Saimm 201502 feb by SAIMM

VOLUME 115

NO. 2

FEBRUARY 2015

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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 DRC

S. Maleba

Johannesburg

I. Ashmole

Namibia

N. Namate

Pretoria

N. Naude

Western Cape

C. Dorfling

Zambia

H. Zimba

Zimbabwe

E. Matinde

Zululand

C. 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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FEBRUARY 2015

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)

* * * * * * * * * * * * * * * * * * * * * * *

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

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 A DVA NCED IN A NY 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.

NO. 2

FEBRUARY 2015

Contents Journal Comment by R. Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

SANCOT Conference Announcement President’s Corner by J.L. Porter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

General Papers Effect of inorganic chloride on spontaneous combustion of coal by Y.-B. Tang, Z.-H. Li, Y.I. Yang, D.-J. Ma, and H.-J. Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

A validation study of the King stratification model by L.C. Woollacott, M. Bwalya, and L. Mabokela. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The behaviour of free gold particles in a simulated flash flotation environment by T.D.H. McGrath, J.J. Eksteen, and J. Heath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103

The role of gravity flow in the design and planning of large sublevel stopes by R. Castro and M. Pineda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

113

Continuous improvement management for mining companies by M. Vanek, K. Spakovska′, M. Mikola′s, and L. Pomothy . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119

A decision analysis guideline for underground bulk air heat exchanger design specifications by M. Hooman, R.C.W. Webber-Youngman, J.J.L. du Plessis, and W.M. Marx . . . . . . . . . . . . .

125

131

The grate-kiln induration machine – history, advantages, and drawbacks, and outline for the future by J. Stjernberg, a, O. Isaksson and J.C. Ion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137

Estimating mine planning software utilization for decision-making strategies in the South African gold mining sector by B. Genc, C. Musingwini, and T. Celik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

145

Thermophysical properties of rocks from the Bushveld Complex by M.Q.W. Jones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153

A new preparation scheme for a difficult-to-float coking coal by column flotation following grinding by Yinfei Liaoa, Yijun Caoa, Zhongbo Hub, and Xiuxiang Taoc . . . . . . . . . . . . . . . . . . . . . . . .

161

Technological developments for spatial prediction of soil properties, and Danie Krige’s influence on it by R. Webster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

165

International Advisory Board

VOLUME 115

NO. 2

FEBRUARY 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 H. Potgieter, Manchester Metropolitan University, United Kingdom E. Topal, Curtin University, Australia

The Journal of The Southern African Institute of Mining and Metallurgy

FEBRUARY 2015

iii

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Editorial Consultant

VOLUME 115

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

he February edition of the Journal contains 12 papers that have something for everyone. For the extractive metallurgists, there are papers on jigging and the flotation of coal and gold. For the mining engineers, there are contributions on stope design, mine refrigeration, and decision-making strategies for the selection of heat exchangers and for mine planning. For the physical metallurgist, there are a number of papers on steelmaking. For the geoscientist, there is a review of soil science. Finally, for those who are prepared to come out of the closet and admit that they ply their trade in the murky world of ‘management’, there is a paper on methods for continuous improvement. It is utterly inappropriate for me to attempt to summarize each of the 12 papers and comment on their relevance and impact, and I will hide under the somewhat weak excuse that this column does not provide enough space for me to do so. Besides, after reading the titles in the Contents, you can simply skip to the paper’s Abstract to decide whether to delve into the detail. So, how do I fill this column with relevant comment? I suffered sleepless nights worrying about this Journal Comment when it occurred to me that every paper, in some way or other, attempts to reduce uncertainty. What is research for, other than to shed light on a topic, make it make more understandable, more known, more predictable, and more useful? As that deep thinker and well-known political scientist (whose name is on the tip of my tongue, but which I just can’t seem to spit out) stated recently with utter conviction: ‘There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don't know we don't know.’ Quite so – I bow to his superior intellect – I could not have put it better myself. It seems that every generation is convinced that uncertainty in their lifetime is increasing, that the world is becoming more, not less, complex. It is frequently said that the more we know, the less we know. Buckminster Fuller, who first reported on the existence of hollow spheres of exactly 60 carbon atoms (so called ‘Buckyballs’), wrote that: ‘Everything you’ve learned in school as ”obvious” becomes less and less obvious as you begin to study the universe. For example, there are no solids in the universe. There’s not even a suggestion of a solid. There are no absolute continuums. There are no surfaces. There are no straight lines.’ To use that great South African expression ‘Ja-wellno-fine ’ – it’s all perfectly clear to me! Whereas I identify with these sentiments on a philosophical and emotional level, they are at odds with the so-called rational side of

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FEBRUARY 2015

my brain that appreciates that the more we know the better the engineering outcomes. Technology is advancing at an increasing pace, only because of incredible advances in knowledge. From a purely technological standpoint, the more we know, the more we know … if you still follow my drift! (I considered deleting the last sentence for fear that I will be placed in the same basket category as the Donald Duck of the infamous ‘Unknown Unknowns’, but I am sure that you are still following my line of clear thought). To return to the mining industry, if you ever wanted evidence of increasing uncertainty, make the time to read the just released ‘Mining Financial Reporting Survey 2014’ prepared by KPMG, which reports that major global mining companies suffered impairment losses of $70 billion in 2013/14. Most of the companies surveyed admitted that falling commodity prices had negatively affected the carrying value of their underlying assets. Some coal mines are temporarily mothballed, while many other mines are cutting back on their output. Add to that the inherent instability of the global economy, climate change, and political uncertainty. Who would want the job of preparing the first draft of any mining company’s annual business plan? – whatever you write is guaranteed to be trashed by your colleagues! In South Africa, we can throw into the unsavoury basket of uncertainties, currency fluctuations, power disruptions caused by load shedding, labour relations, and the Mineral and Petroleum Resources Development Act (MPRDA) which has just been returned to Parliament for redrafting. Despite the large number of ‘known unknowns’ in the mining industry, there is obviously still value to be found and made in mining, but caution, rather than bravado, is currently king in the short term. In the medium to long term, I have no doubt that the saviour will be found in improved technology, as it nearly always is. In South Africa, the key concept is mechanization in mining, and I anticipate that we will soon see an increasing number of papers being published on this topic in the Journal. The only comments that I can add in closing to those feeling the heat in the boardroom is that cowpersons don’t cry. Or as Lt. Col. Frank Slade said: ‘If you make a mistake and get all tangled up, you just tango on’ (handwritten answers as to the origin of the above quotation must be mailed to me on a plain postcard to qualify for the prize – googling the answer is strictly prohibited). Happy reading!

