into emerging markets. Mining companies looking to operate on the African continent face unique challenges. While most companies benefit from long-term certainty and predictability, these market characteristics are even more important to long-term businesses like mining. Mining companies require a degree of political stability, investment-friendliness, appropriate transportation infrastructure, and balanced fiscal regimes to operate successfully. There are several issues prevalent across the African continent that run counter to these requirements, and which contribute to the perception of Africa as a risky destination for business. Poor governance, the prevalence or perception of corruption, tenuous legislative frameworks, fragile security of tenure, and unclear royalty and tax regimes make strategic decisions difficult. Furthermore, long-standing issues such as civil unrest, insurgency, and a history of ethnic conflict pose additional operational risks in certain countries. Besides socio-economic and political complexities, the lack of appropriate infrastructure across Africa is a further barrier for mining companies. The required infrastructure capital is far more than the current infrastructure spend, leaving a substantial spending shortfall. This development constraint leaves investors with little confidence that publicsector infrastructure development will improve sufficiently to facilitate operations. African governments are turning to mining companies themselves to accelerate infrastructure development, linking mining licence issuance to huge infrastructure projects (McNitt, 2013). These multi-billion dollar foreign investments are likely to have a far greater impact on African infrastructure development than publicsector spending. The relationship between mining companies and host countries’ governments is challenging. Of the 54 countries in Africa, 24 rely on relatively few mineral products to generate more than 75% of their export earnings (Monitor Deloitte analysis). Despite this economic dependence on a prosperous mining industry, host governments habitually treat mining companies with suspicion. Mining operations are viewed as operations in isolation without the necessary linkages and benefits to other sectors of the economy or alignment with local aspirations. Furthermore, the history of colonialism across Africa has often resulted in foreign-owned mining companies being viewed by communities as entities with no long-term commitment to the country. Communities often perceive companies as generating wealth and repatriating dividends, leaving behind a damaged environment with little lasting benefit for the community.
Tough choices facing the South African mining industry grassroots levels. The government has been criticized for being seemingly slow to address what the previous Mineral Resources Minister, Susan Shabangu, called South Africa’s ‘evil triplets’ of poverty, inequality, and unemployment (Sowetan, 2011). In this highly political context, proponents of radical state intervention in the South African mining industry have asserted that the mineral wealth of the country ends up in the pockets of ’monopoly capital’ rather than benefiting the broader population (Monitor Deloitte analysis). While the government has ultimately declared that it has no short-term agenda to pursue resource nationalization, the widely reported rhetoric has cost the country a sharp decrease in its attractiveness as a mining destination, resulting in billions of dollars in deferred or abandoned investments (The National, 2013). This negative local sentiment is likely to have gained additional momentum due to the global trend towards resource nationalism and community activism, especially across the developing world. The overarching challenge in Africa (and particularly in South Africa) is to strike an equitable balance of interests, ensuring that mining is productive and profitable, as well as being fair to foreign investors, host states, and affected local communities alike. These challenges, at both a local and global level, make strategy critically important for mining companies.
influence mine profitability, as well as those affecting the company’s reputation and relationship with stakeholders. Adopting a structured approach to making choices at a corporate and business unit level is essential. Strategy is an integrated set of choices that includes both strategic positioning choices and strategic activation choices. Monitor Deloitte assists mining companies to make difficult decisions based on a series of cascading choices, as shown in Figure 3. Mining companies should be able to answer each question successively, working down the cascade. Where a question leads executives to re-evaluate their initial propositions, they can trace back up the cascade to redefine aspects until the strategy is cohesive. These questions allow mining companies to successively focus on key aspects of their high-level and operational strategies, which collectively form the basis for long-term strategic planning and short-term prioritization. The questions shown in Figure 3 can be adapted to the mining context as follows.
What are our aspirations? Mining companies should be able to clearly define both the financial (such as achieving year-on-year increases in average IRR) and non-financial objectives (such as consistently achieving zero harm, or making a positive social impact in host countries). These objectives should be aligned with the company’s overall vision, as they will guide investment decisions.
The strategy of decision-making Strategy is about making choices. Companies choose to do certain things and not to do other things (as opposed to tactics, which are about how to execute on the choices made). The complex operating environment in which mining companies function results in difficult choices. This necessitates a deep understanding of the factors that
Where will we play? Mining companies must choose the resource portfolio that they wish to develop and the countries in which they will operate. They must also decide which parts of the value stream they will target, and where in the project life cycle they should enter or exit.
Figure 3 – Cascading choices
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Tough choices facing the South African mining industry Mining companies should identify sources of sustainable advantage, and use these as the basis for business model development. These choices typically include the mining method, mine design, technology, and sustainability choices. These choices are necessary to achieve the goals and aspirations within the confines of where the company has chosen to play.
How will we configure? Mining companies should ensure that they have the capabilities and skills in place and that they are configured appropriately to successfully implement these strategies.
What are the priority initiatives? In a complex global market, mining companies must prioritize key initiatives and investments in order to execute on the choices made. Using this decision framework, Monitor Deloitte has identified five generic ’tough choices’ that face South African mining executives.
Tough choices facing mining companies Management teams at mining companies with South African operations face a series of tough choices and trade-offs. These are difficult decisions with a broad impact, but ultimately they are critical for long-term survival. Monitor Deloitte has identified five generic questions of particular significance to South African mining companies in light of the global operating context: ➤ How to achieve a step change in profitability and safety performance? ➤ How to attract and retain critical skills? ➤ How to raise the capital needed for South African operations? ➤ What is the best and most sustainable use of capital? ➤ How to balance the conflicting needs of stakeholders? These questions are explored below.
How can mining companies achieve a step change in profitability and safety performance? South African mining companies must simultaneously defend and grow profits, while also ensuring that safety records improve. While South Africa’s mining safety records are steadily improving, mine injury and fatality levels are still above those achieved elsewhere in the world (Business Day, 2013a). With mines becoming progressively deeper and ore grades declining, the unit cost of mine production in South Africa is under significant pressure. The situation is exacerbated by rapidly rising input costs, particularly those of energy and labour. All of the major South African mining companies have been through successive waves of cost reduction and safety improvement initiatives. While these have often been successful, the rate of incremental improvement has not kept pace with the pressures that are inexorably driving up unit costs. Most mines operating in South Africa are in need of a step change in performance. The Journal of The Southern African Institute of Mining and Metallurgy
How can mining companies attract and retain critical skills? Mines continue to face severe frontline and professional skills shortages that affect critical day-to-day operations. Although training programmes have improved, there is still a lack of experienced skills in frontline positions, such as artisans and supervisors, as experienced personnel retire or leave the company. Although current learnerships do produce high volumes of graduates, these graduates often lack necessary hands-on experience. This directly affects output, quality, and safety, while increasing overhead costs. Professional skills are also difficult to attract and retain in mining. The mining industry competes with many other industries for professional talent, and mines are at a disadvantage due to the harsh conditions and remote locations in which they operate. At a global level, South Africa is losing professional skills to other countries as experienced professionals emigrate. Executives are challenged to develop an understanding of the human resource capabilities required, and look to implement structures that attract, develop, and retain these skills. However, the dynamic nature of the industry (and the industries that drive resource demand) means that it will become increasingly challenging to balance the skills required today with the skills needed by mines in future.
How can mining companies raise the capital they need for their South African operations? Investors are starting to attach a risk premium to South African mining investments. This has the effect of increasing the cost of capital to South African mining companies. Several companies have moved to separate their South African assets from their global assets, to help them raise capital for international investments. This leaves their South African assets cash-constrained and struggling to fund expansion projects. Furthermore, many black economic empowerment (BEE) transactions are vendor-financed in a way that leaves the new company cash-constrained and unable to fund expansion projects. In an environment of rising costs and lacklustre commodity prices, South African executives have their work cut out to fund expansion out of operating cash flows.
How can mining companies determine the best and most sustainable use of capital? Capital decisions are complicated by the global and South African factors influencing the current and future operating environment. The increasing regulatory uncertainty and volatile labour conditions in South Africa have substantially increased the country’s inherent operating risk. These factors, coupled with increasing pressures from rising costs, have resulted in mining companies sometimes finding that producing more is not always more profitable. Mining companies have subsequently increased their thresholds for project profitability, abandoning projects that do not promise high enough returns. In addition to local projects, mining companies have a myriad of options to consider elsewhere. The trend towards African exploration promises growth for mining companies willing to absorb the higher operational risks. Beyond the VOLUME 115
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How will we win in chosen markets?
Tough choices facing the South African mining industry choice of geographic focus, mining companies must also assess which commodities are the most profitable and viable under the current conditions, and which commodities are of strategic importance for future growth. Finally, mining companies have the choice of investing in mature mines or developing early-stage operations.
How can mining companies balance the conflicting needs of stakeholders? Mining companies have the unenviable task of balancing the needs of multiple stakeholders. Each stakeholder group has its own unique objectives, often conflicting with those of other stakeholders, as shown in Figure 4. Government looks to maximize revenue to the state while ensuring that mining companies contribute to socio-economic and infrastructure development. Where the government has historically struggled to provide adequate services, mining companies are often used as a vehicle to accelerate change. The role of mining companies is further obscured by the fact that multiple arms of government are often not aligned, with inconsistent policy and populist rhetoric. Calls for distribution of the country’s mineral wealth through resource nationalization have become increasingly popular with politicians looking to garner favour with the country’s impoverished majority. While current government policy is against shortterm resource nationalization, this policy stance may change in future depending on the success of other African countries that have implemented resource-based interventions to drive socio-economic progress. Mining executives should also bear in mind that policy may shift without being considered a ‘radical intervention’ (for example, by increasing royalties or taxes on mining companies). These interventions can nevertheless have a significant impact on profitability and operational sustainability. Similarly, labour, organized labour, and communities also
expect mines to play an active role in socio-economic development. Mines frequently operate in areas with historically poor levels of service provision, and are often on the receiving end of decades of frustration due to a lack of tangible economic development, resulting in social unrest. The perception that international mining companies hoard wealth and do not share it with the communities in which they operate (despite the CSR investments that mining companies make) further threatens the fragile relationship between mining companies and communities. While many shareholders appreciate the value of CSR initiatives, the increasing requirements for mining companies to invest in broad service provision activities makes it difficult for them to balance their responsibility to the shareholders and their responsibility to the community. Mining companies, as is to be expected, look to maximize profit while retaining a social licence to operate. The fluid and increasing government and community expectations mean that mining companies are not always willing or able to deliver social projects to the levels expected. Even when companies are willing to drive social change in their areas of operation, they often do not understand the communities’ needs, and find that fulfilling needs identified by local municipalities sometimes also falls short of meeting community requirements.
Tools to assist decision-making The tough questions facing mining executives require analytical tools as the basis for data-driven decision-making. Monitor Deloitte has identified five tools that can help mining executives understand the key issues underlying these challenging questions, as well as the strategies necessary to mitigate risk and take advantage of opportunities to create sustainable value.
Figure 4 – Overview of the stakeholder landscape
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Tough choices facing the South African mining industry Mining companies can benefit from thinking about the longterm future by using tools such as scenario planning. Scenario planning allows mining companies to organize critical uncertainties about the future, along with predetermined elements, into a manageable set of scenarios that vividly describe potential future states of the world in which stakeholders live. Scenario planning was developed at Royal Dutch Shell in the 1970s as a tool to aid executives in making high-stakes decisions involving large investments and volatile situations, and it is clearly applicable to the mining industry. The foundational proposition of scenario planning is that no-one can predict the future. However, mining companies can choose to adopt a disciplined and imaginative point of view about possible futures by focusing on key interactions among critical uncertainties and how these interactions could reasonably play out. Furthermore, scenario planning also generates early indications that can act as warning signs of danger, or even more valuable early indicators of high-value opportunities, some of which are barely visible or unlikely at the point at which an investment decision is made.
Case study: take a long view A decade ago, miners had great hopes for the investment potential of Zimbabwe. Despite ongoing political turmoil, Harare was signalling a new openness to foreign investors. However, in 2011, the Zimbabwean indigenization minister moved to enforce a previously unenforced law limiting foreign ownership in the mining sector. This left mining companies with three choices: (1) comply with the law, ceding 51% of their stake, (2) refuse to comply and fight for their stake, or (3) walk away from their investment. This presents a tough choice. Scenario planning a decade ago may have thrown up a potential indigenization scenario, and would have helped executives develop a strategy that could survive in this scenario, as well as provide the tools to identify the scenario as it developed.
Tool 2: optimize portfolio The new reality of volatile prices and rising costs means that companies have to optimize their portfolios by acquiring and mining high-quality assets with better grades and strong margins, while ceding low-margin assets to junior miners. Mining company board members and executives face difficult trade-offs between competing strategic objectives, especially when it comes to projects with significant capital requirements. While in-depth financial modelling is critical, decision-makers need to move beyond simply prioritizing projects by value metrics such as NPV or IRR. Companies must assess the tangible and intangible benefits of projects under consideration. While evaluating intangible benefits is often subjective, mining companies can assign quantitative measures to these benefits, allowing projects to be compared on a value basis. Capital allocation models in mining can be further improved by adopting principles from modern portfolio theory. Widely used to assess the value of stocks and other investment instruments, portfolio theory allows mining companies to prioritize projects using a risk-adjusted capital allocation model. Methods that account for risk are especially The Journal of The Southern African Institute of Mining and Metallurgy
crucial for mining companies strongly influenced by global uncertainties such as exchange rates, commodity prices, and political risks, over and above the project-specific risks. Executives are also faced with the decision to allocate capital to growth projects, or sustaining capital to existing projects. As the market expects healthy project pipelines, companies are under pressure to ensure that they are wellpositioned to analyse, select, and implement key projects. Allocating sustaining capital is often more difficult, as the strategic objectives between projects vary greatly, making it difficult to directly compare the return on capital allocations.
Case study: optimize portfolio In June 2013, Sentula Mining announced that it would sell off its coal assets (including its contract mining and exploration operations in Mozambique), as part of a strategy to dispose of non-core assets to focus on its core businesses (Business Day, 2013b) in line with similar disposals by global mining companies such as Rio Tinto and BHP Billiton (Bloomberg, 2013). By focusing its activities on key geographies and commodities in clearly defined parts of the value chain, Sentula Mining made the complex choices of where to play’ and ‘how to win in chosen markets’. This strategic decision will streamline Sentula Mining’s capital allocation process.
Tool 3: innovate aggressively During challenging times such as these, mining companies can choose to pursue a ‘survival strategy’ or a ‘leadership strategy’. Those pursuing a survival strategy will cut costs to the bone while adopting a risk-averse posture and focus on defending their core business. Other companies adopt a leadership strategy, looking to identify unusual opportunities that will enable them to gain ground during the downturn and to make step changes in performance. Mining executives often associate innovation with technology. While this is often the case, there are many different ways in which a company can innovate, as shown in Figure 5. There is no lack of innovative ideas in any business. The challenge is turning these ideas into a step change in results. Good ideas often fall foul of resistance to change, and a failure to understand the whole system of innovations required to make the idea successful. For example, a new mining technology for the mine of the future will inevitably require innovative thinking in skills provision, mine planning, and performance measures. Mining companies should focus their innovation efforts on the few critical projects that will achieve a step change in performance and then move quickly. It is also not necessary to ‘reinvent the wheel’. Many of the most successful innovations started with an idea from outside the company.
Tool 4: engage proactively with stakeholders Mining companies operate in a complex stakeholder environment. As stakeholder understanding is often unstructured, mining companies can adopt a far more analytically rigorous approach to defining and understanding the stakeholder mind-set. Mining companies often take a too narrow view of their stakeholder landscape, missing interdependencies and ‘new’ groups whose interests will be mobilized over the course of the project’s lifespan. Mining companies should develop a sophisticated VOLUME 115
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Tool 1: take a long view
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Figure 5 – Types of innovation in mining
stakeholder map, a living document that evolves over the life of the project and presents new opportunities to improve understanding and communication, and most importantly, to find new common ground. Equipped with a deep understanding of stakeholders’ needs, mining companies must choose to engage constituents in a deliberate and thoughtful manner that takes a long-term view and seeks to build productive relationships. At its core, this integrated, long-term constituent management approach extends beyond a particular project; it is a highly customized, data-driven process that provides a deep understanding of constituents, the interrelationships between them, how they are influenced by prominent issues, and how companies can build platforms to engage these constituents to achieve mutually beneficial objectives.
Case study: engage proactively with stakeholders The Pilbara region of Western Australia, home to the Aboriginal people, has some of the largest iron ore deposits in Australia. The area contains many sacred areas and burial sites. In 2005, Rio Tinto began to explore the possibility of putting in place a comprehensive agreement with local stakeholders. After gathering social data, it built relationships
with key stakeholders and developed community programmes. Seven years later, Rio signed a $2 billion agreement with five Aboriginal groups, giving the company access to 70 000 km2 of traditional land to mine. By understanding communities’ needs and creating shared value through their mining activities, Rio Tinto’s shareholders have benefited as much as the Aboriginal people.
Tool 5: manage costs adaptively Mining firms should make conscious decisions about their overhead ratios. Some companies manage their overhead ratios according to economic cycles, cutting overheads during recessionary periods with either less focus on cost optimization during periods of growth, or actively allowing for increased costs to fuel capabilities that drive growth. Rather than allowing for cyclical cost fluctuations, mining companies should manage their overhead ratio consistently over time. Research has shown that companies that consistently manage their overheads fare better than those with more volatile overheads, as shown in Figure 6. Mining companies can approach adaptive cost management by mapping their costs against four main groups to gain a deeper understanding of where to create
Figure 6 – Managing costs can consistently lead to better returns
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Figure 7 – Analysing return on overheads
Conclusion Mines currently face tough choices around their profitability, attracting and developing key skills, capital raising, capital allocation, and stakeholder engagement. Mining executives need to think strategically about these issues and integrate them into a sustainable long-term strategy. The rising pressure on mining companies to grow profits despite a sub-optimal macro-economic environment and rising costs requires in-depth analysis. Mining executives can use scenario planning to understand possible futures as the basis for informed decision-making in an uncertain environment, and then optimize their portfolio accordingly. Seizing opportunities to innovate, from technological breakthroughs to internal process changes, offers mines a further opportunity to control their future. With limited revenue potential due to unfavourable commodity prices, mining companies may seek to defend their profits by managing costs and streamlining their overhead portfolio to focus on cost categories that drive growth. Mining companies should also welcome innovation to address the critical skills shortages affecting the industry. Scenario planning may also be useful to structure thinking around the kinds of skills that will be required for mining in the future. This will provide the basis for developing strategies to attract, develop, and retain these skills to secure future capabilities. Furthermore, mining executives face difficult capital allocation decisions. By integrating lessons learned from The Journal of The Southern African Institute of Mining and Metallurgy
scenario planning to create an understanding of which projects will develop the mining company’s sustainable advantage in future, mining executives can adopt aspects of modern portfolio theory to analyse and select appropriate projects to deliver shareholder value. Finally, mining companies must take cognisance of their operational context, especially in South Africa. The mining industry must understand and anticipate the needs of various stakeholders. Mining executives can use an analytical approach to understand the stakeholder landscape, ensuring that an effective stakeholder engagement strategy is in place. This strategy should seek to create shared value for stakeholders, resulting in mutually beneficial and productive relationships between the mining company, government, labour, and the community. Even in tough times, mining companies can use strategic thinking and analytical tools to face their tough choices.
References BLOOMBERG. 2 May 2013. European power prices slide to record as coal slumps on surplus. Bloomberg. www.bloomberg.co.za [Accessed 2 July 2013]. BUSINESS DAY LIVE. 5 May 2013. Mine deaths fall, but safety targets missed. BD Live. www.bdlive.co.za [Accessed 26 June 2013]. BUSINESS DAY LIVE. 28 June 2013. Sentula to sell coal assets as losses widen. BD Live. www.bdlive.co.za [Accessed 2 July 2013]. DELOITTE MARKET INTELLIGENCE, May 2013. Global Mining Update – May 2013: Taking the temperature of the market. MCNITT, L. 25 June 2013. A new type of colonialism? AgWeb. www.agweb.com [Accessed 26 June 2013]. SOWETAN LIVE. 3 August 2011. Nationalisation the wrong debate – Shabangu. Sowetan Live. www.sowetanlive.co.za [Accessed 26 June 2013]. THE NATIONAL. 21 June 2013. Glitter comes off South Africa’s gold. The National. www.thenational.ae [Accessed 26 June 2013]. ◆ VOLUME 115
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value. The return on each overhead class can then be calculated, allowing firms to prioritize and optimize costs, focusing on value-creating activities throughout the cycle, as shown in Figure 7.
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http://dx.doi.org/10.17159/2411-9717/2015/v115n6a3 ISSN:2411-9717/2015/v115/n6/a3
Crush pillar support – designing for controlled pillar failure by M. du Plessis* and D.F. Malan†
The aim of any mine design is to ensure that the excavations remain stable for the period they will be in use. Various pillar systems are used to ensure that underground stopes remain stable and that mining activities do not affect the surface infrastructure through either surface subsidence or seismicity. Intermediate-depth platinum mines make use of in-stope pillars designed to fail while the pillars are being cut at the mining face. The pillar stress exceeds the loading capacity and the pillars crush as a result. The aim of the paper is to provide an overview of in-stope crush pillars. This will include the application, behaviour, function, mechanism, impact, and design of a crush pillar system. Keywords crush pillars, controlled failure, limit equilibrium model, pillar layout.
Introduction Safe mining practices are aimed at maximizing the extraction of a particular orebody. Mine stability is a key consideration and the type of layout (i.e. pillar type and spans) must be suitable for the prevailing rock mass conditions. Crush pillar mining appears to be a method unique to South African hard rock mines, with the pillar system being applied to shallow and intermediate-depth gold and platinum orebodies. It allows for a higher extraction than what can typically be achieved with a conventional rigid/elastic non-yielding pillar system. The crush pillar system must, however, be used in conjunction with a barrier pillar system. The crush pillar dimensions are generally selected to give a width to height ratio (w:h) of approximately 2 (Ryder and Jager, 2002). This w:h ratio is selected to ensure that the pillars fail as they are being cut at the mining face. Once the pillar has failed in a stable manner, the residual strength of the pillar contributes to the required panel support by carrying the deadweight load to the height of the uppermost parting on which separation is expected to occur. Closely spaced support elements are typically used between adjacent rows of pillars to provide additional inpanel support. Ozbay and Roberts (1988) suggested that crush pillars should be implemented at depths greater than 400 m below surface. This is based on the assumption that the average face stress The Journal of The Southern African Institute of Mining and Metallurgy
Historic use and design of Merensky crush pillars RPM (Rustenburg Section) was the first platinum mine reported to have used crush pillars (Ozbay et al., 1995). Crush pillars were implemented as early as 1974 on Frank Shaft (now Khomanani Mine) and RPM (Union Section) in 1977 (Korf, 1978). The pillar system was introduced to prevent back breaks as a result of large spans created when the support method was changed from stonewalls (1927) to stonepacks to crush pillars (1974) as mining progressed deeper. Interestingly enough, none of the platinum mine crush pillar sites investigated by Ozbay (1995) made use of barrier pillars in conjunction with the crush pillars.
* Lonmin Platinum, Marikana, North-West province, South Africa. † Department of Mining Engineering, University of Pretoria, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Platinum Conference 2014, 20–24 October 2014, Sun City South Africa. VOLUME 115
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at this depth is large enough to enable crushing of the pillars. In contrast to stable pillar layouts, failure of crush pillars is in fact desired as long as it occurs in a controlled manner. Pillar failure and the resulting load-shedding should ideally be continuous to prevent accumulation of elastic strain energy. Figure 1 is an illustration of the stressstrain relationship of a typical pillar. The initial straight line portion of the curve up to the yield point reflects the elastic response of the pillar. The yield point indicates the onset of inelastic behaviour, whereafter the pillar exhibits strain hardening until it reaches its peak strength. Load shedding then follows until the pillar reaches its residual strength. Crush pillars are designed to function in this residual part of the pillar stress-strain curve.
Crush pillar support – designing for controlled pillar failure
Figure 1 – Diagram illustrating the complete stress-strain behaviour of a pillar (after Ryder and Jager, 2002)
Crush pillar layouts were initially designed using pillar dimensions that were successful in other areas. The pillar dimensions and spacings were then adjusted until the pillars exhibited the required behaviour (Ozbay et al., 1995). The typical range of w:h ratios of the crush pillars varied between 1.5 and 2.5. This accommodated the varying stoping widths (0.9 m to 2 m), the weak footwall rock in some areas, and structural weaknesses in the rock. An alternative design approach was to cut the pillar at a w:h ratio of 2 and then increase or decrease the pillar width until crushing was achieved. Ozbay et al. (1995) stated that the main purpose of the crush pillars was to provide enough resistance to support the rock up to the highest known parting plane (i.e. the Merensky Bastard reef contact at a height of 5–45 m), and not to support the full overburden rock mass to surface. The load requirement of a crush pillar to function as local support can be established by determining the support resistance required, which is dictated by the height of the prominent parting. Support resistance in the order of 1 MPa is quoted (Roberts et al., 2005a), based on the back-analysis of back breaks that occurred at Randfontein Estates and Northam Platinum, where the failures took place at 40 m and 30 m into the hangingwall respectively. Parting heights of 10 m and 20 m would result in a support resistance requirement of approximately 0.3 MPa and 0.6 MPa respectively.
Figure 2 – Photograph of a crush pillar in an underground trial section at Lonmin
Typical crush pillar layouts A typical mining configuration for a crush pillar layout consists of pillars being positioned either adjacent to raises/winzes (dip mining) or strike gullies (breast mining). The pillars are separated in the direction of mining by a holing to allow for either ventilation (vent holing) or to increase extraction (pillar holing). Crush pillar layouts typically consist of approximately 30–33 m wide panel spans (inter-pillar) with slender pillars 2 m, 2.5 m, 3 m, or 4 m wide and 3 m, 4 m, or 6 m in length. The pillars are separated by 0.5 m to 3 m wide holings. In some instances a siding is mined adjacent to the raise or gully to ensure that the failed pillar material does not fall into the travelling way. These sidings are approximately 2–2.5 m deep and are carried a maximum of either 3 m or 6 m behind the panel face (depending on the standard applied by the mining company). Figure 3 is an example of a typical up-dip crush pillar layout. An off-reef haulage links to the reef horizon via a crosscut and a travelling way.
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Figure 3 – Typical layout (up-dip mining) for a narrow tabular reef mine using crush pillars (plan view)
Uncertainty regarding pillar behaviour and design The measured and observed behaviour of a 2:1 Merensky crush pillar is summarized in Figure 4 and Table I. Based on stress measurements, Roberts et al. (2005b) determined that a crush pillar reaches its peak strength at between 3 and 10 millistrains, then fails following a further compression of approximately 5 millistrains along an estimated negative postpeak stiffness slope of 12 GN/m. Following further compression to the extent of 50–90 millistrains, it is assumed that footwall heave occurs as a result of the lateral confinement The Journal of The Southern African Institute of Mining and Metallurgy
Crush pillar support – designing for controlled pillar failure
Figure 4 – The stress strain curve of a 2:1 crush pillar (after Roberts et al., 2005b)
Figure 5 – Stable (line ‘stiff’) and unstable (line ‘soft’) loading of a rock specimen along its complete load deformation curve. Regions A-B, BC, C-D, and D-E represents pre-peak, post-peak, residual strength, and strain hardening respectively (after Ozbay and Roberts, 1988)
Table I
Estimated behaviour of a crush pillar with a w:h ratio of 2:1 (after Roberts et al., 2005b) Behaviour
Value
Unit
A B C D E
Stope closure Peak strength Post-failure slope Residual pillar strength Squat effect
(3 - 10) 75 - 150 12 13 -25 50 - 90
Millistrains MPa GN / m MPa Millistrains
of the foundation. At this point it is assumed that the crushing of the foundation restricts the pillar’s load capacity as the pillar is reliant on the foundation, which is believed to be the limiting load-bearing component. Further compression could result in an increase in the contact friction angle; the result is a ‘squat effect’ with the slope of the stress-strain curve becoming positive. This is assumed to occur when the vertical strain is > 0.4. The value of the peak pillar strength is unknown. The values quoted above are based on estimates as described by Ryder and Ozbay (1990). On most mining operations, the design of the crush pillars is based on trial and error. As the pillar strength is unknown, the pillar sizes are adjusted to obtain the correct behaviour. Several factors affect the behaviour of the crush pillars, and in many cases satisfactory pillar crushing is not achieved. This results in a seismic hazard in many of the mines using crush pillars. If pillar crushing does not occur, once these pillars move to the back area of a stope, some pillars may burst while oversized pillars may punch into the footwall. If pillars are designed in such a way that they are fractured during cutting by the face abutment stresses so that the pillars will already have yielded and reached their residual strength, further compression of the pillars will be associated with an increase in load and stability will be ensured, (Ozbay and Roberts, 1988). The stiffness of the strata must therefore be greater than the post-peak stiffness of the pillar (Figure 5) or violent pillar failure and hangingwall instability will occur (Figure 6 and 7). The pillar design should be aimed at determining pillar dimensions for which the post-peak curve of the pillar is as flat as possible. The Journal of The Southern African Institute of Mining and Metallurgy
Figure 6 – Example of pillar foundation failure. The crushed rock is contained in the siding between the pillar and the pack. The rings on the grout pack snapped during the event. The shattered timber elongate is an indication of the violent nature of this type of behaviour
Figure 7 – Example of pillar bursting. The scattered pillar material was ejected into the siding. The white lines indicate the scatter relative to the stoping width. Timber elongates indicate dynamic loading as a result of the event
There are many factors influencing the behaviour of crush pillars. These factors affect the ability of the pillar to crush as well as the reaction of the strata in response to the pillar when entering a post-peak state. Some of the contributing factors are: VOLUME 115
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Crush pillar support – designing for controlled pillar failure ➤ Mining depth (stress) ➤ The mining height and pillar size (w:h ratio) ➤ Stope layouts, including the position of the pillars and presence of a siding ➤ Strata stiffness and the influence of mining losses (i.e. potholes or unmined ground) or regional pillars ➤ Strength of the pillar foundation relative to the pillar strength and load applied ➤ Peak and residual pillar strength ➤ Mining discipline resulting in over- or undersized pillars.
where S0 and m0 represent the intact strength of the pillar material. Equation [4] can be used to implicitly determine the boundaries between the intact pillar core and the failed edge regions. Equation [3] predicts an exponential increase in the pillar stress away from the edge towards the centre of the pillar. If the pillar width is w and if the pillar is completely failed, assuming that the stress profile is symmetric about the centre of the pillar, the average stress in the pillar (APS, average pillar stress) is given by: [5]
Evaluation of parameters that govern crush pillar behaviour During the past four decades, several parameters have been studied in an attempt to better understand and predict the behaviour of crush pillars. Crush pillar layouts have, however, remained essentially unchanged over this period. To assess some of the key parameters as outlined in the previous section, a simplified model has been derived to investigate some of the parameters governing crush pillar behaviour. The approach applied and results achieved are expanded on in the subsequent sections of the paper. The aim is to understand the impact of these parameters on crush pillar behaviour. All of the results are preliminary and must be substantiated by underground observations and measurement. A trial is being conducted at Lonmin Platinum to calibrate the model and validate the preliminary findings.
Formulation of the limit equilibrium model Malan and Napier (2006) represented the force equilibrium of a material ‘slice’ of a fractured pillar as shown in Figure 8. The slice of fractured material has a mining height H at a distance x from the stope face. The slice is confined by reef-parallel and reef-normal stress components σs and σn respectively, as well as by shear tractions, τ. It is assumed that the edge of the pillar is at x = 0 and that the seam-parallel stress component σs is uniform over the height of the pillar and increases as x increases. From Figure 8 it can be inferred that the equilibrium force balance acting on the slice of height H and unit out of plane width requires that: Hσs(x + Δx) = Hσs(x) + 2τΔx
[1]
Following integration and the application of assumptions and substitutions for σs and τ, the following expressions were derived by Malan and Napier (2006) to express the average horizontal and vertical stress values for a failed pillar.
σs = S(eαx – 1)/m
Substituting Equation [3] into [5] and following the integration of Equation [5], the average pillar stress is expressed by the following relationship (Du Plessis et al., 2011): [6]
Simulation of crush pillar behaviour The evaluation of an analytical limit equilibrium model to simulate crush pillar behaviour was described by Du Plessis et al. (2011). The values predicted by this model were compared to the numerical values obtained from the TEXAN (Tabular EXcavation ANalyzer) simulations. Good correlation was obtained and this serves as a useful validation of the model implemented in the numerical code. In general, the limit equilibrium model appears to be very attractive for simulating pillar failure as the gradual crushing of the outside of the pillar and the transfer of stress to the intact core can be replicated. Du Plessis and Malan (2012) indicated that the analytical solution derived by the author provided a reasonable fit to the underground crush pillar stress measurements conducted by Watson (2010). The impact of pillar width on crush pillar behaviour was investigated by simulating an idealized crush pillar layout (Figure 9) in the TEXAN code. The layout consists of a 30 m × 70 m stope panel with a second panel being mined in a sequential fashion adjacent to this first panel. The layout was simulated as eight mining steps with seven crush pillars being formed in this process. For the second panel, the size of each
[2]
αx
σn = Se
[3]
with α=2μm/H, μ=tanϕ the frictional coefficient, S the cohesion, and m a strengthening parameter. No allowance is made for roof or floor foundation failure and the stress components increase exponentially from the pillar edge. As pointed out by Salamon et al. (2003), a Mohr-Coulomb plasticity model without strain-softening behaviour is inadequate for simulating actual pillar behaviour where rapid load-shedding or ‘bursting’ may occur. To address this shortcoming, it is assumed that initial failure in the seam or reef is controlled by the additional relationship:
σn ≤ S0 + m0σs
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Figure 8 – Force equilibrium of a material slice in the pillar (after Malan and Napier, 2006) The Journal of The Southern African Institute of Mining and Metallurgy
Crush pillar support – designing for controlled pillar failure
mining step was 10 m and the sizes of the crush pillars were varied to obtain the required w:h ratio. Furthermore, mining heights of both 1 m and 2 m were used to compare the impact of pillar width for a constant w:h ratio on pillar crushing. The element sizes used were 0.5 m. The parameters used for the simulations are shown in Table II. Note that these values were chosen arbitrarily and a better calibration of this model based on underground measurements will be required in the future. For pillars simulated with a w:h = 2:1 it appears as if these original parameters used to test the simulation of pillar crushing with a limit equilibrium model (Table II) are very conservative and at the upper limit ensuring that pillar crushing is achieved for the simulations carried out (Du Plessis and Malan, 2012).
Results from numerical simulations Figure 10 indicates the TEXAN modelling results for the various layouts simulating the effect and behaviour of different pillar widths (w:h ratios). All of the results are for pillar D formed in mining step 5. The residual (for smaller w:h range pillars) and peak stress (for larger w:h range pillars) as highlighted in Figure 10 are re-plotted in Figure 11. This compares the results of the numerical analysis and the analytical solution for the same set of input parameters. Note that the larger w:h range pillars did not reach a residual state and therefore the peak stresses of these pillars were used, as highlighted. The preliminary numerical analyses indicated that the pillars with a w:h >2.0 were not yet in a crushed state (at 600 mbs). The 4 m wide pillar (w:h = 2:1) did experience late crushing. Comparative simulations indicated that crush pillars with a w:h ratio of 2 implemented at depths shallower than 600 m will most likely not crush (Du Plessis and Malan, 2012). The simulated pillar behaviour indicated that once the peak strength of a pillar is reached, the stress increase causes complete failure of the core of the pillar and the pillar then moves to a residual state (also shown in Figure 15). Oversized pillars that are typically encountered underground either do not The Journal of The Southern African Institute of Mining and Metallurgy
Assessing pillar behaviour Figure 10 and 11 indicate that pillars of different dimensions (w:h ratio) behave differently. There are limiting factors affecting pillar crushing which include pilllar width, mining depth, regional stability (presence of geological structures), pillar length, etc. It is important to understand which underlying factors play a role and how they affect the behaviour of crush pillars. In this paper some of these characteristics will be highlighted by determining the governing trends. Figure 12 combines the results of the numerical simulations and the analytical solution. Note that the numerical simulation considered that the pillar is initially part of the solid rock mass, and is then formed by the approaching mining face, in order to establish how the pillar behaves pre-
Table II
Parameters used in crush pillar simulations General parameters
Value
Young's modulus Poisson's ratio Stress gradient Depth Reef dip
70 GPa 0.25 0.03 MPa/m 600 m 0°
Crush model parameters
Value
Intact cohesion C0 Residual cohesion Intact slope m0 Residual slope m Bounding friction angle Seam height Seam stiffness modulus
5 MPa 5 MPa 5 3 35° 1 m and 2 m 106 MPa/m
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Figure 9 – Idealized crush pillar layout simulated in the TEXAN code (plan view)
crush or fail violently in the back area. The study highlighted a key attribute of the limit equilibrium models: the pillar stress increases in an exponential fashion towards the centre of the pillar. This may lead to to the formation of unduly high stresses in the core of wide pillars. The simulated results indicated that the cores of these oversized pillars were still intact. As the oversized pillars did not crush at the face when being cut, these pillars move into the back area as the mining face advances. In the back area, the pillar is at a higher stress. The change in stress caused by a mining increment is lower than when the pillar is formed at the face. The pillar may therefore either not crush (particularly when oversized) or fail violently, as the stresses on these pillars are much higher and the loading environment has become much softer as the pillar is no longer close to the face abutment. The slope of the postpeak load deformation relationship becomes flatter with increasing w:h ratios (Salamon and Oravecz, 1976). Salamon predicted that the softest loading system (strata stiffness) will present the greatest danger of uncontrolled failure The analytical solution of a completely crushed pillar, as indicated in Figure 11, provides a two-dimensional solution (infinitely long pillars) to a three-dimensional problem. If very long pillars are simulated in TEXAN, the residual pillar stress of the failed pillars moves closer to the analytical solution (pillars with w:h ≤ 2:1). The residual pillar stress of these failed pillars touches or lies just below the analytical solution curve.