R. Paul

The Journal of The Southern African Institute of Mining and Metallurgy

NFERENC O C T E2 O C

Due to the global increase in urbanisation, pressure is being placed on governments and the public sector to provide expanded services such as safe and reliable public transport, electricity, gas, water and sewage facilities. This results in further development of road, rail and metro infrastructure. However, the availability of space for this necessary infrastructure in the urban environment is becoming a major challenge. In order to keep up with this increasing demand, Civil designers and Contractors are having to resort to tunnelling more than ever before and, in order to deliver these services timeously, mechanised underground excavation and support installation is proving to be cost effective. The fast, efficient and safe abstraction of raw mineral reserves is of strategic importance for leading mining companies. However, rising labour costs, coupled with labour unrest, impact heavily on the ability of companies to achieve these goals. The South African mining sector needs to mechanise at a faster pace in order to remain globally competitive. This is especially true when developing stopes and vertical shafts. A typical deep level mine has a life of 30 to 40 years, meaning that shafts are not sunk regularly and the specialised expertise may not be readily available.

OBJECTIVES The conference is intended to promote interaction and closer communication between personnel and companies in the mining and civil industries, and to create a platform where expertise and experience gained in mechanised underground excavation can be shared.

BACKGROUND

are hosting the

SAN

The South Afican National Committee on Tunnelling in affliation with

Mechanised Underground Excava on THEME This conference is in response to the Civil and Mining industry being under immense pressure to deliver projects fast, efficiently and as safely as possible. Mechanised underground excavation and support installation is proving to be an invaluable and cost effective tool in the execution of a project. Technology exists for mechanised excavation where tunnels can be excavated from as small as 300mm to in excess of 18 metres in order to access ore bodies, build road or railway tunnels, facilitate the installation of utilities, construct storage caverns for gas and oil, etc. It is recommended that delegates interested in the mining application of tunnel boring attend both days. PRESENTERS AND TOPICS INCLUDE:

WHO SHOULD ATTEND The conference should be of value to: • All stakeholders involved with underground excavation • Stakeholders involved in the shaft sinking arena • Mine executives and management • Civil construction companies • Stakeholders from Government, local Municipalities and Water Authorities • Engineering design and consulting companies • Project management practitioners • Mine owners and entrepreneurs • Technology suppliers and consumers • Health, safety and risk management personnel and officials • Government minerals and energy personnel • Research and academic personnel.

23–24 April, 2015 - Conference 25 April 2015 - Technical Visit Elangeni Maharani, Durban For further information contact: Conference Co-ordinator, Yolanda Ramokgadi SAIMM, P O Box 61127, Marshalltown 2107 Tel: +27 11 834-1273/7 · Fax: +27 11 833-8156 or +27 11 838-5923 E-mail: yolanda@saimm.co.za · Website: http://www.saimm.co.za

Mechanised excavation – mining and civil industry Dr Karin Bap• p• ler, Herrenknecht Sea Outfalls, utility tunnelling Swen Weiner, Herrenknecht Utility tunnelling, the Durban Aqueous tunnel beneath the harbour entrance Frank Stevens, (ex Deputy Head, Water and Sanitation, eThekwini Municipality), President of IMESA Mechanised excavation – mining Danie Roos, Herrenknecht Vertical excavation utilising the V Mole System Allan Widlake, Murray & Roberts Cementation Use of the EPB TBM on Gautrain Alain Truyts, Gibb Point Road Micro Tunnel Montso Lebitsa, Hatch Cutting in Stoping Rod Pickering, Sandvik Mining and Construction Cutting Technology – Past, Present and Future Trends Prof. Jim Porter, President SAIMM, (Jim Porter Mining Consulting) Mechanised excavation in the civils industry – Past, Present and Future Ron Tluczek, SANCOT Chairman (Executive – Geotechnical, Africa AECOM ZA) Mechanised Sprayed Concrete Chris Viljoen, Functional Head, Hydropower, Dams, Tunnels and Geotechnics SMEC

Conference Announcement

tʼs iden s e r P er Corn

n 30 January 2015, the Honourable Minister of Mineral Resources, Advocate Ngoako Ramatlhodi, issued a statement announcing the 2014 health and safety statistics for the South African mining industry. To quote, ‘There has been a marked improvement in health and safety in the sector over the past twenty years, as result (sic) of renewed focus by the Department as well as collaboration with key stakeholders.’ Results over this period show an 86% reduction for all mine fatalities, thereby achieving the lowest ever number of fatalities in the mining sector in 2014. I am not commenting on specific statistics because, as we know today, all fatalities are ultimately preventable. And yet, not so long ago (when I was a young mining engineer!), this statement would not have been accepted by many in the industry. Safety achievements were measured in months – today they are measured in years. Of course, various industry pundits have widely diverging opinions on how these remarkable statistics were achieved, ranging from the degree of stakeholder collaboration to the impact of industrial action on underground shifts worked. Personally, I would like to believe that the pace of cultural change, technology adoption, and leadership style is now having a material and lasting effect. It caused me to cast about in my recent reading for new ideas and innovations that may have a significant impact on the mining industry in the years ahead. Here are a few that I came across. 1. The ‘Internet of Things’ Many futurists predict that within the next 5 to 10 years just about any device we can imagine could be controlled through an IP address. One drawback is how to power these devices. Enter wireless charging with sound waves. In this process, conceived at the University of Pennsylvania, mechanical vibrations (sound) are turned in to electrical energy. There are plenty of vibrations in mines and we need to monitor our physical environment more efficiently with remotely powered devices. First products are due to ship in 2017. 2. Application of new materials IBM Research has (accidentally, like all good inventions) developed a new form of tough, hard recyclable plastic (thermoset) called ‘Titan’ together with its derivative ’Hydro’ which has a property that when cut it automatically closes and re-bonds. If these technologies were combined with work being done by the University of Manchester (2010 Nobel prize in physics) in building new ‘super’ materials at an atomic level (think graphene) could we one day have a lightweight, robust exoskeleton that can be worn by underground workers, providing them with their own environmental bubble and safety cocoon? These technologies are still in the laboratory. 3. Safety vision Forty per cent of 40-year-olds need glasses and 8% of all men suffer from colour deficiency. This has a direct impact on how people can work – especially in hazardous or difficult conditions. The University of California and MIT have developed vision correcting displays that are in the pre-production phase and could potentially be fitted to all devices that use digital screens (e.g. on machine dashboards) and are ‘tuned’ to the user’s specific eyesight. Most sight impairments could be compensated for with the technology – especially in developing countries where it is sometimes easier to get a mobile phone than a pair of prescription glasses. 4. Energy from low-grade heat According to the US Environmental Protection Agency, a third of all wasted energy is ’lost’ at temperatures below 100°C. It is reported that MIT has developed new efficient battery electrodes that can convert temperature differentials to electricity at temperature differentials of around 50° C. This is done by exploiting the thermogalvanic effect (look it up!). Given the temperature gradients we have in our deep gold and platinum mines, are we potentially sitting on an undiscovered new energy source for Eskom? Enough of this – there is plenty of innovation out there if you look and apply your own imagination. Lastly, please remember that the SAIMM annual banquet is on Saturday 14 March at the Sandton Convention Centre. It is an ideal time to refresh old acquaintances, make new ones, and have some rest and recreation at a time where everyone seems too busy. I spoke earlier about changing leadership styles. Taking a table at the banquet is a solid investment in the motivation of your team and maintaining energy levels, even when budgets are constrained. [Sources: Time Magazine, Fortune Magazine, Scientific American, and the internet].

The Journal of The Southern African Institute of Mining and Metallurgy

FEBRUARY 2015

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J.L. Porter President, SAIMM

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 Effect of inorganic chloride on spontaneous combustion of coal by Y.-B. Tang, Z.-H. Li, Y.I. Yang, D.-J. Ma, and H.-J. Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 The effect of five inorganic chloride compounds on the low-temperature oxidation of coal was investigated experimentally by oxidizing chloride-loaded coal samples and model compounds. The results demonstrate that inorganic chlorides can play an inhibitory role in the spontaneous combustion of coal. A validation study of the King stratification model by L.C. Woollacott, M. Bwalya, and L. Mabokela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 This paper presents a study on the ability of the King stratification model to describe the density stratification patterns that are achieved in a bed of particles under idealized conditions. Good agreement was obtained between measured and modelled data, which gives strong endorsement of the mathematical appropriateness of the core equation in the King model. The behaviour of free gold particles in a simulated flash flotation environment by T.D.H. McGrath, J.J. Eksteen, and J. Heath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 The response of free gold particles to flash flotation was determined using a free milling gold gravity concentrate and a gold powder as the gold source. Trends in free gold flotation kinetics, as well as size and milling effects, were identified for gold recovery based on the different feed types, reagent dosages, and residence times. The role of gravity flow in the design and planning of large sublevel stopes by R. Castro and M. Pineda. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 This paper discusses the role and influence of gravity flow on the design and planning of large sublevel stopes, with and without vertical dilution, based on laboratory experiments. The results of the investigation are used to develop guidelines, which would complement the currently used geotechnical considerations, towards the design and planning of large sublevel stoping operations. Continuous improvement management for mining companies by M. Vanek, K. Spakovskaâ&#x20AC;˛, M. Mikolaâ&#x20AC;˛s, and L. Pomothy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

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This paper proposes ideas for the development and application of processes that provide for continuous improvement of production and management, using the OKD mine, a producer of hard coal in the Czech Republic, as an example. The KAIZEN methodology, of Japanese origin, was chosen as the principal method of continuous quality improvement. A decision analysis guideline for underground bulk air heat exchanger design specifications by M. Hooman, R.C.W. Webber-Youngman, J.J.L. du Plessis, and W.M. Marx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 A step-by-step guide to all factors contributing to technical specifications for underground bulk air heat exchangers has been developed from environmental factors and engineering and technical requirements. The guide makes it possible to design a quick and easy fit-for-purpose technical specification for underground heat exchangers.

These papers will be available on the SAIMM website

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‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking by K.B. Nagashanmugam, M.S. Pillai, and D. Ravichandar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 This paper describes the methodology and application of the Salem Box Test, which was developed at JSW Steel Limited, to predict the suitability of coke for blast furnace use, and illustrates the advantages in making adjustments to the coal blending ratio, detecting coal contamination, and preventing bulk production of inferior coke. Experimental results show that the test is acceptable as a screening tool for regular use. The grate-kiln induration machine – history, advantages, and drawbacks, and outline for the future by J. Stjernberg, a, O. Isaksson and J.C. Ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 The grate-kiln method for producing iron ore pellets for ironmaking yields a superior and more even iron ore pellet quality compared with the straight grate process. However, certain issues exist with the grate-kiln plant, which are discussed in this paper together with some proposed practical solutions. Estimating mine planning software utilization for decision-making strategies in the South African gold mining sector by B. Genc, C. Musingwini, and T. Celik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 This paper discusses a new methodology for defining and measuring mine planning software utilization in the South African gold mining sector within an evolving data-set framework . The methodology is useful to stakeholders who are reviewing existing software combinations or are intending to purchase new software in the near future and want to estimate the comparative attractiveness of a certain software package. Thermophysical properties of rocks from the Bushveld Complex by M.Q.W. Jones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 This paper presents a compilation of thermal conductivity, heat capacity, and density data on rocks from the Bushveld Complex. Rocks encountered in deep platinum mines are particularly well characterized, and this has important implications for prediction of mine refrigeration requirements. A new preparation scheme for a difficult-to-float coking coal by column flotation following grinding by Yinfei Liaoa, Yijun Caoa, Zhongbo Hub, and Xiuxiang Taoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 The test work presented in this paper shows that column flotation after grinding is beneficial in obtaining low-ash products from difficult-to-float coking coal. Consistently better flotation results, in terms of product ash content and recovery of combustible matter, demonstrated that column flotation is more efficient than conventional flotation for the feed material tested. Technological developments for spatial prediction of soil properties, and Danie Krige’s influence on it by R. Webster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 In the 1970s when soil scientists learned of the work of Daniel Krige and Georges Matheron’s theory of regionalized variables, they then applied the mainstream geostatistical methods of spatial analysis and kriging to map plant nutrients, trace elements, pollutants, salt, and agricultural pests in soil, which has led to advances in modern precision agriculture. This paper illustrates the most significant advances, with results from research projects.

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http://dx.doi.org/10.17159/2411-9717/2015/v115n2a1 ISSN:2411-9717/2015/v115/n2/a1

Effect of inorganic chloride on spontaneous combustion of coal by Y.-B. Tang*†, Z.-H. Li†, Y.I. Yang†, D.-J. Ma†, and H.-J. Ji†

Chlorine-containing minerals are commonly present in coal. Associated minerals such as pyrite can undergo exothermic reactions. Consequently, it is of great significance to study the effect of inorganic chloride on the spontaneous combustion of coal. In this study, the effects of five inorganic chlorides (sodium chloride, magnesium chloride, potassium chloride, calcium chloride, and zinc chloride) on the spontaneous oxidation of coal were investigated. Analysis of the gaseous products of coal oxidization at low temperatures (323K to 453K) showed that the presence of inorganic chlorine in coal markedly decreases O2 consumption and the generation of CO and CO2. Samples of raw coal and chlorine-loaded coal were oxidized for 36 hours under the same experimental conditions. Infrared diffuse reflectance spectroscopy results showed that inorganic chloride can inhibit the oxidative decomposition of some functional structure components (methyl, methylene, methine, and hydroxy) in the coal. The influence of inorganic chloride on the oxidation characteristics of the functional groups in coal during spontaneous combustion was investigated using benzyl alcohol and 1-phenylpropanol as model compounds, which were tested under the same experimental conditions as the coal samples. The oxygen consumption of model compounds with and without the addition of inorganic chloride further suggested that inorganic chloride may hinder the oxygenolysis of these structures during low-temperature oxidation. This phenomenon can be attributed to the radical reaction from the perspective of radical chemistry. It can therefore be concluded that inorganic chlorides play an inhibitory role in the spontaneous combustion of coal. Keywords coal, spontaneous combustion, inorganic chloride, gaseous products, model compounds, FTIR.