Crush pillar support – designing for controlled pillar failure
Figure 10 – TEXAN simulation – effect of pillar width on pillar performance (600 mbs; 2 m pillar height)
Figure 11 – Results from the analytical solution of a completely crushed pillar (Equation [6]) and numerical simulation for pillars with various w:h ratios (600 mbs)
cutting whilst being formed at the face and post-cutting as it moves into the back area as the mining face continus to advance. Figure 12 was compiled to establish how the state of stress of each cut pillar changes and compares in relation to the analytical solution during this pillar-forming stage. The behaviour of each pillar is indicated by the arrows representing the stress relation as a result of mining. The range includes the initial state of stress (after mining step 1), the increase in stress as the pillar is formed (towards the maximum), and where applicable the reduction in pillar stress to the residual state (pillar crushing). From Figure 12 it is apparent that the stress range of the larger pillars (w:h > 2:1) is situated far below the analytical solution. Line A represents the initial pillar stress (all pillars) and line B the peak pillar stress (for the failed pillars only). From the figure it is clear that the pillars with a larger width-toheight ratio (i.e. w:h > 2:1) are at a much lower initial stress. The pillars are therefore able to absorb the change in stress as
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they are formed and mining progresses. The state of peak pillar stress is therefore not reached and pillar crushing is not achieved. For these pillars a much higher level of initial stress is required. As mentioned earlier, various factors affect the initial stress state of the rock mass and pillar. These include pillar width, mining height, mining depth, pillar length (width-to-length ratio >5), and the presence of geological structures (regional stability). Figure 13 compares the stress profile from the edge of pillar D, over the pillar holing and ahead of the mining face, for both a 1 m and 2 m mining height (for mining step 5). For a lower mining height, a narrower pillar is required to maintain the same w:h ratio. As mentioned, late pillar crushing was achieved for the the 4 m wide pillar (2 m stoping height; SW = 2). The 2 m wide pillar (1 m stoping height; SW = 1) did experience pillar crushing while the pillar was being formed at the mining face (also refer to Figure 14, which compares the stress change per mining step). In both cases, significantly large face stresses were achieved in the region approximately 2 m ahead of the mining face. These were sufficiently large to
Figure 12 – Pillar stress trends for different pillar widths (simulated pillars at 600 mbs)
Figure 13 – Simulated vertical stress along portion of section b´- b´ in Figure 9. Note that the same w:h ratio is maintained in the simulations for both stoping widths The Journal of The Southern African Institute of Mining and Metallurgy
Crush pillar support – designing for controlled pillar failure
initiate early pillar crushing while the pillar was being formed at the face. This is, however, not the case for larger pillars with w:h > 2:1. Lower pillar edge stress is achieved for the pillars with larger w:h ratios (110 MPa for a pillar with w:h = 3:1 compared to 189 MPa for pillar with w:h = 2:1). This effect is highlighted in Figure 12 and Figure 15. Figure 15 indicates the exponential stress increase towards the centre of the pillar. This could, however, lead to excessively high stresses in the cores of wide pillars where a crushed state is not reached. Note that the stress profile for the w:h = 2:1 pillar is for mining step 5 and just before the pillar completely crushes and moves to a residual state in mining step 6 (refer to Figure 14, which indicates the stress sequence as a result of mining). At this point the pillar has completely crushed from the outside towards the centre of the pillar. The pillar core also reaches a maximum stress limit, after which complete failure of the pillar is reached and the pillar moves to a residual state (as can be seen for a w:h = 1.5 pillar). As mentioned, the core of an over-sized pillar does not reach this maximum stress limit and remains intact. Considering the aforementioned factors influencing the stress state of a pillar, a zone defining possible pillar crushing can be identified. Figure 16 distinguishes between zone 1 (pillar crushing) and zone 2 (no pillar crushing). The window for achieving pillar crushing therefore becomes smaller as the pillar width increases (as indicated by zone 1 pinching out at a w:h of approximately 2.5). Line C defines a possible linear extrapolation of the anticipated residual pillar stress. In theory, the residual state of the pillar should coincide with the curve representing the analytical solution. The amount of stress change required per increment of mining for these large pillars does, however, become substantial. As indicated by the results of the numerical modelling, pillar crushing is typically not achieved by these larger pillars. Although the pillars are at a higher stress, the incremental stress increase reduces as the pillars move towards the back area (refer to Figure 10). A critical stress level is therefore not reached whereby the pillars crush completely (the pillar core remains intact). This highlights the importance of crush pillars requiring a sufficiently high initial stress level to ensure that the pillar can fail throughout and move to a residual state. The peak strength of the pillar must be exceeded while the pillar is close to the mining face to cause complete failure of the core. It is for this The Journal of The Southern African Institute of Mining and Metallurgy
Figure 15 – Simulated vertical stress along section a´ – a´ in Figure 9 for pillars with different w:h ratios (600 mbs)
Figure 16 – Zone of pillar crushing for various pillar widths (600 mbs)
reason that the analytical solution may be of practical use only for pillars with low width-to-height ratios (w:h approx. 2:1). Numerical simulations conducted for pillars with w:h ≥ 2.5 indicated that the pillars did not crush even at 1000 m below surface. The wider pillars are therefore able to absorb the change in stress as the pillar is formed and mining progresses. In the back area the change in stress per mining increment decreases and the pillar core remains solid. From the preliminary results obtained, it is hypothesized that the zone of pillar crushing can essentially be extended as indicated in Figure 17. The cut-off zone extends just beyond a pillar w:h ratio of 2 and stretches towards the analytical solution curve. This is based on numerical simulation for various scenarios including mining depth, pillar width, pillar height, and the impact of infinitely long pillars.
Conclusion This paper provides a general overview of crush pillars. Although the function of crush pillars is well understood (the residual state of the pillar must support the deadweight to the uppermost unstable parting), the behaviour of pillars in VOLUME 115
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Figure 14 – Effect of pillar width on pillar behaviour (comparison of a 1 m and 2 m mining height on w:h = 2:1 pillar)
Crush pillar support – designing for controlled pillar failure It should be emphasized that all of these results and conclusions are based on the parameters assumed for the limit equilibrium model. Regarding further work, rock testing is required to determine the post-peak properties and behaviour of the Merensky Reef. This will improve the confidence in the derived analytical solution as a tool for estimating the residual strength of crush pillars. Furthermore, underground measurements are required to back-analyse the crush pillar behaviour and calibrate the model. A trial mining site using crush pillars has been established at Lonmin Platinum.
Acknowledgement The work described in this paper forms part of the PhD study of Michael du Plessis at the University of Pretoria. The contribution of Dr John Napier with regards to the development of the limit equilibrium model as well as the TEXAN code is greatly appreciated. Figure 17 – Proposed extrapolated zone of pillar crushing based on preliminary modelling results
underground mines is, in some instances, unpredictable, resulting in pillar seismicity. Although the concept that the pillar design should be aimed at determining pillar dimensions for which the post-peak curve of the pillar is as flat as possible is widely accepted, several factors influence the ability of a pillar to enter a residual state while the pillar is being formed at the mining face. If the pillar does not crush while in close proximity to the stiff face abutment, the transition to a soft loading environment as the pillar moves into the back area could cause the pillar to fail violently. A derived limit equilibrium model and its implementation in a numerical boundary element code were used to predict the potential residual state of crush pillars. This included simulating an idealized crush pillar layout to determine the stress of a crush pillar prior to formation (while still part of the mining face), when cut at the face, and when the pillar is in the back area of a stope as the mining face advances. The results indicate that there are many factors affecting pillar crushing, including pillar width, mining depth, mining height, regional stability, and pillar length. These factors have an impact on the initial stress state of the pillar. The comparative simulations indicated that a pillar with a w:h > 2:1 may not crush completely and could pose a seismic risk in the back area of a stope. Furthermore, the initial stress required to ensure the crushing of a 2:1 pillar core is achieved only at mining depths greater than 600 m below surface. The peak strength of the pillar must be exceeded while the pillar is close to the mining face to cause complete failure of the pillar core. It is for this reason that it is envisaged that the analytical solution might be of practical use only for pillars with low width-to-height ratios. A zone of pillar crushing exists around the analytical solution curve. The window for pillar crushing, however, decreases as the pillar width increases and is influenced by the initial stress state of the pillar. Mining losses such as potholes also affect the initial stress state of crush pillars in close proximity to these geological structures. The influence of potholes was also simulated in order to to understand the impact on crush pillar behaviour, but the results are not presented in this paper.
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References DU PLESSIS, M. MALAN, D.F. and NAPIER, J.A.L. 2011. Evaluation of a limit equilibrium model to simulate crush pillar behaviour. Journal of the Southern African Institute of Mining and Metallurgy, vol. 111, no. 12. pp. 875–885. DU PLESSIS, M. and MALAN, D.F. 2012. The simulation of crush pillar behaviour in the tabular layouts of the Bushveld Complex. Proceedings of Eurock 2012, Stockholm, Sweden, 28–30 May 2012. International Society for Rock Mechanics. KORF, C.W. 1978. Stick and pillar support on Union Section, Rustenburg Platinum Mines, Association of Mine Managers of South Africa. pp. 71–82. MALAN, D.F. and NAPIER, J.A.L. 2006. Practical application of the TEXAN code to solve pillar design problems in tabular excavations. SANIRE 2006 Symposium - Facing the Challenges, Rustenburg, South Africa. South African National Institute for Rock Engineering. pp. 55–74. OZBAY, M.U., and ROBERTS, M.K.C 1988. Yield pillars in stope support. Proceedings of the SANGORM Symposium in Africa, Swaziland. South African National Group on Rock Mechanics, Johannesburg. pp. 317–326. OZBAY, M.U., RYDER, J.A., and JAGER, A.J. 1995. The design of pillar systems as practiced in shallow hard-rock tabular mines in South Africa. Journal of the South African Institute of Mining and Metallurgy, vol 95, no. 1. pp. 7–18. ROBERTS, D.P., ROBERTS, M.K.C., and JAGER, A.J. 2005a. Alternative support systems for mechanised stopes, PlatMine project report 2004-0189. Miningtek Division, CSIR, Johannesburg. ROBERTS, D.P., ROBERTS, M.K.C., JAGER, A.J., and COETZER, S. 2005b. The determination of the residual strength of hard rock crush pillars with a width to height ratio of 2:1. Journal of the South African Institute of Mining and Metallurgy. vol. 105. pp. 401–408. RYDER, J.A. and JAGER, A.J. 2002. A Textbook on Rock Mechanics for Tabular Hard Rock Mines. Safety in Mines Research Advisory Committee (SIMRAC), Johannesburg. pp 287, 298–299. RYDER, J.A. and OZBAY, M.U. 1990. A methodology for designing pillar layouts for shallow mining. International Symposium on Static and Dynamic Considerations in Rock Engineering, Swaziland, 10-12 September 1990. International Society for Rock Mechanics. SALAMON, M.D.G. and ORAVECZ, K.I. 1976. Rock Mechanics in Coal Mining. Coal Mining Research Controlling Council. Chamber of Mines, Johannesburg. SALAMON, M.D.G., BADR, S., MENDOZA, R., and OZBAY, M.U. 2003. Pillar failure in deep coal seams: numerical simulation. Proceedings of the 10th Congress of the International Society for Rock Mechanics. South African Institute of Mining and Metallurgy, Johannesburg. pp. 1011–1018. WATSON, B.P. 2010. Rock Behaviour of the Bushveld Merensky Reef and the Design of Crush Pillars. PhD thesis, School of Mining Engineering, University of the Witwatersrand, Johannesburg, South Africa. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a4 ISSN:2411-9717/2015/v115/n6/a4
The application of pumpable emulsions in narrow-reef stoping by S.P. Pearton*
from many of the regulations imposed on Class 1 explosives. The increase in safety and security obtainable through this classification, together with the physical properties of pumpable emulsions, allows for significant advantages over alternative explosives technologies available for use in the narrowreef environment. In addition to the improved safety during transportation, storage, and handling, pumpable emulsions can be pumped between transport vessels, through shaft pipelines, and into the blast-hole, thereby reducing labour requirements. Given the possible benefits available through the implementation of pumpable emulsions within narrow-reef operations, a study was undertaken in order to gain an understanding of the factors essential to their successful implementation. Through this understanding, a project was formulated that would allow for the development of a suite of underground emulsion technologies and UN class 5.1 pumpable emulsion formulations suitable for application in the South African narrow-reef environment.
Synopsis Pumpable emulsion explosives have been available to surface and underground massive mining operations for decades, and their unique properties offer significant advantages in terms of improved safety, reliability, and performance. However, the benefits of pumpable emulsions have been unavailable to narrow-reef mining operations due to the lack of technology necessary for their successful implementation in this challenging environment. Despite efforts to promote and enhance the safety and performance of bulk emulsions for narrow-reef stoping, little research has been undertaken to advance the pump technologies required for their implementation. This has resulted in a gap in knowledge and technology, and as a consequence the successful implementation of a pumpable emulsion system has consistently eluded the narrow-reef environment. The purpose of the following investigation was to evaluate the viability of pumpable emulsion explosives for use in South African narrow-reef mining operations. By approaching the problem from multiple perspectives, this research aimed firstly to propose a theoretical framework and suite of equipment suitable for the implementation of pumpable emulsions within the narrow-reef environment. Through the development of this suite of pumpable emulsion technology, tests could be undertaken on the proposed narrow-reef emulsion formulation and pumpable emulsion technology to obtain the necessary understanding of the performance of the system under controlled operating conditions prior to its implementation in the broader mining industry. Keywords blasting technology, explosives, pumpable emulsions.
Explosives selection for narrow-reef blasting applications
Bulk emulsion explosives have been used in large-scale mining operations across the globe for decades. The reason for their extensive use lies primarily in the advantages of bulk emulsion explosives over alternative explosive technologies, both in terms of safety and blast performance. Despite these benefits having been attainable for some time, the scale and cost of equipment required for the implementation of such technologies has resulted in their use being limited to large-scale mining operations. This limitation continues to exist, despite the ever-growing demand for increased levels of safety and security in the narrow-reef environment. As pumpable emulsions are insensitive to initiation prior to sensitization they are classified as UN Class 5.1, and as such are free The Journal of The Southern African Institute of Mining and Metallurgy
* BME, a division of the Omnia Group Š The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Platinum Conference 2014, 20â&#x20AC;&#x201C;24 October 2014, Sun City South Africa.
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Introduction
In order to understand the benefits available through the implementation of pumpable emulsion systems, a discussion of available explosives is important for comparative purposes. Toward the end of the 20th century the use of dynamite explosives was phased out of narrow-reef mining operations in favour of various forms of ammonium nitrate (AN)based explosives. Despite the commonality of AN in the various explosives, considerable
The application of pumpable emulsions in narrow-reef stoping differences in physical properties and performance characteristics still exist between the various types of AN-based explosives. As the results obtained in blasting operations are, in part, dependent on the physical properties and performance characteristics of the selected explosive, the optimal choice of explosive will differ depending on the blast design, the desired outcome of the blast, and the geological and environmental conditions in which the blast takes place.
ANFO ANFO was first introduced into South African underground tabular mining operations in 1963, and by 1975 accounted for approximately 60% of the commercial explosives consumption within the sector. Not only did ANFO increase the level of safety of commercial explosives, but it was less expensive than dynamites, ‘less arduous’ to handle, and as a result of its bulk form it allowed for 100% coupling within the blast-hole, improving the efficiency of energy transfer from the blast-hole into the surrounding rock mass. Although the bulk nature of ANFO initially appeared to be beneficial to mining operations, it allowed for the unprecedented overcharging of blast-holes. This high mass of fully-coupled low velocity of detonation (VOD) explosive increased the extent of damage to the hangingwall and increased levels of overbreak (Mosenthal, 1990). Due to the hygroscopic nature of ANFO it is also less sensitive to detonation when exposed to humid conditions and its use will result in poor and inconsistent blast results in wet mines. The combination of these factors reduces the efficiency of blasting operations and increases the overall cost of mining due to undesirable blast results and high levels of explosives waste. Although ANFO is manufactured at an approximate bulk density of 0.8 g/cm3, the blow loading of ANFO in underground operations increases the density of the explosive, thereby increasing the relative bulk strength (RBS) of the explosive. As the air pressure available at the time of loading determines the force at which the ANFO granules are propelled through the charging lance and into the hole, the crushed particle size and compaction of the prill within the blast-hole will vary depending on the available air pressure, the strength of the prill, and the loading technique used. Loaded densities achieved through the use of pneumatic loaders commonly range from 0.94 to 1.1 g/cm3 depending on the abovementioned variables (Brinkmann, 1994). This high blow-loaded density further exacerbates the problem of the overcharging of blast-holes due to the increased energy within the blast-hole. As ammonium nitrate crystals undergo a phase change at 32°C, the control of product shelf life is important in order to limit the degradation of ANFO through temperature cycling (Mulke, 1966). Repeated cycling of ANFO across 32°C results in the degradation of the original prill and significantly increases the density achieved through pneumatic loading, as well as the quantity of ANFO blown into the air during loading operations. As these factors affect the density of blow-loaded ANFO and this in turn affects the VOD, a broad and inconsistent range of VOD results will be experienced when using ANFO. In an attempt to reduce the extent of damage caused by the overcharging of blast-holes with ANFO, explosives manufacturers have reduced the relative bulk strength of ANFO. Despite these efforts, limited success is evident and ANFO
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has been largely excluded from consideration in mines with poor ground conditions (Kruger, 2010). In addition to the undesirable performance profile, the UN Class 1 classification of ANFO increases the burden on the transportation and storage of ANFO as required in accordance with South African law.
Cartridged explosives The way in which an explosive is packaged has both practical and financial implications for mining operations. Two types of packaged AN-based cartridges exist for use in underground blasting applications, namely watergel (slurry) and emulsion cartridges. As packaged explosives such as cartridges are pre-sensitized at a manufacturing facility, better equipment and a higher level of control can be exercised in achieving consistent quality product. In addition, as cartridges are manufactured ready to load into the blasthole, they are also easy to handle in confined and difficult underground conditions. Despite these benefits, the use of packaged explosives entails numerous disadvantages. Owing to their presensitized nature, most packaged explosives are classified as Class 1 explosives and as such are subject to the regulations of the Department of Mineral Resources (DMR), Chief Inspectorate of Explosives (CIE), and the Department of Labour (DOL) throughout manufacture, delivery to, and storage on mining operations. This is a significant disadvantage in the current South African environment given the stringent legislation regulating the transportation and control of Class 1 explosives. In addition to increased regulation, the manufacture of pre-packaged explosives necessitates increased investment and operating expenses on the side of the manufacturer, in turn resulting in higher prices than for packaged explosives than for bulk products. As packaged products also require additional labour for offloading and handling on the shaft, transportation
Figure 1 – Effect of coupling ratio on radial compressive strain and fall time (Saffy, 1961) The Journal of The Southern African Institute of Mining and Metallurgy
The application of pumpable emulsions in narrow-reef stoping
Cartridged watergel explosives Watergel or slurry explosives as they are also known were first implemented in South African opencast mines in 1968. Watergel explosives constitute â&#x20AC;&#x2DC;a colloidal suspension of solid AN particles suspended in a liquid AN solution and gelled using cross linking agentsâ&#x20AC;&#x2122; (Aimone, 1992). Gelling agents such as guar gum are used to thicken the explosive matrix while fuel oils are added to the matrix to enable detonation to take place. In order to increase the sensitivity of watergel explosives, sensitizing agents such as TNT, nitrostarch, Composition B, ethyl alcohol, and glass micro-bubbles are added to the formulation, while aluminium is added to increase the energy released during detonation. Due to the comparatively poor intimacy of the fuel and oxidizer phases of watergel explosives, they have a lower VOD and as such a lower detonation pressure than emulsion explosives (Spiteri, 1998). Owing to the lower detonation pressure, the strain wave induced through the detonation of the charge will be lower than that of high-VOD emulsion explosives, while the period of time in which high-pressure gases act on the rock mass will be greater. Typical VOD values for smaller diameter watergel charges fall within the range of 3200 m/s to 3700 m/s (Brinkmann, 1990), while explosive densities may be as high as 1.35 g/cm3, allowing for a high energy concentration during loading (Spiteri, 1998). Due to the presence of water in the watergel formulation, the resistance of watergel explosives to accidental initiation is good.
Cartridged emulsion explosives Emulsion explosives first entered South African underground tabular mining operations in cartridged form in the early 1980s. Emulsion explosives are composed of two immiscible liquids with an aqueous oxidizer phase and a fuel oil phase making up the explosive. During the manufacturing process, the aqueous ammonium nitrate phase of the emulsion is divided repeatedly through a blending process, forming microscopic droplets of oxidizer suspended within the oil The Journal of The Southern African Institute of Mining and Metallurgy
matrix. As a result of the microscopic size and even distribution of oxidizer droplets within the matrix, the intimacy of the oxidizer and fuel within emulsion explosives is better than in ANFO and slurry explosives. During detonation, the high degree of intimacy between the two phases of the emulsion explosive allows for a faster reaction between the fuel and oxidizer, thereby resulting in a higher VOD. As this intimacy allows for a more efficient reaction, smaller volumes of noxious gases are released during detonation (Svard and Johansson, 1999). Another benefit of the high intimacy between the fuel and oxidizer phase of an emulsion is that the addition of mechanically or chemically induced nitrogen gas bubbles is sufficient to sensitize the base emulsion to allow for detonation. For this reason no other sensitizing chemicals need be added to an emulsion, and as a result, the resistance of emulsions to accidental initiation is substantially less than even that of watergel explosives. Typical VOD values for emulsions in small-diameter blast-holes range from 4500 m/s to 5100 m/s (Brinkmann, 1990), with average density values in the region of 1.15 g/cm3 to upper limits as high 1.35 g/cm3 (Spiteri, 1998). As emulsions are insoluble in water, they are ideal for use in wet mining operations where they are able to displace water within wet blast-holes due to their high initial density.
Pumpable emulsion explosives Pumpable emulsions represent the forefront in explosives safety and efficiency in underground mining operations due to their Class 5.1 classification and bulk form. In order to reduce the sensitivity to allow for the UN Class 5.1 classification, pumpable emulsions are manufactured with a higher water content than Class 1 cartridged emulsions. This, together with the larger bubble size introduced through onsite chemical sensitization, results a marginally lower VOD specific to the water content and overall sensitivity to initiation of the emulsion formulation. While pumpable emulsions share many of the performance characteristics of cartridged emulsion explosives, the physical properties of pumpable emulsions allow for greatly improved operational efficiency. Advantages of pumpable emulsions include a reduction in labour for the transportation and loading of explosives, a reduction in charging time due to the high loading rate of charging equipment, and a 100% coupling ratio between the explosive charge and the blast-hole wall. As full coupling increases the efficiency with which the detonation pressure and brisance produced in the detonation front are transmitted into the rock mass, the overall efficiency of the blast is improved (Saffy, 1961). As cartridged explosives possess a coupling ratio of only 80% to 90%, they are unable to achieve the same blasting efficiency as bulk explosives. This is evident in Figure 2, where the shock energy delivered to the rock through the primary reaction zone (PRZ) is calculated and compared for a high-energy cartridged emulsion (RBS 153, VOD 4500 m/s) and average energy pumpable emulsion (RBS 132, VOD 3600 m/s) (Brinkmann, 1990). From the figure it is evident that the efficiency of decoupled explosive charges is impacted initially by a decrease in loaded mass per metre as a result of decoupling, and secondly by a loss in shock energy delivered to the rock mass as a result of the decreased efficiency in the transmission of the strain wave through the air cushion VOLUME 115
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throughout the operations, and during loading of the blastface, considerable additional labour expenses are incurred. One consideration of great significance in the use of packaged explosives is the reduction in coupling experienced during the loading of the explosive. The degree of decoupling is dependent on a number of variables in the manufacturing and loading of the explosive. These factors include the manufactured stiffness of the cartridge, the thickness and strength of the wrapping or sleeving material, the elevation of the operation where it is used, the temperature at time of use, the ratio of the manufactured cartridge size to hole size, and specific operator practices such as the force applied to tamp the cartridge and the number of cartridges inserted in a hole prior to tamping (ISEE, 1998; Saffy, 1961). As a decrease in coupling ratio is directly proportional to the loss in shock energy or the strain wave delivered to the rock mass, a significant reduction in explosive efficiency results from the use of decoupled cartridged explosives (Figure 1). In order to compensate for the poor efficiency of shock transmission to the rock mass, a greater mass of explosive is necessary per blast-hole to break a fixed mass of rock, further compounding the additional time and labour required to load the blast-face.
The application of pumpable emulsions in narrow-reef stoping
Figure 2 â&#x20AC;&#x201C; Theoretical energy comparison of a fully coupled bulk emulsion charge with a decoupled cartridged emulsion in 36 mm blastholes. Cartridged emulsion mass loss is calculated with a coupling ratio of 85% (Pearton, 2014)
around the explosive charge. Pumpable emulsions are thus able to concentrate a greater proportion of the available explosive energy both at the bottom of the hole and throughout the length of the column, facilitating better breakout of the toe and increasing advance rates. An additional advantage limited to bulk emulsions and other non-sensitized liquid explosives systems is the ability to adjust the density and therefore the energy available within each blast-hole. This can be achieved through the use of a single-base blasting agent coupled with a range of specified density sensitizing agents. Although significant advantages are available through the use of bulk explosives systems, the sensitization of bulk explosives at the blast-face presents a degree of risk in daily blasting operations. The introduction of loaders or charging equipment necessary for the loading of the blast-face increases the opportunity for equipment failure or poor labour practices that could result in insensitive explosive, which would in turn result in undesirable blast results or the complete failure of the blast. In order to reduce the possibility of error, correct equipment selection and operator training is of greater importance with unsenzitised pumpable explosives systems than with pre-sensitized cartridged explosives.
The impact of poor explosive selection on mine profitability The previous section discussed the range of commercial explosives currently available within the South African narrow-reef mining industry. As each of these explosives differs in physical properties and performance characteristics, the effect of each on the rock mass will differ. As underground operations and rock types are not all the same, the optimal explosive for each operation will differ according to the desired set of outcomes for each operation. Should an explosive and round design be chosen without consideration for the broader implications of the selection, the downstream financial implications of the decision could easily exceed the cost of explosives for the mining operations. As downstream implications affect almost all activities within the mine, rock
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breaking is arguably the single most crucial and influential area of the mining operation and as such will have the greatest impact on the generation of profits (de Graaf, 2010). According to Brinkmann (1994), the three most important considerations in daily blasting operations include the advance achieved per blast, the fragmentation of ore, and the degree of overbreak experienced in stoping operations. These three considerations are influenced by geological and environmental conditions, the energy within the blast, the quality of drilling and blasting practices, and the performance characteristics of the explosive. While these factors may have the greatest influence on profits within a mining operation, they are often overlooked by production personnel due to lack of awareness, production pressure, and the demand for direct savings on explosives to meet short-term financial targets (Prout, 2010).
The importance of advance per blast and blasting rate The advance achieved per blast is vital to the success of a mining operation as it is directly responsible for the liberation of payable ore from the solid rock mass (Cunningham and Wilson, 1991). Advance per blast is affected by multiple factors, including the properties of the rock mass, geological considerations, blast design, drilling accuracy, explosives selection, the initiation system, the timing of the round, and the use of effective stemming products (Prout, 2010). These factors when applied in the correct manner increase the breakout of the toe of the blast-hole and the overall efficiency of the blast. Through calculation, Cunningham and Wilson (1991) proposed that for underground narrow-reef operations, only a small increase in advance is required to justify the use of a more expensive explosive in order to improve the general results of the blast. Given current commodity prices, this increase in advance is often no more than millimetres in length. Table I compares the cost of explosives to the revenue generated through a single blast on a 30 m production panel within a gold mining operation. It is evident from the calculation that with an average grade of 6 g/t, the cost of explosives will be recovered in the first centimetre of advance. From this calculation it can be seen that the best suited explosive should be selected for a blasting application, as the direct cost of explosives is negligible when compared to the financial implications of greater advance rates and improved blast efficiency. As the advance achieved in blasting operations is a function of the performance and reliability of an explosive, factors such as the physical properties of explosives also need to the taken into consideration during the selection process. The use of water-soluble explosives such as ANFO in high humidity or wet operations presents the risk of blast failure and the loss of advance should the sensitivity of the explosive be reduced through the hydroscopic properties of the explosive (Mulke, 1966). Should only one blast in one hundred fail as a result of poor explosive selection, resulting in a loss in advance of only one metre, this would represent an equivalent loss in advance of one centimetre per panel per blast. Through the calculation above it can be seen that the loss of this single centimetre of advance per blast represents a financial loss greater than the cost of explosives for every blast and thus would have justified the use of an explosive at The Journal of The Southern African Institute of Mining and Metallurgy
The application of pumpable emulsions in narrow-reef stoping Table I
Comparison of revenue and the direct cost of explosives for a panel (gold mining) 30 1.2 1.0 (83%) 2.7 97 6.0 0.6 $ 1260 R 10.20 R 413 248 R 241 006 0.9%
double the expense in order to prevent the failure of a single blast. From this, it is evident that the direct cost of explosives is negligible when compared to the financial implications of daily blasting operations. Any loss in advance experienced within a mining operation represents a piece of ground that needs to be drilled and blasted a second time (Brinkmann, 1994).
Fragmentation and mine call factor As previously discussed regarding the advance per blast, multiple factors are responsible for the degree of fragmentation achieved in blast results. The most important factors in determining the fragmentation within a specific round design include the specific energy of the explosive, the powder factor within the round, the timing of the round, the quality of drilling and blasting practices, and the VOD of the selected explosive (Prout, 2010; Lindsay, 1991). While the mass and VOD of the explosive determine the extent of fracturing surrounding the blast-hole, Brinkmann (1994) found by experimentation that the specific energy within the round had by a significant margin the greatest effect on the size of fragmentation achieved in the blast. This is illustrated in Figure 3, where the specific energy per cubic metre is plotted against fragment size for 10%, 50%, and 90% screen pass rates (Brinkmann 1994). Due to the increase in pressure within the blast, rock particles will be accelerated to higher velocities and the size of rock fragments will be further reduced on collision with the excavation walls (Brinkmann, 1994). From his experiments Brinkmann (1994) noted a practical limit to the specific energy that could be applied in an attempt to reduce the maximum fragment size achieved in the blast. As indicated in Figure 3, only a small reduction in large particle size is experienced for increasing specific energy beyond approximately 8 MJ/m3. In determining the required fragment size for a specific mining operation, logistical considerations, the geological properties of the rock mass, and the distribution of mineralization in the ore need to be taken into consideration (Brinkmann, 1994). Fragment size is of particular importance for carbonaceous gold reefs on the Witwatersrand due to the detrimental effect of excessive fragmentation on mine call factor. Several case studies have highlighted the severe financial implications that can result from the excessive use of high-energy explosives in such operations (Kruger, 2010; The Journal of The Southern African Institute of Mining and Metallurgy
Burden (m) Lines Blast-holes per face Mass per hole (kg) Mass per face (kg) Powder factor (kg/m3) Cost per kg explosive Cost per fuse Cost of explosives Cost of accessories Total cost of explosives Cost per ton broken
0.5 2 120 0.8 96.0 2.1 R 10.00 R 10.00 R 960 R 1200 R 2160 R 22.22
Brinkmann, 1994). Another repercussion of excessively fine fragmentation is increased operating expenses for autogenous mills due to the requirement for additional steel balls (Brinkmann, 1994). Excessively large fragmentation will similarly have an impact on the operating costs of the mining operation. As the powder factor of the blast increases, within acceptable limits, the average fragment size produced by the blast will decrease, increasing the efficiency of handling and size reduction activities downstream. Areas of low efficiency and increased expenditure as a result of oversize ore in the muck pile include (Prout, 2010; Cunningham and Wilson, 1991; Brinkmann, 1994): ➤ Damage to support and blasting barricades ➤ Secondary blasting activities ➤ Cleaning cycle times ➤ Efficiency and wear on scrapers ➤ Grizzly maintenance costs ➤ Orepass blockages ➤ Maintenance of loading boxes ➤ Equipment running costs ➤ Crusher throughput and maintenance. From the above discussion it is clear that a fundamental understanding is required of the effect of fragmentation size on the profitability of a specific mining operation in order for
Figure 3 – Influence of specific energy on fragment size in blast results (Brinkmann, 1994) VOLUME 115
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Panel length (m) Panel height (m) Advance (m) Rock density (kg/m3) Tons per blast (t) Average grade (g/t) Gold produced per blast (kg) Gold price ($ per oz) Exchange rate (R/$) Rand gold price Revenue per blast Explosives cost as % of revenue
The application of pumpable emulsions in narrow-reef stoping the correct explosive and round design to be selected for the application. Given the financial implications of fragmentation on the profitability of an operation, it is essential to understand the effect of both excessive fines and oversize material on the profitability of the mine (Brinkmann, 1994).
Overbreak and dilution One of the greatest areas of concern in narrow-stope mining operations is the degree of overbreak. Overbreak results from the penetration of high-pressure gases into the rock mass surrounding the excavation, resulting in the breakout of excess rock from the hangingwall (Brinkmann, 1994). As with advance and fragmentation, the primary factors responsible for the degree of overbreak include the energy of the explosive, the energy within the round design, the quality of drilling and blasting practices, sequential firing of blastholes, and the VOD of the selected explosive. As the powder factor within a round design increases, the magnitude of the shock wave and the volume of high-pressure gases produced within the blast-hole increase. On detonation, these highpressure gases penetrate the hangingwall resulting in the breakout of the hanging. As rock type and geology play a significant role in the extent of overbreak, these factors need to be taken into consideration during the selection of explosives and blast designs. A study undertaken by Cunningham and Wilson (1991) on the comparative overbreak experienced through the use of Dynagel and ANFO explosives on a gold mining operation revealed an average overbreak of approximately 18% for Dynagel in comparison to approximately 33% for ANFO. Despite the similar VODs of Dynagel and ANFO explosives, a greater level of overbreak was experienced through the use of ANFO as a result of the overcharging of blast-holes (Cunningham and Wilson, 1991). This comparison illustrates the importance in controlling the energy within the blast in order to prevent overbreak. As higher VOD explosives produce a greater strain wave with a shorter period within the blast-hole, limited time is available for the penetration of high-pressure gases into rock strata surrounding the excavation. Higher VOD explosives can thus be applied together with lower overall energy and reduced burden sizes in order to control the extent of damage to the hangingwall in stoping operations (Lindsay, 1991). Given their large width-to-height ratio, narrow-reef operations are particularly sensitive to overbreak due to the additional volume of rock that will be broken out as a result of only minor levels of overbreak in the hanging (Brinkmann, 1994). As overbreak is largely responsible for the liberation of waste, payable ore is diluted, decreasing the head grade and increasing all expenses associated with the downstream handling and processing of ore (Prout, 2010; Swart, Human, and Harvey, 2004; Brinkmann, 1994). Through overbreak, mine infrastructure is indirectly allocated to the handling and processing of waste rock and the production of gold is therefore restricted. This increases expenses incurred through activities such as tramming, shaft expenses and time limitations, mucking, and additional support requirements, all in proportion to the level of overbreak, thereby limiting potential revenue through reduced gold production (Prout, 2010; Cunningham and Wilson, 1991). Due to ore processing inefficiencies, an increase in the volume of rock processed to recover a specific quantity of
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gold will in addition result in an increase in the total gold losses incurred (Pickering, 2005). This is a result of gold being trapped within the larger volume of rock and concentrate during the beneficiation process. As expenses related to the handling and processing of ore increase and the actual mass of gold extracted in the plant decreases, the effect of overbreak on the bottom line of a mining operation is significant. â&#x20AC;&#x2DC;If mining width is not controlled the profitability of the mining operation will sufferâ&#x20AC;&#x2122; (Pickering, 2005).