Introduction Spontaneous combustion of coal is a serious problem that often occurs in the coal industry (Jones and Townend, 1945; 1949. Although several theories have been proposed to account for the phenomenon (Wang, 2006; Li, 1996; Wang, 1999; Lopez, 1998), the definitive mechanism of coal spontaneous combustion is still unknown. However, it is acknowledged that coal spontaneous combustion is a kind of oxidizing reaction (Pis et al., 2996; Itay, Hill, and Glasser, 1989). Hence, the relevant parameters during coal oxidation at low temperatures can be used to indicate the tendency of coal to spontaneously combust (Jones et al., 1998; Wang, Dlugogorski, and Kennedy, 2003). The Journal of The Southern African Institute of Mining and Metallurgy

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* College of Mining Technology, Taiyuan University of Technology, Taiyuan. † School of Safety Engineering, China University of Mining and Technology, Xuzhou. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Nov. 2012; revised paper received May 2014. FEBRUARY 2015

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Synopsis

Coal is a complex material consisting of combustible maceral and mineral components, and which contains dozens of minor elements besides various major elements including C, H, O, N etc. (Zhu, 2008). Furthermore, coal contains major impurity elements such as carbonates, sulphates, chlorides, and silicates, most of which are associated with the mineral components (Tang and Zhao, 2008; Zhang, 2009), and which are likely to influence the propensity of coal to undergo spontaneous combustion. For example, coal rich in pyrite is more likely to undergo spontaneous combustion, due to the presence of Fe2+ ions (Cole et al., 1987). Although the mechanism of this reaction has been studied extensively, there has been little work on the effect of other elements on coal spontaneous combustion to date. Many scholars have investigated the effect of adding different inorganic compounds to coal as possible fire retardants in order to control spontaneous combustion (Beamish and Arisoy, 2008; Carras and Young, 1994). A number of investigations have been carried out on the effect of mineral matter on coal liquefaction, coal char combustion, and coal pyrolysis etc. (Ma, 2011; Hanzade, Reha, and Aysegül, 1999; Li, Lu, and Jiao, 2009). However, there are few systematic studies of the influence of specific elements on coal spontaneous combustion. Chlorine is a common trace element in coal, occurring mainly as an accompanying mineral (rock salt, potassium salt, bischofite, and hydrophilite etc.) Caswell, Holmes, and Spears, 1984; Vassilev, Eskenazy, and Vassileve, 2000). We therefore investigated the effects of five inorganic chlorides on coal spontaneous

Effect of inorganic chloride on spontaneous combustion of coal combustion. In addition, we tested model compounds under the same experimental condition as supporting research, in the light of coal molecular structure and organic chemistry (Benjamin, 1984; Shinn, 1984). Investigations using model compounds can provide references for showing the oxidation characteristics of the functional groups in coal during the process of spontaneous combustion (Li, Wang, and Song, 2009). Based on the macromolecular structural model of coal (Matthews, van Duin, and Chaffee, 2011), two kinds of model compounds were employed to investigate the effect of inorganic chloride on some specific active groups during the spontaneous combustion of coal.

experiments can be divided into two groups: coal with inorganic chloride added and coal treated with deionized water only. The experimental set-up is shown in Figure 1. The coal sample (40 g) was placed into the sample tank, which included two vent lines (inlet and outlet). The sample tank consisted of a cylindrical container with the height of 105 mm and diameter of 48 mm. The hole located in the centre of the sample tank was fitted with a temperature sensor, the top of which was in the geometric centre of the sample tank. Dry air at a flow rate of 20 ml/min was provided from a compressed gas cylinder. During the reaction, the sample tank was heated from 323K to 453K at a rate of 1K/min. The gaseous products were analysed by gas chromatography (SP501N-type, Beijing East & West Analytical Instruments Co. Ltd.). The influence of inorganic chloride on the oxidation characteristics of the functional groups in coal during spontaneous combustion was investigated using model compounds. Each selected model compound must contain only one representative oxidative active group, which should be a common structure of coal. According to the molecular structure, coal contains aromatic structures and functional groups such as hydroxyls and alkanes. Therefore, benzyl alcohol and 1-phenylpropanol were adopted as the model compounds in this experiment (Table II). Firstly, 0.02 mol of model compound was mixed with 10 g acetone, 0.01 mol inorganic chloride, and 40 g inert support (Figure 2). The parameters of the inert support are shown in Table III. This mixture was then dried for 12 hours in the vacuum drying oven to ensure that the acetone completely evaporated and that the model compound was uniformly attached to the inert support. After the abovementioned pretreatment, the model compounds were tested under the same experimental conditions as the coal samples.

Experimental The test samples were collected from the no. 3 coal seam at Xutuan, Huaibei City, Anhui Province, China. The samples were crushed to a grain size of between 0.180 mm and approximately 0.250 mm for testing. The analysis (including macerals and minerals) of the no. 3 coal seam at Xutuan is shown in Table I. The additives, chemically pure (>99%) sodium chloride, potassium chloride, magnesium chloride, calcium chloride, and zinc chloride, were purchased from Sinopharm Chemical Reagent Co. Ltd. The samples were prepared by dissolving 0.05 mol of each reagent in 7 ml of deionized water and adding the solution to 100 g of coal sample with constant stirring. The samples were allowed to stand in sealed containers for 24 hours in order for fully equilibrate the solution with the coal. Before each experiment, the coal sample was dried at 40°C for 12 hours in a vacuum drying oven (Shanghai Saiou Testing Equipment Co. Ltd) in order to exclude the interference of moisture with spontaneous combustion. ‘Control’ samples were prepared using the same procedure, but without the chloride addition. The samples used in the

Table I

Analysis of no. 3 coal seam, Xutuan Moisture

Ash

(%)

Volatiles Calorific value Fixed carbon Sulphur (%)

(MJ/kg)

(%)

2.12

20.9

35.76

27.04

50.76

0.21

Organic components

Inorganic components

Vitrinite (%) Inertinite (%) Liptinite (%) Carbonate (%) Oxide (%) Sulphide (%) 65.7

27.4

——

4.1

2.6

0.2

Figure 1—Experimental set-up

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Effect of inorganic chloride on spontaneous combustion of coal Table II

The model compounds of coal spontaneous combustion Name

Active group

Benzyl alcohol

—CH2—OH

1-phenylpropanol

Molecular formula

—CH(OH)—CH2—

(a) Oxygen

Figure 2—The outward appearance of 6201# supporter

Table III

(b) Carbon monoxide

Parameters of the inert support Name

Material

Granularity/ Bulk density Surface area, mm g/ml m2/g

6201# supporter/ Diatomite 0.180–0.250 molecular sieve

0.4–0.55

4–5

In order to further elucidate the reaction principles governing the effects of inorganic chloride on spontaneous combustion of coal, the structural changes in the coal molecule before and after low-temperature oxidation were investigated using Fourier transform infrared spectroscopy (FTIR). The coal samples were oxidized for 36 h under the abovementioned experimental settings, then examined by infrared spectroscopy at a frequency in the range 400–4000 cm-1. Each sample was scanned 32 times.

Results and discussion Analysis of gaseous products

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(d) Heating rate

Figure 3—Analyses of product gas from the experimental set-up VOLUME 115

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As shown in Figure 3, the proportions of the gases produced by low-temperature oxidation of coal were varied by additions of inorganic chloride. During the entire process of low-temperature oxidation, the oxygen concentration in air flowing through the control sample, treated with only deionized water, decreased from 20.46% to 5.79% between 323K and 453K. For the samples loaded with inorganic chloride, the decrease was slower. Furthermore, the trends

Effect of inorganic chloride on spontaneous combustion of coal for sodium chloride and potassium chloride were similar, with the oxygen concentration falling to 15.03% and 16.08% respectively at a temperature of 453K. In the case of calcium chloride and zinc chloride, the oxygen concentration declined slowly until 373K, and then dropped sharply to 10.67% and 10.45%, respectively. Notably, the oxygen concentration with magnesium chloride decreased only slowly up to 373K, and then remained relatively stable at around 19.0% to 18.4%. The CO concentration in the product gas from the lowtemperature oxidation of coal increased from zero to 4056 ppm between 323K and 453K. The increase was less from coal treated with inorganic chlorides. For sodium chloride and potassium chloride additions, CO concentrations remained at a low level (below 200 ppm) until 383K and then rose rapidly to 1903 ppm and 1151 ppm respectively at a temperature of 453K. The upward trends for calcium chloride and zinc chloride were quite similar to that for coal treated with only deionized water, although the CO concentrations were slightly lower, diverging only when the reaction temperature reached 443K. The CO concentration for magnesium chloride remained low at 153 ppm throughout the entire reaction. The CO2 concentrations generally follow the same trends as those for CO. The CO2 content of the product gas for magnesium chloride-treated coal was also lowest among the five chloride-treated samples. The CO concentrations from sodium chloride and potassium chloride are consistently higher those from calcium chloride and zinc chloride samples across the entire temperature range. However, at temperatures higher than 413K the CO2 concentrations from the calcium chloride and zinc chloride samples exceed those from sodium chloride and potassium chloride samples. In summary, treatment with inorganic chloride decreased the oxygen consumption by the oxidation reaction at low temperatures, especially from 393K to 453K, and lower amounts of CO2 and CO were generated at the same reaction temperatures. The results suggest that inorganic chloride can effectively inhibit the low-temperature oxidation of coal. The study shows that of the five chlorides tested, potassium chloride has a medium inhibitory effect on the low-temperature oxidation of coal. The relevant data for

potassium chloride was therefore investigated using infrared spectroscopy and model compounds.

Infrared spectroscopy The chief characteristic of the infrared spectrum is that the frequencies of vibration of the same types of chemical bonds are very similar and always appear within a certain range. Table IV and Figure 4 depict the attribution of the major peaks in the sample from seam no. 3 of Xutuan colliery, according to the coal chemistry and infrared spectroscopy (Speight, 1971). Figure 4 shows that oxidation for 36 hours results in a significant change in the organic structure of XT coal, which affects the tendency to undergo spontaneous combustion. This change is most apparent at temperatures between 333K and 453K.t At first, there is no apparent change of all the absorption peaks in the infrared spectra induced by the heating and oxidization at 333K. However, it is clear that after low-temperature oxidation at 453K, the peaks corresponding to -OH,-CH2-, and -CH3 are weakened, which suggests that the activity of these structures (methyl, methylene, methine, and hydroxy) in coal molecule is damaged to some extent. The peak value of the corresponding region of C-H (aromatic ring) and C=C (aromatic ring) decreases slightly, which indicates that the main structure of the aromatic ring or fused ring has not been destroyed during the low-temperature oxidation process. Under these conditions, apart from the absorption peak of the vibration of aromatic ring C=C and C-H bonds, all other absorption peaks decrease to varying degrees. In comparison, after the addition of inorganic chloride, the functional groups on the surface of coal molecules change slightly. In general, there is no great change in the intensity of most of the absorption peaks, including the stretching vibrations of aromatic ring C=C and carbonyl O-H as the temperature passes 333K; while the absorption peaks of CH2- and C-O decrease slightly after the XT coal is heated at 453K for 36 hours. This indicates that the inorganic chloride inhibits the oxidation and decomposition of some functional groups in the coal during low-temperature oxidation.

Figure 4â&#x20AC;&#x201D;Infrared diffuse reflectance spectrogram (IDRS) of coal samples under different reaction conditions

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Effect of inorganic chloride on spontaneous combustion of coal Table IV

Infrared spectrum absorption peaks of coal sample Number A B C D E F G H I J K L M N

Peak position,cm-1

Functional groups

Attribution

3697–3625 3624–3610 3550–3200 3056–3032 2935–2915 2857–2851 1910–1900 1605–1595 1460–1405 1384–1367 1330–1110 1060–1020 900–850 540–475

-OH -OH -OH -CH -CH2-CH2-

Free -OH bond -OH (self-association hydrogen bonds) Stretching vibration of –OH (phenols, alcohols, carboxylic acids, peroxides) Stretching vibration of -CH in aromatics Antisymmetric stretching vibration of –CH2- in naphthenic or aliphatic Symmetric stretching vibration of –CH2 in naphthenic or aliphatic Vibration of C-C, C-H in benzene Stretching vibration of C=C in aromatic ring or fused ring Antisymmetric deformation vibration of -CH3 Shear vibration of -CH3 Stretching vibration of C-O Stretching vibration of R-O Plane deformation vibration of CH in aromatics where H atoms are substituted. Characteristic peaks of S-S or S-H

C=C -CH3 -CH3 C-O R-O S-S or S-H

Model compounds Similar to the coal molecular structrure (Shi, Deng, and Wang, 2004), the aromatic ring in the model compounds is relatively stable and the side chain is easily oxidized. It can be seen from Figure 5 that potassium chloride can inhibit the oxidation of benzyl alcohol and 1-phenylpropanol. During the oxidation of benzyl alcohol, the oxygen concentration in the product gas decreased from 20.74% to 20.55% between 328K and 368K. In particular, the concentration fell sharply to 19.96% when the temperature rose to 428K. However, after the addition of potassium chloride, the oxygen concentration declined more slowly, dropping to only 20.43% from 328K to 428K. Similarly, potassium chloride suppressed the oxidation of 1-phenylpropanol. Between 328K and 428K, the oxygen concentration in the product gas fell from 20.81% to 19.75% after loading potassium chloride into 1-phenylpropanol, compared with 20.74% to 18.99% without potassium chloride. These results suggest that inorganic chloride can inhibit the oxidation of methyl, methylene, methine, and hydroxy groups in model compounds to varying degrees.