Theft of explosives One of the greatest factors driving the implementation of Class 5.1 blasting agents within South African mining operations is the theft of commercial explosives for use in illicit activities. As cartridged explosives are pre-sensitized and easy to handle, they have become the preferred explosive for use in activities such as ATM bombings and illegal mining. In an attempt to reduce the extent of crimes committed with commercial explosives, the Inspector of Mines, under the auspices of the Department of Mineral Resources (DMR), has targeted the control of explosives throughout commercial blasting applications in an attempt to prevent the flow of Class 1.1 cartridged explosives from mines into the community. Despite the continued focus of the police on the theft of explosives and the increase in arrests seen in recent years, levels of ATM bombings are still high, presenting an unacceptable risk to the community. This trend, as evident in Table II, has become one of the greatest motivations behind the increase in Section 54 mine stoppages experienced in recent years. Should the improper control of a Class 1 explosive on the operation result in a Section 54 notice being issued to the mine a significant loss in revenue will be incurred. As the average downtime experienced through a Section 54 notice is 3.9 days and over this period three blasts are potentially lost per panel, the loss in revenue per notice totals 1.6% per annum. This equates to a loss in revenue of R815 298 per Section 54 per panel, thereby exceeding the total cost of explosives for the panel for the year. As Class 5.1 blasting agents are not explosives until sensitized at the face it is not possible for them to be used in criminal activities without the correct components and equipment required for their sensitization. As the use of this equipment in such applications is highly implausible, criminals will continue to use pre-sensitized cartridged explosives until it is no longer possible to do so. The viscous nature of pumpable emulsions both before and after the sensitization process also prevents the relocation of
Table II
Number of ATM bombings per year in South Africa Fiscal year 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 (South Africa.info, 2012 and ISS, 2011)
Number of ATM bombings 431 387 247 399 251
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The application of pumpable emulsions in narrow-reef stoping Table III
Impact of Section 54 notices on gold mine revenue Value per panel Panel length (m) Panel height (m) Drilled length (m) Volume per blast (m3) Rock density (t/m3) Tons per blast Recovered grade (g/t) Gold recovered per blast (kg) Gold price ($ per oz) Exchange rate R/$ Value per blast (R)
30 1.2 1.13 40.6 2.7 110 6 0.658 1260 10.2 271 766
Loss of revenue – Section 54 Blasts per year (per panel) Revenue per year (per panel) (mill) Lost blasts per section 54 (per panel) Loss in annual revenue per Section 54 (per panel)
192 52.2 3 R 815 298 1.6%
Loss of revenue – blast performance 1% failure of blasts as % of explosives costs 1 cm loss in advance as % of explosives costs
R 2718 R 2265
106% 88%
sensitized emulsion without disturbing the chemically induced nitrogen gas bubbles within the sensitized emulsion. As the disturbance of these gas bubbles would return the emulsion to its non-explosive state, it would again become undetonable and impossible to use in illicit activities. Given the implications of explosives selection and the importance of the leveraging effects of explosives as discussed above, the correct application of explosives through correct drilling and blasting practices is fundamental to the success of any blasting operation. As production personnel are often unaware of the broader implications of their decisions, significant financial implications often originate in daily blasting activities. The state of health of a mining operation is determined by the effectiveness of daily drilling and blasting activities on the operation (Cunningham and Wilson, 1991).
skilled technician with each unit in order to ensure the correct performance of the charging unit. Given the inflexibility of narrow-reef operations, a large number of pumps are necessary for the implementation of pumpable emulsions on such operations. As a result it is no longer possible for every portable charging unit (PCU) to be accompanied by a trained technician to ensure the quality of explosive manufactured at the blast-face. With this in mind, the reliability of the PCU and its ability to deliver consistent sensitized emulsion without continual calibration were deemed essential to the success of the project. Given the level of skills and training within the workforce, safe operation of the pump technology was paramount throughout all operating conditions and all possible failure modes. All possible risks were to be identified, and multiple fail-safe modes incorporated into the charging equipment design so as to eliminate the possibility of dangerous pumping conditions. As technicians were no longer available during daily charging operations, it would no longer be possible for skilled personnel to check the quality of explosive delivered to each blast-hole during charging operations. For this reason a decision was made to remove the ability of individuals to adjust the manufacturing parameters of sensitized emulsion in the underground environment. In order to allow this to take place, it was essential that the charging equipment delivers consistent and repeatable results such that uniform settings applied to all charging equipment would produce consistent sensitized emulsion on all pumps in use throughout the operation. This outcome needed to be achieved despite variable operating conditions that included temperature fluctuations and changing air or hydropower pressure throughout the operation based on both workplace and time of day.
Operation of the narrow-reef emulsion system As shown in Figure 4, the narrow-reef emulsion system has been specifically designed for use in confined stoping operations. The system utilizes re-useable bags to supply
The development of new pump technology for tabular mining operations
The Journal of The Southern African Institute of Mining and Metallurgy
Figure 4 – Portable pump as implemented in the Rand Uranium narrowreef trial VOLUME 115
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Possibly the greatest challenge faced in the introduction of pumpable emulsion systems to narrow-reef operations is the management of pump technology required for their implementation. As pumpable emulsions are transported underground as a Class 5.1 oxidizer, the ability to ‘manufacture’ explosives in the underground environment is determined almost entirely by the reliability and consistency of the equipment used in charging operations. While it is comparatively simple to guarantee the quality of explosives manufactured at a central production facility, the ability to identify ‘out of spec’ sensitized emulsion in underground operations depends on the ability and training of the pump operator. Traditional emulsion charging units previously utilized for the implementation of pumpable emulsions on mechanized underground operations are complex machines, and as such necessitate the allocation and training of a
The application of pumpable emulsions in narrow-reef stoping emulsion and sensitizer to the pump, allowing the emulsion system to be used in previously inaccessible areas within mining operations. The PCU has a weight of only 14 kg, allowing the pump to be carried by a single operator, and is able to deliver a fixed mass of explosive per blast-hole through the activation gun on the charging lance. As the PCU utilizes sealed emulsion bags that do not require continuous re-filling by a charging assistant (such as in the case of open tanks), only a single operator is required for the operation of the PCU.
Distribution of emulsion BME’s Megapump emulsion is delivered to site in 30 t tankers and either stored in a silo on surface or pumped through a vertical pipeline to an underground storage facility. When applicable, the emulsion is pumped into emulsion transfer cassettes for transportation through the shaft before being transferred into refilling stations at the entrance to the working places. When required, emulsion bags can be refilled from the refilling stations and transported into the panel to be connected to the PCU for loading the stope face. One on the greatest advantages available through the use of the BME PCU is the Closed Emulsion SystemTM used to deliver emulsion to the pump. By eliminating the use of polyethylene bags and open containers for the transportation of emulsion to the pump, the Closed Emulsion SystemTM is able to eliminate waste during the refilling of emulsion containers and while transferring emulsion into the PCU. In addition to the considerable saving through the elimination of waste, BME’s Closed Emulsion SystemTM also acts to prevent rock and foreign objects from contaminating the emulsion, reducing the risk of damage to the PCU. Key to the success of the Closed Emulsion SystemTM is the high stability and long shelf life of BME’s Megapump emulsion formulations. As Megapump can be pumped
multiple times without damaging the integrity of the emulsion, it is possible for it to be pumped through multiple transfer tanks before being pumped into emulsion bags for transport to the blast-face. In addition, the high stability of Megapump emulsion allows for it to be pumped through the shaft, reducing the shaft time necessary for the transportation of explosives and increasing shaft availability.
The influence of equipment on the feasibility of the pumpable emulsion system When considering the overall cost of an explosives system, three broad areas of costs need to be borne in mind. These three areas include the direct costs of explosives; the logistical, capital, and operating expenses for the system; and the downstream implications of the explosive system on daily mining activities and revenue generation. While bulk pumpable explosives systems are able to offer a reduction in the direct cost of explosives, logistics, and storage requirements, and allow for increased levels of efficiency throughout the mining cycle, initial capital is required for the procurement of charging equipment and storage facilities. In order to justify the increase in capital expenditure required for the implementation of the pumpable emulsion system, an adequate level of equipment utilization needs to be achieved in order to offset the costs incurred through the implementation of the system. The importance of utilization in the overall cost of the explosives system is illustrated in Figure 5. As the capital and maintenance expenses required for the implementation of a charging unit increase, the utilization of charging unit also needs to increase in order to offset the increase in fixed and operating expenditure. Although costs will vary for different mining operations, Figure 5 illustrates the importance of maximizing the utilization of charging equipment. Manufacturing and maintenance data used in the calculation of the cost curves was obtained from original
Figure 5 – Effect of equipment utilization on the overall cost of explosives (calculation excludes explosives waste, initiation systems, and magazine facilities)
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The application of pumpable emulsions in narrow-reef stoping
Conclusion Through the comparison of commercial explosives available for use in narrow-reef mining operations, a number of improvements in safety and operational efficiency have been proposed through the implementation of pumpable emulsions. Arguably the greatest advantage of pumpable emulsions lies in their UN Class 5.1 classification. This classification has less stringent requirements for legislation and control than those applicable to Class 1 explosives, allowing for considerable advantages throughout the transportation and storage of blasting intermediates. Additional advantages of the non-explosive classification of the system are evident in the prevention of the theft of explosives and the downstream use of commercial explosives in criminal activities. Class 5.1 blasting intermediates can be transported with other materials, saving tramming and shaft time as well as allowing for longer storage periods underground. The bulk nature of pumpable emulsions gives them a number of advantages over pre-packaged explosives systems. Of greatest significance in the use of bulk explosives is the full coupling of the explosive within the blast-hole. Through full coupling, pumpable emulsions are able to increase the energy available at the toe of the blast-hole, as well as the efficiency with which shock energy is transmitted from the explosive into the surrounding rock mass. From this study it is evident that pumpable emulsions are able to provide narrow-reef operations with increased levels of flexibility, efficiency, and control that are unavailable or limited through the use of alternative commercially available explosives. This increase in performance and efficiency throughout the mining operation renders pumpable emulsions a financially desirable alternative to existing explosives systems within the narrowreef environment.
References BRINKMANN, J.R. 1990. An experimental study of the effects of shock and gas penetration in blasting. Third International Symposium on Rock Fragmentation by Blasting, Brisbane Australia, 26-31 August 1990. The Journal of The Southern African Institute of Mining and Metallurgy
BRINKMANN, J.R. 1994. Controlled blasting and its impact on profits. School: Drilling and Blasting in the Narrow Reefs and their Effect on the Profitability of Gold Mines, Welkom, South Africa. South African Institute of Mining and Metallurgy, Johannesburg. CANADIAN INDUSTRIES LIMITED (CIL). 1968. Blasters Handbook. 6th edn. CIL Explosives Division, Montreal. CUNNINGHAM, C. and WILSON, J. 1991. Blast surveys: getting to grips with realities at the rockface. Rescue ‘91: Survival Initiatives for the Mining Industry, Welkom, South Africa, 16 June 1991. South African Institute of Mining and Metallurgy, Johannesburg. DE GRAAF, W. 2010. Explosives. Drilling and Blasting 2010, Muldersdrift, South Africa, 8 June 2010. Southern African Institute of Mining and Metallurgy, Johannesburg. DYNO NOBEL. 2006. Trench blasting with dynamite. Trench Blasting Guide. Dyno Nobel Inc. www.dynonobel.com HUSTRULID, W.A. 1999. Blasting Principles for Open Pit Mining, General Design Concept. AA Balkema, Rotterdam, Netherlands. ISEE. 1998, Blasters Handbook. 17th edn. International Society of Explosives Engineers, Cleveland, Ohio. ISEE. 2011. Blasters Handbook. 18th edn. International Society of Explosives Engineers, Cleveland, Ohio. KRUGER, D. 2010. Blasting explosives for narrow reef stoping of gold. Drilling and Blasting 2010, Muldersdrift, South Africa, 8 June 2010. Southern African Institute of Mining and Metallurgy, Johannesburg. LINDSAY, R.A. 1991. Improved explosives technology, has it been worth the effort? Rescue ‘91. Survival Initiatives for the Mining Industry. South African Institute of Mining and Metallurgy, Johannesburg. MULKE, H.C. 1966. The measurement and analysis of the velocity of detonation of ANBA in small diameter drill holes. Thesis, University of the Witwatersrand. PICKERING, R.B.G. 1996. Colloquium:.Deep Level Mining - the Challenges. South African Institute of Mining and Metallurgy, Johannesburg. PICKERING, R.G.B. 2004. .The optimization of mining method and equipment. International Platinum Conference: ‘Platinum Adding Value’, Sun City, South Africa, 3-7 October 2004. Symposium Series S38. South African Institute of Mining and Metallurgy, Johannesburg. pp. 111–116. PEARTON, S. 2014. Evaluating the viability of pumpable emulsion explosives for use in narrow reef mining operations. MSc Research Report. Department of Mining Engineering, University of the Witwatersrand. PROUT, B. 2010. Choosing explosives and initiating systems for underground metalliferous mines. Drilling and Blasting 2010, Muldersdrift, South Africa, 8 June 2010. Southern African Institute of Mining and Metallurgy, Johannesburg SAFFY, A.A. 1961. An experimental Investigation of the factors influencing the efficient use of explosives in short drill holes. Thesis, University of the Witwatersrand. SPITERI, 1998. A new gereration Watergel explosive. Colloquium, Explosives – What’s New, University of the Witwatersrand, 29 July 1998. University of the Witwatersrand and South African Institute of Mining and Metallurgy. SVARD, J. and JOHANSSON, C. 1999. How environmental and transport regulations will effect blasting - explosives of the future. Fragblast 1999. Sixth International Symposium for Rock Fragmentation by Blasting, 8-12 August 1999. South African Institute of Mining and Metallurgy, Johannesburg. ◆ VOLUME 115
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equipment manufacturers (OEMs), and comparative labour and logistics costs include the cost of unit operators and technicians for mechanized equipment as well as the equipment necessary for the transport of explosives throughout the operation. As charging equipment cannot easily be moved within the narrow-reef environment and blasts are limited in size and undertaken only once per day, the utilization of equipment in the narrow-reef environment will be inherently poor. Under such conditions, the quantity of explosive that can be pumped through a charging unit will be limited to a range of 1 to 5 t per month, depending on the requirements of the panel. In order to allow for the feasible implementation of the narrow-reef emulsion system, the system not only needed to achieve the technical requirements discussed previously, but also needed to cost considerably less than traditional charging equipment. For this reason design of the PCU was optimized on an ongoing basis such that manufacturing and maintenance expenses could be reduced to acceptable levels.
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http://dx.doi.org/10.17159/2411-9717/2015/v115n6a5 ISSN:2411-9717/2015/v115/n6/a5
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions exposed to sulphuric acid and sodium chloride media by J. van der Merwe*† and D. Tharandt*†‡
The corrosion behaviour of 304L stainless steel laser-cladded with various amounts of ruthenium (Ru) was evaluated in solutions of sulphuric acid and sulphuric acid plus sodium chloride at 25°C and 45°C by open-circuit potential and cyclic potentiodynamic polarization tests. In general, the addition of Ru to the stainless steel increased its corrosion resistance in 1 M H2SO4, as well as in 1 M H2SO4 plus 1 wt% NaCl. This was observed for a number of parameters such as corrosion rate, corrosion potential, open-circuit potential, and current density. However, increasing the amount of Ru added beyond a certain level did not result in further improvement in corrosion protection. For each environment there is an optimal Ru concentration for the best corrosion protection. For example, in 1 M H2SO4 at 25°C, 2.44 wt% Ru shows the least active surface in terms of corrosion. Further research into ruthenium coatings on stainless steels is recommended. Keywords corrosion protection, ruthenium, laser cladding, 304 stainless steel.
Introduction Corrosion is responsible for significant economic loss in all types of industries due to equipment failures and additional maintenance requirements. It can be prevented or reduced by applying surface coatings to the metal. The evaluation of a more corrosion-resistant surface coating on 304L stainless steel is the subject of this investigation. It is well known that the corrosion resistance (electrochemical and pitting corrosion) of all types of stainless steels is significantly increased by alloying it with small amounts of platinum group metals (PGMs) (Sherif et al., 2009). Ru is by far the least expensive metal of the PGM family and therefore is most applicable in industry for the passivation of stainless steels. It has been observed that the addition of small amounts of Ru improves the corrosion resistance of stainless steel (Potgieter, Ellis, and van Bennekom, 1995). Bulk alloying is still considered expensive, and since corrosion is a surface phenomenon, recent research (Lekala, van der Merwe, and Pityana, 2012) indicates a tendency to add the alloy only to the surface, where corrosion protection is most required. The Journal of The Southern African Institute of Mining and Metallurgy
Experimental procedure Test materials Stainless steel 304L base-plate 5 mm in thickness was used as the substrate for all the test samples. A mixture of 304L stainless steel powder and ruthenium powder was used to clad the base-plate using a laser surfacecladding technique. The ruthenium powder was added to the stainless steel powder in varying ratios to obtain target Ru contents in the coating of 1 wt%, 2 wt%, 3 wt%, 4 wt%, and 5 wt%.
* School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa. † DST/NRF Centre of Excellence for Strong Materials, University of the Witwatersrand, Johannesburg, South Africa. ‡ Worley Parsons, Johannesburg. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Platinum Conference 2014, 20–24 October 2014, Sun City South Africa. VOLUME 115
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Synopsis
It is now generally known (Potgieter, Ellis, and van Bennekom, 1995) that during active corrosion, ruthenium additions increase the resistance of stainless steel to anodic dissolution and lower the hydrogen overpotential. This implies that ruthenium inhibits the corrosion of the alloy by a combination of these two mechanisms. During active dissolution, ruthenium increases the corrosion potential and lowers the critical as well as the passivation current density. Potgieter, Ellis, and van Bennekom (1995) showed that stainless steel alloyed with minor ruthenium additions passivates spontaneously due to the formation of a stable passive surface layer with a significantly increased corrosion resistance. This shifts the corrosion potential of these alloys towards more noble (more positive) values. The mechanism of corrosion also depends on the medium of exposure.
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions The plates were plasma-cut into approximately 40 x 60 mm sections and cleaned with acetone before cladding. The cladded portion was 20 x 30 mm, providing approximately 600 mm2 of cladding. Figure 1A shows the stainless steel after laser cladding, from which the samples were cut.
Laser surface alloying technique The laser cladding was performed using a 4.4 kW Rofin Sinar diode-pumped Nd:Y AG laser. The 1.064 μm radiation was delivered via a 400 μm core diameter step index optical fibre to a 200 mm focal length collimator. The collimated beam was focused with a 300 mm focal length lens. The optical assembly was mounted on a KIKA KR60L30HA sixaxis articulated arm robot to control the welding process. The laser spot size was 2 mm in diameter. The stepover for all the samples, i.e. the centre-to-centre distance of successive weld beads, was 0.8 mm. The laser power used was 1200 W and the scan speed was 2 m/min bi-directional. The carrier shield gas was argon at a flow rate of 3 standard L/min. The cladded plate was cut into a number of approximately 5 x 5 mm samples for assessment of the alloyed surface and the cross-sectional microstructure, as well as electrochemical tests.
Scanning electron microscopy (SEM) The samples for SEM tests were mounted separately in Bakelite® powder using a mounting press. Two samples for each composition were mounted – one such that the alloyed surface could be examined, and the second sample such that the cross-section of the weld could be examined. The samples were ground in stages to 1200 grit size using silicon carbide paper, as were the samples for electrochemical testing. The samples were then polished using 3 μm diamond powder on an automated polishing machine. The samples were cleaned with ethanol and dried with compressed air. The clean and dry samples were then electrolytically etched in 10 wt% oxalic acid solution for 30 seconds. The microstructures were evaluated using a Zeiss Axiotech 25 HD microscope. Figure 1B shows a mounted sample used for microscopic evaluation. SEM is a semi-quantitative method of chemical analysis that provides only an indication of composition. The chemical composition of the alloyed surface was measured by scanning its surface area as well as the cross-sectional area of the sample using the energy dispersive spectroscopy (EDS) capability of the Zeiss Sigma field emission SEM. EDS was conducted at a working distance of approximately 8.5 mm and an acceleration voltage of 20.0 kV. The overall composition was determined by averaging the measured compositions.
Figure 1 – Samples used for the experimentation. A: laser-cladded base-plate. B: mounted sample for microscopic evaluation. C: ground sample for electrochemical testing
mounted sample), a platinum counter-electrode, and a silver/silver chloride reference electrode. The electrochemical polarization measurements were carried out by an autolab potentiostat. Nova software was used to simulate the test procedures as well as to analyse the resultant potentiodynamic polarization curves. The potentiodynamic polarization procedure consisted of the following consecutive steps: ➤ Open-circuit potential (E vs time) for 12 hours ➤ Anodic scan from -500 mV to +1100 mV at a scan rate of 1 mV/s ➤ Polarization at -500 mV for 5 minutes ➤ Anodic scan from -500 mV to +1100 mV at a scan rate of 1 mV/s. The tests were conducted according to the ASTM G5 standard. The corrosive environment for each sample was altered by varying the medium and the temperature. The two solutions used were 1 M sulphuric acid and 1 M sulphuric acid with 1 wt% sodium chloride. The samples were exposed at temperatures of 25°C and 45°C; the temperatures were kept constant by a thermostat-controlled water bath.
Results and analysis Energy dispersive spectroscopy The compositions of the laser cladded surfaces, as obtained by EDS, are shown in Table I.
Table I
Compositions of the laser-alloyed surface measured by EDS
Electrochemical tests The samples prepared for electrochemical tests were coldmounted in epoxy resin such that the alloyed surface would be exposed to the corrosive environment. An example of a 1200 grit ground sample, ready for testing, is shown in Figure 1C. The electrochemical tests were conducted in an electrochemical cell consisting of the working electrode (the cold-
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Ru (wt%) Expected
1 2 3 4 5
Sample area 1
Sample area 2
Sample area 3
Average
0.72 0.84 2.90 1.81 5.24
0.16 0.79 3.22 1.80 4.19
2.65 3.70 4.57
0.44 0.82 2.92 2.44 4.67
The Journal of The Southern African Institute of Mining and Metallurgy
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions powder; in those areas the Ru concentration was very close to that expected. In other areas Ru islands were observed where the Ru concentration was up to 100 wt%. A consistent coating thickness was obtained, but the coating was not homogeneous at higher Ru concentrations and could thus not produce a consistent protective layer. Figures 2 and 3 show examples of the images obtained and analysed; the white spots represent pure Ru. The average compositions of these cladded samples are given in Table III.
Table II
Composition of the 304L stainless steel baseplate, wt% Fe Ni Cr Mn
71.0 8.0 19.1 1.4
The stainless steel base-plate was also analysed. The composition is shown in Table II. The EDS results show that the expected Ru compositions were not attained on all samples, and the composition varied significantly between different areas on the same sample. This is a result of the actual cladding procedure, since the variation of the base-plate composition was negligible. On some samples, ruthenium-rich stringers were observed where the ruthenium was well mixed with the stainless steel
Electrochemical testing A number of parameters were used to indicate corrosion resistance. These included the corrosion rate (mm/a); the observed corrosion potential, Ecorr (V); the passivation exchange current density, ipass (A/cm2); the open-circuit potential, OCP (V); and the critical exchange current, icrit (A).
1 M H2SO4 solution at 25°C Increasing the Ru concentration within the cladded layer was expected to improve corrosion resistance. From the log i vs E curve (Figure 4) it is evident that small additions of Ru to the stainless steel surface protect in 1 M H2SO4 solution. The passivation current density for the samples containing Ru is orders of magnitude smaller than when no Ru is added. However, passivation does not occur over a large potential range at a specific current density normally associated with a stable protective film on the surface. For samples with Ru cladding, the current density increases in this passivation region as the potential increases until the transpassive potential is reached. The expected step increases with increasing Ru concentration are not evident. Averaging all the results and looking at a combination of all the abovementioned parameters for corrosion protection, a very clear ranking order is observed. The order of decreasing corrosion resistance in 1 M H2SO4 solution at ambient temperatures is 2.44 wt%, 0.82 wt%, 2.92 wt%, 4.67 wt%, 0.44 wt%, stainless steel blank, and 0 wt% Ru. The stainless steel blank sample (no cladding) shows a typical curve with distinct active, passive, and transpassive regions. The cladded sample with no Ru added indicates very low corrosion resistance in this environment, with a more active corrosion potential indicating that a welded region would be more susceptible to corrosion, as expected. At 0.44 wt% Ru, the benefit of corrosion protection can be seen, and at higher Ru concentrations more noble corrosion potentials are seen, indicating the formation of a more stable passive layer. Samples that exhibited high corrosion potentials had a lesser tendency to corrode since a high potential energy is required to break down or corrode the alloy. High positive
Figure 2 –Surface of the laser-cladded 3 wt% Ru sample
Figure 3 – Cross-section of the laser-cladded 5 wt% Ru sample
Table III
Sample 3 wt% Ru surface 5 wt% Ru cross-section
Fe
Ni
Cr
Si
Mn
Ru
66.26 64.41
9.81 9.53
18.91 18.39
0.79 0.83
1.58 1.60
2.66 5.24
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Compositions of the cladded samples in Figures 2 and 3, wt%
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions
Figure 4 – Log i vs E example curves for different Ru compositions in 1 M H2SO4 at 25°C
Figure 5 – OCP graphs after exposure to 1 M H2SO4 at 25°C
Table IV
Indicators of corrosion rates using average sample measurements Sample
Ecorr obs (mV)
jcorr (nA/cm2)
Polarization resistance
Corrosion rate (mm/a)
OCP (mV)
0.551 3.841 0.166 0.009 0.007 0.016 0.081
-12 -242 134 305 204 336 234
(kΩ) SS blank 0 wt% Ru 0.44 wt% Ru 0.82 wt% Ru 2.44 wt% Ru 2.92 wt% Ru 4.67 wt% Ru
-257 -243 -222 77 134 121 15
47393 287344 14254 776 587 1409 57109
potentials are an indication of spontaneous passivation of the surface of the sample (Papavinasam, 2013). From the trend shown in Table IV, it is clear that the maximum corrosion protection is attained at 2.44 wt% Ru, closely followed by 0.82 wt% Ru (both of which indicated spontaneous
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11.4 3.3 23.3 322.1 826.8 290.4 309.3
passivation), then 4.67 wt% Ru, 2.92 wt% Ru, 0.44 wt% Ru, no Ru, and finally the stainless steel blank. Figure 5 shows that a stable potential was achieved over a short period of time; most notably for the 2.44 wt% Ru sample. The attained potentials with Ru addition were in the The Journal of The Southern African Institute of Mining and Metallurgy
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions Table V
Indicators of corrosion rates using average sample measurements Sample
Ecorr obs. (mV]
jcorr (nA/cm2]
Polarization resistance
Corrosion rate (mm/a)
OCP (mV0
2.98 5.36 6.41 0.56 0.15
-357 -365 54 114 192
(kΩ) SS blank 0 wt% Ru 0.82 wt% Ru 2.92 wt% Ru 4.67 wt% Ru
-336 -352 -269 -239 -193
256383 461480 551340 48370 13089
noble po-tential region. Higher, positive OCPs were obtained with increasing Ru, peaking at 2.92 wt% Ru, followed by 0.82 wt%, 4.67 wt%, 0.44 wt%, and 2.44 wt% Ru.
1 M H2SO4 + 1 wt% NaCl solution at 25°C Addition of sodium chloride to the sulphuric acid solution increased the corrosivity of the environment due to the chloride ions attacking the passive layer formed on the surface of the cladded material. A clearer relationship between increasing Ru concentration and improved corrosion protection became evident. Figure 6 shows that the corrosion potential increased with increasing Ru content. The presence of chloride ions in the solution accelerated the damage to the passive layer. The effects of variations in Ru concentrations in the samples were also observed in the tests with the acid and salt solution. Table V shows a slightly different trend with regard to specific corrosion protection values, but clearly indicates that cladding with stainless steel alone, without any Ru, decreases corrosion resistance, while increasing the amount of Ru added improves the corrosion protection significantly in 1 M H2SO4 with 1 wt% NaCl. Samples with 0.44 and 2.44 wt% Ru addition were damaged during the experimental stage and no further results could be obtained from these. The attained open-circuit potentials were negative only
0.34 0.20 4.74 4.06 20.78
for the cladding without Ru and the stainless steel blank samples, while the addition of Ru brought them into the noble region The clear trend is, in order of increasing corrosion protection: 0 wt% Ru, stainless steel blank, 0.82 wt% Ru, 2.92 wt% Ru, and 4.67 wt% Ru.
1 M H2SO4 + 1 wt% NaCl solution at 45°C At higher temperature (45°C) in an acid environment with the presence of chloride ions there was no significant improvement in corrosion resistance with increasing Ru content. The stainless steel blank as well as the 0 wt% Ru sample behaved similarly, and all samples containing Ru behaved very similarly, as can be seen from Figures 7 and 8. A notable improvement in corrosion protection is observed with the addition of 0.82% Ru, but no additional benefit was observed at higher Ru contents.
Discussion The laser cladding method appears to have resulted in lower Ru compositions in the cladding than in the powder used. Although a uniform coating depth of almost 1 mm was obtained, Ru distribution was not uniform. The high variability of the Ru content of the cladding is the most likely cause of the variability of the electrochemical results, since each fresh surface exposed had a slightly different
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Figure 6 – Log i vs E example curves for different Ru compositions in 1 M H2SO4 plus 1 wt% NaCl at 25°C
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions
Figure 7 – Log i vs E example curves for different Ru compositions in 1 M H2SO4 plus 1 wt% NaCl at 45°C
Figure 8 – OCP graphs after exposure to 1 M H2SO4 plus 1 wt% NaCl at 45°C
composition and structure. Repeatable results were obtained with the stainless steel blank sample, but the cladding introduced significant variability. As can be seen from Figures 2 and 3 as well as Table I, the Ru concentration is highly variable between specific small areas within the plate. It should be noted that the EDS results are only for comparing the various samples – EDS is by no means the most accurate method for determining chemical composition. The polarization curves clearly indicate an improved corrosion resistance with the addition of Ru in 1 M H2SO4 and 1 M H2SO4 plus 1 wt% NaCl, especially at higher temperatures. The reason for this is as follows. Since the over-
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potential of the standard hydrogen reduction reaction is low, the addition of Ru shifts the equilibrium reaction to more active potentials, and this shift is sufficient in a reducing medium to move the system into the passive region and thus reduce the actual corrosion rate of the sample. McGill (1990) also observed this initial rapid corrosion of stainless steel followed by passivation in non-oxidizing acid media such as sulphuric acid (H2SO4). The solution concentration is critical, as too high a concentration results in no passivation since the dissolution rate is too fast (McGill, 1990). Potgieter and Brookes (1995) observed that adding too small an amount of Ru can increase corrosion rates as it increases the efficiency The Journal of The Southern African Institute of Mining and Metallurgy
Corrosion resistance of laser-cladded 304L stainless steel enriched with ruthenium additions of the cathodic hydrogen reaction. Therefore, passivation is induced only if the passivation potential of the PGM-metal alloy is less than the overpotential of the hydrogen evolution reaction on the alloying PGM. Potgieter and Brookes (1995) conclude that there is a maximum amount of ruthenium that can be added to stainless steel to increase corrosion resistance, beyond which no further improvement is gained. The actual amount depends on the exposure medium and temperature. This is corroborated by the results of the current investigation. The OCP was expected to increase with time as the formation of a passive region occurs by the dissolution of oxides that accumulate into a protective layer. Ruthenium is expected to become concentrated on the surface as the other components oxidize, thereby stabilizing the protective layer by preventing it breaking down. It was thus expected that increasing the Ru content would increase that stabilizing effect, consequently increasing the resistance of the material to corrosion. OCP values of the claddings containing Ru were in the positive region, but did not simply increase with increasing Ru content. There is a definite optimal Ru concentration for a particular environment. It is important in the development of any new product or alloy to ensure that it is cost-effective, or there will be only very limited scope for application. In this case, the various compositions of ruthenium in the stainless steel need to be evaluated for cost-effectiveness and compared to other existing materials offering similar levels of corrosion protection. The cost of only the ruthenium metal was taken into account for this evaluation. A 5 wt% Ru coating is required, and it could only be 100–200 μm thick if it were to replace a 316 stainless steel or SAF2205. Such thickness is difficult to achieve at this stage with the laser cladding process. If a 3 wt% Ru coating was sufficient, it could be up to 500 μm thick to compete with SAF2205 on the material costs only. This thickness can be achieved more easily with the laser cladding. A different cladding technique needs to be investigated in order to reduce the thickness of the applied ruthenium alloy. Hastelloy C276, on the other hand, is so expensive that almost any quantity of ruthenium alloyed with 304L stainless steel would be cost-effective providing that it can demonstrate equivalent corrosion protection. Therefore, especially for smaller components, this technology could be very beneficial and improve the lifespan considerably.
potential, OCP, and current density. ➤ Corrosion protection did not improve with increasing additional of Ru beyond a certain level. There is an optimal Ru concentration to be added for a specific environment. By averaging the results a clear ranking order is obtained: for the 1 M H2SO4 solution at 25°C the order of increasing corrosion protection is 0 wt%, 0.44 wt%, 2.92 wt%, 4.67 wt%, 0.82 wt% and best at 2.44 wt%. The addition of 1 wt%NaCl changes that to 0 wt%, 0.82 wt%, 2.92 wt% and 4.67 wt%Ru. ➤ Chloride ions attack the passive layer formed on stainless steel, increasing the corrosion rate. This is also applicable to the Ru-cladded samples, even though corrosion protection is substantially improved. ➤ Increasing the temperature of the 1 M H2SO4 plus 1 wt% NaCl environment to which the samples were exposed increased the corrosivity. Test results showed that the passive layers formed were stable as the temperature increased. A notable improvement in corrosion protection is observed with the addition of Ru under these conditions, but adding Ru above a certain level does not result in any additional benefit for the Ru range observed. ➤ Ruthenium-cladded samples behaved differently in different environments, and hence their application should be carefully selected and evaluated against available types of stainless steel.
References LEKALA, M.B., VAN DER MERWE, J.W., and PITYANA, S.L. 2012. Laser surface alloying of 316L stainless steel with Ru and Ni mixtures. International Journal of Corrosion, vol. 2012. pp. 1–4.
MCGILL, I.R. 1990. Platinum metals in stainless steels – a review of corrosion and mechanical properties. Platinum Metals Review, vol. 34. pp. 85–97.
PAPAVINASAM, S. 2013. Corrosion Control in the Oil and Gas Industry. Elsevier, Amsterdam. Chapter 2. pp. 49.
POTGIETER, J.H. and BROOKES, H.C. 1995. Corrosion behavior of a high-chromium duplex stainless steel with minor additions of ruthenium in sulfuric acid. Corrosion Engineering, vol. 51, no.4. pp. 312–320.
Conclusions
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POTGIETER, J.H., ELLIS, P., and VAN BENNEKOM, A. 1995. Investigation of the active dissolution behaviour of a 22wt% chromium duplex stainless steel with small ruthenium additions in sulphuric acid. ISIJ International, vol. 35. pp. 197–202.
SHERIF, EL-SAYED M., POTGIETER, J.H., COMINS, J.D., CORNISH L.A., OLUBAMBI P.A., and MACHIO C.N. 2009.Effects of minor additions of ruthenium on the passivation of duplex stain-less-steel corrosion in concentrated hydrochloric acid solutions. Journal of Applied Electrochemistry, vol. 39. pp. 1385–1392. VOLUME 115
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➤ The laser surface cladding method was successfully used to add small amounts of ruthenium to a stainless steel cladding in a non-porous and well-adhered layer of uniform thickness. The variability in terms of chemical composition and ruthenium distribution in the cladding need to be improved in future studies. ➤ The addition of Ru to the stainless steel increased its corrosion resistance in 1 M H2SO4, as well as in 1 M H2SO4 plus 1 wt% NaCl. This was observed for a number of variables such as corrosion rate, corrosion
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http://dx.doi.org/10.17159/2411-9717/2015/v115n6a6 ISSN:2411-9717/2015/v115/n6/a6
Fire and brimstone: The roasting of a Merensky PGM concentrate by R.I. Rambiyana*, P. den Hoed†, and A.M. Garbers-Craig‡
Synopsis Four sulphide minerals – pyrite (FeS2), pyrrhotite (Fe1–xS), pentlandite ([Ni,Fe]9S8), and chalcopyrite (CuFeS2) – contain the base metals and most of the iron in concentrates of platinum group metals (PGMs). In the pyrometallurgical processing of PGM concentrates these sulphides form a matte during smelting, and iron and sulphur are removed from the matte during the converting process. This paper discusses the roasting of Merensky concentrate in air before smelting, with the purpose of reducing the matte load to the converter. Roasting tests were conducted in a bench-scale rotary kiln at temperatures from 350°C to 700°C. The concentrate tested contained 17.4% sulphur and consisted of 23% pyrrhotite, 16% pentlandite, 11% chalcopyrite, and 2% pyrite. The particles were fine (d50 = 22 μm), and all the sulphide particles were liberated. Roasting in air at 550°C and 650°C for 20 minutes removed respectively 60% and 70% of the sulphur. The iron in the sulphides was oxidized to Fe3O4 (magnetite) at temperatures below 500⁰C and to Fe2O3 (haematite) at temperatures above 550⁰C. At 700⁰C the bed sintered and copper oxides formed. At temperatures below 450°C oxidation was incomplete: pyrrhotite remained and only 30% of the sulphur was removed. Smelting tests were conducted to assess matte fall and the deportment of copper and nickel to matte. It was evident that roasting resulted in lower matte falls (a drop of approximately 60%) compared with matte falls from unroasted concentrate. The iron and sulphur levels in the matte were reduced to below 3.5% and 22% respectively. This paper also briefly describes the mechanisms by which pyrrhotite, chalcopyrite, and pentlandite are oxidized during roasting. For chalcopyrite, the mechanism proceeds through an intermediate solid solution phase, which extends from Cu1.02Fe1.04S2 to Cu2.04Fe0.72S2 to a copper-rich solid solution of bornite (Cu4Fe1.4S4–Cu2S). The oxidation of pentlandite proceeds through a monosulphide solid solution (Ni0.39Fe0.53S–Ni0.74Fe0.15S) to a solid solution of heazlewoodite ([Ni,Fe]3±xS2). These mechanisms are explored in relation to chemical thermodynamics and microstructures.
m, and the freeboard 8 m, while the grate diameters at Thompson are 5.5 m, and the freeboard 6.4 m. The expanded freeboard begins 6.5 m above the grate (Warner et al., 2007). The roasters at Thompson process approximately 50 dry t/h of concentrate. The bed temperatures of the roasters at Thompson and Sudbury are respectively 600°C and 760°C. Both roasters operate under oxidizing atmospheres of air or oxygen-enriched air. Approximately 40% of the sulphur is removed from the concentrate at Thompson, and 70% at Sudbury (Pandher and Utigard, 2010). Roasting can in principle be applied to PGM concentrates, which besides gangue minerals (mostly minerals in the pyroxene group) comprise pyrite (FeS2), pyrrhotite (Fe1–xS), chalcopyrite (CuFeS2), and pentlandite ([Ni,Fe]9S8). The base metal sulphides are the very same minerals roasted in the copper and nickel industries. In the ConRoast process developed at Mintek, PGM concentrates are dead-roasted to remove all of the sulphur (Jones, 1999). Subsequent smelting under reducing conditions produces a Cu-Ni-bearing alloy, which collects the PGMs. Many of the aims of roasting could be met by partially oxidizing the base metal sulphides. Partial roasting would desulphurize the concentrate, oxidize some or most of the iron, and the roasted concentrate would still produce a matte.
Keywords roasting, PGM, concentrate, pyrrhotite, pentlandite, chalcopyrite, smelting, matte, base metal.
Producers of nickel and copper have been partially roasting concentrates to reduce levels of sulphur and volatile impurities such as arsenic, antimony, and lead for many years (US Environmental Protection Agency, 1995). At Sudbury (Glencore) and Thompson (Vale Inco), concentrate is roasted in fluidized beds (two at each plant) before smelting. The grate diameters of the roasters at Sudbury are 5.6 The Journal of The Southern African Institute of Mining and Metallurgy
* Centre for Pyrometallurgy, Department of Materials Science and Metallurgical Engineering, University of Pretoria, now working for Anglo American Platinum. † Anglo American Technical Solutions. ‡ Centre for Pyrometallurgy, Department of Materials Science and Metallurgical Engineering, University of Pretoria. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Platinum Conference 2014, 20–24 October 2014, Sun City South Africa. VOLUME 115
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Introduction
Fire and brimstone: The roasting of a Merensky PGM concentrate This study focused on partial roasting of a Merensky concentrate. It examined how the conditions of roasting affect the degree of desulphurization, which phases form, how these phases affect matte fall, and the deportment of base metals to the matte. Mechanisms for the oxidation of pyrrhotite, pentlandite, and chalcopyrite were also determined.
Experimental Sample The concentrate sample — a product of flotation — originated from the Merensky Reef in Limpopo Province, South Africa. The bulk modal analysis of the concentrate, as determined with a Mineral Liberation Analyser (MLA), is given in Table I. The main sulphide minerals present in the concentrate were pyrrhotite (Fe1–xS), pentlandite ([Ni,Fe]9S8), and chalcopyrite (CuFeS2); together accounting for 50.1% of the mass of the concentrate. The metal sulphides in sub-samples showed a degree of variation with respect to phase compositions (Rambiyana, 2015). Attaching a fixed set of conditons to a degree of oxidation is therefore problematic.
Figure 1 – View of the rotating-tube furnace (RTF)
Procedure Roasting tests were conducted in a rotating-tube furnace (Figure 1). This reactor provided good gas-solid mixing and contact for the duration of a test and readily dissipated heat from the exothermic reactions. The furnace was fitted with a quartz work tube, 100 mm in internal diameter and 1150 mm long. The work tube was externally heated, which resulted in a hot zone of 550 mm. The tube was fitted with lifters to ensure good gas-solid mixing and contact. The roasting tests were conducted under isothermal conditions at temperatures ranging from 400°C to 650°C and a residence time of 20 minutes. The work tube was purged with air at a flow rate of 42 NL/min. Roasting was conducted with the reactor run in continuous mode.
Table I
Mineral composition of the Merensky concentrate (bulk modal analysis) Mineral
Mass % Base metal sulphides
Pyrrhotite (Fe1–xS) Pentlandite ([Ni,Fe]9S8) Chalcopyrite (CuFeS2) Pyrite (FeS2) Bornite (Cu5FeS4) Other sulphides
22.9 16.0 11.2 2.0 0.1 0.2 Gangue
Enstatite (Mg2Si2O6) Other
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Figure 2 – View of the angular reciprocating capsule (ARC)
The sulphation reactions were tested in an angular reciprocating capsule that was heated in a horizontal splitshell furnace. This capsule rotated about its longitudinal axis, but alternated between clockwise and anticlockwise (Figure 2). The capsule had an internal diameter of 44 mm, a working zone of 120 mm, and a volume of 954 cm3. It was also fitted with lifters. The temperature of the bed was measured with a K-type thermocouple. Tests were carried out on 50 g samples held at 500°C under controlled atmospheres (air at 1 and 2 bar) for one hour. The pressure in the capsule was recorded for the duration of the tests. The capsule provided 0.0148 moles of oxygen for every 1 bar of pressure. Smelting tests were conducted in a vertical-tube furnace. The mullite work tube had an internal diameter of 80 mm and a hot zone of 150 mm. Unroasted concentrate, concentrate roasted at 550°C, and concentrate roasted at 650°C were smelted in alumina crucibles. A flux consisting The Journal of The Southern African Institute of Mining and Metallurgy
Fire and brimstone: The roasting of a Merensky PGM concentrate of 10 g Al2O3 and 10 g CaO for every 100 g of concentrate was added to the charge. The charge was mixed well and smelted at 1500°C for 30 minutes. The sample was furnacecooled.
Analytical techniques The products of roasting and smelting were examined and analysed by an array of techniques. These included X-ray diffraction analysis (XRD); scanning electron microscopy (SEM), using energy dispersive spectrometry (EDS); quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN); and quantification of the amount of ferromagnetic material. XRD analysis was performed with a PANalytical X’Pert Pro powder diffractometer in θ–θ configuration with an X’Celerator detector and variable divergence and receiving slits. The radiation was Fe-filtered Co-Kα (λ=1.789 Å). Phases were identified by means of X’Pert Highscore Plus software. SEM-EDS was performed with a Mineral Liberation Analyser to identify phases and analyse their compositions. QEMSCAN-EDS was used to create mineral maps of the samples. The percentage ferromagnetic material was determined with a Satmagan 135 (Rapiscan Systems). This instrument measures the ferromagnetic signal from a sample and correlates it with magnetite content.
the oxidation of Fe3O4 to Fe2O3 is relatively slow and that the oxidation rate increases with temperature. At temperatures below 500°C oxidation did not proceed much beyond Fe3O4. As temperatures increased from 500°C to 650°C, increasing amounts of Fe2O3 formed and less Fe3O4. The reaction occurred at the interface between pyrrhotite (Fe1–xS) and the iron oxide. The reaction might proceed by means of a shrinking core but, given that the pyrrhotite is porous, it might occur at several loci within each particle. As the conversion of Fe3O4 to Fe2O3 also occurred during the oxidation of the base metal sulphides, it is discussed in more detail in a subsequent section of this paper. This ‘duplex layer’ has been observed in other studies on the roasting of base metal sulphides (Xia, Pring, and Brugger, 2012).
Chalcopyrite (CuFeS2) Chalcopyrite particles in the initial stages of oxidation had
Results Roasting mechanisms Roasting tests focused on two temperatures, 450°C and 650°C. These temperatures were chosen based on data published in the literature for the pertinent systems Fe-Ni-S and Fe-Cu-S. Rates of oxidation are too slow at temperatures below 400°C, while particles sinter and stick to the wall of the work tube at temperatures above 700°C. Tests were run in air at temperature for residence times ranging from several minutes to 25 minutes. Most tests were run with the sample passing through the kiln in 20 minutes. The phase chemistry of the roasted samples was analysed using SEMEDS and XRD. By combining the conditions of roasting and phase chemical results with phase diagrams and thermochemical data, the mechanisms for the oxidation of the three primary sulphide minerals in the PGM concentrate were postulated.
Figure 3 – Backscattered electron (BSE) image of bornite(ss) around a core of chalcopyrite. 1 - Cu0.9Fe0.98S2; 2 - Cu4.73Fe0.94S4
Pyrrhotite (Fe1–xS)
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Figure 4 – Backscattered electron (BSE) image of bornite(ss) occupying a core that was formerly chalcopyrite. 1 - Cu5.77Fe0.24S4 VOLUME 115
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Pyrrhotite reacted with oxygen to form iron oxide. This reaction was rapid compared with the oxidation of the base metal sulphides. In the 400—650⁰C temperature range pyrrhotite disappeared within 20 minutes. Chemical thermodynamics predicts that oxidation should result in the formation of Fe2O3 (haematite) (Muan and Osborn, 1965: Figure 12). However, Fe3O4 (magnetite) formed as an intermediate phase. These observations are consistent with the assumption that in the temperature range 400–650⁰C,
Fire and brimstone: The roasting of a Merensky PGM concentrate
Figure 6 – TG-DTA curves of Merensky concentrate roasted in air and argon (heating rate 5°C/min)
Figure 5 – Backscattered electron (BSE) image of a particle of mss and iron oxide after oxidation (formerly pentlandite). 1 - Ni0.69Fe0.18S; 2 Ni0.78Fe0.12S; 3 – Fe oxide rim
compositions at the iron-rich end of the bornite solidsolution series. Chalcopyrite occupied a core that shrunk as bornite(ss) developed around it (Figure 3). Iron oxide (Fe2O3 or Fe3O4) formed on the surface of the bornite(ss). The disappearance of chalcopyrite was rapid. Further oxidation proceeded by the reaction of oxygen with iron and sulphur in bornite(ss), which retained its structure but became increasingly depleted in iron and (somewhat less) in sulphur (Figure 4). It was difficult to accurately determine the change in bornite(ss) phase compositions radially with SEMEDS, as the particles were too fine. It is therefore not clear whether the chalcopyrite disappeared before bornite(ss) started to oxidize. Cu-rich end-members of the bornite(ss) were not detected with chalcopyrite cores in a particle. Reactions were faster at higher temperatures. Chalcopyrite disappeared after roasting for 20 minutes at 450°C and 650°C. However, the bornite(ss) was richer in copper at higher temperatures and richer in iron at lower temperatures after 20 minutes. This empirical evidence is in disagreement with the phase relations in the system Fe-Cu-S (Rambiyana, 2015). An intermediate solid solution (iss) was found to be stable at temperatures above 400°C, occupying a region of the phase diagram between CuFeS2 (chalcopyrite) and the bornite(ss). The presence of iss could not be confirmed in this study.
Pentlandite ([Ni,Fe]9S8) An initial rapid reaction resulted in pentlandite losing iron through oxidation to Fe3O4 and transforming into the mss (monosulphide solid solution) phase, which became progressively depleted in iron through further oxidation. An individual particle could exhibit variable compositions of the mss phase (Figure 5). More Fe3O4 (magnetite) formed and was oxidized to Fe2O3. The Ni-rich mss finally became unstable and underwent a structural change to heazlewoodite (ss). This agrees with the work of Zamalloa and
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Figure 7 – Change in pressure and temperature in the angular reciprocating capsule during the roasting of Merensky concentrate
Utigard (1996), who identified (Ni,Fe)3±xS2 (heazlewoodite) in particles roasted at 747⁰C. This reaction sequence was observed at all temperatures above 400⁰C. Pentlandite disappeared after 10 minutes at all temperatures above 400⁰C. Heazlewoodite (ss) appeared after 20 minutes at temperatures above 650⁰C.
The absence of sulphates The formation of sulphates during roasting is undesirable as sulphates retain sulphur. Sulphate formation can, however, be avoided by considering the thermodynamic relations depicted in the predominance diagrams of Fe-Ni-S-O and FeCu-S-O. High partial pressures of SO2 and O2 tend to stabilize the metal sulphates FeSO4 and Fe2(SO4)3. To avoid the formation of sulphates during roasting of base metal sulphides, pSO2 should therefore be kept low (<1%). However, higher partial pressures of SO2 may be established inadvertently – fixed beds of fine particles promote high SO2 partial pressures within the bed, as SO2 is unable to diffuse rapidly to the free surface of the bed. Examples include particulate beds contained in crucibles or boats used in thermogravimetry and in work utilizing muffle furnaces. Disturbing the bed frees the ‘trapped’ SO2. This was The Journal of The Southern African Institute of Mining and Metallurgy
Fire and brimstone: The roasting of a Merensky PGM concentrate accomplished in the rotating-tube furnace by the action of the lifters in the work tube. Compressed air was also forced in at the feed end of the work tube and extracted by means of an extraction duct inserted at the discharge end. A high pO2 and a low pSO2 were therefore maintained in the tube. No sulphates were detected in any of the products from tests conducted in the rotating-tube furnace. Roasting of concentrate in a thermobalance and the angular reciprocating capsule, however, produced metal sulphates, as indicated by a gain in mass shown by the thermobalance (Figure 6). The sample started to gain mass from approximately 350°C, and the gain became significant between 400°C and 620°C (Figure 6). It is at these temperatures, under high SO2 partial pressures, that metal sulphates can be expected to form. At temperatures greater than 620°C the sample lost mass. This loss is associated with the decomposition of sulphates, which are thermodynamically unstable at high temperatures. The temperatures for the thermal decomposition of ferrous sulphate, copper sulphate, and nickel sulphate are reported in the literature (Kolta and Askar, 1975). The two peaks in the TG curve — one at 725°C, the other at 830°C — indicate the decomposition of different sulphates at their respective thermal stability limits. Sulphates were also formed in the angular reciprocating capsule, as high SO2 partial pressures were established by maintaining a closed system. The pressure in the capsule increased while the capsule was being heated to temperature (Figure 7). The initial increase in pressure can be attributed principally to the thermal expansion of gas (air) in the freeboard. The pressure dropped when the temperature exceeded approximately 200°C, and the final recorded pressure was lower than the pressure in the system at the beginning of the test. From these measurements, and from an understanding of the conditions that promote sulphate
Figure 8 – Sulphur and magnetite contents in the concentrate as a function of roasting temperature (roasting in air for 20 minutes)
formation, it can be concluded that oxygen was withdrawn from the gas phase and reacted with the sulphides to form sulphates.
Sulphur removal and the formation of iron oxides The primary objective of roasting is to lower the levels of sulphur in a concentrate. A second objective is the preferential oxidation of iron in the base metal sulphides so that the iron can be removed in the slag phase during smelting. Iron is oxidized to either Fe2O3 or Fe3O4. Where possible, magnetite formation should be avoided as iron-bearing spinels form a viscous intermediate layer between the matte and slag layers during smelting. This viscous layer results in increased levels of matte entrainment in, and PGM losses to, the slag. The build-up of Fe3O4 or other spinels during smelting also reduces furnace capacity (Jones, 1999). The amount of either Fe3O4 or Fe2O3 in the product is also linked to temperature (Muan and Osborn, 1965). The unroasted concentrate contained 17.4% sulphur. The degree
Table II
Compositions of phases shown in Figure 9 (wt%) A† 1
2
B‡ 3
1
C (insert in B) 2*
1
2
D Matrix
Digenite*
Silicate
Spinel#
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S 37.6 34.5 31.6 26.7 21.3 — — 28.23 21.3 — — Fe 58.6 40.0 35.8 — — 2.0 2.4 — — 15.0 60.2 Ni 3.8 20.8 16.4 73.4 — 62.0 70.7 71.77 — 1.4 8.7 Cu — 4.7 16.3 — 78.7 26.0 26.2 — 78.7 — — Pt — — — — — 10.0 — — — — — Pd — — — — — — 0.8 — — — — Mg — — — — — — — — — 4.2 Al — — — — — — — — — 6.0 9.9 Si — — — — — — — — — 19.1 — Ca — — — — — — — — — 21.5 — O — — — — — — — — — 32.8 21.2 * These phases are digenite(ss) with Cu9.3S5 † A phase assemblage of (1) Fe0.9Ni0.5S (pyrrhotite[ss]), (2) (Fe0.6Ni0.3Cu0.06)9S8(pentlandite[ss]), and (3) Cu9S5 (digenite) in a matrix of pentlandite(ss) is postulated ‡ The phase assemblage comprises (1) heazlewoodite(ss) and (2) digenite(ss), which is intergrown with heazlewoodite(ss) in a macro- and micro-texture # The spinel composition matches the following stoichiometry (Fe0.7Ni0.3)(Al0.7Fe1.3)O4
Fire and brimstone: The roasting of a Merensky PGM concentrate Table III
Compositions of phases shown in Figure 10B (wt%) 1
2
3
4
5*
6#
7
Fe
1.46
1.76
2.22
15.94
62.95
38.56
1.65
2.31
Ni
3.19
5.36
1.73
7.97
15.01
15.15
3.98
Cu
95.35
88.96
4.56
2.21
6.30
5.47
Al
5.98
9.96
21.09
Si
20.97
Ca
23.40
94.58
Mg
8
93.72
6.25 20.67 23.81
Cr
2.44
O
3.88
3.20
27.42
16.91
16.60
27.99
* A spinel, (Mg0.16Ni0.24Fe2+0.61)(Al0.64Fe3+1.36)O4 #
A spinel, (Mg0.38Ni0.38Fe2+0.24)(Al1.15Cr0.07Fe3+0.78)O4
Figure 9 – Backscattered electron (BSE) images of phases observed in the smelted samples. (A) matte from unroasted concentrate; (B) matte from concentrate roasted at 550°C; (C) insert appearing in (B); (D) matte and slag from concentrate roasted at 550°C. (Phase compositions are given in Table II)
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Fire and brimstone: The roasting of a Merensky PGM concentrate
Figure 10 – Backscattered electron (BSE) images of the smelted concentrate roasted at 650°C. (A) Typical microstructure, (B) Typical phase assemblage at higher magnification (phase compositions are given in Table III)
Smelting When concentrate is smelted, PGMs are collected in the matte phase. The principal aim of partially roasting the concentrate before smelting is to decrease the matte fall, thereby resulting in a higher PGM concentration in the matte phase. It is thus important to know how a roasted concentrate will behave on smelting. Bench-scale smelting tests were therefore conducted on the roasted concentrate, using alumina crucibles. The smelting tests were evaluated according to the matte fall (as a percentage of material charged to the crucible), the deportment of Ni and Cu to the matte, the collection of PGMs in the matte (if this could be measured accurately on a small scale), as well as the composition of the matte. The Journal of The Southern African Institute of Mining and Metallurgy
Since roasting lowered the sulphur concentrations in the concentrate by approximately 60%, from 17.4 % to 6.5 % at 550°C, while the concentration of magnetite formed decreased from approximately 4.8% to 3.1% as the temperature increased from 450°C to 550°C, it was decided to do the smelting tests only on the roasted concentrate samples of lower magnetite content, i.e. the samples that were roasted at 550°C and 650°C. Matte was collected from the smelting of fresh concentrate, while matte associated with an alloy phase was collected from smelting of concentrate roasted at 550°C (Figure 9). Smelting of the concentrate roasted at 650°C produced only an alloy. In all three tests the matte or alloy collected in fine beads and prills, which did not fall under gravity to the bottom of the crucible. The viscosity of the slag was therefore too high to facilitate the coalescence of matte or alloy into a button within 30 minutes. Dispersed matte hindered the direct measurement of matte fall. Matte fall was subsequently estimated at 45% for the unroasted concentrate and 15% for the concentrate roasted at 550°C, using a least-squares regression of concentrate and matte compositions within the framework of a mass balance. These values are lower than the matte falls calculated from all sulphides that can possibly report to the matte: 52% for the unroasted concentrate, 21% for the concentrate roasted at 550°C, and 17.6% for the concentrate roasted at 650°C. Although matte (from the unroasted and 550°C roasted concentrates) and alloy (from the 650°C roasted concentrate) did not coalesce, the compositions of matte and alloy beads and prills as well as the slags could be determined. The matte from the concentrate roasted at 550°C comprised Ni3S2 (heazlewoodite), Cu9S5 (digenite), Cu1.97S (djurleite), and Cu2S (chalcocite) (Table II). The PGM-containing alloy was associated only with nickel-copper-iron alloys (Figure 9C). Some nickel was lost to the slag (estimated in a mass VOLUME 115
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of desulphurization increases with increasing temperature, as reactions are faster at higher temperatures and, for the same duration of oxidation (20 minutes), the extent of desulphurization is higher. Since the concentrate sample consisted of different proportions of pyrrhotite, pentlandite, and chalcopyrite, the overall degree of desulphurization reflects the joint extents of oxidation of these different sulphide phases. At 550°C the sulphur content in the roasted concentrate dropped to 6.5% (a decrease in sulphur content of approximately 60%), while at 650°C it dropped to just over 5% (a decrease of 70%). The magnetite content also decreased with temperature (Figure 8). Oxidation produced both Fe2O3 and Fe3O4. At approximately 400°C, after 20 minute of roasting, the magnetite content was at its highest and decreased steadily with increasing temperature. Thermodynamics predicts that haematite is the stable form of iron oxide in air, in the temperature range used in this study (Muan and Osborn, 1965. The presence of magnetite therefore reflects nonequilibrium conditions.
Fire and brimstone: The roasting of a Merensky PGM concentrate balance calculation to be less than 10% in silicates and spinel). Copper was not detected in any of the phases in the slag. The formation of a Cu-rich alloy in the smelted product of the concentrate roasted at 650°C was unexpected (Figure 10). With 5% residual sulphur in this roasted product, up to 10% matte was expected to form. The alloy was entrained in an iron-rich spinel in a silicate matrix of variable composition (Table III).
and Metallurgical Engineering at the University of Pretoria. At Anglo American our thanks go to Dr L.J. Bryson (Head of Hydrometallurgy) and Dr R.P. Schouwstra (Head of Mineral and Process Research) for the use of the facilities at Technical Solutions as well as their technical support. This work is based on the research supported in part by the National Research Foundation of South Africa (Grant number TP1208219517).
References Conclusions ➤ It is possible to selectively oxidize the iron in sulphides that are present in a Merensky concentrate, leave the nickel and copper in sulphide phases, and produce a matte on smelting. This was achieved by roasting the concentrate at 550°C for 20 minutes ➤ The oxidation of the iron-containing sulphide phases at temperatures between 500°C and 650°C proceeded as follows: • Pyrrhotite oxidized to magnetite, which in turn oxidized to haematite. The extent of haematite formation increased with roasting temperature • Chalcopyrite oxidized to form a bornite(ss) phase, with compositions close to the Fe-rich end member at 550°C and a Cu-rich end member at 650°C • Pentlandite oxidized to monosulphide solid solution (mss). The mss composition tended towards the NiS (millerite) end-member of the solution with oxidation at higher temperatures. ➤ Roasting at 550°C lowered the sulphur levels by at least 60%, from 17.4% to 6.5% total sulphur, and at 650°C, sulphur was lowered by 70%, from 17.4% to 4.9% total sulphur ➤ The magnetite content of the calcine roasted at 550°C was double that of unroasted concentrate ➤ As long as the oxygen partial pressure in the rotary furnace remained high (close to that of air), metal sulphates did not form ➤ Smelting tests revealed that concentrates roasted at 550°C for 20 minutes produced matte and a greatly reduced matte fall. The iron levels in the matte were below 3.5%. The sulphur content of this matte was just above 20%, compared to a sulphur content of more than 30% for matte from unroasted concentrate ➤ The matte produced from roasted concentrate resembled the converter matte in composition.
The authors would like to thank Lonmin for supplying the concentrate, and gratefully acknowledge the technical support of Dr Lloyd Nelson and Rodney Hundermark from Anglo American Platinum, as well as Rian Bezuidenhout and Burger van Beek from Lonmin. A special word of thanks goes to our colleagues in the Department of Material Science
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JONES, R.T. 1999. Platinum smelting in South Africa. South African Journal of Science. vol. 95, no. 11–12. pp. 525–534.
KNOWLTON, T.M. 2002. A review of catalytic fluidized-bed reactors in the chemical and petrochemical industries. IFSA 2002, Industrial Fluidization South Africa. Luckos, A. and Den Hoed, P. (eds). Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 3–31.
KOLTA, G.A. and ASKAR, M.H. 1975. Thermal decomposition of some metal sulphates. Thermochimica Acta, vol. 11, no. 1. pp. 65–72.
MUAN, A. and OSBORN, E.F. 1965. Phase Equilibria among Oxides in Steelmaking. Addison-Wesley, Reading, MA.
PANDHER, R. and UTIGARD, T. 2010. Roasting of nickel concentrates. Metallurgical and Matererials Transaction B, vol. 41, no. 4. pp. 780–789.
RAMBIYANA, R.I. 2015. Partial roasting of a PGM concentrate. MEng dissertation, University of Pretoria, South Africa.
US ENVIRONMENTAL PROTECTION AGENCY. 1995. Compilation of Air Pollutant Emission Factors. Volume 1: Stationary Point and Area Sources. Chapter 12. Office of Air Quality Planning and Standards (OAQPS) and Office of Air and Radiation (OAR) Research Triangle Park, NC.
WARNER, A.E.M., DIAZ, C.M., DALVI, A.D., MACKEY, P.J., TARASOV, A.V., and JONES, R.T. 2007. JOM World Nonferrous Smelter Survey. Part IV: Nickel Sulphide. JOM, vol. 59, no. 4. pp. 58–72.
XIA, F., PRING, A., and BRUGGER, J. 2012. Understanding the mechanism and
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kinetics of pentlandite oxidation in extractive pyrometallurgy of nickel. Minerals Engineering, vol. 27-28. pp. 11–19.
ZAMALLOA, M. and UTIGARD, T.A. 1996. The behaviour of Ni-Cu concentrate in an industrial fluid bed roaster. Canadian Metallurgical Quarterly, vol. 35, no. 5. pp. 435–449.
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The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a7 ISSN:2411-9717/2015/v115/n6/a7
Strategic and tactical requirements of a mining long-term plan by B.J. Kloppers*, C.J. Horn*, and J.V.Z. Visser*
An integrated mining company must include in the strategic LTP all the activities of mining, including those related to mineral processing and beneficiation as well as commercial marketing and sales. The LTP therefore has to be more than a mere technical mining document of what is planned to be mined where and how in the future – the LTP becomes a strategic tool to guide the entire organization into the future. The strategic LTP needs to be an aligned view of the organization’s future outlook: a long-term plan delivering on the company’s strategic intent, balanced by a short-term tactical plan.
Synopsis The long-term plan (LTP) in a mineral resource company is defined by the quality of the mineral resource and represents the result of a series of trade-offs to fulfil internal organizational as well as external business and legislative requirements, ensuring ultimate delivery on the defined organizational strategy. The LTP should as a whole align to a coherent and well-defined organizational strategy, working towards a clearly defined objective while still allowing a tactical response to short-term requirements of the organization. The ability to respond tactically to changes in environment, like the unprecedented five-month strike in 2014 on the platinum belt following the 2012 Marikana incident, is a measure of the flexibility of the plan given the agreed strategy. This paper describes the Lonmin process of linking company strategy with long-term planning, tactical planning, and the execution of the plan through an annual planning cycle to maximize organizational flexibility. This flexibility enables mining companies to respond to the many internal and external forces that impact on both strategy formulation and delivery of results that meet shareholder expectations.
Different planning horizons
Introduction The strategic long-term plan (LTP) of a mineral resources company can be described as the scheduled mining plan for the available mineral resource area based on current knowledge of the orebody and its mineral resource classification. The long-term robustness of the strategic plan is typically impacted by a combination of changing economic, market, and technical environments. Therefore the LTP has to be updated and reviewed on an annual basis, resulting in a near-term tactical as well as long-term strategic plan in response to changes (Smith, 2011). The annual update of the LTP ensures fulfilment of legislative and statutory requirements of the Mine Works Programme in terms of the Mineral and Petroleum Resources Development Act (DMR, 2002) as well as the resource classification in the mineral resources and reserves statement in terms of the South African Code for the Reporting of Exploration Results, Mineral Resources, and Mineral Reserves (SAMREC, 2007). The Journal of The Southern African Institute of Mining and Metallurgy
Strategic planning Kear (2006) states that strategy is the broad plan to attain some objectives in the future and
* Lonmin Platinum, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the Platinum Conference 2014, 20–24 October 2014, Sun City South Africa.
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Keywords planning cycle, long-term plan, organizational strategy.
Long-term planning focuses on strategy, determining the ‘where’ or destination in future, while short-term planning focuses on the execution process representing the ‘how’ to get there in the short term. Long-term strategic planning is supported by shorter term operational planning, also referred to as tactical planning. Kear (2006) states that tactical and strategic planning are done in two very different planning environments, with different levels of an organization with different skill sets working in these distinct environments. These environments are also referred to as the ‘realms of planning’ by Lane et al. (2010), where three worlds within planning are presented as strategic planning, capital planning, and operation planning.
Strategic and tactical requirements of a mining long-term plan that strategic planning focuses on identifying those objectives with a view of more than 10 years ahead. Smith et al. (2008) describe strategic planning as dealing with components of the business and decisions that deal with long-term value generation. Strategic planning can also be described as a process of selecting options or making choices of what the long-term goal should be. In the analogy where strategy determines the destination, strategic planning will involve certain trade-offs of alternative destinations identifying the most desirable long-term destination. To be successful, the long-term strategy needs to be constantly assessed in the light of short-term progress made towards the intended destination, which in turn is determined by the tactical plan within an ever-changing business environment.
Figure 1 – Indication of economic trade-offs (adapted from Lane et al., 2010)
Tactical planning The tactical plan can be described as the short-term or operational plan aimed at solving the ‘how’ of getting to the strategic destination. Smith et al. (2008) describe tactical planning as a process that is interlinked with strategic planning and revolves around the routine operational planning requirements, ensuring delivery on the long-term strategy. This requires that operational performance be tracked and measured with regard to its alignment with strategy, and corrective action taken when required. It is during the execution phase of the tactical plan that executives are faced with the challenges of enabling the overall strategy.
Executive’s dilemma Identification of the appropriate long-term strategy involves trade-offs between a desired future state and the cost of opportunities lost as a result of choosing the specific future state. These trade-offs have been described as the ‘executive’s dilemma’ by Lane et al. (2010) with the question ‘what should the strategic objective be?’ A modification of their model of the executive’s dilemma is depicted in Figure 1. This model deals exclusively with issues in the economic realm with reference to production levels, capital efficiency, value, and profit. The dilemma lies not in the fact that tradeoffs exist between the economic drivers, but rather in understanding the opportunity cost of options given up due to the selection of a strategy based on a specific economic driver. Table I outlines potential trade-offs that may exist when following a strategy to maximize value based on a single economic driver. The strategic intent shown for each of these drivers viewed in isolation will enhance long-term value.
Clear trade-offs exist between the economic drivers when the strategic intent is to maximize value, since various combinations of these drivers or components of cash flow can be used to maximize value. It should also be noted that tactical requirements sometimes have to be traded off against long-term strategic requirements. The need to preserve cash in a severe short-term downturn of the economy will trigger a ‘cash conservation’ tactical plan, while the long-term strategy might still require capital investment in future growth based on a bullish long-term view. The true dilemma in identifying and selecting the appropriate long-term objectives is much broader than just the economic realities. The trade-offs that require the highest level of analysis due to uncertainty and risk fall outside of the financial realm where the financial impact is driven by non-financial factors. It is in these areas of decision-making where economic modelling and scenario planning taking risk and uncertainty into account plays an important role – identifying and quantifying the potential economic impact of non-financial decisions in diverse scenarios. The discipline of economic modelling makes trade-off decisions in this realm less complex because of the ability to express the impact of various decisions in terms of a common monetary value using a process of scenario analysis and ranking (Ballington et al., 2004).
Triple bottom line (3BL) The need to understand and measure the impact of business is focused not only on the financial, but also the environmental and social spheres of influence as described by Spreckley (1981), who identified a need for ‘social enterprise
Table I
Trade-offs between economic drivers (adapted from Lane et al., 2010) Economic driver
Possible strategic intent (management levers)
Resultant strategic trade-offs
Revenue
Increase production / sales
Costs
Reduce unit costs
Capex
Increase capital efficiency
Potentially impacting costs and/or capital efficiency by introducing high unit cost expansion projects requiring additional capital Potentially impacting revenue by cutting high-cost production and/or capital efficiency by introducing expansion capital projects Potentially impacting revenue and/or fixed cost dilution by stopping low-efficiency projects
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Strategic and tactical requirements of a mining long-term plan
Model of key strategic drivers Strategy formulation, building on the models of a triple bottom line and the notion of an executive’s dilemma (answering the question ‘what should the strategic objective be?’), should therefore be considered against an expanded set of drivers that includes economic drivers as well as internal and external forces. Expanding on the model of the executive’s dilemma, by bringing in the realities of the non-financial factors in the 3BL model, ensures better informed evaluation of options traded off by capturing potential risks that would otherwise not have been identified as opportunity costs when viewing economic drivers exclusively. The factors to be traded off in determining strategy can collectively be referred to as strategic drivers; internal forces are identified as a proxy for people and external forces are used as a proxy for environment from the 3BL model. In the adapted model depicted in Figure 2, the economic trade-offs are still as important, being the trade-off to best generate free cash flow and ultimately value. The emphasis changes by also considering the non-financial factors related back to 3BL. This system of strategic drivers is in a perpetual state of flux. The strategic trade-off of how to best generate value by harmonizing the economic drivers is constantly influenced by changing internal and external forces or drivers of strategy.
Figure 2 – Strategic drivers The Journal of The Southern African Institute of Mining and Metallurgy
Strategic planning will result in a plan that best matches the economic drivers to deliver on a single long-term strategic view, with some flexibility to alter course based on changes in the environment. Tactical planning will, however, be bombarded by the turbulent environment due to short-term changes in the internal as well as external strategic drivers like industrial action or exchange rate and commodity price volatility. This change in external and internal forces in the short term requires that the strategic intent be re-assessed on a continual basis to ensure alignment between where the strategy aims to go and what the tactical implementation allows. In some instances, the changes in the short-term drivers might require drastic changes in tactical execution having a material impact on delivery of the strategic LTP. This would require a total overhaul of the strategy, for example, the impact of the five-month strike on the platinum belt and changes in the industrial relations climate, which could trigger a strategic move towards mining methods with increased reliance on mechanization and reduced labour dependence. The unprecedented five-month Association of Mineworkers and Construction Union (AMCU) strike that took place from January to June in 2014 is an example of an external force that has to be evaluated in strategic planning. The loss of production had significantly different results on the share prices of the three companies concerned. Although
Figure 3 – Share price movements following the AMCU strike (source: Sharenet, 2014) VOLUME 115
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audits’ in an attempt to highlight a more holistic impact of execution of a business strategy. Over time this need for broader reporting and measurement gave rise to the phrase ‘triple bottom line’, which was coined by Elkington in 1999 and comprises profits, people, and environment (Elkington, 1999). This concept has since gained tremendous momentum in management and business literature. Measuring a strategy against the triple bottom line by including people and the environment is particularly apt in the mining industry. One only has to consider the recent industrial action in the platinum belt, which highlighted the socio-economic impact of mines on their surrounding communities and how the general well-being of the communities affects labour productivity. These spheres have the potential to disrupt the ultimate delivery of the strategy, thus proving some of the trade-offs made in selection of the strategy obsolete if not considered at the onset.