HCl* + H* → H2 + Cl* The newly generated Cl* can further react with combustible materials. The continuous reaction is capable of removing considerable amounts of OH*, H*, and O*. As a result, chlorides can inhibit the spontaneous combustion of coal.

Radical reactions Spontaneous combustion of coal produces CO, CO2, and other products, which has been verified in underground and laboratory test work. This phenomenon can be explained by the chain-transfer of radicals. According to free radical theory, the initial stage of coal spontaneous combustion can be attributed to the radical reactions (Li, 1996). The lowtemperature oxidation of coal can generate numerous free radicals, such as H*, OH*, and O*. The continuous cyclic generation of free radicals not only leads to spontaneous chain reactions, but also brings about heat accumulation in the coal body, which will eventually result in spontaneous combustion. However, with chlorine present in the coal, Cl* would be produced with increasing temperature. The reaction of Cl* and OH*, and of H* and O*, would inhibit spontaneous combustion. The reactions are as follows:

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Figure 5—Oxygen concentration change in experiments with the model compounds VOLUME 115

FEBRUARY 2015

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HCl* + OH* → H2O + Cl* HCl* + O* → OH* + Cl*

Effect of inorganic chloride on spontaneous combustion of coal LI, Z.H., WANG, Y.L., and SONG, N. 2009. Experiment study of model compound

Conclusion This study revealed that inorganic chloride can inhibit the spontaneous combustion of coal. The important indicators of the spontaneous combustion process, such as O2 consumption and production of CO and CO2, decreased significantly when inorganic chlorides were added to coal samples. Of the five reagents used in the investigation, magnesium chloride has the best inhibiting effect on the low-temperature oxidation of coal. We conclude that inorganic chloride can inhibit the oxidation of methyl, methylene, methine, and hydroxy groups in the low-temperature oxidation process of model compounds of coal. This phenomenon can be explained to some extent by the process of radical reaction. This may play an important role in coal spontaneous combustion if the raw coal has a high chlorine content. There is plenty of scope for extending this work to investigate the effect of other elements on coal spontaneous combustion.

oxidation on spontaneous combustion of coal. Procedia Earth and Planetary Science, vol. 1. pp. 123–129. LI, Z.H. 1996. Mechanism of free radical reactions in spontaneous combustion of coal. Journal of China University of Mining and Technology, vol. 125. pp. 111–114. LOPEZ, D. 1998. Effect of low-temperature oxidation of coal on hydrogentransfer capability. Fuel, vol. 77. pp. 1623–1628. MA, Y.J. 2011. Thermodynamics simulation and experimental study of volatile trace elements in coal during combustion. Henan Polytechnic University, Jiaozuo. pp. 32–51. MATHEWS, J.P., VAN DUIN, A.C.T., and CHAFFEE, A.L. 2011. The utility of coal molecular models. Fuel Processing Technology, vol. 92. pp. 718–728. ITAY, M., HILL, C.R., and GLASSER, D. 1989. A study of the low temperature

Acknowledgement This work was supported by the Project of China National Natural Science Foundation (No. 51074158) and the Fundamental Research Funds for the Central Universities (No. 2012LWBZ10).

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http://dx.doi.org/10.17159/2411-9717/2015/v115n2a2 ISSN:2411-9717/2015/v115/n2/a2

A validation study of the King stratification model by L.C. Woollacott*, M. Bwalya*, and L. Mabokela†

This paper presents a study on the ability of the King stratification model to describe density stratification patterns that are achieved under idealized conditions. Tests were conducted in a batch jig using artificial particles in seven density classes. All particles in a density class had essentially the same density, size, and shape. Tests were conducted for particle systems involving from two to seven components. Good agreement was obtained between measured and modelled data to an extent that gave strong endorsement of the mathematical appropriateness of the core equation in the King model. Somewhat ambiguous results were found with regard to claims about the independencies of the single experimentally-determined parameter required by the model. Keywords mineral jigs, jigging, batch jigging, stratification, mineral beneficiation.

Introduction Several mineral beneficiation processes are based on stratifying particles in a bed according to their density. Although this approach to separating minerals has a long history, it has proved quite difficult to develop reliable models that can describe or predict density stratification in a satisfactory way. Mehrotra and Mishra (1997) provide an excellent review of the different modelling approaches that have been used. One of the most promising models of stratification currently available is that due to King (King, 1987; Tavares and King, 1995; King, 2001). The model has been shown to give very good agreement with experimental data for systems of PVC cubes, simple coal systems, and coal and marble mixtures (Vetter, 1987; King, 1987), and reasonable agreement for stratification of an iron ore (King, 1987). In each case the systems were binary in nature. Tavares and King (1995) extended the range of experimental validation by showing good fits of the model with Vetter’s (1987) data for ternary systems of PVC cubes, and of coalmarble mixtures. They also reported additional validation work that involved multi-component coal systems and continuous jigging. That work focused on testing a model of continuous jigging based on the King stratification model combined with models for the splitting The Journal of The Southern African Institute of Mining and Metallurgy

The King stratification model: a summary The King model envisages that density stratification of particles in a bed is the result of a dynamic equilibrium between two vertically opposing fluxes of particles – a stratification flux and a diffusive flux. The stratification flux is driven by the reduction in potential energy that occurs when particles of different densities stratify (Mayer, 1964). The diffusive flux is driven by ‘random walk’ diffusion processes that are considered to be Fickian in nature. When particle motion has reached a state of dynamic equilibrium, the two opposing fluxes are equal and the concentration profiles

* School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg. † Mintek Johannesburg. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jul. 2014; revised paper received Sep. 2014. VOLUME 115

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Synopsis

efficiency and for flow patterns in a jig. Although that work constituted a test of the model of continuous jigging, as opposed to a test of the stratification model, it is significant that good fits of the experimental data were obtained – a result that provides further support for the veracity of King’s stratification model. Since the model was first published in 1987 and then extended in 1995 it has received little attention in the literature. This is rather surprising given, firstly, the considerable potential which the abovementioned validation work demonstrates, and secondly, that the model has some inherent restrictions which suggest that further development is worthwhile given its demonstrated potential. This paper makes a contribution in this regard by presenting additional validation work. It begins with a summary of the model and thereafter presents the findings from a validation study based on stratification in a batch jig.