Strategic and tactical requirements of a mining long-term plan the mining operations of all the companies in the Rustenburg area ground to halt as a result of the strike (i.e. external factors were the same), the share prices behaved very differently as can be seen in Figure 3. This may be explained by the difference in internal factors and specific trade-off in economic drivers within each company, as well as the market sentiment regarding the vulnerability of the companies as a result of the strike. The strategic and tactical responses of these companies will be better understood in the years to come. Changes in the strategic outlook after an event like the prolonged strike prompted discussions in the media of a mechanization solution to reduce the reliance on labour, mitigating the increasing risk associated with labour relations in the platinum industry. In this instance, any decisions to change towards mechanization will have to be evaluated in the context of the broader strategy by means of scenario planning.
Scenario planning The strategic plan has a long-term view based on a specific scenario, a selected future view as translated through internal business assumptions (labour productivity, cost inflation etc.), as well as external global assumptions (commodity prices, exchange rates, inflation indices etc.). Scenario planning allows testing of various alternative strategies (or different plan versions supporting the same strategy). Multiple explicit scenarios are generated during the scenario planning process, each based on a different future view. Each of these scenario plans is tested for valuation under different global assumption sets. A key output of the scenario-based planning process is the identification of the specific scenario plan or grouping of scenario plans resulting in the maximization of an agreed objective function. In mining, the objective function is almost always maximization of shareholder value within the context of the accepted risk profile of the organization. A scenario-based valuation analysis, using the ’hill of value’ methodology adapted from Hall (2003) is depicted in Figure 4. Multiple scenario plans are generated by changing the internal management levers available in long-term planning (production rates, project sequencing, mining methods etc.), each supporting a different future view. In this illustrative example a valuation is generated for each combination of a scenario plan and global assumption set, and the resultant valuation for each combination depicted on a surface diagram. The diagram in Figure 4 allows identification of the optimal scenario that will maximize value, being ‘scenario B’ indicated by ‘flag 1’ at the highest point. If the current strategic LTP is represented by ‘scenario D’ resulting in the lower valuation indicated by ‘flag 2’, further analysis can be done to determine how to best progress the strategic LTP to ‘scenario B’ in order to maximize value. Scenario planning creates an understanding about the problem at hand through a process where assumptions and mental models about the future are critically examined, leading to a better understanding of the uncertainties facing the business (Cardoso and Emes, 2014). Scenario planning is not done to predict the future, but simply to understand what various future states could look like (Schoemaker, 1995).
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Figure 4 – Trade-offs to optimize the objective function (adapted from Hall, 2003)
Smith et al. (2008) describe how scenario planning can also be used to communicate the inherent uncertainty in investment decision-making, ultimately leading to a better understanding of possible future world views and consequently strategy formulation. The aim of scenario planning is therefore to better understand and communicate different views of potential future states. The scenario planning process allows for informed adjustments to the strategic and tactical plan to proactively adapt to changes in the environment. Scenario planning supporting evaluation of long lead time investments in natural resources has a long history, with Royal Dutch/Shell using it extensively as far back as the 1970s (Schoemaker, 1995). A 2009 report by the World Economic Forum in association with the International Finance Corporation and Mckinsey & Company (World Economic Forum, 2009) focused on scenario planning in the mining and metals sector, highlighting key areas where scenario planning can add value to: ➤ Enhance the robustness of a chosen strategy ➤ Improve strategic decision-making by revealing uncertainties and allowing for proactive planning ➤ Improve understanding of uncertainties by revealing the links and trade-offs between strategic drivers ➤ Increase planning flexibility and ability to cope with change ➤ Facilitate multi-stakeholder engagement and understanding in the planning environment. Scenario planning allows the impact of different future views to be ranked and compared side-by-side at the current decision point. In the mining industry, economic modelling and net present value analysis are commonly used to express scenarios at the decision point. The commercial use of economic modelling and net present value analysis to assess the impact on value of multiple scenarios, allowing comparison and ranking, is well documented (Ballington et al., 2004; Lane et al., 2010). Economic modelling forms an integral part of long-term decision-making at Lonmin Platinum, consequently enhancing decision-making in the scenario planning and strategy formulation process (Hudson et al. 2008). Scenario planning is a constant process that supports study work in the project pipeline, evaluation of the longterm impact of tactical plans, the formulation of guidelines to the budgeting process, and ultimately to evaluate and support strategy formulation. The ever-enduring scenario planning The Journal of The Southern African Institute of Mining and Metallurgy
Strategic and tactical requirements of a mining long-term plan process needs to be linked to a fixed business cycle to provide specific feedback at specified points in the cycle. Scenario planning without these links and feedback loops can easily evolve into ‘planning for the sake of planning’ that over time becomes far removed from what is achievable in a tactical plan.
Long-term planning cycle at Lonmin Lonmin’s long-term plan has evolved over time and many of the leading practices described by Smith et al. (2006) and Smith (2011) have been introduced into the planning cycle. The requirements of structured planning according to a repeatable cycle are not unique to South African mining. The strategic planning cycle is also identified as one of the main features of the planning process at major oil and gas companies. Grant (2003) identified in his analysis of strategic planning within a turbulent environment experienced by the oil majors (similar to the current environment of South African mining) that the cycle is to a large part generic between organizations surveyed. The cycle identified follows the path of strategic planning feeding planning guidelines to budget and long-term planning ending with reviews and a feedback loop back to strategic planning. This cycle can be compared to the planning cycle proposed in the strategic framework for South African platinum mining by Smith (2011) in his doctoral thesis. The planning cycle allows for multiple feedback loops between each of the stages in the process. This allows development of a tactical plan that keeps track of the requirements of the strategy on the one hand, but also feeds the tactical and operational limitations back into the process of strategy formulation. The planning cycle should be designed in such a manner that it allows information to flow freely between the tactical and strategic levels, with the ability to adapt strategy if the environment dictates it, but also to inform strategy when tactical execution needs to deviate from strategy.
Figure 5 illustrates how strategy is linked to the tactical short-term plan (or two-year budgeting cycle) at Lonmin. The Lonmin strategy drives strategic planning through a mine extraction and processing strategy that aims at the optimal extraction of the available mineral resource. This is done through the management of a project pipeline, where projects (mostly shafts and concentrator projects) are ranked as part of a portfolio optimization process establishing which projects are prioritized, considering the availability of capital and value these projects generate. The mining and processing extraction strategy provides top-down goals that form the guiding principles of the annual mining and processing long-term plans. The long-term plans in turn provide top-down goals to the two-year budgeting cycle. During the budgeting two-year period, short-term quarterly planning and control ensure that the tactical plans are executed according to the overall strategy. The long-term planning cycle supports annual external reporting of mineral resource and mineral reserves as well as the mine works programme. This cycle is unpacked in more detail in Figure 6, which shows how the feedback loops operate along a two-year timeline. Three cycles are illustrated in Figure 6, the Strategic Planning Cycle illustrated horizontally across a two-year timeline, the Life of Business Plan Cycle illustrated in the centre, and finally the Budget Cycle across the same timeline. Commitment to the overall long-term strategy is key to an organization’s success. Horn (2012), in the development of a goal-setting measurement tool for the South African mining industry, found overwhelming evidence that goal-setting (in this case following a defined strategy) correlates positively with organizational commitment. Strategic planning at Lonmin occurs annually with the gathering of the company executives in March, where topdown goals (TDGs) that are derived from this session feed into the scheduling of the mining LTP. The previous year’s
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Figure 5 – Linking strategy and tactical planning at Lonmin
Strategic and tactical requirements of a mining long-term plan
Figure 6 – Long-term and short-term planning cycle
Figure 7 – Ownership and review of the LTP
plan is used as a base and also provides input to the LTP. Lonmin makes use of a bottom-up planning process that allows ownership and organizational commitment. Figure 6 further illustrates a process of single- and multidisciplinary reviews to ensure governance and compliance with technical standards. This review and ownership process is unpacked in more detail in Figure 7. Once the technical mining plan is complete with all the governance and quality assurance steps applied, a draft life-
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of-business plan (LOBP) is prepared for the executive committee (Exco). This new plan is used to update the capital ranking model, which allows for further optimization to establish the final LOBP. The final LOBP becomes the input document for the strategic session taking place in the following year, which leads to the cycle repeating itself. The outcomes of both the strategy session and the final LOBP are presented to the board in March of each year. Figure 6 further indicates how the life-of-business The Journal of The Southern African Institute of Mining and Metallurgy
Strategic and tactical requirements of a mining long-term plan
Ownership and review of the LTP Organizational commitment to the LTP at Lonmin is enabled through a bottom-up process where each shaft/operation generates its own LTP aligned with the previous LTP as well as TDGs, as illustrated in Figure 7. At operational level the various line managers, which starts at mine overseer level together with the technical specialists (geology, planning, rock engineering etc.), are responsible for the shaft/operation LTP. The review, governance, and auditing of the LTP at shaft/operation level takes place through a series of single-disciplinary and multidisciplinary reviews (SDRs and MDRs) by the Lonmin Group managers together with the line managers. This process ensures ownership as well as strategic and tactical alignment within organizational governance and quality standards.
Conclusion Strategic planning takes a long-term view and should constantly be broken down into tactical plans that can be implemented and monitored operationally. The process of formulating long-term strategy, testing the strategy in an operational plan, and providing feedback that influences future strategy should be embedded in planning and institutionalized in an annual planning cycle. The Lonmin strategic long-term planning process described in this paper follows leading practices that enable ownership, tactical flexibility, and strategic alignment to ensure all stakeholder expectations are met. The challenge to the industry is the ability of organizations to balance the requirement to respond quickly to volatile market conditions, like the prolonged platinum strike following the Marikana event, with the often time-consuming formal process which ensures quality, governance, and control of the strategic long-term plan. The Journal of The Southern African Institute of Mining and Metallurgy
References BALLINGTON, I., BONDI, E., LANE, G., and SYMANOWITZ, J. 2004. A practical application of an economic optimisation model in an underground mining environment. Proceedings of the Orebody Modelling and Strategic Mine Planning Conference, Perth, WA, 22–24 November 2004. pp. 215. CARDOSO, J.F. and EMES, M.R. 2014. The use and value of scenario planning, Modern Management Science and Engineering, vol. 2, no. 1. pp. 19–42. DEPARTMENT OF MINERAL RESOURCES (DMR). 2002. Mineral and Petroleum Resources Development Act, Act 28. Government Gazette. 2002. Pretoria. ELKINGTON, J. 1999. Cannibals with Forks: The Triple Bottom Line of 21st Century Business. New Society Publishers, Stony Creek, CT. GRANT, R.M. 2003. Strategic planning in a turbulent environment: evidence from the oil majors. Strategic Management Journal, vol. 24, no. 6. pp. 491–517. HALL, B.E. 2003. How mining companies improve share price by destroying shareholder value. Proceedings of the CIM Mining Conference and Exhibition, Montreal, Paper 1194. HORN, C.J. 2012. Developing a measure for goal-setting in the mining industry in South Africa. Association of Mine Managers of South Africa. HUDSON, J.H.K., MAYNARD, M., and KLOPPERS, B. 2008. The application of economic modelling to enhance decision-making at Lonmin Platinum. Proceedings of the Third International Platinum Conference, Sun City, South Africa, 5–9 October 2008. Southern African Institute of Mining and Metallurgy. Johannesburg. pp. 343–348. KEAR, R.M. 2006. Strategic and tactical mine planning components. Journal of the Southern African Institute of Mining and Metallurgy, vol. 106. pp. 93–96. LANE, G.R., MILOVANOVIC B., and BONDI E. 2010. Economic modelling and its application in strategic planning. The 4th Colloquium on Diamonds Source to Use, Gaborone, Botswana, 1-3 March 2010. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 161–172. SCHOEMAKER, P.J.H. 1995. Scenario planning: a tool for strategic thinking. Sloan Management Review, vol. 36, no. 2. pp. 25–40. SHARENET 2014. Sharenet Java Technical Analysis Charts. http://www.sharenet.co.za/charts/index.php?load=LON [Accessed 23 June 2014]. SMITH, G.L. 2011. A conceptual framework for the strategic long term planning of platinum mining operations in the South African context. PhD thesis, University of the Witwatersrand, Johannesburg. SMITH, G.L. PEARSON-TAYLOR J., and ANDERSEN D.C. 2006. The evolution of strategic long-term planning at Anglo Platinum. Proceedings of the Second International Platinum Conference, Sun City, South Africa, 8–12 October 2006. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 301–305. SMITH, G.L., SURUJHLAL S.N., and MANYUCHI K.T. 2008. Strategic mine planning— communicating uncertainty with scenarios. Proceedings of the Third International Platinum Conference. Sun City, South Africa, 5–9 October 2008. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 335–342. SOUTH AFRICAN MINERAL RESOURCE COMMITTEE (SAMREC). South Africa 2007. The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (The SAMREC Code), 2007 Edition, amended July 2009. SPRECKLEY, F. 1981. Social Audit - A Management Tool for Co-operative Working. Beechwood College. WORLD ECONOMIC FORUM. 2009. Mining & Metals Scenarios to 2030. Geneva. http://www.weforum.org/reports/mining-metals-scenarios-2030 [Accessed 19 May 2014]. ◆ VOLUME 115
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planning cycle links to short-term planning. From Figure 6 one can see that the draft LOBP provides input through TDGs into the short-term budgeting cycle. The draft LOBP through TDGs provides inputs in order to allocate available capital for the quarter 3 forecast plan, which is part of the two-year budgeting process. This plan makes use of the December mining face positions and a mining schedule of 33 months is planned. This schedule allows for two objectives to be achieved; firstly it provides for a 9-month short-term forecast of the current financial year in order to enable market guidance. Secondly, it enables a 24month budgeting process. The plan is signed off in March of every year. Two other short-term plans are formulated during the budgeting cycle, namely the quarter 2 and quarter 4 forecast plans. Both these plans enable refinement of the short-term plan and improve market guidance. These are tactical plans that respond to the many internal and external factors affecting the plan. In summary, the Lonmin planning cycle enables strategic scenarios to affect short-term execution as well as feedback of how short-term internal and external forces may influence, and in some cases completely alter, the overall strategy of the company in a controlled, well-informed manner. The review and ownership of the plan is described in the next section.
STRONG ON ROBUSTNESS
renault-trucks.co.za
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a8 ISSN:2411-9717/2015/v115/n6/a8
Integration of imprecise and biased data into mineral resource estimates by A. Cornah* and E Machaka†
Mineral resources are typically informed by multiple data sources of varying reliability throughout a mining project life cycle. Abundant data which are imprecise or biased or both (‘secondary data’) are often excluded from mineral resource estimations (the ‘base case’) under an intuitive, but usually untested, assumption that this data may reduce the estimation precision, bias the estimate, or both. This paper demonstrates that the assumption is often wasteful and realized only if the secondary data are naïvely integrated into the estimation. A number of specialized geostatistical tools are available to extract maximum value from secondary information which are imprecise or biased or both; this paper evaluates cokriging (CK), multicollocated cokriging (MCCK), and ordinary kriging with variance of measurement error (OKVME). Where abundant imprecise but unbiased secondary data are available, integration using OKVME is recommended. This re-appropriates kriging weights from less precise to more precise data locations, improving the estimation precision compared to the base case and to Ordinary Kriging (OK) of a pooled data-set. If abundant secondary data are biased and imprecise, integration through CK is recommended as the biased data are zero-sum weighted. CK consequently provides an unbiased estimate with some improvement in estimation precision compared to the base case. Keywords Mineral resource estimation, data integration, cokriging, ordinary kriging with variance of measurement error.
Introduction In mining projects it is common that the resource estimate is informed by multiple overlapping sources of ‘hard’ data. Sample information for the attribute(s) of interest may have been acquired from various generations and types of drilling campaigns (such as diamond, sonic, reverse circulation, or percussion). In addition, for brownfield sites, production data such as channel samples or blast-hole samples may also be available. Each data source is likely to be associated with a different level of precision and accuracy, as demonstrated by quality control metadata or twinned drill-holes. In addition, the various data sources often feature differences in sample support. At the outset of the resource estimation process, various key decisions are required; one of these is whether all of the available hard data should be incorporated into the estimation, or whether some data should be The Journal of The Southern African Institute of Mining and Metallurgy
Integration of imprecise or biased data Numerous case studies concerning resource estimation based upon multiple generations of drill-hole data are documented in the literature (for recent examples see Collier et al., 2011 and Smith et al., 2011). Other authors have previously compared techniques for integrating data of different types and reliability. For example, Emery et al. (2005) compared five geostatistical techniques for integrating two subsets of error-free (assumed) and imprecise data into a resource estimate, including ordinary kriging (OK) of the pooled data-set; separate OK of each data-set and subsequent combination of the estimates by weighted average; cokriging (CK) of the two data sources; lognormal kriging with a filtering procedure; and indicator kriging to determine e-type estimates of grade. Emery et al. (2005) contend that the kriging techniques tested are not very sensitive to the level of sampling error and that the quantity of data prevails over the quality; they conclude that imprecise and unbiased measurements should never be discarded in the estimation paradigm despite their poor quality. However, Abzalov and
* Anglo American, formerly of QG Consulting. † Kumba Iron Ore. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received May 2014 and revised paper received Jan. 2015. VOLUME 115
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Synopsis
excluded on the basis of imprecision or bias or both. Inclusion of additional data reduces the information effect (Journel and Huijbregts, 1978), but by excluding one or more data sources the practitioner judges that the benefit of that data (in terms of minimizing estimation error) is outweighed by its imprecision or bias or both. However, in practice this judgement is rarely quantified and fails to consider the various geostatistical techniques available to account for imprecision and bias during estimation.
Integration of imprecise and biased data into mineral resource estimates Pickers (2005) compared kriging with external drift (KED) and collocated cokriging (CCK) against OK for the integration of two different generations of sample assays, finding that KED and CCK significantly improved the accuracy of grade estimation when compared with conventional OK. This paper firstly re-examines the case where imprecise but unbiased secondary data are available for incorporation into the resource estimate; the imprecise and biased case is then considered. Various techniques are trialled, including integration through CK, multicollocated cokriging (MCCK), and OK with variance of measurement error (OKVME). CK is classically the estimation of one variable based upon the measured values of that variable and secondary variable(s). Estimation uses not only the spatial correlation of the primary variable (modelled by the primary variogram) but also the inter-variable spatial correlations (modelled by crossvariograms). By making use of data from a secondary variable(s) as well as the primary variable, CK aims to reduce estimation variance associated with the primary variable (Myers, 1982; Olea, 1999); it also seeks to enforce relationships between variables as measured within the data. CK coincides with independent OK if all variables are sampled at all locations (isotopic sampling) and the variables of interest are also intrinsically correlated (see Wackernagel, 2003); this is a condition known as autokrigeability (Journel and Huijbregts, 1978; Matheron, 1979; Wackernagel, 2003). However, in the isotopic sampling but non-intrinsic case, real and perceived difficulties are usually deemed to outweigh the benefit of CK over OK (see Myers, 1991; Künsch et al., 1997; Goovaerts, 1997). Various authors including Journel and Huijbregts (1978) and Goovaerts (1997) suggest that CK is worthwhile only where correlations between attributes are strong and the variable of interest is under-sampled with respect to secondary variables (heterotopic sampling) (see Wackernagel, 2003). Even in the heterotopic sampling case, secondary data that are co-located or located near unknown primary data tend to screen the influence of distant secondary data; the high degree of data redundancy can produce large negative weights and instability within CK systems (Xu et al., 1992). In addition, fitting a positive definite linear model of coregionalization (LMC) to multiple attributes is often considered problematic (Journel, 1999), although significant improvements have been made in automated fitting routines (for example see Goulard and Voltz, 1992; Künsch et al., 1997; Pelletier et al., 2004; Oman and Vakulenko-Lagun., 2009; Emery, 2010). Furthermore, the inference of crossvariograms is problematic in the displaced heterotopic sampling case where the sample locations of the secondary variable do not coincide with the primary variable. In this paper CK is used to integrate dislocated heterotopic primary and secondary sample data representing the same attribute in cases where the latter is imprecise or biased or both, but spatially correlated to the former. CCK was proposed by Xu et al. (1992) to avoid the matrix instability associated with CK discussed above. It is usually applied in cases where primary data is sparsely distributed but secondary data is quasi-exhaustively measured (for example seismic attributes in petroleum applications). Under a Markov-type screening hypothesis (Xu et al., 1992), the primary data point screens the influence of any other data
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point on the secondary collocated variable. This assumption allows CK to be simplified to CCK in that only the secondary data located at the estimation target is retained within the CK system; the neighbourhood of the auxiliary variable is reduced to only one point: the estimation location (Xu et al., 1992; Goovaerts, 1997). Under the Markov model the crosscovariance is available as a rescaled version of the primary covariance (Xu et al., 1992). MCCK makes use of the auxiliary variable at the estimation location and also at sample locations where the target variable is available, but not at other locations where only the auxiliary variable is available (Rivoirard, 2001). This study investigates the possibility of integrating the secondary sample data, which represents the same attribute by MCCK. Another alternative is to integrate data sources of varying precision using OKVME (Delhomme, 1976). The method requires that the precision of each sample incorporated into the estimation is known. The approach also requires the assumption that measurement errors represent pure nugget effect; that is, the measurement errors are non-spatial noise which is independent of the data values (see Wackernagel, 2003). The specific measurement error variance of each data point is added to the diagonal terms of the left-hand kriging matrix. The response is re-appropriation of the kriging weights in favour of sample locations with low measurement error variance and against samples with high measurement error variance. In practice it is unlikely that the precision of every measurement is known: sampling errors may be broadly assessed for each campaign based upon quality control metadata, allowing progressive penalization of progressively imprecise data. Details of procedures for measuring and monitoring precision and accuracy in geochemical data are given by Abzalov (2008, 2009).
Modelling Experimentation was carried out using a two-dimensional iron ore data-set that comprises a set of exploration samples from a single geological unit configured at approximately 75 m × 75 m spacing. These samples were derived from diamond drill core and for the purpose of this experiment are assumed to be error-free; herein they are referred to as the ‘primary data’, Z1. An exhaustive ‘punctual ground truth’ model of Fe grades within the data-set area was generated at 1 m × 1 m spaced nodes by conditional simulation (CS) of the primary data (Journel, 1974; Chilès and Delfiner, 1999). CS allows multiple images of the deposit to be generated which are realistically variable but are ‘conditional’ in the sense that they honour the known but limited drill-hole sampling data from the deposit. In this case, a single realization of Fe grade (which adequately represented the statistical moments of the input data) was selected from a set of 25 that were generated using the turning bands algorithm (Journel, 1974). This ‘punctual ground truth’ realization was averaged into 50 m × 50 m mining blocks as shown in Figure 1 to provide a ‘block ground truth’ (Z) against which estimations based upon the primary and secondary data could be compared. The top left-hand plot in Figure 1 portrays the primary data locations (shown as solid black discs) and the CS realization built from this data, the histogram of Fe grades The Journal of The Southern African Institute of Mining and Metallurgy
Integration of imprecise and biased data into mineral resource estimates
Figure 1 – Conditional simulation of punctual ground truth (top left) with drill-hole locations shown as discs; punctual ground truth histogram (bottom left); ground truth up-scaled to 50 m x 50 m blocks (top right); and block ground truth histogram (bottom right)
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Six unbiased normal error distributions were applied to the extracted Z2 samples with absolute error standard α deviation (σ Z2) ranging between 0.25 and 3 (see Figure 2), termed unbiased cases hereafter. Because the error distributions are symmetric and unbounded, imprecision has no implication with respect to bias. In addition, six positively biased error distributions centred upon absolute +1% were applied, and six negatively biased distributions centred upon α absolute -1% were applied, each with the same range in σ Z2 (termed biased cases hereafter). An OK block estimate incorporating the primary data only represents the base case (ZOK 1 ) and is compared against the block ground truth in the scatter plot shown in Figure 3. In bedded iron ore data-sets, the Fe distribution is typically negatively skewed (see Figure 1), the data is characteristically heteroscedastic (i.e. subsets of the data show differences in variability), and the proportional effect usually exists (local variability is related to the local mean; see Journel and Huijbregts, 1978). Figure 3 indicates that the variance of estimation error is least where the local mean is greatest but increases as the local mean declines. This implies VOLUME 115
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pertaining to this is shown below; the re-blocked realization is shown in the top right graphic and the histogram pertaining to this below. The figure demonstrates the key features of up-scaling from punctual to block support under the multigaussian model assumptions that underpin the CS realization (see Journel and Alabert, 1989): the range and skewness of the distribution is reduced, but the mean is preserved. In order to provide a series of abundant secondary sample data-sets (Z2), ‘virtual’ samples were extracted from the punctual ground truth. Multi-generational data sources often overlap, but are rarely exactly coincident unless they represent re-assay of older pulps or rejects. The Z2 data locations were therefore made irregular and dislocated from Z1 in order to provide a realistic representation of a multigenerational drilling campaign (the displaced heterotopic sample arrangement; see Wackernagel, 2003). Regular 25 m × 25 m locations were adjusted by easting and northing values drawn from a uniform distribution over [−3,3] to provide the Z2 extraction locations. This geometry results in Z2 to Z1 frequency of approximately 10:1.
Integration of imprecise and biased data into mineral resource estimates
Figure 2 – Secondary data locations shown as coloured discs with primary data locations shown as black discs (left); unbiased error distributions which were applied to the secondary data locations also shown (right)
Errors in geochemical determinations can be considered analogous to estimation errors in this case, and the CVAVR(%) metric is thus used as the basis for comparison between estimation precision in this paper. In the base case estimate shown in Figure 3, CVAVR(Z, ZOK 1 )=0.88.
Analysis of imprecise but unbiased cases
Figure 3 – Comparison of base case estimation against the ground truth; rho represents the Pearson correlation coefficient
that the proportional effect exists in this data-set with local variability, and consequently estimation error, greatest in lower grade areas and least in high-grade areas. This is common in iron ore data-sets where the low-grade areas may pertain to deformed shale bands, discrete clay pods, diabase sills, or unaltered banded iron formation. The Pearson correlation coefficient (shown as rho in Figure 3) measures the linear dependency between Z and and thus quantifies the precision of the estimate. ZOK 1 However, following a review of the various precision metrics used in geochemical data-sets, Stanley and Lawie (2007) and Abzalov (2008) both recommend using the average coefficient of variation (CVAVR(%)) as the universal measure of relative precision error in mining geology applications:
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The Z1 and unbiased but increasingly imprecise Z2 data-sets OK CK ), CK (Z1,2 ), were integrated into estimation through OK (Z1,2 MCCK OKVME ). Given the displaced MCCK (Z1,2 ), and OKVME (Z1,2 heterotopic geometry of the data-sets, the Z2 locations adjacent to Z1 were migrated into collocation to allow crossvariography in CK cases, and to allow implementation of MCCK. The resulting loss of accuracy represents a compromise of these methods in the common displaced heterotopic sample geometry case and is discussed further below. The resulting estimates are quantitatively compared against the base case CVAVR(Z, ZOK 1 ) and against each other in Figure 4. The figure firstly shows that CVAVR(Z, ZOK 1 ) is α independent of σ Z2; secondly, that pooling the primary and OK ) results in secondary data and estimating using OK (Z1,2 α OK CVAVR(Z,Z1,2 )=0.56 where σ Z2=0. Incorporating secondary samples with zero measurement error through OK clearly improves estimation precision and is therefore preferable to α OK ) excluding them. However, as σ Z2 increases, so CVAVR(Z,Z1,2 also increases rapidly (indicating that estimation precision declines). Figure 4 shows that, in this particular case, where σαZ2=1.5 there is no benefit to including Z2 samples in the OK estimate: OK σαZ2=1.5 → CVAVR(Z,Z1,2 ) ≈ CVAVR(Z, ZOK 1 ).
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Integration of imprecise and biased data into mineral resource estimates
Figure 4 – Average coefficient of variation for each of the estimation approaches, with increasing standard deviation of measurement error associated with the secondary samples in the unbiased cases
OK σαZ2>1.5 → CVAVR(Z,Z1,2 ) > CVAVR(Z, ZOK 1 )
α
Figure 4 shows that where σ Z2=0, some improvement in estimation precision is achieved by incorporating secondary CK ) compared to ZOK samples through CK (Z1,2 1 . However, in this circumstance the improvement in accuracy is not as great as OK : that gained through Z1,2 CK OK σαZ2=0 → CVAVR(Z,Z1,2 ) > CVAVR(Z, Z1,2 )
This is due to the vagaries of CK compared to OK, which α were discussed above. However, as σ Z2 increases, the benefit α OK CK of Z1,2 declines relative to Z1,2. In this case, where σ Z2≈1.4, OK CK CVAVR(Z, Z1,2 )≈CVAVR(Z,Z1,2), both of which are more precise than the base case. Where the precision of the secondary data is poorer than this, integrating it through CK is preferable to integrating it through OK. In this case: CK OK σαZ2>1.4 → CVAVR(Z,Z1,2 ) < CVAVR(Z, Z1,2 )
Unlike OK, integrating the secondary data using CK provides an improvement in estimation precision compared to the base case in all of the cases tested: CK ) < CVAVR(Z, ZOK CVAVR(Z,Z1,2 1 )
As discussed above, MCCK is an approximation of full CK and some loss of estimation precision is to be expected in this case by the migration of data locations required by noncolocation of primary and secondary data-sets. This is evident within the results shown in Figure 4: MCCK mirrors The Journal of The Southern African Institute of Mining and Metallurgy
fully the CK result, with some loss of estimation precision α α where σ Z2<2. However where σ Z2>2 the difference between the two approaches is negligible, suggesting that at higher σαZ2 levels the simplifications associated with MCCK represent a worthwhile trade-off compared to CK. In addition, integration of secondary data through MCCK is preferable to α the base case in all σ Z2 cases that were tested: MCCK CVAVR(Z,Z1,2 ) < CVAVR(Z, ZOK 1 )
Finally, Figure 4 shows the results of integrating OKVME ). In all secondary data through OKVME estimation (Z,Z1,2 α σ Z2 cases that were tested, this approach outperformed the base case: OKVME ) < CVAVR(Z, ZOK CVAVR(Z,Z1,2 1 ) OKVME The figure confirms that the Z1,2 result converges on where zero error is associated with the secondary variable:
OK Z1,2
OKVME σαZ2=0 → CVAVR(Z,Z1,2 ) = CVAVR(Z, ZOK 1 )
However, as the precision of the secondary data declines, OKVME OK Z1,2 outperforms Z1,2 by an increasing margin; it also α CK MCCK and Z1,2 . Therefore in all of the σ Z2 out-performs both Z1,2 cases that were tested the following relationship holds: OKVME CK σαZ2>0 → CVAVR(Z,Z1,2 ) < CVAVR(Z, Z1,2 ) MCCK OK < CVAVR(Z, Z1,2 ) < CVAVR(Z, Z1 )
To further elucidate the OKVME technique, the cumulative kriging weights allocated to each sample during the OK and OKVME estimations were recorded. The mean α values for each σ Z2 that was tested are shown in Figure 5. VOLUME 115
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Where measurement errors are large, the OK estimate using the pooled data-set is less precise than the base case, which excludes the secondary data. In the discussed case study:
Integration of imprecise and biased data into mineral resource estimates
Figure 5 – Mean cumulative kriging weights in OKVME at increasing standard deviation of measurement error associated with secondary samples
OK The figure firstly shows that in the Z1,2 case the mean cumulative OK weight that is applied to the secondary samples exceeds that applied to the primary samples; this is because the secondary samples are typically in closer proximity to block centroids than the primary samples. As the secondary samples are also more numerous than the primary samples, they are clearly more influential in the OK estimation. The figure confirms that the OK weight is independent of OK σαZ2; this results in the rapidly increasing CVAVR(Z, Z1,2 ) with α increasing σ Z2, as shown in Figure 4. Figure 5 also shows that where no measurement error is associated with the secondary samples, the OKVME weights converge on the OK α weights. As σ Z2 increases the OKVME weights are rebalanced α in favour of the Z1 samples. In this case, where σ Z2 is 0.5 or greater the mean cumulative OKVME weight assigned to Z1 samples exceeds that assigned to the Z2 samples. Given that in the case study Z2 samples are significantly more numerous than Z1, a small decrease in the mean cumulative weight applied to the former must be balanced by a larger increase in the mean cumulative weight applied to the latter, due to the requirement that the weights sum to unity.
Analysis of imprecise and biased cases The imprecise and biased Z2 data-sets were also integrated into estimation through OK, CK, MCCK, and OKVME. Mean estimated grades are compared against each other and the ground truth mean in Figure 6.
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The figure firstly shows that the base case ZOK 1 is OK , unbiased with respect to the ground truth. In the Z1,2 estimate weights associated with the pooled Z1 and Z2 samples sum to unity; consequently bias associated with Z2 α samples is directly transferred into the estimate in all σ Z2 OKVME cases, as is shown in Figure 6. The Z1,2 weights associated with the pooled Z1 and Z2 samples also sum to unity. However, because increasing weight is re-appropriated α from Z2 to Z1 samples as σ Z2 increases, less bias is retained α within the estimate in the higher σ Z2 cases compared to OK Z1,2 ; this is also evident in Figure 6. CK MCCK and Z1,2 estimations, Z1 sample weights In the Z1,2 sum to unity and Z2 samples are zero-sum weighted. Figure 6 confirms that as a consequence the resulting estimations are unbiased, regardless of the bias associated with Z2, and α regardless of σ Z2. Consequently, CK or MCCK represent the only viable options to integrate Z2 data that are imprecise and biased. The positive and negative bias cases are coincident CK MCCK and for Z1,2 . for Z1,2
Conclusions If precise, unbiased, and abundant secondary data are available, their basic integration through OK of a pooled dataset is pertinent (see Table I). The estimate is directly improved compared to the base case (exclusion of the secondary data) by reduction of the information effect. However, abundant secondary data that are imprecise or biased or both are often excluded from resource estimations by practitioners, typically under an intuitive but untested The Journal of The Southern African Institute of Mining and Metallurgy
Integration of imprecise and biased data into mineral resource estimates
Figure 6 – Estimated mean grades for each of the approaches, with increasing standard deviation of measurement error associated with the secondary samples in the biased cases. CK estimates for negative and positive bias are coincident, as are the MCCK estimates
Table I
Summary of recommendations for integration of secondary data into resource estimates Secondary data accuracy
Secondary data precision
Recommended integration approach
Good Good Poor Poor
Good Poor Good Poor
OK OKVME CK CK
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Acknowledgements The authors would like to sincerely thank Dr Michael Harley of Anglo American, Scott Jackson of Anglo American, Dr John Forkes (independent consultant), and John Vann of Anglo American; their excellent comments and constructive criticism of earlier versions of this paper significantly improved its final content.
References ABZALOV M.Z. and PICKERS, N. 2005. Integrating different generations of assays using multivariate geostatistics: a case study. Transactions of the Institute of Mining and Metallurgy, Section B: Applied Earth Science, vol. 114, no.1. pp. 23–32. ABZALOV, M. 2008. Quality control of assay data: a review of procedures for measuring and monitoring precision and accuracy. Exploration and Mining Geology, vol. 17, no. 3–4. pp. 131–141. ABZALOV, M. 2009. Use of twinned drillholes in mineral resource estimation. Exploration and Mining Geology, vol. 18, no. 1–4. pp. 13–23. CHILÈS, J. and DELFINER, P. 1999. Geostatistics – Modelling Spatial Uncertainty. John Wiley and Sons, Hoboken, New Jersey. COLLIER P., CLARK G., and SMITH R. 2011. Preparation of a JORC Code compliant resource statement based on an evaluation of historical data – a case study from the Panguna deposit, Bougainville, Papua New Guinea. Eighth International Mining Geology Conference, Queenstown, New Zealand, 2224 August 2011. Australasian Institute of Mining and Metallurgy. pp. 409–426. VOLUME 115
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assumption that inclusion will result in loss of estimation precision or that the estimation may be biased, or both. Experimentation outlined in this paper demonstrates that this may be true if the secondary data are not handled appropriately in the estimation; also that such a decision is generally wasteful. Where imprecise but unbiased secondary data are available, it is recommended to integrate them into the estimation using OKVME (see Table I). This provides an improvement in estimation precision compared to the base case and compared to OK of a pooled data-set. CK and MCCK also provide some improvement in estimation precision compared to the base case, but do not outperform OK of a pooled data-set if abundant secondary data are relatively precise, and never out-performed OKVME in the unbiased cases that were tested. If secondary data are associated with bias in addition to imprecision, CK is recommended as the biased data are zerosum weighted (see Table I). CK consequently provides an unbiased estimate but with some improvement in estimation precision compared to the base case. MCCK suffers some loss of estimation precision compared to CK where the secondary data are relatively precise, but is similar to CK where the secondary data are less precise.
Integration of imprecise and biased data into mineral resource estimates DELHOMME, J.P. 1976. Applications de la Theorie des variables Regionalisees Dans Les Sciences de L’Eau. Thése de Docteur – Ingénieur, Universitie Pierre et Marie Curie, Paris. EMERY, X., BERTINI, J.P., and ORTIZ, J.M. 2005. Resource and reserve evaluation in the presence of imprecise data. CIM Bulletin, vol. 90, no. 1089. pp. 366–377. EMERY, X. 2010. Iterative algorithms for fitting a linear model of coregionalization. Computers and Geosciences, vol. 36, no. 9. pp. 1150–1160. GOOVAERTS, P. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press. GOULARD, M. and VOLTZ, M. 1992. Linear coregionalization model: tools for estimation and choice of cross-variogram matrix. Mathematical Geology, vol. 24, no. 3. pp. 269–286. JOURNEL, A., 1974. Geostatistics for the conditional simulation of ore bodies, Economic Geology, vol. 69. pp. 673–688. JOURNEL, A.G. and Huijbregts, C.J. 1978. Mining Geostatistics. Academic Press, London. JOURNEL, A.G. and Alabert, F. 1989. Non-Gaussian data expansion in the earth sciences. Terra Nova vol. 1. pp. 123–134.
MYERS, D.E. 1991. Pseudo-cross variograms, positive definiteness, cokriging. Mathematical Geology, vol. 23, no. 6. pp. 805–816. OLEA, R. 1999. Geostatistics for Engineers and Earth Scientists. 1st edn. Oxford University Press, Oxford. OMAN, S.D. and VAKULENKO-LAGUN, B. 2009. Estimation of sill matrices in the linear model of coregionalization. Mathematical Geosciences, vol. 41, no. 1. pp. 15–27. PELLETIER, B., DUTILLEUL, P., LAROCQUE, G., and FYLES, J.W. 2004. Fitting the linear model of coregionalisation by generalized least squares. Mathematical Geology, vol. 36, no. 3. pp. 323–343. RIVOIRARD, J. 2001. Which models for collocated cokriging? Mathematical Geology. vol. 33, no.2. pp. 117–131. SMITH, D., LUTHERBORROW, C., ERROCK, C., and PRYOR, T. 2011. Chasing the lead/zinc/silver lining – establishing a resource model for the historic CML7, Broken Hill, New South Wales, Australia. Eighth International Mining Geology Conference, Queenstown, New Zealand, 22-24 August 2011. Australasian Institute of Mining and Metallurgy. pp 181–189.
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MATHERON, G. 1979. Recherche de simplification dans un problem de cokrigeage. Centre de Géostatistique, Fountainebleau, France. MYERS, D.E. 1982. Matrix formulation of co-kriging. Mathematical Geology, vol. 14. pp. 249–257.
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XU, W., TRAN, T.T., SRIVASTAVA, R.M., and JOURNEL, A.G. 1992. Integrating seismic data in reservoir modeling: the collocated cokriging alternative. 67th Annual Technical Conference of the Society of Petroleum Engineers, Washington DC. Proceedings paper SPE 24742. pp. 833–842. ◆
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http://dx.doi.org/10.17159/2411-9717/2015/v115n6a9 ISSN:2411-9717/2015/v115/n6/a9
Stochastic simulation for budget prediction for large surface mines in the South African mining industry by J. Hager*, V.S.S. Yadavalli*, and R. Webber-Youngman*
This article investigates the complex problem of a budgeting process for a large mining operation. Strict adherence to budget infers that financial results align with goals. In reality, the budget is not a predetermined entity but emerges as the sum of the enterprise’s operational plans. These are highly interdependent, being influenced by unforeseeable events and operational decision-making. Limitations of stochastic simulations, normally applied in the project environment but not in budgeting, are examined and a model enabling their application is proposed. A better understanding of budget failure in large mines emerges, showing that the budget should be viewed as a probability distribution rather than a single deterministic value. The strength of the model application lies with the combining of stochastic simulation, probability theory, financial budgeting, and practical scheduling to predict budget achievement, reflected as a probability distribution. The principal finding is the interpretation of the risk associated with, and constraints pertaining to, the budget. The model utilizes a four-dimensional (space and time) schedule, linking key drivers through activity-based costing to the budget. It offers a highly expressive account of deduction regarding fund application for budget achievement, emphasizing that ’it is better to be approximately right than precisely wrong’. Keywords probabilistic logic, Monte Carlo, simulation, NPV, budget.
Introduction One of the biggest questions confronting senior management of a mine, regardless of its size or mining methodology, is: ‘Why does the budget of a mine become so far removed from reality that it ends up being useless?’ The extraction of minerals is an expensive endeavour, with budgets often amounting to billions of rands, and unlike a manufacturing process, is based on a resource that is depleting with each production year. There are also factors unique to mining that make the environment challenging, including the extreme volatility of commodity markets and surety of the mineral reserves. The usual approach to dealing with these challenges is to use a rigorous budgeting process. The budget is the single most important document that regulates the production of a mine. All the strategies, tactics, and plans are ultimately based on attaining the targets set in the budget. Investors and executive managers The Journal of The Southern African Institute of Mining and Metallurgy
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Synopsis
of resource companies judge performance and make decisions primarily based on the budgets of the mines. The budget, however, is expressed in exact amounts, which obscure (or ignore) the variability of the mining environment. Deviations from the budget are often waived as uncontrollable elements or force majeure such as more rain than expected, unfavourable exchange rates, or strikes. Revisions to the underlying inputs (physical standards) that drive the budget are sometimes considered, but then such physical standards are also stated as exact values, ignoring their inherent variability. Random and seasonal fluctuations are aggregated into single values and treated as deterministic. Interdependability and accompanying (common-mode) risk is neglected. The result is that the budget does not have a fair chance of accurately forecasting reality. The budgeting process for a large mine is especially complicated and arduous, needing detailed inputs from every department over a six-month period before it is finally compiled. Although management does measure the budget carefully, its action is only retrospective – i.e. the fact is known only after the budget has failed (either negative or positive) – and the autopsy then turns into finding a scapegoat. Decisions about the application of scarce capital sometimes appear to be somewhat arbitrary. There is no decision-making methodology established that dictates where funds applied (spent) will have the greatest impact on the budget. The importance of increasing the confidence in achieving the budget, while simultaneously giving the assurance that the budget is accurate and ‘strict’ enough, cannot be over-emphasized. This article proposes a methodology that addresses the lack of budgeting accuracy by addressing the inherent uncertainty in a mining operation.
Stochastic simulation for budget prediction for large surface mines Methodology To achieve the necessary budget accuracy, a detailed modelling tool is required. The model should be able to replicate the actual mining both in time and actual spatial translation – i.e. travelling distances and specific physical attributes must be coupled to the mine layout and assets utilized, taking cognisance of the particular equipment fleet and uniqueness of the beneficiation process, as well as the specific geological factors that govern the resource. The model should be able to replicate the budget from first principles to within 1% accuracy. Once the detailed tool is in place and calibrated, the key operational performance drivers of the budget are determined. These are the drivers that have the most influence on the budget, and also have the largest variance. The main concept is that if two or more key drivers (that have a large impact) have large variances (as opposed to their budget assumptions) and are interdependent of each other, the probabilities of each can be multiplied to give a new probability. This new probability will have a larger ’spread’ than either of the individual drivers. This leads to instability in terms of budget achievement. The problem with the above is that if too many drivers with too large a spread are chosen, the resultant probability will be unrealistic and unusable, i.e. multiplication of a lot of fractions quickly approaches zero (this is in all probability the main reason why stochastic simulation is unsuccessful in the budget process and is therefore never applied). It is therefore clear that the key drivers must be carefully selected. These drivers should be compiled from different secondary probabilities that can be influenced (or manipulated) to optimize the distribution of the primary probability. This leads to the investigation of how these first-order (prime) probabilities can be influenced or manipulated to increase confidence, so the budget can be achieved. The logical deduction is that it will be mostly through the application of money, i.e. to fix something, buy more, pay someone to do it, etc. This culminates in the final objective, to provide a realistic budget, expressed as a probability distribution, and show where scarce capital should be applied to achieve the optimum return. The basic assumption is that all parameters that can influence the budget will conform to some type of probability distribution. The following distributions were considered: triangular, normal, and Weibull. These will be sufficient to describe any deviation. Due to the ability of a threeparameter Weibull distribution to closely approximate a normal distribution, the normal distribution was ignored.
Probabilistic logic and ‘stochasticity’ The basic aim of probabilistic logic is to make use of probability theory in combination with logic. Probability theory is used to handle uncertainty, while deductive logic is used to exploit structure. One of the problems with probabilistic logic is that it tends to multiply the computational complexities of the probabilistic and logical components, resulting in an answer that is too vague to have practical meaning. Jøsang (2009) remarks that probabilistic logic by itself finds it impossible to express input arguments with degrees of ignorance as, for examples, reflected by the expression ‘I don’t know’. The generally accepted practice, to
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provide values without supporting evidence, will generally lead to unreliable conclusions, often described as the ‘garbage in - garbage out’ syndrome.
Risk and uncertainty Risk (and the chance of loss), i.e. in the event that the situation can lead to both favourable and unfavourable outcomes, is the probability that the event outcome will be unfavourable, i.e. an unwanted event, while uncertainty is the indefiniteness associated with the event, i.e. the distribution of all the possible outcomes. Uncertainty is an intangible value (Elkjaer, 2000). The main problem with the budget is that it uses only point estimates. Discrete estimates by themselves, are insufficient for good decisions (US Air Force, 2007) or a good budget. The underlying probability distributions inherent to the production process will influence the outcome, for example no two trucks travel at exactly the same speed – and no two shifts produce exactly the same saleable product. It is therefore obvious that the answer to achieving the budget lies in the uncertainty of these cornerstones of production, which must be understood so that the probability of success may be improved (or positively influenced). It is clear that the single deterministic point value for a budget is a fallacy, since the chance of achieving it exactly in a highly complex environment is zero. As SAP® is widely used in large mining environments, Table 600, which is a summary of the main cost buckets of the budget, was analysed as a first step. This was further distilled by using a standard Pareto analysis to determine the most important cost buckets. A Monte Carlo simulation was then used to give the distribution of outcomes. (This simulation failed, as is explained below). From the literature it is clear that Monte Carlo simulation is used mainly for capital budgeting of large projects (Clark, 2010). Such simulations are concerned with the cost of the project, while this model concentrates on the uncertainty inherent in the production process and regards cost fluctuations as risk i.e. uncontrollable (but explainable), for example, price increases in diesel, electricity, etc. The cost buckets were then combined to describe the cost function, broken down into fixed and variable cost. Income (through product sales) was added to allow the results to be expressed as a net profit (prior to tax and cost of capital). The probability distributions were assumed to be triangular with a lowest, highest, and most probable value (US Air Force, 2007). This methodology did not work, since the multiplication of uncertainty leads to a wider spread of probabilities – to the extent that it is clearly an irrational approach and most probably is the reason why Monte Carlo simulation is not used in the standard budgeting process. A different approach was indicated, and the drum–buffer–rope (DBR) production planning methodology from the theory of constraints (TOC), as originally proposed by Eliyahu M. Goldratt in the 1980s, was considered. Schragenheim and Dettmer (2000) summarize the drumbuffer-rope as striving to achieve the following: ➤ Very reliable due-date performance ➤ Effective exploitation of the constraint ➤ As short a response time as possible, within the limitations imposed by the constraints. The problem with the DBR methodology is that although The Journal of The Southern African Institute of Mining and Metallurgy
Stochastic simulation for budget prediction for large surface mines
Analysis of the problem The budget needs to be expressed not as a single number, but as a range within a probability distribution. The position of the budget point relative to the median is important, i.e. a budget above the median indicates a greater chance of failure, and below a greater chance of success. The shape of the distribution is also important, as a narrow spread implies a greater chance of success, while a broader spread equates to a higher risk environment with a greater chance of failure (Figure 1). The variability (distribution) of most of the key drivers can be changed through the application of funds – i.e. training of personnel, appointing more personnel, buying and commissioning more production units, or better maintenance to improve reliability. However, not all key drivers can be influenced through application of money, for example, geological variability. Furthermore, the interdependence obscures the relationships between the drivers to the extent that is impossible to define the value of changing an individual driver without detailed modelling. The detailed modelling tool must accurately simulate the schedule that will supply the activities to be priced for the budget – activity-based costing – and the model must not The Journal of The Southern African Institute of Mining and Metallurgy
break down under probabilistic simulation, but keep the integrity of the mine plan and three-dimensional geographical exploitation intact. The main inputs to any mining budget are derived primarily from the past, namely: historical costs and performance, strategy with regard to exploitation, stripping, equipment replacement, and marketing. Normally, the mine will have a life-of-mine pit shell that outlines the mineable area. Within these limits, the mine will then develop a schedule. The most important driver of the schedule and hence the budget is the market forecast. It is of no, or very little, use to produce product that cannot be sold. Constraints imposed by infrastructure such as rail or harbour capacity are normally viewed as part of the overall marketing plan. A great deal of time is spent on price forecasts – for the very obvious reason that it is imperative to know what prices will be realized. Next, the market plan is married to the production constraints or bottleneck, normally the plant capacity. The beneficiation plant is usually the largest capital investment, and has a fixed production ceiling that limits the total throughput. The schedule is then broken down into base components. Firstly, the ROM tonnages from the different mining benches are determined and allocated to different beneficiation plants, honouring the spatial constraints. Secondly, the specific metallurgical characteristics of the material to be delivered to the plants are calculated – namely yield, plant efficiency, and other modifying factors like misplaced material, etc. The calculations are based on physical standards and norms with the assumption that physical standards are changeable and can be influenced by the amount of money available, whereas norms are a given. Utilization through shift rosters and number of operators employed is then added to the equation. Broadly speaking, the budget may be divided into two distinct parts, namely CAPEX (sustainable and other) and OPEX (salaries, electricity etc.) The correct way to cost a schedule is to link the tonnages to the activities. This is commonly referred to as activity-based costing (ABC). This results in a budget that tries to reflect reality. However, the shortcoming is that it is based on fixed events – i.e. events that are supposed to occur. No exceptions
Figure 1 –- Probability distribution – shape and size VOLUME 115
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the beneficiation plants (specifically the tipping bins of these plants) are normally defined (through TOC) as the bottleneck, the analogy is not a true one as the mining process differs from the manufacturing production process. It should rather be described as a trail run with a specific obstacle that all the runners have to cross. It is clear that if trucks are seen as a buffer, the logical response would be to over-truck the constraint. However, in the analogy of trail running, this is the worst possible decision. More athletes trying to cross the same obstacle at the same time results in more interference with each other and a slower throughput. Envision athletes on a trail run. Some run faster and some slower. Some stumble and block others. There is no rope (communication once the athletes are running), and this is exactly the problem with production haul trucks. Breakdowns, bad road conditions or secondary work on the road, intersections etc. cause unpredictable delays that can be handled only by stochastic methods. The logical solution is to express the budget as a probability distribution through examining the effect of the inputs in a logical way. By managing these distributions, the final shape and position of the budget distribution may be influenced. The understanding of the difference between the risk and uncertainty clearly indicates that the focus must be on ‘controllable factors’, as the assumption that these factors may be influenced by money (i.e. either men, material, or equipment), holds true. This will also allow the model to indicate to management where to optimally apply funds to have maximum impact on the achievement of the budget. The examination of the system through the above leads to defining the ’heartbeat’ of the operation – ROM must move, and for a large surface (open pit) mine it should be on wheels – i.e. trucks. So by measuring and understanding the truck cycle, the inherent uncertainty can be quantified as a probability distribution. These distributions can be manipulated through the application of money and will directly influence the production and therefore attain the budget.
Stochastic simulation for budget prediction for large surface mines are allowed in the budget. In reality, nothing is absolutely fixed and this is nowhere more apparent than in the intricate and highly complex environment of a big mining operation. The only way to address this is by way of a different approach, and this leads to the introduction of risk and uncertainty, which logically implies the use of stochastic modelling of the budget to reflect uncertainty within the predicted cash flow. To find the significant (i.e. key) drivers of the budget, a classical Pareto analysis was done on the budget’s main cost buckets (Figure 2). The following analysis demonstrates the complexity of the problem. For example, the cost of diesel is influenced through price fluctuations, over which the mine has no control. However, if it is influenced by production, i.e. higher production will require more diesel, but if there are better standards (fewer litres per ton produced), the mine will require less diesel than budgeted. It is therefore clear that a different approach is required to find the real drivers that will meet the requirements of a distribution that can be manipulated. In re-examining the approach, the following alternative view of the process is proposed. The process (in a mining environment) can now be summarized as follows: ➤ The budget utilizes assets (production units) to mine and to supply ROM to the plant ➤ The plant beneficiates and delivers product to be sold. To use any asset for continuous production, three things are required, namely capital, utilization, and maintenance. In using the assets, the main drivers that will influence the budget can now be stated as: ➤ Capital. Only three things can happen to capital expenditure – it may be replaced, sustained, or increased ➤ Use of assets. Assets are either being used or maintained. If they are in use, they can be used productively. The level of utilization will depend on the skills level of the people and the number of full-time employees (FTEs). Both may be changed by applying more money i.e. more people can be employed, or they can be trained better. The same basic argument can be applied to maintenance. More money can be spent on better maintenance (replace before failure etc.), employing more FTEs, and/or training them better. Figure 3 shows the detail.
Plant yield (which provides the link between the budget and the geology) is one of the most important drivers in a budget, as the quantity and quality of the product drives the total income stream of a mine. In the geological environment, boreholes are drilled to set spatial parameters. As an example, for a coal mine, coal from the boreholes is analysed in a laboratory to determine a washability curve that gives the various qualities at specific densities. The information is then spatially configured through a database coupled to a geological model. The model uses different types of growth algorithms, statistical methods etc. to predict the information in-between the boreholes – normally given in a grid (or block) format. Since the predictions are not absolute but rather an approximation of the truth, this imparts a ‘probability’ flavour to the process. It should be noted that the interpretation of washability gives a singular deterministic value for a specific block of coal. However, as the analysis is done in a laboratory, there will be a difference in the results, as the operational procedure (i.e. production) occurs under dynamic conditions. It is sufficient to note that the yields will rarely be better than expected. The next problem is caused by the operational procedure followed in product bed-building. Production beds are normally required to conform to very strict quality specifications. It is standard practice, to build a product bed with a slightly higher than required quality, as it is easier to add lowgrade material rather than above-grade in the beneficiation environment. When the bed is of too high a grade, nobody worries, and there may even be some bonuses. However, if the bed is out of specification on the ‘poor’ side, the company may incur large penalties or even rejection of the product by the customer. Because of this principle, and coupled to the fact that the beneficiation curve is not linear, it is common knowledge that one never gains on the upswing what is lost on the downswing. The schedule determines the time when a specific mining block will be beneficiated. The methodology proposed here, is to take cognisance of the ‘stochasticity’ and to introduce variability with a triangular stochastic distribution as suggested by Clark, Reed, and Stephan (2010). With this triangular distribution, the Arena® model will simulate operator error and variability, which will represent reality much closer than utilizing a single
Figure 2 – Pareto analysis of main cost buckets
Figure 3 – Interaction of Table 600 with the production process
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The Journal of The Southern African Institute of Mining and Metallurgy
Stochastic simulation for budget prediction for large surface mines
Rigidity of the mine plan The development or mining of an open pit follows very strict rules, i.e. the pit may be described in terms of a series of consecutive pit shells, governed by the need to keep the slopes at stable angles and have roads and ramps in place for access to specific mining blocks. Although some deviations are possible, for a given budget period the interrelationship between the different material types will have a fixed correlation, for example the pit slope has to be maintained, so the percentage distribution between benches will stay the same, but with increased production the slope will move faster, and with decreased production, it will move slower.
The haul truck – defining the heartbeat A truck carries a payload that is not a fixed tonnage but may vary considerably. There are specific factors that cause this, e.g. the load-tray design, loader operator expertise, loose bulk density of the material (i.e. after blasting) and the type of material, which all vary considerably for any given pit. Overloading will lead to spillage, and exceeding the maximum carrying capability will cause damage to the truck. A truck moves material from a given point to a fixed destination – normally from a series of mining benches to a plant or crushing facility, or in the case of overburden to a waste dump. A truck haul cycle consists of the following components: full hauling, queue at bin, tip, empty hauling, queue at loader, spot, load, and the cycle starts again. It is clear that there is a rigidly defined or fixed number of production hours per year, day, or month during which the truck may be utilized. During this time the truck must operate not only productively, but also be maintained. Waiting times (times not spent hauling) should be as short as possible.
Probabilistic methodology The logic component is clearly defined in the budget process. Combining this with a probabilistic approach aims to determine: The Journal of The Southern African Institute of Mining and Metallurgy
➤ What do the confidence levels look like for a given budget? ➤ Could the application of probabilistic logic influence the inherent risk of non-compliance with the budget? ➤ Will a stochastic approach allow the budget owner to establish a target probability with a higher confidence level? To answer these questions, the impact on the budget or the achievement thereof must be simulated in a stochastic environment. The problem with this statement is that setting up the model and running it takes up to 40 minutes per run. A true stochastic simulation would therefore take approximately 2 years to complete.
Probabilistic cash flow model From the literature it is clear that stochastic simulation is not applied to the prime financial budget, but is used to assess either the risk or the cost associated with large projects. A systematic approach to modelling of the budget is needed that will allow simulation of results under a variety of possible scenarios. In other words, simulated net cash flow with extreme movements within the controllable budget inputs, such as fluctuations in the norms and standards that underpin the budget, is required. In summary, the model predicts the potential loss or profit in relation to the budget over a defined period, reflecting a probability distribution for which a given confidence interval can be assumed for the achievement (or non-achievement) of the budget. (Budget risks such as higher inflation, higher diesel prices, underperforming assets, and declining revenues cannot be ignored, since for a large mine the influence of these risks may be significant enough to threaten the company’s ability to fund new projects, pay dividends, and impair cash flow).
Model interaction – probabilistic methodology (Figure 4) A methodology that will keep the space-time interrelationship intact, honour the integrity of the mine plan while taking cognisance of the budget complexity, and meet the simulation criteria with regard to computing time constraints is needed. As explained, the initial phase is a modelling tool that will link the mining schedule to the budget. The Xpac® model, which drives the tonnage schedule on which the budget is based, is used to obtain bench information for tons, hours, cycles, payload, and destination – i.e. from where (which blast block) to which plant or overburden dump. Next, the translation model describes the budget in terms of tons. Simultaneously, the costing (ABC) model is used to give inputs to the exposure model for the different variables. The exposure model balances the bench ratios etc. The fundamentals and statistics interact to derive a model with economic logic – in other words, a basic cash flow model underpinned by logic. The macroeconomic variables or drivers that have a significant influence on the budget performance can now be entered and distributions for the identified drivers applied. Risk is derived from random, unexpected deviations from the forecasts. Finally, a stochastic process (an Arena® Dynamic simulation model) is used to simulate values of the variables by randomly picking observations from their variance/covariance matrix. VOLUME 115
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deterministic value. As expected, the yield will form a distribution around the budget figure. Although the uncertainty assumed for the evaluation of the Dereköy copper deposit (Erdem et al., 2012) demonstrates a probability curve of NPV, it ignores the time component in relation to the actual mining of the deposit. This is a serious shortcoming, as a financial budget is by default a forecast of monetary flow over time. The mining operator can influence this to a large extent – for example, high-grading early on will increase the NPV, etc. The geology and other mining conditions are given inputs to the budget. These are accessed through the mining schedule, which links time-based production outcomes to the budget. This is done with scheduling software (XPAC®), where the yield and plant relationships that will exist sometime in the future are derived through a time-based production schedule. The resultant product mix will impact on logistics and marketing constraints if more than one product is sold. This solution may then be used to calculate the revenue or income, culminating in the final budget figure, expressed as a net profit. The above description is a somewhat simplified version of the actual process, but based on logic and demonstrably accurate enough to deal with the myriad of confusing interrelationships that exist in such a complex environment.
Stochastic simulation for budget prediction for large surface mines Deriving the budget description in a mathematical expression The budget ƒ can be described from Xeras® in terms of fixed (Fc) and variable costs (Vc). The variable costs are a function of ROM tons, which are a function of operational performance (OP). Budget (Pareto-based) cost function ƒ = Fc_Other + Fc_Salaries + (Vc_Salaries x tons) + Fc_Energy + (Vc_Energy x tons) + Fc_Diesel + (Vc_Diesel x tons) + Fc_Plant Maintenance + (Vc_Plant Maintenance x tons) + Fc_Maintenance + (Vc_Maintenance x tons) + Fc_Explosives + (Vc_Explosives x tons) Budget Income ƒ = (AvePrice x tons) ROM tons can be described by the operational performance drivers. These drivers can be described by probability distributions which can be measured and managed and influenced. The relationship between the operational performance drivers and tons can be determined with a function. The main operational performance drivers are: ➤ Maintenance (availability and utilization) ➤ Operators (FTEs, skills, production rate) ➤ Fleet units.
Stochastic simulation The Arena® Dynamic simulation model was used to simulate the cash flow model analysis. The objective of the model is to vary chosen business drivers in order to obtain a net cash flow distribution for the budget. The model uses Excel® driver inputs (per destination per bench) obtained from an Xpac® life-of-mine schedule. Typical driver inputs like cycle times, bench ratios, payloads, fleet hours, and physical standards are read in by the model. The model then uses probability distributions to independently vary the drivers like cycle times, payloads, and fleet hours, also making provision for force majeure events and operator absence. The model adjusts the driver values and then ensures that the fleet size and bench ratio are kept constant in order to simulate new bench tons and product tons. The model has product prices per bench, per destination, and per product, and also has the variable and fixed costs as derived from the Table 600 Budget, in order to calculate a net cost and net income. Ten thousand variable runs of each independent driver are simulated and the values are recorded in order to apply a statistical analysis of the net profit spread using an Excel® input sheet with built-in formulae for evaluation. Because Arena® does not use ’time’ in the sense that the scheduling model does, an extra iteration to limit the total production hours available had to be implemented. This increased the complexity of the model without influencing the stability. The final Monte Carlo model was also expanded to be able to ‘randomize’ more than one parameter simultaneously so that influence on the budget of any combination of parameters can be tested . The interaction between the different environments and accompanying models is depicted in Figure 4.
The curves fitted are three-parameter Weibull curves. The maximum likelihood estimation (MLE) method is generally considered to be the best method for estimating the curve parameters for a two-parameter Weibull curve (balancing resources and accuracy), but poor with three-parameter methods (Cousineau, 2009). Therefore the method for estimating the shape of the distribution is a modified MLE, which intelligently identifies the offset parameter before applying the MLE. The accuracy of the resulting curve has proven to be consistently adequate during testing on real data. Some results are shown below in graphical format as probability distribution and cumulative probability distribution curves. The following are examples of curve-fitting to real data as obtained from the dispatch sequel server database: The payload distribution (depended on the material density) and total cycle times are shown. Not shown, but fitted, were: empty hauling time, spot time, queuing, loading, full haul, dump, and reassign time. From a visual inspection it is clear that the methodology applied, i.e. using a three-parameter Weibull curve-fitting technique, yields the desired results. Typical results obtained are shown in Figures 5 and 6.
Results The following results are based on a real case study. The budget has been normalized so as not to release sensitive information. The answers are given in profit units, called net profit, and expressed as millions of rands. In the analysis that follows, it must be borne in mind that the budget was completed at least 3 months prior to the start of the budget year. The cycle time and payload information that were used were the actual for 3 months into the budget, as well as the preceding 3 months, i.e. 6 months of real-time data. All examples refer to a large open pit mine.
Cycle and payload In this particular example, the mine had a problem, prior to budgeting, with the standards used cycle times. They either were under pressure not to drop the physical standards too much, or did not fully understand the implication of the trend that they were seeing, or a combination of both. It would
Data used The data for the probability distributions is obtained from the mine’s history through a sequel server database. Values are generated fitting Weibull distributions with an Excel®-linked spreadsheet.
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Figure 4 – Model interaction The Journal of The Southern African Institute of Mining and Metallurgy
Stochastic simulation for budget prediction for large surface mines Production hours (FMs) In the following example, the influence of lost production hours is examined (see Table II). A triangular distribution is deemed to be the best fit to describe this problem, as depicted in Figure 10. The mine has on average two trucks down, either through an accident or an unforeseen rebuild. Section 54 (Mine Health and Safety Act) stoppages cause a loss of on average four production days. The rest of the loss is made up of ‘truck standing no operator’ (dispatch code). The fit for the data is a triangular distribution with a mean of 21 340 production hours, less 10% plus 5% (these events are seen as a force majeure, hence the terminology FM.) The mean drops to 1519 against the budget of 1862, with a very narrow distribution as indicated (Figure 10).
Yield (influence) Because yield causes a distribution around the budget line (Figures 9 and 10) it gives a target of only 1802 against the budget of 1862, as expected.
Murphy (if everything that can go wrong, goes wrong) Figure 5 – Payload data fit
It is clear that if all of the above events occur, then the results (called ‘Murphy’) are catastrophic, with a mean of only 1345 units.
Example of capex optimization The following example demonstrates the power of the model to determine where money should be spent. In striving to achieve the budget, the mine now has the option of: ➤ Spending R10 million on upgrading the roads and improving the rolling resistance. This gives a minimum advantage of 2 minutes per cycle and a maximum of 4 minutes per cycle ➤ Alternatively, buy two additional trucks for R75 million, which will add 2 x 5500 hours = 11 000 hours for the year. The results are compared in Table III and Figures 11 and 12. The mean moves from 1401 to 1542 with two extra trucks, or 142 units. If the cycle is adjusted by 2 minutes, (through better roads) it moves to 1570, generating 159 units. A saving of 4 minutes will give 248 units. It is clear that the better option will be to spend money on the roads instead of buying more trucks.
Conclusion
appear that they thought that the longer cycle times could be countered by increasing the payloads that the trucks were carrying. In other words, they ‘under-budgeted’ on payloads. Figures 7 and 8 show the situation. The budget was set at 1862 units (Table I). The effect of the poor cycle times at 50% results in a target of 1401 – below the budget. It is clear that the effect of the cycle time deterioration was not apparent when the budget was compiled. The strategy of countering the poor cycle time performance with loading (1933 units at 50%) is obviously not working as the increase in payload moves the target to only 1554 units compared with a budget of 1862, clearly indicating that the budget will be at risk. The Journal of The Southern African Institute of Mining and Metallurgy
Monte Carlo simulation is not widely used in the industry as a budgeting tool, although there are a few examples of it being used mainly for capital budgeting and the prediction of the variations within the budget. The main reason for it not being used in the normal budget process is that the multiplication effect of the distributions of the key budget drivers leads to a spread in the budget distribution that gives an unreliable conclusion, or no conclusion at all. The strength of the probabilistic logic model lies in the determination of the main drivers (first-order) that are independent of each other and can be influenced through the application of money. Probability logic offers a highly expressive account of deduction of where funds should be applied to optimally influence the achievement of the budget. The probabilistic logic model circumvents the original problem of expressing the budget as a single deterministic VOLUME 115
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Figure 6 – Cycle time data fit
Stochastic simulation for budget prediction for large surface mines Table I
Cycle time and payload * Description
Base case
Cycle
Payload
Cycle and payload
0
R m net profit
R m net profit
R m net profit
R m net profit
R m net profit
1 064 1 401 1 756 1 395 1 430
1 730 1 933 2 142 1 931 1 981
1 155 1 544 1 986 1 544 1 540
-
Low (5%) Mean (50%) High (95%) Median Mode • 0 indicates a control
1 862 1 862 1 862 1 862 1 862 run
Figure 7 – Cumulative probability distribution – cycle time and payload
Figure 9 – Cumulative probability distribution – yield and FMs added to cycle time and payload
Figure 10 – Probability distribution – yield and FMs added to cycle time and payload
Figure 8 – Probability distribution – cycle time and payload
Table II
Yield and FMs added to cycle time and payload Base case
Cycle and payload
HRS/FM
Yield
Murphy
Description
R m net profit
R m net profit
R m net profit
R m net profit
R m net profit
Low (5%) Mean (50%) High (95%) Median Mode
1 862 1 862 1 862 1 862 1 862
1 154 1 554 1 987 1 544 1 580
1 472 1 519 1 570 1 519 1 524
1 731 1 802 1 861 1 802 1 803
977 1 354 1 774 1 341 1 357
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Stochastic simulation for budget prediction for large surface mines Table III
Capex optimization Base case
Cycle
Cycle 2 min saving
Cycle 4 min saving
2 New trucks + cycle
Description
R m net profit
R m net profit
R m net profit
R m net profit
R m net profit
Low (5%) Mean (50%) High (95%) Median Mode
1 862 1 862 1 862 1 862 1 862
1 065 1 401 1 757 1 395 1 397
1 190 1 570 1 975 1 562 1 545
1 241 1 649 2 084 1 641 1 643
1 179 1 542 1 928 1 536 1 545
robustness of the model proposed lies in the fact that it differentiates between the primary drivers and secondary drivers which, while appearing to be important, generate so much noise that the answers become invaluable or worthless. Testing of a real budget proved the ability of the model and the value that may be unlocked through this novel approach.
Acknowledgements The authors wish to thank Professor Kris Adendorff for his valuable comments.
Acronyms
Figure 11 – Cumulative probability distribution – capex optimization
➤ Arena® - Simulation software ➤ force majeure – Act of God, i.e. unforeseen and uncontrollable ➤ Murphy – Refers to Murphy’s Law, an adage typically stated as ‘Anything that can go wrong will go wrong’ ➤ SAP® - Enterprise software used in the industry ➤ Table 600 – A generic budget summary used in SAP® ➤ Xeras® - Software from the Rung suite for costing schedules ➤ XPAC® – Scheduling software from the Runge suite, widely used in mine planning
References CLARK, V., REED, M., and STEPHAN, J. 2010. Using Monte Carlo Simulation for a Capital Budgeting Project. Management Accounting Quarterly, vol. 12, no. 1. pp. 20–31.
value by using the related activity-based costing, so that when standards change the influence is clearly reflected in the new probability distribution of the budget. The robustness of the model is guaranteed through the exploitation part of the model that directly links the deviation in standards to production. Correcting standards through the application of men, materials, or money is something that management has been trained to do and is good at. The impact and value of changing the standards are directly reflected in the probability of achieving the budget. The stochastic model uses real data wherever possible. Hubbard (2010) makes the point that the model should only be accurate enough, and states that uncertainty can be overcome by adding more complexity to the model. This is precisely wrong in the stochastic modelling environment. The The Journal of The Southern African Institute of Mining and Metallurgy
ELKJAER, M. 2000. Stochastic budget simulation. International Journal of Project Management, vol. 18, no. 2. pp.139–147. http://linkinghub.elsevier.com/retrieve/pii/S0263786398000787. ERDEM, Ö., GÜYAGÜLER, T., and DEMIREL, N. 2012. Uncertainty assessment for the evaluation of net present value : a mining industry perspective. Journal of the Southern African Institute of Mining and Metallurgy, vol. 112, no. 5. pp.405–412. HUBBARD, D.W. 2010. How to Measure Anything: Finding the Value of “Intangibles” in Business. 2nd edn. Wiley. JØSANG, A. 2009. Subjective Logic. Representations, vol. 171 (January). pp. 1–8. http://persons.unik.no/josang/papers/subjective_logic.pdf. US AIR FORCE. 2007. Cost Risk and Uncertainty Analysis Handbook. Tecolote Research Inc., Golera, CA. pp. 153–178. SCHRAGENHEIM, E. and DETTMER, H.W. 2000. . Simplified drum-buffer-rope. A whole system approad to high velocity manufacturing. Goal Systems International, Port Angeles, WA, USA. ◆ VOLUME 115
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Figure 12 – Probability distribution – capex optimization
COUSINEAU, D. 2009. Nearly unbiased estimators for the three-parameter Weibull distribution with greater efficiency than the iterative likelihood method. British Journal of Mathematical and Statistical Psychology, vol. 62, part 1. pp.167–91. http://www.ncbi.nlm.nih.gov/pubmed/18177546 [Accessed 18 September 2013].