A validation study of the King stratification model of particles of different density stabilize. King developed expressions for the two fluxes in a bed of mono-sized spheres. Equating expressions for the two fluxes leads to the differential equation (Equation [1]) (Tavares and King, 1995, King, 2001), which is at the heart of the King model. [1] In this equation, Cj(h) is the volumetric concentration of particles having a density ρj in the very thin horizontal layer in the bed located at a relative height h to h+dh from the bottom of the bed, i.e. h=H/Hbed where H is the actual height of the bottom of the thin layer and Hbed is the height of the bed. The variation of Cj(h) with h is termed the concentration profile. – In Equation [1], ρ(h) is the average density of the particles in the thin layer at h and can be calculated from Equation [2] [2] The stratification parameter, α, is a composite parameter which, as Equation [3] indicates, takes into account the nature of the diffusive flux as described by the diffusion coefficient, D, and the nature of the stratification flux as – described by the ‘specific penetration velocity’ u. The other terms in α are the gravitational acceleration, g, the volume of each particle, v, and the depth of the bed, Hbed. The stratification constant has the units of inverse density and, significantly, is not a function of particle density or of the composition of the feed to the jig. [3] To determine the concentration profile, Equation [1] must be integrated to give Equation [4]. (Note that in this equation, k is used to represent the relative height instead of h– h within the integral, i.e. ∫0 ρ (k) dk) [4] 0

Cj is the concentration of component j at the bottom of the bed (i.e. h = 0) and is related, through Equation [5], to f Cj , the concentration of component j in the feed to the jig. [5] Equation [4] can be solved analytically for binary systems (King, 1987; 2001) but a numerical procedure is required for multicomponent systems involving more than two components (Tavares and King, 1995; King, 2001). They suggest an iterative procedure beginning with an estimate of ρ–(u), integrating numerically, and normalizing successive 0 0 estimates of Cj to satisfy the constraint that ∑all j Cj = 1.

Application to batch jigging In batch jigging, a density separation is performed by splitting the stratified bed horizontally at some height hsplit to form two layers – the upper layer consisting predominantly of less dense particles and the lower layer consisting predominantly of denser particles. However, for the purposes of experimentation and parameter estimation of parameter the

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bed can be split into a number of slices, where any slice i consists of the particles found between h=bi and h=ti. Here bi and ti are respectively the relative height of the bottom and top of slice i. The concentrations of the various components in each slice can be determined experimentally and the value of the stratification parameter that leads to the best fit of the experimental data can be established by an appropriate regression procedure. In such a procedure, the experimental i data can be expressed in several forms: namely as Cj , the → concentration of component j in slice i; or as C j, the cumulative concentration of component j up to h (i.e. the concentration of j in the lower part of the bed from h=0 to h= ← hsplit); or as C j, the cumulative concentration down to h (i.e. the concentration of j in the upper part of the bed from h=1 to →i ←i h=hsplit); or as R j or R j , the recovery of the component j to the lower or upper layers respectively. The corresponding model values relating to the upper and lower layers in the bed can be calculated from the concentration profile, Cj, using Equation [6] or [7] and appropriate values for bi and ti. [6] [7]

An investigation into the veracity of the King model The rationale behind the study reported in this paper is that, at very least, the model should be able to describe stratification behaviour under ideal conditions. Accordingly, tests were conducted in a batch jig in which reproducible and tightly controlled conditions could be maintained. A pilotscale jig was kindly made available by Mintek in Johannesburg. Wall effects were minimized by using a reasonably large, cylindrical jigging chamber. The diameter of the chamber was 300 mm, which was well over 17 times the diameter of the particles used in the study. In addition, the particles selected for the study were ideal in nature in that they were all essentially the same size and the particles in each density class all had essentially the same density, as indicated in Table I. The particles used were colour-coded ‘density tracers’ which had been manufactured for conducting investigations on the performance of DMS separators in the coal industry. Although the particles were not the ideal spherical shape assumed by the King model, all particles had the same shape. As shown in Figure 2, this shape was essentially that of a squat cylinder with a domed top surface and an indented bottom surface. The diameter of the particles was 17 mm and the height between 7 and 8.5 mm. The small degree of variation in the density and the dimensions of the tracers is shown in Table I.

Experimental details As shown in Figure 1, the jig chamber was made up of rings that were 25 mm in height and had an internal diameter of 300 mm. The rings were mounted on top of each other on the jig support screen to make up a cylindrical jigging chamber about 0.3 m high. After the rings had been clamped together, the sample to be tested was poured into the chamber, which The Journal of The Southern African Institute of Mining and Metallurgy

A validation study of the King stratification model Table I

Specific gravities and dimensions of the density tracers Tracer colour

Orange

Yellow

Green

Pink

Blue

Red

Woody

Code

No. of tracers measured Specific gravity (SG) Nominal SG Measured SG Std deviation % std deviation

2.1 2.130 0.0039 0.18

1.9 1.916 0.0332 1.73

1.7 1.714 0.0014 0.08

1.6 1.579 0.0013 0.08

1.5 1.520 0.0021 0.14

1.4 1.417 0.0107 0.76

1.3 * * *

Tracer volume Average (ml) Std deviation (ml) % std deviation

1.854 0.0291 1.57

1.809 0.0468 2.59

1.693 0.0296 1.75

1.687 0.0247 1.47

1.605 0.0196 1.22

1.807 0.0529 2.93

1.416 0.0196 1.36

Tracer dimensions (mm) Diameter Average height

17 8.2

17 8.0

17 7.5

17 7.4

17 7.1

17 8.0

16 7.3

*The woody tracers were smaller than the other tracers and so were used only when a seven-component system was needed. There was uncertainty about their effective density because they were porous. Accordingly, the nominal value of 1.3 was taken as their SG

was then flooded with water and subjected to the jigging cycle shown in Figure 2. The jig cycle was generated pneumatically and was controlled by a PLC so that the jigging conditions in every test were identical. In each test, a hutch water flow of 500 ml/min was maintained. The duration of each test was 20 minutes, which had been shown to be well in excess of the time required for the concentration profiles in the bed to reach a state of equilibrium. After each test the bed height was measured, the rings were unclamped, and slices were removed progressively from the top of the bed by inserting a slicing device between the rings. Except for the top slice, each slice constituted a layer of the bed 25 mm thick. Six tests were undertaken to investigate how well the model fitted the experimental data for particle systems involving from two to seven components. About 3000 particles were used for each test which amounted to between 7 and 11 kg per sample, depending on the density of the particles. Preliminary tests had indicated that this was sufficient to produce a bed height of around 100 to 150 mm, which would allow the bed to be sliced horizontally into 4–6 fractions. This had been shown to give a satisfactory definition of the concentration profiles within the constraints of the experimental conditions, namely that the bed could be sliced only in 25 mm increments, that the particles were relatively large (i.e. around 8×17 mm), and that tests involving only the seven densities indicated in Table I could be undertaken. The Journal of The Southern African Institute of Mining and Metallurgy