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http://dx.doi.org/10.17159/2411-9717/2015/v115n6a10 ISSN:2411-9717/2015/v115/n6/a10
Q-coda estimation in the Kaapvaal Craton by D.J. Birch*, A. Cichowicz*, and D. Grobbelaar*
The Q-coda method for estimating the quality factor Q(f)= Qo(f)n was used to characterize seismic wave attenuation in a region of the Kaapvaal Craton that includes the mining areas of the Bushveld Complex and Witwatersrand Basin. Seismic waveform data, collected by locally distant stations of the South African National Seismograph Network (SANSN), consisted of mining-related events with magnitudes ranging from ML 1.8 to ML 4. Q was calculated for nine different source-receiver pairs spanning the study region. A weighted average Q based on the number of available data gave an estimated attenuation relation for the study region of Q(f) = 327 f 0.81. Keywords seismic wave attenuation, coda waves, Q-coda method.
Introduction Earthquake activity in South Africa is significantly more prevalent in the mining regions, especially gold mining, than anywhere else in the country (Saunders et al., 2008). This has led to a concentration of seismograph stations of the South African National Seismograph Network (SANSN) around mining areas. An accurate understanding of the attenuation is important to seismograph networks. It affects not only the results of day-to-day monitoring such as magnitude calculations, but also advanced seismological studies such as determining the characteristics of the seismic source. With the increase in the number of seismograph stations around the gold and platinum mining areas, a study to determine the attenuation of seismic waves is this region is necessary. The region under investigation stretches from the gold mines at Klerksdorp in the south to the platinum mines at Rustenburg in the north (Figure 1). The study region lies at the centre of the Kaapvaal Craton and includes the economic mineral rich formations of the Witwatersrand Supergroup and Bushveld Complex. The Q-coda method for estimating the attenuation parameter, Q, was applied to the gold and platinum mining districts of South Africa. Data from stations of the South African National Seismograph Network (SANSN) at local distances were used. The The Journal of The Southern African Institute of Mining and Metallurgy
Q-coda method The extent to which backscattered S-waves are generated is dependent on the heterogeneity of the shallow crust and is quantified as the attenuation of the medium by the parameter Q. Aki and Chouet (1975) proposed two extreme models to account for the observations on the coda of seismograms.
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data-set includes only mining-related events initiated at shallow depths (< 5 km). S-wave coda decay has been widely used to characterize the local, lateral heterogeneity of the shallow crust. The method, developed by Aki (1969) and Aki and Chouet (1975), is a statistical approach to the coda wave amplitude decay characterized by a single backscattering model. Aki (1969) described the origin of coda waves from local earthquakes as backscattered waves and regarded them as a superposition of secondary waves, sometimes being referred to as the S-coda or QS. Aki (1969) proved that they could be treated entirely statistically and that they were caused by heterogeneities in the shallow crust because of their short period nature. The strict interpretation of Q, referred to as the quality factor, according to Langston (1989) is that it is the attenuation effect due to scattering of the elastic waves by elastic heterogeneity. The general approach to estimating the attenuation in a country with little tectonic seismicity and a short history of recorded seismicity (Saunders et al., 2008) is to use models that have been derived for similar stable continental regions. One such region is Norway, which is characterized by small to moderate, crustal earthquakes (Bungum et al.,1991). OttemĂśller and Havskov (2003) use a quality factor of QS/Lg(f) = 470 f 0.7 for Norway.
Q-coda estimation in the Kaapvaal Craton and attenuation, which are responsible for the coda amplitude decay. The source-receiver distance is small (< 100 km) and considered negligible when compared with the travel path of the backscattered waves. Thus, the sampling volume for which Q is calculated is much larger than the source volume where the seismicity is generated. In the single backscattering model, scattering is considered to be a weak process and the loss of seismic energy through scattering is neglected (Aki and Chouet, 1975). The single backscattering model expresses the medium function as follows: [2]
Figure 1 – Locality of the study region
Both models led to similar formulae that allowed for the separation of the effects of the earthquake source and attenuation on coda spectra. The single backscattering model is most frequently used to determine Q (Scherbaum and Kisslinger, 1985; Lee et al., 1986; Woodgold, 1990; Gupta et al., 1998; Yun et al., 2007; Parvez et al., 2008). Aki and Chouet (1975) also noted several important properties of coda waves that were highlighted and explained by Sato (1977). One such property is that the coda wave power spectra of local earthquakes of different magnitudes and locations but from the same sampling volume have a characteristic time-dependent decay. This serves as proof that the coda wave train is the result of scattered S-waves for local earthquakes. The coda wave amplitude AC(f,T) we observe on a seismogram is a function of the seismic source S(f), the site effect Z(f), the instrument transfer function I(f), and the medium function C(f,T). AC(f,T) = S(f).Z(f).I(f).C(f,T)
[1]
The medium function describes the geometrical spreading
where T = lapse time; γ = geometrical spreading factor; f = frequency; Q = coda quality factor. The lapse time is the time that has elapsed since the earthquake origin time. The geometrical spreading factor is assumed to be equal to unity for body waves and 0.5 for surface waves. This model allows us to separate the medium function from the rest by taking the natural logarithm on both sides.
[3] For a particular central frequency, f, the least-squares fit for a plot of ln(AC(f,T).T) vs. T allows us to solve for the quality factor, Q. The frequency dependence of Q was modelled as a powerlaw relationship of the form: Q(f) = Qofn
[4]
where Q = Qo at 1 Hz and n characterizes the frequency dependence of the attenuation in the medium. A procedure to determine Q was written in MATLAB, using the following steps: ➤ The P- and S-arrivals of raw seismograms were repicked to ensure accuracy. The seismograms were then trimmed for faster processing (Figure 2) ➤ The trimmed seismograms were filtered at central
Figure 2 – Raw seismogram from KOSH recorded at station PRYS showing the P- and S-arrivals and the 20-second coda waves interval
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Figure 3 – Results of bandpass filtering at central frequencies 6, 10, 12, and 24 Hz
Data description Waveform data were collected for seismic events located in the vicinity of the platinum mines near Rustenburg, the gold mines of the Central and West Rand, which form part of the Witwatersrand Basin, and the gold mines near Klerksdorp. These recordings were written at locally distant stations of the SANSN (viz. BFSD, KSR, MOAB, PRYS, SLR and TLEK, see Figure 6). The data includes densely clustered, small magnitude (ML 1.8 to ML 4) events. Three seismograph stations of the SANSN are located in the Klerksdorp area, resulting in source-receiver distances for The Journal of The Southern African Institute of Mining and Metallurgy
these events of 0–30 km. In all the other cases, the seismograph stations were located 50–70 km from the seismicity. Scherbaum and Kisslinger (1985) estimated the sampling volume for the coda waves of lapse time T as an ellipsoid with axes a=Δ and b=vs.T/2, where Δ is the hypocentral distance and vs the S-wave velocity, and the source and station positioned at the foci. For shallow mining events located at distances > 50 km and with depths less than 5 km (the deepest mining), we can assume that hypocentral distance is approximately equal to epicentral distance. The average shear wave velocity in the upper 3 km was assumed to be 3.5 km/s (Kgaswane et al., 2012) and the coda wave duration was T = 20 seconds. The maximum sampling volume would therefore take the shape of an ellipsoid with the major axis a = 70 km and minor axis b = 35 km. Since events are spread across the different mining areas, the total approximate sampling volume has an approximate diameter of 140 km. In the Klerksdorp area, BFSD, MOAB, and TLEK, sample roughly the same area defined by a diameter of 35 km. Figure 6 shows the source-receiver pairs that were selected to calculate a quality factor for this study. Some stations were used twice with nearby seismic events from two different areas. A total of nine different pairs were used. The geology of the various sampled volumes is also displayed in Figure 6. The data mainly sampled rock formations of the Transvaal Supergroup, with sources located in the igneous rocks of the Bushveld Complex and sedimentary formations of the Witwatersrand Supergroup.
Results and discussion The results are listed in Table I. The quality factor was calculated using the vertical components of the waveforms only, since site effects cause amplification on the horizontal components at specific frequencies, which would contaminate the results. VOLUME 115
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frequencies of 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, and 24 Hz, using an 8-pole Butterworth bandpass filter (Figure 3) ➤ The coda waves were extracted from 20-second time windows starting at 2Ts (Ts is the arrival time of Swave less the origin time of the event). For lapse times > 2Ts the general form of the coda is established (Rautian and Khalturin, 1978) ➤ The rms coda amplitudes were calculated for windows of 0.5 seconds ➤ The slope of the coda wave amplitude decay was extracted from least-squares fits of the data, which allowed us to solve for Q at each frequency (Figure 4) ➤ A power-law relationship (Equation 4) was modelled using a least-squares fit of the data (Figure 5). Certain quality control measures were employed. The signal-to-noise ratio of the coda was calculated by taking the rms amplitude of a 5-second window of noise before the Parrival and comparing it with that of a 5-second window at the end of the 20-second coda wave interval. Waveforms with a ratio of less than 2.0 were not used. Any coda amplitude decay with a positive slope was rejected. Leastsquares fits of the coda amplitude decay with relative standard errors of greater than 50% were rejected.
Q-coda estimation in the Kaapvaal Craton
Figure 4 – Coda amplitude decay for central bandpass frequencies 6, 10, 12, and 24 Hz with a least-squares fit
Figure 5 – Q-coda attenuation relationship calculated for KOSH events recorded at station PRYS. The standard deviations for Q0 and n are given in parentheses
The attenuation parameter Qo varied between 597 and 115. A low Qo corresponds to a high attenuation. The parameter n, which describes the power-law relationship between Q and frequency, varied between 1.23 and 0.62. A value greater than unity would suggest that higher frequencies attenuate less than the lower frequencies. This is difficult to explain, but it could be related to the fact that higher frequencies are more susceptible to small-scale heterogeneities, which give rise to S-wave scattering and, ultimately, the coda waves. In this case, a higher frequency wave would be scattered more often, becoming more
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prevalent in the coda and appearing to be attenuated less than the lower frequency waves. The number of available data in the different areas varied significantly, with the lowest average number of data per frequency being only 9, while the highest was 496. It is, therefore, best to calculate a weighted average as opposed to treating the individual results equally. This was done based on the average number of data per frequency. The average was also calculated for comparison. The weighted average yielded a quality factor of 327 f 0.81. The ellipses 1, 7, and 8 from Figure 6 deviated from the The Journal of The Southern African Institute of Mining and Metallurgy
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Figure 6 â&#x20AC;&#x201C; The SANSN stations and locations of seismic events that were used to calculate Q for various sections of the study region. Dashed lines delineate the approximate sampling volumes
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in the upper end of the frequency range. This may be attributed to similarities in the geology of the areas, since the Traansvaal Supergroup is prominent in all three sampling volumes. Another possibility is that the stations may be experiencing site effects, which would amplify the ground motion at certain frequencies.
Conclusions A Q-coda estimation following the single backscattering model of Aki and Chouet (1975) was carried out for nine different source-receiver pairs using seismicity recorded by locally distant stations of the SANSN. Magnitudes of the seismic events ranged from ML 1.8 to ML 4. Q was calculated for the vertical component of bandpass filtered seismograms at central frequencies of 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, and 24 Hz. The majority of the results from the individual areas VOLUME 115
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relatively low attenuation observed in the remaining sampled volumes, yielding values for Qo between 186 and 115 and for n between 1.23 and 0.99. The geology does not provide an explanation for this, since the three stations BFSD, MOAB, and TLEK would have sampled very similar rock masses. These three areas also do not have the lowest numbers of data available, suggesting that a lack of data is most likely not the cause. The least-squares fits for the attenuation power-law relationships are plotted in Figure 7, Figure 8 shows the relationships plotted on a geological map of the region. Figure 9 allows for an easier comparison of the results. Apart from three outliers, the curves show good agreement. The pairings of BFSD with data from the West Rand, SLR with data from the Central Rand, and KSR with data from Rustenburg (areas 1, 3, and 5 from Figure 6) deviate from the rest with a much higher quality factor (lower attenuation)
Q-coda estimation in the Kaapvaal Craton
Figure 7 â&#x20AC;&#x201C; Plots of the least-squares fits for the Q-coda attenuation power-law relationships for each of the nine source-receiver pairs. The standard deviations for Q0 and n are given in parentheses
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Q-coda estimation in the Kaapvaal Craton Table I
Q-coda attenuation relationships derived from the vertical components of the waveforms Ellipse
Source area
Station
no. 1 2 3 4 5 6 7 8 9
Av. number of
Qo
Qo std. dev.
n
n std. dev.
Q = Qo f n
115 390 597 356 421 367 131 186 529 344 327
15 128 106 14 70 29 11 27 67 169 115
1.23 0.74 0.72 0.73 0.84 0.74 1.06 0.99 0.62 0.85 0.81
0.06 0.14 0.07 0.02 0.07 0.03 0.04 0.06 0.05 0.2 0.15
115 f 1.23 390 f 0.74 597 f 0.72 356 f 0.73 421 f 0.84 367 f 0.74 131 f 1.06 186 f 0.99 529 f 0.62 344 f 0.85 327 f 0.81
data per freq. Rustenburg KSR Rustenburg SLR Cent. Rand SLR West Rand PRYS West Rand BFSD Klerksdorp PRYS Klerksdorp BFSD Klerksdorp MOAB Klerksdorp TLEK Average Weighted average
43 9 24 496 37 105 116 56 53
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Figure 8 – The Q-coda attenuation relationships plotted on a geological map of the region
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Figure 9 – Q-coda relationships with frequency plotted for comparison
showed good agreement with each other, while three of the nine areas showed a significantly higher Q in the upper end of the frequency range. The results confirm that a low-attenuation model is suited to this stable continental region, although Q(f) is slightly lower than what was proposed for Norway. A calculation of the weighted average, based on the number of available data, yielded a quality factor vs. frequency power-law relation of Q(f) = 327 f 0.81 for the study region. We conclude that while the local attenuation may vary significantly, a suitable attenuation model for the region is given by the weighted average of the individual constituents.
OTTEMÖLLER, L. and HAVSKOV, J. 2003. Moment magnitude determination for local and regional earthquakes based on source spectra. Bulletin of the Seismological Society of America, vol. 93, no. 1. pp. 203–214.
References
RAUTIAN, T.G. and KHUALTURIN, V.I. 1978. The use of the coda for determination of the earthquake source spectrum. Bulletin of the Seismological Society of America, vol. 68, no. 4. pp. 923–948.
AKI, K. 1969. Analysis of the seismic coda of local earthquakes as scattered waves. Journal of Geophysical Research, vol. 74, no. 2. pp. 615–631. AKI, K. and CHOUET, B. 1975. Origin of coda waves: source, attenuation and scattering effects. Journal of Geophysical Research, vol. 80, no. 23. pp. 3322–3342. BUNGUM, H., ALSAKER, A., KVAMME, L.B., and HANSEN, R.A. 1991. Seismicity and seismotectonics of Norway and nearby continental shelf areas. Journal of Geophysical Research, vol. 96. pp. 2249–2265. GUPTA, S.C., TEOTIA, S.S., RAI, S.S., and GAUTAM, N. 1998. Coda Q estimates in the Koyna Region, India. Pure and Applied Geophysics, vol. 153. pp. 713–731 JOHNSON, M.R., ANHAEUSSER, C.R., and THOMAS, R.J. (eds). 2006. The Geology of South Africa. Geological Society of South Africa and Council for Geoscience, Johannesburg/Pretoria. 691 pp. KGASWANE, E.M., NYBLADE, A.A., DURRHEIM, R.J., JULIÀ, J., DIRKS, P.H.G.M., and WEBB, S.J. 2012. Shear wave velocity structure of the Bushveld Complex, South Africa. Tectonophysics, vol. 554–557. pp. 83–104. LANGSTON, C.A. 1989. Scattering of long-period Raleigh waves in Western North America and the interpretation of Coda Q measurements. Bulletin of the Seismological Society of America, vol. 79, no. 3. pp. 774–789.
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LEE, W.H.K., AKI, K., CHOUET, B., JOHNSON, P., MARKS, S., NEWBERRY, J.T., RYALL, A.S., STEWART, S.W., and TOTTINGHAM, D.M. 1986. A preliminary study of coda Q in California and Nevada. Bulletin of the Seismological Society of America, vol. 76, no. 4. pp. 1143–1150.
PARVEZ, I.A., SUTAR, A.K., MRIDULA, M., MISHRA, S.K., and RAI, S.S. 2008. Coda Q estimates in the Andaman Islands using local earthquakes. Pure and Applied Geophysics, vol. 165. pp. 1861–1878.
SATO, H. 1977. Energy propagation including scattering effects single isotropic scattering approximation. Journal of Physics of the Earth, vol. 25. pp. 27–41.
SAUNDERS, I., BRANDT, M., STEYN, J., ROBLIN, D., and KIJKO, A. 2008. The South African National Seismograph Network. Seismological Research Letters, vol. 79, no. 2. pp. 203–210.
SCHERBAUM, F. and KISSLINGER, C. 1985. Coda Q in the Adak seismic zone. Bulletin of the Seismological Society of America, vol. 75, no. 2. pp. 615–620.
WOODGOLD, C.R.D. 1990. Estimation of Q in Eastern Canada using coda waves. Bulletin of the Seismological Society of America, vol. 80, no. 2. pp. 411–429.
YUN, S., LEE, W.S., LEE, K., and NOH, M.H. 2007. Spatial distribution of coda in South Korea. Bulletin of the Seismological Society of America, vol. 97, no. 3. pp. 1012–1018. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a11 ISSN:2411-9717/2015/v115/n6/a11
Geometallurgical model of a copper sulphide mine for long-term planning by G. Compan*, E. Pizarro†, and A. Videla*
One of the main problems related to mining investment decisions is the use of accurate prediction models. Metallurgical recovery is a major source of variability, and in this regard, the Chuquicamata processing plant recovery was modelled as a function of geomining-metallurgical data and ore characteristics obtained from a historical database. In particular, the dataset gathered contains information related to feed grades, ore hardness, particle size, mineralogy, pH, and flotation reagents. A systemic approach was applied to fit a multivariate regression model representing the copper recovery in the plant. The systemic approach consists of an initial projection of the characteristic grinding product size (P80), based upon energy consumption at the particle size reduction step, followed by a flotation recovery model. The model allows for an improvement in the investment decision process by predicting performance and risk. The final geometallurgical model uses eight operational variables and is a significant improvement over conventional prediction models. A validation was performed using a recent data-set, and this showed a high correlation coefficient with a low mean absolute error, which reveals that the geometallurgical model is able to predict, with acceptable accuracy, the actual copper recovery in the plant. Keywords geometallurgical modelling, multivariate regression, recovery prediction.
Introduction Good investment decisions are based on reliable decision-making tools. In the geometallurgical field, decision-making tools are commonly models designed to predict ore or operational characteristics such as dilution, particle size, throughput, recovery, etc. The results lead to better design, investment, and operational decisions. Geometallurgical models can generate the information necessary to make better design evaluations in order to support business plans. The Batu Hijau throughput model (Wirfiyata and McCaffery, 2011) is a good example of how geometallurgical models can be applied to optimize the production plan and to improve the plant circuit. The changes made led to a throughput risk reduction of approximately 5%. Geometallurgical models can be developed using physical kinetic methods or statistical models. An example of a physical kinetic model is the geometallurgical modelling of the Collahuasi flotation circuit (Suazo et al., 2010), in which the flotation rate constant was modelled by using the collision-attachmentThe Journal of The Southern African Institute of Mining and Metallurgy
* Mining Engineering Department, Pontificia Universidad Católica de Chile, Chile. † Applied Technology Director, Chuquicamata Underground Mining Project, CODELCO. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Dec. 2014 and revised paper received Feb. 2015.
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detachment approach. The variables were gas dispersion properties, flotation feed particle size distribution, and operational and equipment parameters. Another example of the use of physical kinetic models to predict recoveries in flotation is the calculation of induction times, i.e., the time required for thinning and rupture of the water film between particles and bubbles (Danoucaras et al., 2013). Adopting the microkinetics modelling approach and using induction time, it was possible to obtain similar results to the actual recovery of galena in four different size classes. Physical kinetic models are difficult to implement and update timeously, and in such cases statistical models can be developed based upon different kinds of operational data, such as mineralogy, hardness, particle size, hydrodynamic characteristics, and alteration. Here, it is important to note that input data may come from two sources: laboratory data or plant operational data. In the first case, there are more uncertainties related to the scale-up process (Ralston et al., 2007) than for operational-related data. In the current study, Chuquicamata plant operational data on mill feed grades, ore hardness, particle size, mineralogy, pH, and reagents, representing several months of operation, was collected to predict recovery at the plant. As stated by Coward et al. (2009) the variables that have the most impact on the processes of mining and treatment must be identified to determine those that need to be measured. Geometallurgical models need to be well specified. In the current study, a recent
Geometallurgical model of a copper sulphide mine for long-term planning technique that allows selection of variables based on the Ftest was used to select the best regression variables set. The use of a correct set of variables made it possible to formulate an accurate prediction using as little information as possible. As shown by Berry (2009), reliable geological data input and correct interpretation include two-thirds of all the problems experienced as a consequence of using geometallurgical models. Recovery is one of the most important variables for a mining project, and defines the performance of the mineral concentration process. In economic terms, the income of a concentrator can be calculated in a simple form as follows: E=P·g·T·R
[1]
where P is the net price of the valuable metal, which is usually defined by the market; g is the ore feed grade, which is defined as the result of the cut-off grade policy applied by the mine plan; and T is the mill plant throughput, which should be fulfilled to achieve a fixed production capacity. Finally, the recovery is an intrinsic variable of the concentration process and it can be managed in long-term planning by operative decisions. It is well known that accurate forecasting of recovery is important due to its significance for the economic viability of a project and because it is a variable of major impact on processing plant results. A good recovery prediction model makes it possible to take mitigation and control actions to guarantee a minimum return on investment. Different approaches have been taken in modelling recovery. It is common to use variables related only to the flotation cell (Nakhaei et al., 2012; Danoucaras et al., 2013; Hatton and Hatfield, 2012), although a systemic approach which is linked to grinding and flotation models could yield better results (Bulled and McInnes, 2005).
Hypothesis The aim of this study is to model ore recovery as a function of operational data and ore characteristics obtained from an historical database at the Chuquicamata A2 plant with the
idea of contributing to long-term management decisions and planning mitigation initiatives in the event of major deviations in plant performance. The data-set gathered was modified using a multiplier factor for confidentiality purposes. In this study, a multivariate regression method was applied to develop a recovery model based on known operational variables. A general form for a regression model is as follows: [2] where yi is the variable to be modelled, βj is a set of constant parameters, and xij are the regressors or explanatory variables for j going from 0 to n. Each variable in the data-set was normalized for easier interpretation of the constant parameters as shown in Equation [3]: [3] where xi represents the average of the variable along the historical set of data. Therefore, a higher constant value βj means higher significance of the variable xi in the regression.
Chuquicamata plant description The Chuquicamata copper mine is owned and operated by CODELCO, a Chilean state-owned company since 1976. It is located in the northern part of Chile, near Calama, 215 km northeast of Antofagasta. Chuquicamata produces copper cathodes from concentrate obtained at three different plant facilities named A0, A1, and A2, each with different process technologies, depending upon the time they were built. A simplified process flow chart is shown in Figure 1 The A0 plant was built in 1952. It operates a conventional milling circuit designed to process 74 kt/d. It consists of 13 grinding circuits in parallel, each having one rod mill (10×14 ft, 597 kW) and two ball mills (10×12 ft, 597 kW). The A1 mill was built in 1983. It is designed to process 38 kt/d and operates three parallel grinding circuits, each having
Figure 1 – Chuquicamata plant flow sheet
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Geometallurgical model of a copper sulphide mine for long-term planning one rod mill (13.5×18 ft, 1305 kW) and one ball mill (16.5×21 ft, 2610 kW). Finally, the A2 mill, built in 1989, operates two parallel SAG lines, each with one SAG mill (32×15 ft, 8203 kW) and two ball mills (18×26 ft, 3729 kW). Additionally, the A2 mill plant has another smaller grinding line with two ball mills (13×18 ft, 1305 kW). Each grinding circuit is followed by a rougher froth flotation circuit. The rougher concentrate from the three mills goes through a single cleaner, scavenger, re-cleaner, and rescavenger circuit that produces a copper-molybdenum concentrate. The concentrate is then transported to a selective molybdenum concentration plant, where separate copper and molybdenum concentrates are ultimately obtained.
Plant operational data analysis The historical data-set contains information related to feed grades, hardness, particle size, mineralogy, pH, and reagent dosages for froth flotation. The data-set consists of 29 variables controlled daily between January and October 2013. Table I shows each variable under study. An exhaustive statistical analysis was performed as an initial effort to model the recovery response function. First, a normality test was conducted to understand the intrinsic variation in recovery. Figure 2 shows the normal distribution test applied to the data. Over the period under analysis, copper recovery at the A2 plant averaged 86.6% with a coefficient of variation of 2.5%. As the figure shows, the data fits well to a normal distribution. Although the coefficient of variation could be considered acceptable, it is still necessary to identify the source of variations in the recovery response for accurate predictions. With long-term evaluation studies it is common practice to use a fixed average recovery, for instance 86%, for every year of the term of evaluation. This average is subsequently corrected by variations in copper feed grade. It is expected that a geometallurgical model that has a good correlation with operational and feed characteristic parameters will lead to better results and constitute a better tool for investment decisions.
Ore feed sources The A2 plant receives ore feed from three different mines:
Figure 2 – Normality test result for recovery at A2 plant
Chuquicamata (DCH), which contributes 77% of all the ore feed to the plant, Radomiro Tomic (RT), which contributes 19%, and finally Minitro Hales (DMH), which contributes 4%.
Ore feed grindability Another important characteristic regarding ore feed is its work index. Defined by Bond (1963), the work index (Wi) is the specific energy (kWh/t) required to reduce a particulate material from an infinite grain size to 100 μm. Therefore high work indexes are associated with ores which are more difficult to grind. As defined by Bond, the specific energy required for a given grinding process is related to the work index by the following expression: [4]
where E is the specific energy consumption (kWh/t) and P80 and F80 are the 80% passing sizes (μm) of the product (P) and feed (F).
Table I
Parameter
Variables
Ore size
% +65# P80 as calculated by Bond’s equation As each origin Work Index mass weighted based on the origin's throughput Chalcocite, digenite, covellite, chalcopyrite, enargite, bornite, pyrite, molybdenite, and sphalerite mass-weighted based on the origin's throughput Copper (Cu), iron (Fe), arsenic (As), molybdenum (Mo), zinc (Zn), lead (Pb) Water pH Froth flotation circuit pH Lime consumption index Two collectors One frother Three sets of fitted parameters
Work index Ore mineralogy Grades pH
Reagents Flotation model
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Variables in the data-set
Geometallurgical model of a copper sulphide mine for long-term planning An analysis of the data-set revealed that the work index at the DCH mine is greater than that observed at RT and DMH. The DCH ore has an average work index of 14.3 kWh/t, whereas the work indexes for the RT and DMH ores are 12.8 kWh/t and 12.5 kWh/t respectively. These differences indicate, as result of applying Equation [4], that it would not be possible to achieve the same throughput for the same P80 for the different ores from DCH, DMH, and RT. In fact, an increment in the proportion of DCH ore feed will lead to an increment in the grinding product size P80.
arsenic grade, which is very high in the DMH ore, 100 ppm on average, or more than 30 times that in the RT ore and three times higher than in the DCH ore. The most important issues concerning arsenic are the smelting penalties incurred and the environmental issues, given that arsenic is a dangerous contaminant and must be disposed of properly. Predicting recovery in long-term mine plans with ore blends from several sources is a complex task, usually leading to non-linear behaviour. Because of this, a geometallurgical model can be a useful tool for planning purposes if interactions, both positive and negative, between variables are well captured and nonlinear relationships are recognized at an early stage.
Ore feed mineralogy The mineralogy of the ore feed differs depending on its origin. The main mineral species in the mill feed were chalcocite (Cu2S), digenite (Cu9S5), covellite (CuS), chalcopyrite (CuFeS2), enargite (Cu3AsS4), bornite (Cu5FeS4), pyrite (FeS2), molybdenite (MoS2) and sphalerite (ZnS). Copper feed grades were measured by atomic absorption analysis. Each feed sample was assayed for copper (Cu), iron (Fe), arsenic (As), molybdenum (Mo), zinc (Zn), and lead (Pb). Results are summarized in Table II and Table III. As can be seen, chalcopyrite and pyrite are the most important species in the DCH feed ore. The presence of pyrite could cause recovery problems, because pyrite competes with chalcopyrite for recovery by the collector during froth flotation. Chalcocite is an important species in the RT and DMH ores, and enargite is also significant in the DMH ore. Table III shows the distribution of feed grades. As shown, the copper grade in the DMH ore is significantly higher than in the DCH and RT ores. Another important difference is the
The systemic approach A systemic approach was applied to develop a multivariate regression model representing the copper recovery in the Chuquicamata plant. The systemic approach consisted of reducing all unit operations to a two-step sequential process, grinding and concentration (Figure 4). In this study, a projection of the average P80 has been done based upon energy consumption in the grinding step, and the resulting particle feed size is the input for the froth flotation recovery model.
Geometallurgical multivariate regression model As stated previously, the aim of this study is to generate a geometallurgical model for copper recovery in the A2 plant. The data-set collected from Chuquicamata was used to establish a relationship between the most significant variables controlling the operational performance of the plant.
Table II
Mineralogy of feed according to source DCH
Chalcocite Digenite Covellite Chalcopyrite Enargite Bornite Pyrite Molybdenite Sphalerite Others
RT
DMH
Copper content (%)
Average in feed (%)
Coeff. var. (%)
Average in feed (%)
Coeff. var. (%)
Average in feed (%)
Coeff. var. (%)
79.85 78 66.5 34.65 48.4 63.3 0 0 0 -
0.12 0.19 0.21 0.62 0.09 0.15 0.78 0.05 0.06 97.74
45 15 19 18 69 24 16 21 55 -
0.54 0.00 0.12 0.26 0.00 0.14 0.26 98.67
25 316 45 23 316 70 30 -
0.72 0.01 0.02 0.12 0.69 0.07 1.41 96.96
47 181 101 61 76 117 29 -
Table III
Feed grades according to source DCH
DMH
Coeff. var. (%)
Average in feed
Coeff. var. (%)
Average in feed
Coeff. var. (%)
0.87 2.16 28.20 32.90 48.19 8.92
16.65 45.20 50.76 19.76 45.33 38.38
0.68 1.09 3.13 10.24 11.69 1.41
7.98 15.90 5.06 28.33 10.97 61.15
1.88 2.39 100.46 10.37 63.74 36.43
13.82 17.63 30.78 95.02 50.98 22.31
Cu (%) Fe (%) As (ppm) Mo (ppm) Zn (ppm) Pb (ppm)
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Average in feed
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Geometallurgical model of a copper sulphide mine for long-term planning
Figure 3 – The systemic approach sequence
The main variables controlling copper recovery were selected using a technique based on the Fisher test (F-test). This approach is different from other commonly-used techniques based on simple correlations between variables and redundancy (Boisvert et al., 2013). The method consists of adding or removing variables from the model, and calculating iteratively an F-statistic and p-value for each variable in the model. The p-value is the probability of obtaining a test statistic result at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true. If the model contains j variables, then for any variable Xr the F-statistic is defined as follows: [5] where n is the number of observations, SSE(j−Xr) is the squared error for the model that does not contain Xr, SSEj is the squared error, and MSEj is the mean squared error for the model that contains Xr. If the p-value calculated for any variable in the model is greater than a defined significance level (α), then the variable is removed. After that, if there are no more variables with a p-value to be removed, the method continues trying to add a variable on the basis of its F-statistic and p-value. If the pvalue corresponding to the F-statistic for any variable not in the model is smaller than a significance value α, then the variable is added. The entire process is then repeated in an iterative procedure. The method ends when there are no more variables to be removed or added (Montgomery and Runger, 2002).
of only 7.11% over the historical database under analysis, which indicates a poor copper recovery prediction. In order to develop a model for the recovery in the A2 plant, the previously mentioned variable selection methodology was applied to the overall data-set. Results of the variable selection method for the data-set collected are shown in Table V. Column 2 of Table V shows the correlation constant βj of each component. The laboratory flotation model represents the application of Equation [6], Wi DMH is the work index of the DMH mill feed ore, constant is β0 in the model as defined in Equation [2], Cp is the chalcopyrite grade of the ore feed, P80 is the mill plant product size, Fe is the iron grade of the mill feed, Dg is the digenite, CuS is the soluble gopper grade, and Mo is the molybdenite present in the ore feed. This regression model (Table V) achieves a coefficient of determination (R2) of 56.6%. Although these results represent a significant improvement with respect to the standard method, they were not completely satisfactory, therefore a second-degree polynomial fit was attempted by adding second-degree terms for each of the variables. The
Table IV
Parameters for copper recovery prediction model as described by Equation [6] K θ T
87.694 0.216 0.108
Analysis of variables used in the regression data-set Table V
First regression model results Variable
[6] where K is the recovery obtained at infinite residence time in the flotation circuit, T is a constant parameter, and θ is the copper grade in the tailings. Using a regression technique, the equation can be fitted to the historical data. Results are shown in Table IV. This model has a small statistical significance, with an R2 The Journal of The Southern African Institute of Mining and Metallurgy
Lab. flotation model Wi DMH Constant Cp P80 Fe Dg CuS Mo
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Constant
P-value
123 -28 -13.24 6.5 6.5 -4.1 -2.82 -2.76 1.37
0.000 0.051 0.000 0.143 0.007 0.004 0.000 0.027
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Chuquicamata has historically used a method to project recovery as a relationship between recovery and copper feed grade as described by the following equation:
Geometallurgical model of a copper sulphide mine for long-term planning final set of variables selected for the model were iron (Fe) and copper sulphide (CuS) feed grades, molybdenite (Mo), chalcopyrite (Cp), digenite (Dg) grades, the work index of the ore from DMH (WiDMH), and product size (P80). Table VI shows the coefficients for this new regression model. The final model has a correlation coefficient of 75.6% and coefficient of determination (R2) of 57.2%. Table VII shows a summary of the statistics for the model.
Results and discussion The final response function for copper recovery, based on the available information collected in the A2 mill plant, is as follows:
Table VI
Final regression model results Variable
Constant
P-value
Lab. flotation model Constant (WiDMH)2 Fe Fe2 P802 Cp2 CuS Dg2 Mo2
356.286 -264.744 -15.008 12.244 -6.699 4.036 2.937 -2.876 -0.923 0.599
0.000 0.002 0.035 0.287 0.165 0.098 0.000 0.000 0.033 0.028
Table VII
[7]
Figure 4 shows a Q-Q plot of the measured copper recovery vs the predicted recovery during the period under analysis. The confidence interval is 97.5%. The actual historical recovery is compared with the fitted model in Figure 5. The statistical analysis shows that recovery is more sensitive for specific variables. These variables are copper feed grade (Cu), iron feed grade (Fe), and the work index of the DMH ore. This implies that for long-term projections, better control over these variables will have a beneficial impact on the recovery projections and control variability. Each variable in the model has a constant coefficient that shows its impact on the copper recovery projection. As expected, the significant plant variables affect the recovery in the same way that empirical evidence does. Four variables behave as expected: copper, chalcopyrite, the work index of DMH, and soluble copper. The first two have a positive impact. As empirical evidence shows, when copper grade
Final model statistics Correlation coefficient Coefficient of determination (R2) Adjusted R2 Standard error Mean absolute error
75.61% 57.17% 52.64% 3.25 2.43%
Figure 4 – Q-Q plot for real copper recovery vs fitted model
Figure 5 – Historical series for real copper recovery vs fitted model
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Geometallurgical model of a copper sulphide mine for long-term planning
Figure 6 â&#x20AC;&#x201C; Historical series for real copper recovery and average fixed value
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Validation The recovery regression model was validated using monthly plant data between January and July 2014. The model prediction shows a correlation coefficient of 89.7% and a mean absolute error of 2.75%. Results of the validation are shown in Table VIII and Figure 7. The low mean absolute error and high correlation coefficient obtained indicate that the developed model is able to predict, within an acceptable range, the real copper recovery in the plant. The developed geometallurgical model is a significant improvement compared to the current fixed value used for copper recovery. Also, the regression equation shows that it is able to capture with confidence any variation in the feed ore characteristics.
Conclusions The results presented show that is possible to improve, with an acceptable certainty, recovery estimations for a concentration process based on operating and ore characteristics data. The database used included information relating to feed grades, ore hardness, particle size, mineralogy, pH, and reagent mix for froth flotation. The final regression model used only copper, iron, and soluble copper feed grades, molybdenite, chalcopyrite, digenite, the work index of the ore from DMH, and P80.
Table VIII
Validation statistics Correlation coefficient Coefficient of determination (R2) Standard error Mean absolute error
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increases, copper recovery also increases. The presence of chalcopyrite has the same effect as copper grade, because in porphyry copper deposits such as DCH, RT, and DMH, chalcopyrite is one of the minerals that increases the copper grade of the ore. On the other hand, the work index of the ore from DMH mine and soluble copper both have a negative, or inverse, impact on copper recovery. The work index affects the liberation, so when the work index increases it becomes more difficult to liberate copper-bearing minerals from the ore, therefore recovery decreases. Soluble copper grade has an expected adverse effect on flotation recovery, because soluble copper ore does not float under sulphide copper flotation conditions. There are two variables which have unexpected behaviour: P80 and digenite. P80 has a positive impact on the estimated recovery, and digenite a negative one. Both behaviours are the opposite to what is expected from the empirical evidence. It is known that when P80 increases copper liberation decreases, therefore recovery should decrease. The model shows the opposite behaviour, which could imply that the mill plant operates at the lower boundary of optimal grinding size. Digenite also has an unexpected impact on copper recovery, and further investigation of this phenomenon is needed. In addition there is one variable, Fe, which is presented in the regression model in first and second degrees. This variable has the expected overall effect of reducing copper grade recovery when Fe is increased in the ore feed. The final model shows a great advantage compared with using an average fixed value to project recovery in mediumand long-term studies with a variable ore feed. If a fixed value of 86% for recovery were used to predict copper recovery in the period under analysis, as shown in Figure 6, the mean absolute error would have been 3.9%. The final model gives a mean absolute error in the same period of only 2.4%, which represents a significant 1.5% improvement in recovery.
Geometallurgical model of a copper sulphide mine for long-term planning
Figure 7 – Validation results for the developed model
A methodology based on a systemic approach and an adequate selection of variables was used to adjust a multivariate regression model that represented the copper recovery. The best model found used 8 out of 32 variables collected in the database. The model fit achieved a correlation coefficient of 75.6% with a mean absolute error of 2.4%, which is acceptable for medium-term projections purposes. Model validation was performed for the developed regression model. Results show a correlation coefficient of 89.7% and a mean absolute error of 2.75% between the real observations and the predicted values. These high correlation and low error values indicate that the model has the ability to predict recovery variability with an acceptable confidence, which shows the model is an improvement compared to the use of a fixed value. This improved forecasting capacity assists investment decisions and would allow optimization of production plans, due to its ability to identify low- and highrisk options. The usefulness of these improvements in forecasting capacities, evaluating risks, and defining risk values for mitigation control will be the focus of a forthcoming study. The model variables impact the recovery as expected, with the exception of P80 and digenite. Further study is needed to elucidate the unexpected behaviour of these variables on recovery.
References BERRY, M. 2009. Better decision-making from mine to market by better assessment of geological uncertainty. AusIMM Project Evaluation Conference, Melbourne, Vic., 21-22 April 2009. Publication Series no 3/2009. Australasian Institute of Mining and Metallurgy, Carlton, Australia. pp. 15–19. BOISVERT, J., ROSSI, M., and EHRIG, K. 2013. Geometallurgical modeling at Olympic Dam Mine, South Australia. Mathematical Geosciences, vol. 45. pp. 901–925. BOND, F.C. 1963. Metal wear in crushing and grinding. 54th Annual Meeting of the American Institute of Chemical Engineers, Houston, TX. AIChE, New York.
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BULLED, D. and MCINNES, C. 2005. Flotation plant design and production planning through geometallurgical modelling. Centenary of Flotation Symposium, Brisbane. Australasian Institute of Mining and Metallurgy. pp. 809–814.
COWARD, S., VAAN, J., DUNHAM, S., and STEWART, M. 2009. The primary-response framework for geometallurgical variables. Seventh International Mining Geology Conference, Perth: Australia. Australasian Institute of Mining and Metallurgy. pp. 109–113.
DANOUCARAS, A.N., VIANNA, S.M., and NGUYEN, A.V. 2013. A modeling approach using back-calculated induction times to predict recoveries in flotation. International Journal of Mineral Processing, vol. 124. pp. 102–108.
HATTON, D.R. and HATFIELD, D.P. 2012. A probabilistic equation for flotation simulation. Minerals Engineering, vol. 36–38. pp. 300–302.
MONTGOMERY, D.C. and RUNGER, G.C. 2002. Multiple linear regression. Applied Statistics and Probability for Engineers. Wiley, New York. pp. 411–467.
NAKHAEI, F., MOSAVI, M.R., SAM, A., and VAGHEI, Y. 2012. Recovery and grade accurate prediction of pilot plant flotation column concentrate: neural network and statistical techniques. International Journal of Mineral Processing, vol. 110–111. pp. 140–154.
RALSTON, J., FORNASIERO, D., GRANO, S., DUAN, J., and AKROYD, T. 2007. Reducing uncertainty in mineral flotation – flotation rate constant prediction for particles in an operating plant ore. Mineral Processing, vol. 84. pp. 89–98.
SUAZO, C.J., KRACHT, W., and ALRUIZ, O.M. 2010. Geometallurgical modelling of the Collahuasi flotation circuit. Minerals Engineering, vol. 23, no. 2. pp. 137–142.
WIRFIYATA, F. and MCCAFFERY, K. 2011. Applied geo-metallurgical characterisation for life of mine throughput prediction at Batu Hijau. Fifth International Conference on Autogenous and Semiautogenous Grinding Technology, Vancouver, Canada, 25–29 September 2011. Canadian Institute of Mining, Metallurgy and Petroleum. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n6a12 ISSN:2411-9717/2015/v115/n6/a12
Introduction to the production of clean steel by J.D. Steenkamp* and L. du Preez†
Iron ore In iron ore, iron (Fe) is present in an oxidized state as Fe3+ in haematite (Fe2O3) or a combination of Fe2+ and Fe3+ in magnetite (Fe3O4) (Poveromo, 1999). To produce steel, the oxygen is removed from the Fe in order to reduce its oxidation state to zero, Fe0. In pyrometallurgical processing the principles of chemistry are applied to achieve this objective.
Synopsis This paper introduces the concept of clean steel production from a pyrometallurgist’s perspective to the broader metallurgical community. A simplistic overview of the steelmaking process from iron ore to car body manufacturing is followed by an introduction to the South African steel industry and the technologies that it utilizes. The process is illustrated by an overview of the flow sheet and technologies for the production of clean steel at Saldanha Steel, South Africa. Keywords ironmaking, steelmaking, clean steel.
Ironmaking
When confronted with the term clean steel, my friend – who is an expert in minerals processing – conjures up images of washing his car. Although, in pyrometallurgical terms, steel cleanliness affects the body of his car, clean steel production does not include soapy water. This paper presents an overview of what clean steel means to a pyrometallurgist, as well as an explanation of its relevance to South Africa.
(Very) simplistic process overview Car manufacturing In car manufacturing, the mass of the steel body structure typically ranges between 200 and 400 kg (WorldAutoSteel, 2013). Hydroforming is the manufacturing process used to convert a sheet of steel into the panel of a car door or other shape required (Singh, 2003). In a typical hydroforming process cycle, the sheet metal blank is placed onto the lower tool, the die is closed, and fluid pressure applied to one side of the blank. The pressure is sufficient to cause the blank to deform plastically and take the shape of the tool cavity (Singh, 2003). During plastic deformation, nonmetallic inclusions will cause the steel to deform. Typical nonmetallic inclusions are Al2O3 as a product of deoxidation – explained later on – and a solid solution of the spinel MgAl2O4 and Al2O3 (Pistorius, Verma, and Fruehan, 2011). These inclusions form during the steelmaking process. The Journal of The Southern African Institute of Mining and Metallurgy
* Mintek. † ArcelorMittal Saldanha Steel. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Nov. 2014 and revised paper received May 2015.
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Introduction
To reduce the oxidation state of Fe, the iron ore is contacted with a reagent that has a greater affinity for oxygen. In commercial ironmaking, carbon (C) is used to achieve this objective (Sundholm et al., 1999) as oxygen (O) has a higher affinity for C than for Fe under certain conditions. These conditions are governed by the rules of thermodynamics (Turkdogan and Fruehan, 1999). According to the Oxford Dictionary (Oxford University Press, n.d.), pyrometallurgy is the branch of science and technology concerned with the use of high temperatures to extract and purify metals. Therefore, in pyrometallurgy, processing temperature is one of the parameters manipulated to exploit the rules of thermodynamics. Carbon, when used to reduce the oxidation state of the Fe, is referred to as the reducing agent (Sundholm et al., 1999). C reacts with the O associated with the Fe when the C is present as solid carbon, as carbon dissolved in a metal phase, or as carbon present in a gaseous phase as carbon monoxide (CO) gas (Burgo, 1999; Turkdogan and Fruehan, 1999). Different technologies are available (Feinman, 1999) to exploit these reactions, with the blast furnace being the main technology (Burgo, 1999). The liquid pig iron tapped from the
Introduction to the production of clean steel blast furnace is saturated in C, with typical contents of 3.5% to 4.4% (Burgo, 1999). Unfortunately, iron ores contain not only Fe but also other elements, all present at higher oxidation states in the form of minerals. These elements include silicon (Si), manganese (Mn), phosphorus (P), aluminium (Al), magnesium (Mg), calcium (Ca), titanium (Ti), potassium (K), sodium (Na), and sulphur (S) (Burgo, 1999; Poveromo, 1999). During the ironmaking process, the oxidation states of some of these elements – especially Si, Mn, and P – are reduced to zero as well. Apart from Fe and C, the liquid pig iron tapped from a blast furnace therefore also contains Si (1.5%), Mn (1.0–2.0%) and P (<0.4%) as major components (Burgo, 1999). In ironmaking, coal and anthracite, or products derived from them, are used as sources of C (Sundholm et al., 1999). As in the case of iron ores, the coal and anthracite contain not only carbon, but also other elements in the form of gangue minerals – most importantly S. This S, together with the S present in the iron ore, will report to the pig iron, resulting in S contents of 0.05% or less, typical for liquid pig iron tapped from a blast furnace (Burgo, 1999).
Steelmaking To produce steel suitable for car body manufacturing, the liquid pig iron tapped from a blast furnace has to be refined to reduce the C, Si, Mn, S, and P content. In the first refining step, O2 gas is blown under controlled conditions into the liquid pig iron, where it reacts preferentially with C, Si, and Mn (Fruehan, 1998), increasing their oxidation states from zero to C2+ or C4+ as CO or CO2 gas, and to Si4+ as SiO2, and Mn2+ as MnO, both of the latter reporting to a liquid slag phase. By controlling temperature and slag chemistry, P is also oxidized from zero to a higher oxidation state, and S to S2+ by forming CaS, both reporting to the slag phase (Miller et al., 1998). As with ironmaking, different steelmaking technologies are available (Fruehan and Nassaralla, 1998; Jones, Bowman, and Lefrank, 1998; Miller et al., 1998) to exploit these reactions, with the basic oxygen furnace (BOF) being widely applied in the treatment of liquid pig iron (Miller et al., 1998). During tapping of the BOF, the refined steel is separated from the slag. After O2 blowing, the dissolved O content of the steel is too high for casting purposes and has to be reduced. This reduction is achieved by adding Al or Si, which react with the dissolved O to form Al2O3 or SiO2 (Kor and Glaws, 1998). Under ideal conditions the reaction products report to the slag phase. Under non-ideal conditions, the reaction products remain in the steel as nonmetallic inclusions. Therefore, steel producers actively manage the inclusion content and morphology of their products through the introduction of Ca additions and soft purging with Ar gas. The treatment of the steel described here occurs in the ladle furnace (LF) (Kor and Glaws, 1998). Treatment in the LF also includes further reduction of the S content of the steel through synthetic slag additions as well as final adjustments to the chemical composition of the steel (Kor and Glaws, 1998). During tapping of the steel from the primary vessel (typically a BOF) or arcing at the ladle furnace, nitrogen (N) in the air dissolves into the steel. Many of the alloys and synthetic slag components added to the steel at the ladle
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furnace contain water, which has the potential to increase the dissolved hydrogen (H) content of the steel. In many instances the levels of these gases have to be reduced with vacuum oxygen degassing (VOD) technology – one of the technologies available (Kor and Glaws, 1998). VOD technology is also used to reduce the carbon content of ultralow carbon steel grades (Kor and Glaws, 1998).
Summary Figure 1 presents a summary of the iron- and steelmaking processes. ➤ In iron ore, Fe is present in an oxidized state. Iron ore contains not only Fe but also other components, such as Si, Mn, P, and S present in gangue minerals. ➤ During the ironmaking process the desired Fe is reduced from ore together with Si, S, P, and Mn. The pig iron product is saturated in C. ➤ During the steelmaking process, different refining steps drastically reduce the levels of acceptable elements dissolved in the iron, such as C, Si, and Mn, as well as unacceptable elements such as S, P, N, and H, while at the same time mitigating the effect of nonmetallic inclusions.
Iron- and steelmaking technologies applied in South Africa In South Africa, iron and steel are produced at a number of steelmaking plants (Jones, n.d.) using various technologies. ArcelorMittal South Africa operates four steelworks named after the towns they are based in. EVRAZ Highveld Steel is based in Emalahleni, and Columbus Stainless in Middelburg; SCAW Metals operates a steelworks in Germiston, Cape Gate in Vanderbijlpark, and SA Steelmakers in Cape Town (Delport, 2014).
Figure 1 – A very simplistic overview of clean steel production The Journal of The Southern African Institute of Mining and Metallurgy
Introduction to the production of clean steel The largest integrated steel plant is the ArcelorMittal South Africa Vanderbijlpark Works (ArcelorMittal, n.d.-a). The Vanderbijlpark Works utilizes blast furnaces for the production of pig iron and rotary kilns for the production of direct reduced iron (DRI) – a product in the solid state (ArcelorMittal, n.d.-b). Both pig iron and DRI are produced from a combination of lumpy iron ore and sintered ore fines. Basic oxygen furnaces (BOFs) are applied for the production of primary steel. The electric arc furnaces (EAFs) were decommissioned in 2012 (Mathews, 2012). Secondary steelmaking is conducted in ladle furnaces, followed by degassing in either a Ruhrsthal Heraeus (RH) degasser or vacuum arc degasser (VAD). ArcelorMittal South Africa operates three other plants (ArcelorMittal, n.d.-b): Saldanha Works, Newcastle Works, and Vereeniging Works. The process flow sheet and technologies applied at Saldanha Steel are discussed in the next section in more detail. At the Newcastle Works, iron is made from lumpy and sintered iron ore in a blast furnace. BOF technology is used for primary steelmaking, and ladle furnace and RH degasser technologies for secondary steelmaking. The Vereeniging Works does not have an ironmaking facility. During the primary steelmaking step, scrap is melted in EAFs. Secondary steelmaking technologies applied are the ladle furnace and vacuum degassing. At EVRAZ Highveld Steel, iron is made using a combination of rotary kilns for solid-state reduction and open-bath furnaces for final reduction and melting (Steinberg, Geyser, and Nell, 2011). The choice of technology was driven by the high levels of titania (TiO2) present in the magnetite ore. As the pig iron tapped from the open-bath furnaces contains high levels of vanadium, primary steelmaking is conducted in two steps: a soft oxygen blow at the shaking ladles to remove the vanadium, followed by treatment in BOFs. Ladle furnace technology is the only secondary steelmaking technology applied.
The only facility for the production of stainless steel in South Africa is Columbus Stainless, which uses EAF technology to melt recycled steel scrap together with metal alloys containing Cr and Ni, and an argon-oxygen decarburizer (AOD) for refining (Columbus Stainless, n.d.). No secondary steelmaking technologies are described.
Production of clean steel at Saldanha Steel ArcelorMittal Saldanha Works was commissioned in 1998, with a unique combination of production units (Figure 2). A Corex unit produces hot metal, and the surplus gas is fed into a Midrex unit that produces DRI. Both products are fed into a twin-shell Conarc furnace, which is a hybrid EAF/BOF. The steel is then treated in a ladle furnace where final chemical adjustments are made before being sent to a thin slab caster (TSC). Slab temperatures are equalized in a roller hearth furnace (RHF) where surplus gas from the ironmaking units is burned. In a seven-stand hot strip mill (HSM), the slabs are reduced to their final thickness, mainly ≤1.5 mm. A temper mill (TM) improves the flatness of the strip, and a packaging unit puts strapping around it for shipment to customers mainly in East and West Africa. The Corex unit produces hot metal in a configuration of a split blast furnace. In the bottom section, the melter-gasifier, coal and coke are gasified with 95% pure O2 to form CO gas and energy. The gas is fed upwards into the second section, a shaft pre-loaded with iron ore, iron pellets, and coke moving downward. The gas strips the extra oxygen from the iron ore, and the metallized pellets gravitate into the high-temperature zone of the melter where they are further reduced and melted. In the process, some C, Si, P, and S from the coal goes into solution into the molten iron. Most of the SiO2 and Al2O3 from the coal is removed by the lime and dolomite added to make a fluid slag. The excess gas is cleaned by a wet scrubber, and the CO2 is removed by vacuum pressure swing adsorption (VPSA).
The Journal of The Southern African Institute of Mining and Metallurgy
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Figure 2 – Flow sheet applied at Saldanha Steel for the production of mild steel from iron ore
Introduction to the production of clean steel in refractory-lined ladles is stirred by argon injected from the bottom of the ladle. The temperature is once again increased through the use of an electric arc. Aluminium additions remove the dissolved oxygen from the steel and the CaO from the lime combines with sulphur to remove the sulphur from the steel. At the ladle furnace, alloy additions take the steel to the final specification. No late addition of aluminium is allowed as it can form smaller oxide inclusions that are difficult to float out. For 10 minutes before calcium injection, low stirring with argon is carried out to help float out inclusions. Just before the ladle is sent to the TSC, calcium is injected to combine with alumina particles present in the steel bath to form a lower melting point calcium-aluminate that will remain liquid during the casting process. Another 5 minutes of soft stirring helps to remove some of the aluminates. The TSC utilizes a water-cooled copper mould to freeze the liquid steel and produce slabs for the rolling mill. The ascast thickness of the slabs is 85 mm. To prevent oxidation of the steel or aluminium in the steel, contact with the atmosphere is limited. A refractory shroud with argon around it protects the steel during transfer from the ladle to tundish. The tundish serves as a buffer between ladles to enable continuous casting. An artificial slag covers the top of the steel in the tundish to prevent oxidation and decrease energy loss. During transfer from the tundish to the mould, the steel is protected with a submerged entry nozzle (SEN) made from alumina. Owing to the alumina construction, there is a risk that small alumina inclusions, products of earlier oxidation, could stick to the perimeter of the hole and reduce steel flow by clogging. Besides shielding from the atmosphere at the TSC, several steps at the LF ensure that the steel is clean enough to be cast and rolled to a 1 mm sheet. Table I summarizes the typical chemical compositions and temperatures of the hot metal and steel at the different production stages.
The gas is then heated and passed into a shaft containing iron ore and pellets, similar to that of the Corex unit. Here the oxygen is more thoroughly stripped by the gas, resulting in >90% metallization (typically 90–92%), and DRI is produced. All impurities from the ore and pellets remain in the DRI. The DRI is cooled by injection of liquefied petroleum gas (LPG), a mixture of propane and butane, which ‘cracks’ and deposits carbon on the DRI. The hot metal can then be desulphurized by the addition of calcium carbide (CaC2), which combines with the S dissolved in the hot metal to form CaS in a slag on top of the ladle. From here the hot metal is charged into one of the twin shells of the Conarc. The name is derived from ‘converter and arc furnace’. In converters, steel is produced from hot metal by the injection of oxygen via a top lance. In an arc furnace, steel scrap and DRI are melted using electrical energy delivered by carbon electrodes. The Conarc at Saldanha Works consists of two refractorylined vessels that share one oxygen top lance and one electrode gantry between them. In the first step, oxygen is blown to combine with the Si, P, and C to form oxides that report to the slag floating on top of the iron, or as gaseous CO. Burned lime (CaO) and dolomite (CaMgO2) are added to help with removal of P, and to protect the MgO refractories. The CO gas is withdrawn from the furnace and combusted with air before being sent to the baghouse for the removal of fine particles. After the blowing phase, DRI is charged while electrical energy is added via three electrodes. Once again, lime and dolomite are added to form the slag together with SiO2 from the DRI. At the same time, a door lance blows oxygen into the metal to oxidize the remainder of the carbon in the bath. To help promote a foaming slag, carbon is blown into the slag to react with the oxidized iron (FeO) that inevitably forms when oxygen is injected. The foam protects the refractories from arc flare and increases the energy transfer from the arc to the bath. When the steel reaches 1630°C it is tapped into a ladle, taking care not to transfer the furnace slag with the steel. During tapping, additions of lime, dolomite, and alumina form a new synthetic slag. Carried-over furnace slag will lead to an increase in silicon when aluminium additions reduce the SiO2. The ladle furnace consists of two stations where the steel
School on production of clean steel The Center for Iron and Steelmaking Research at Carnegie Mellon University (CMU) in Pittsburgh, Pennsylvania, USA has a proud history in iron- and steelmaking. Collaboration between CMU and the South African iron and steel industry
Table I
Chemical composition and temperature of hot metal and steel Hot metal tapped from Corex
C (%) P (%) Si (%)
Specification 4.3–4.8 0.12–0.16 0.5–0.8
Typical 4.3–4.8 0.12–0.14 0.5–0.9
S (%) Al (%) Ca (%)
0.020–0.060 -
0.02–0.06 -
Temp (°C)
1300–1380
1250–1350
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DRI from Midrex
Specification
-
Typical 1.2–1.4 0.12–0.14 3.0–6.0 as SiO2 0.02–0.06 -
VOLUME 115
Primary steel after blow in ConArc
Primary steel when taped from ConArc
Secondary steel after treatment at ladle furnace
Specification 0.2–0.5 0 0
Typical 0.3–0.5 0.12–0.14 0.0
Specification 0.040–0.050 <0.015 0
Typical 0.040–0.050 <0.015 0.0
Specification 0.045–0.060 <0.015 <0.030
Typical 0.045–0.065 <0.015 <0.030
0.020–0.050 -
0.02–0.06 -
0.010–0.030 -
0.02–0.06 -
1550–1580
1560–1600
1630–1660
1630–1670
<0.008 0.025–0.045 0.0020– 0.0025 1585–1595
<0.008 0.025–0.045 0.0020– 0.0025 1596–1685
The Journal of The Southern African Institute of Mining and Metallurgy
Introduction to the production of clean steel has included a school on steelmaking presented by Professor Richard Fruehan in Vanderbijlpark in 1996. Professor Chris Pistorius from the University of Pretoria joined Professor Fruehan in 2008 and is now the POSCO Professor of Iron and Steelmaking in the Department of Materials Science and Engineering at CMU. Professor Pistorius continues the collaboration between CMU and the South African iron and steel industry by addressing a number of topics under the headings ‘controlling dissolved elements’ and ‘controlling microinclusions’. The work done by Professor Pistorius describes the relevant process conditions (temperatures, oxygen activity, slag basicity, stirring) in the blast furnace, steelmaking converter, EAF, ladle furnace, and caster; and investigates ways in which dissolved elements are controlled both on a theoretical and on a practical level. On the topic of micro-inclusions, Professor Pistorius has examined the principles of control, sources of micro-inclusions, and techniques used to assess micro-inclusions. A future ‘Clean Steel’ event would not only benefits South African steel producers, but also allow suppliers of raw materials and consumables to obtain a better understanding of their clients’ perspectives on iron- and steelmaking; and inform downstream consumers of the challenges faced by their suppliers.
JONES, J.A.T., BOWMAN, B., and LEFRANK, P.A. 1998. Electric furnace steelmaking. Making, Shaping and Treating of Steel - Steelmaking and Refining Volume. 11th edn. Fruehan, R.J. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 525–660.
Conclusions
MILLER, T.W., JIMENEZ, J., SHARAN, A., and GOLDSTEIN, D.A. 1998. Oxygen steelmaking processes. The Making, Shaping and Treating of Steel Steelmaking and Refining volume. 11th edn. Fruehan, R.J. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 475–524.
Acknowledgements This paper is published with the permission of Mintek. Thanks are due to Elzaan Behrens for flow sheets of the different steelworks, and to Professor Chris Pistorius for his helpful comments.
References ARCELORMITTAL. Not dated (a). Vanderbijlpark works overview. www.arcelormittalsa.com/Operations/VanderbijlparkWorks/Overview.aspx Accessed 17 Nov. 2014. ARCELORMITTAL. Not dated (b). Operations production processes. www.arcelormittalsa.com/Operations/ProductionProcesses.aspx Accessed 17 Nov. 2014. BURGO, J.A. 1999. The manufacture of pig iron in the blast furnace. Making, Shaping and Treating of Steel - Ironmaking Volume. 11th edn. Wakelin, D.H. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 699–740. COLUMBUS STAINLESS. Not dated. Simplified process for making stainless steel. www.columbus.co.za/processes.html Accessed 17 Nov. 2014. DELPORT, H.M.W. 2014. Personal communication. The Journal of The Southern African Institute of Mining and Metallurgy
FRUEHAN, R J. 1998. Overview of steelmaking processes and their development. Making, Shaping and Treating of Steel - Steelmaking and Refining Volume. 11th edn. Fruehan, R.J. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 1–12. FRUEHAN, R.J. and NASSARALLA, C.L. 1998. Alternative oxygen steelmaking processes. Making, shaping and Treating of Steel - Steelmaking and Refining Volume. 11th edn. Fruehan, R.J. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 743–759.
JONES, R.T. Not dated. Pyrometallurgy in Southern Africa. http://www.pyrometallurgy.co.za/PyroSA/ Accessed 17 Nov. 2014. KOR, G.J.W. and GLAWS, P.C. 1998. Ladle refining and vacuum degassing. Making, Shaping and Treating of Steel - Steelmaking and Refining Volume. 11th edn. Fruehan, R.J. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 661–713. MATHEWS, C. 2012. Steel producers - prolonged downturn. www.financialmail.co.za/business/money/2012/11/14/steel-producers--prolonged-downturn
OXFORD UNIVERSITY PRESS. Not dated. Pyrometallurgy, n. http://www.oxforddictionaries.com/definition/english/pyrometallurgy Accessed 17 Nov. 2014. PISTORIUS, P., VERMA, N., and FRUEHAN, R.J. 2011. Calcium modification of alumina and spinel inclusions in aluminum-killed steel. http://saimm.co.za/about-saimm/branchessaimm/58-saimmpretoria/173-calcium-modification-of-alumina-inclusions-in-steel-reaction-mechanisms POVEROMO, J.J. 1999. Iron ores. Making, Shaping and Treating of Steel Ironmaking volume. 11th edn. Wakelin, D.H. (ed.). AISE Steel Foundation Pittsburgh, Pensylvania. pp. 547–642. SINGH, H. 2003. Introduction to hydroforming. Fundamentals of Hydroforming. Society of Manufacturing Engineers, Michigan. pp. 1–17. STEINBERG, W.S., GEYSER, W., and NELL, J. 2011. The history and development of the pyrometallurgical processes at Evraz Highveld Steel & Vanadium. Southern African Pyrometallurgy 2011. Jones, R.T. and Den Hoed, P. (eds). Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 63–76. SUNDHOLM, J. L., VALIA, H. S., KIESSLING, F. J., RICHARDSON, J., BUSS, W. E., WORBERG, R., SCHWARZ, U., BAER, H., CALDERON, A., and DINITTO, R.G. 1999. Manufacture of metallurgical coke and recovery of coal chemicals. Making, Shaping and Treating of Steel - Ironmaking volume. 11th edn. Wakelin. D.H. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 381–546. TURKDOGAN, E.T. and FRUEHAN, R.J. 1999. Fundamentals of iron and steelmaking. Making, Shaping and Treating of Steel - Ironmaking volume. 11th edn. Wakelin. D.H. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 37–160. WORLDAUTOSTEEL. 2013. Steel elimnates weight gap with aluminium for car bodies. http://www.worldsteel.org/dms/internetDocumentList/pressrelease-downloads/2013/Steel-closes-gap-with-aluminium-for-carmaking/Steel closes gap with aluminium for car making.pdf. ◆ VOLUME 115
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The conversion of iron ore to steel requires several processing steps. For each processing step, different types of technologies are available and applied in South Africa. The plant at Saldanha Steel is an example of an integrated minimill that produces steel from iron ore. For a pyrometallurgist producing steel, the term clean steel refers to the control of the dissolved elements and the nonmetallic inclusions in the steel. A school focusing on the transfer of knowledge on both aspects would be beneficial not only to pyrometallurgists producing steel, but also to their suppliers and clients.
FEINMAN, J. 1999. Direct reduction and smelting processes. Making, Shaping and Treating of Steel - Ironmaking Volume. 11th edn. Wakelin, D. (ed.). AISE Steel Foundation, Pittsburgh, Pensylvania. pp. 741–780.
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Crown Plaza, Johannesburg · 19–20 August 2015 THEME The theme of the conference is ‘Geostatistical Geovalue—Rewards and Returns for Spatial Modelling’, a theme which emphasizes the improvement in, or addition to, value that spatial modelling can bring to the process of mine evaluation and mineral resource and reserve estimation. Spatial modelling of earth related data to estimate or enhance attributed value is the principle domain of geostatistics, the broad content of the Danie Krige Commemorative Volumes, and the focus of this conference.
OBJECTIVES The conference provides authors who have recently published papers in the SAIMM’s Danie Krige Commemorative Volumes, a platform to present their research. In addition an invitation to geostatisticians, resource estimation practitioners, and those with an interest in geostatistics to present new papers for inclusion in the proceedings is now open. The conference will explore advances in technology and methodologies, and case studies demonstrating the application of geostatistics. It will cross the commodity boundaries, with applications presented from precious to base metals, and diamonds. This is a valuable opportunity to be involved in constructive dialogue and debate, and to keep abreast with the best practice in this specialist field.
WHO SHOULD ATTEND The conference provides a platform for: • local and international geostatisticians • geologists • engineers • researchers • software vendors • mineral resource managers and practitioners, across the mining industry • consultancy and academia, to present their work and contribute to the advancement of this field.
BACKGROUND Geostatistics constitutes a globally accepted technical approach to mineral resource-reserve estimation and the basic toolkit for mine evaluation practitioners. Following the call for papers and the publication of the Danie Krige Commemorative Volumes, the SAIMM invites submission of papers for the Danie Krige Geostatistical Conference to be held in Johannesburg, South Africa, 19–20 August 2015. CONFERENCE SUPPORTER
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Sponsorship opportunities are available. Companies wishing to sponsor or exhibit should contact the Conference co-ordinator.
Conference Announcement
INTERNATIONAL ACTIVITIES 2015 14–17 June 2015 — European Metallurgical Conference Dusseldorf, Germany Website: http://www.emc.gdmb.de 14–17 June 2015 — Lead Zinc Symposium 2015 Dusseldorf, Germany Website: http://www.pb-zn.gdmb.de 16–20 June 2015 — International Trade Fair for Metallurgical Technology 2015 Dusseldorf, Germany Website: http://www.metec-tradefair.com 6–8 July 2015 — Copper Cobalt Africa Incorporating The 8th Southern African Base Metals Conference Zambezi Sun Hotel, Victoria Falls, Livingstone, Zambia Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 15–16 July 2015 — Virtual Reality and spatial information applications in the mining industry Conference 2015 University of Pretoria 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 6–7 August 2015 — MINPROC 2015: Southern African Mineral Beneficiation and Metallurgy Conference Vineyard Hotel, Newlands, Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 19–20 August 2015 — The Danie Krige Geostatistical Conference: Geostatistical geovalue —rewards and returns for spatial modelling Crown Plaza, Johannesburg Contact: Yolanda Ramokgadi
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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 25–27 August 2015 — Coal Processing – Unlocking Southern Africa’s Coal Potential Graceland Hotel Casino and Country Club Secunda Contact: Ann Robertson Tel: +27 11 433-0063 26–28 August 2015 — MINESafe 2015—Sustaining Zero Harm: Technical Conference and Industry day Emperors Palace Hotel Casino, Convention Resort, Johannesburg Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za 28 September-2 October 2015 — WorldGold Conference 2015 Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Gauteng, 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 12–14 October 2015 — Slope Stability 2015: International Symposium on slope stability in open pit mining and civil engineering In association with the Surface Blasting School 15–16 October 2015 Cape Town Convention Centre, Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 20 October 2015 — 13th Annual Southern African Student Colloquium Mintek, Randburg, Johannesburg Contact: Yolanda Ramokgadi 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
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28–30 October 2015 — AMI: Nuclear Materials Development Network Conference Nelson Mandela Metropolitan University, North Campus Conference Centre, Port Elizabeth Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 8–13 November 2015 — MPES 2015: Twenty Third International Symposium on Mine Planning & Equipment Selection Sandton Convention Centre, Johannesburg, South Africa Contact: Raj Singhal E-mail: singhal@shaw.ca or E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za
2016 14–17 March 2016 — Diamonds still Sparkle 2016 Conference Botswana Contact: Yolanda Ramokgadi 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 13–14 April 2016 — Mine to Market Conference 2016 South Africa Contact: Yolanda Ramokgadi 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
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17–18 May 2016 — The SAMREC/SAMVAL Companion Volume Conference Johannesburg Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za May 2016 — PASTE 2016 International Seminar on Paste and Thickened Tailings Kwa-Zulu Natal, South Africa Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 9–10 June 2016 — 1st International Conference on Solids Handling and Processing A Mineral Processing Perspective South Africa Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 1–3 August 2016 — Hydrometallurgy Conference 2016 ‘Sustainability and the Environment’ in collaboration with MinProc and the Western Cape Branch Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 16–19 August 2016 — The Tenth International Heavy Minerals Conference ‘Expanding the horizon’ Sun City, 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
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21–22 October 2015 — Young Professionals 2015 Conference Making your own way in the minerals industry Mintek, Randburg, 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
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Webber Wentzel
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Weir Minerals Africa
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The Journal of The Southern African Institute of Mining and Metallurgy
Forthcoming SAIMM events...
IP PONSORSH EXHIBITS/S ng to sponsor ishi Companies w ese t at any of th and/or exhibi contact the events should rdinator -o conference co ssible as soon as po
SAIMM DIARY 2015 or the past 120 years, the Southern African Institute of Mining and Metallurgy, has promoted technical excellence in the minerals industry. We strive to continuously stay at the cutting edge of new developments in the mining and metallurgy industry. The SAIMM acts as the corporate voice for the mining and metallurgy industry in the South African economy. We actively encourage contact and networking between members and the strengthening of ties. The SAIMM offers a variety of conferences that are designed to bring you technical knowledge and information of interest for the good of the industry. Here is a glimpse of the events we have lined up for 2015. Visit our website for more information.
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◆ CONFERENCE Copper Cobalt Africa in association with The 8th Southern African Base Metals Conference 6–8 July 2015, Zambezi Sun Hotel, Victoria Falls, Livingstone, Zambia ◆ CONFERENCE Virtual Reality and spatial information applications in the mining industry Conference 2015 15–16 July 2015, University of Pretoria, Pretoria ◆ CONFERENCE MINPROC 2015: Southern African Mineral Beneficiation and Metallurgy Conference 6–7 August 2015, Vineyard Hotel, Newlands, Cape Town ◆ CONFERENCE The Danie Krige Geostatistical Conference 2015 19–20 August 2015, Crown Plaza, Johannesburg ◆ CONFERENCE MINESafe 2015—Sustaining Zero Harm: Technical Conference and Industry day 26–28 August 2015, Emperors Palace Hotel Casino, Convention Resort, Johannesburg ◆ CONFERENCE World Gold Conference 2015 28 September–2 October 2015, Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Muldersdrift ◆ SYMPOSIUM International Symposium on slope stability in open pit mining and civil engineering 12–14– October 2015 In association with the Surface Blasting School 15–16 October 2015, Cape Town Convention Centre, Cape Town ◆ COLLOQUIUM 13th Annual Southern African Student Colloquim 2015 20 October 2015, Mintek, Randburg, Johannesburg
For further information contact: Conferencing, SAIMM P O Box 61127, Marshalltown 2107 Tel: (011) 834-1273/7 Fax: (011) 833-8156 or (011) 838-5923 E-mail: raymond@saimm.co.za
Website: http://www.saimm.co.za
◆ CONFERENCE Young Professionals 2015 Conference 21–22 October 2015, Mintek, Randburg, Johannesburg ◆ CONFERENCE AMI: Nuclear Materials Development Network Conference 28–30 October 2015, Nelson Mandela Metropolitan University, North Campus Conference Centre, Port Elizabeth ◆ SYMPOSIUM MPES 2015: Twenty Third International Symposium on Mine Planning & Equipment Selection 8–12 November 2015, Sandton Convention Centre, Johannesburg, South Africa