Results Stratification patterns in the jig The experimental data obtained, together with the best model fits to that data, is shown in Figures 4 to 10. Plots of component recoveries as a function of split height are the most compact way to present the data, and these are ←i presented first (Figure 4). The plots show R j , the component recoveries to the top (lighter) fraction when the bed is split at a relative height h. The curves in the plots represent the

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Figure 1—Details of construction of the jig chamber

The compositions of the particle systems tested are shown in Table II. In most cases the proportions of each component were approximately similar, but arbitrary adjustments were made to obtain round numbers. This approach was violated for the two-component system because insufficient red tracers were available to make up a 50:50 mixture of the required bed volume. The woody coloured tracers were used only when the seven-component system was investigated because they were slightly smaller than the other tracers and because, being slightly porous, their effective density in a jigging environment was uncertain; measured SGs varied between 1.28 when measured dry and 1.35 after they had been soaked in water. The SG value used for this component in the analysis of the seven-component system was 1.3, the nominal value given by the manufacturers of the tracers. The shape of the tracers is shown in Figure 3.

A validation study of the King stratification model Table II

Compositions of the six particle systems investigated Volumetric composition (%) Tracer SG Tracer colour* 2 components (BR) 3 components (OYG) 4 components (OYBR) 5 components (OYGBR) 6 components (OYGPBR) 7 components (OYGBPBRW)

2.130 Orange

1.916 Yellow

1.714 Green

38 25 21 15 15

25 25 18 12 13

37 20 17 11

1.579 Pink

11 12

1.520 Blue 70

1.417 Red 30

25 20 30 14

25 21 14 14

1.3 Woody

* The particle systems tested are identified by a code that uses the initial letter of the tracer colour to indicate which components made up that system

Figure 3—Shape of the density tracers

recoveries calculated using the model while the points represent the experimental data. It should be noted that for each particle system, only a single experimentally determined parameter value (for α) was used to generate the recovery curves for each component in that system. The actual values of the parameters for the different systems are presented and discussed in the following section. If the model provides a good description of the stratification patterns achieved, then the experimentally determined recoveries and the model values should coincide. As can be seen from Figure 4, the level of agreement between the model and experimental values is remarkably good. Figure 5 to 10 compare experimentally determined concentrations in the jig bed with the data generated by the model. In each figure, the right-hand column of plots shows ← the variation of the cumulative concentrations, C j, of the upper layer of the bed as a function of h. Each curve indicates what the model predicts the concentration of component j would be in the top (lighter) fraction of the bed if it were to be split at a relative height hsplit=h. The filled circles represent experimental points. These should fall on the relevant curve if the model is providing an accurate description of stratification behaviour. As can be seen, the alignment of the experimental and modelled values is remarkably good. Only with two points in the four-component system is there some substantive misalignment, and even there the experimental and modelled values differ by no more than 5%. The left-hand columns of the plots in Figures 5 to 10 compare the measured component concentrations in each i slice, Cj , shown as filled circles, with the model-predicted values shown as unfilled circles. To provide perspective when

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comparing these values, the concentration profile, Cj, for each component is shown as a dotted curve. This curve refers to the concentration of component j in the very thin layer at h, whereas the filled and unfilled points refer to the much thicker slices removed experimentally from the particle bed, the vertical centres of these slices being located at h. i Accordingly, at the relevant value of h, the Cj points for the thick slices should have a value somewhere near the value of Cj (for very thin layers), but these values should not necessarily coincide, because they refer to layers of different thicknesses. As can be seen from the figures, there is a generally good agreement between the experimentally measured and modelled values and, in most cases, these values are close to the curve. However, the agreement is not always as good as is the case with the cumulative concentrations (the right-

Figure 4—Component recoveries to the upper layer of the stratified bed The Journal of The Southern African Institute of Mining and Metallurgy

A validation study of the King stratification model

Figure 5—Concentration plots for the two-component system shown in Figure 4

Figure 7—Concentration plots for the four-component system shown in Figure 4

hand plots in the figures) and some significant differences occur; in eight cases, the difference is as large as 10% and in two cases it is 16%. It is to be expected that the coincidence of measured and modelled data for the concentration profiles would not be as good as with the cumulative concentration data because the former is inherently more sensitive to experimental error. When slicing the particle bed, the slicer is forced through the bed and particles that lie in the path of the slicer are forced upwards or downwards into the layer above or below the slicer. In this process, particles can be misplaced to the wrong layer. In addition, because the particles are relatively large, misplacement errors can also occur because the particle The Journal of The Southern African Institute of Mining and Metallurgy

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Figure 6—Concentration plots for the three-component system shown in Figure 4

displacement disturbs adjacent particles and, in some cases, may scour out adjacent particles to the upper or lower layer. While such misplacement errors are associated with all the data in the study, the error is doubled when determining the concentrations of slices removed from the bed. This is because the slicing error occurs at both the top and bottom of the slice. When the data is cumulated from the top of the bed, the error is associated only with the bottom of the slice and the effect of the error diminishes as the data is cumulated downwards through the bed. The situation is similar when cumulating from the bottom of the bed. Considering all results taken together, it can be concluded that the model was able to give reasonable to good descriptions of the concentrations of slices taken from the bed, and remarkably good descriptions of the cumulative concentrations and recoveries associated with the upper fraction of the bed when it is split at any particular height h. With regard to the lower fractions split from the bed, a similar positive conclusion is reached both by implication and by examining the relevant plots (not shown in this paper). The implication of these conclusions is that given the feed composition to a batch jig, the density of each component in the feed, and a single experimentally determined parameter, α, the model is able to give reliable descriptions of the stratification that would be achieved in a batch jig under equilibrium conditions.

A validation study of the King stratification model

Figure 9a—Concentration plots for the six-component system shown in Figure 4 (plots for components 1 to 3)

Figure 8—Concentration plots for the five-component system shown in Figure 4

The value of the stratification parameter A significant feature of the King stratification model is its claim that the value of the stratification parameter, α, is independent of feed composition and the densities of the components in the feed. According to Equation [3], α is a function only of particle volume, the height of the bed, Hbed, – and the stratification dynamics in the bed represented by u and D. Bed height varied in the different tests so the value of α is not expected to be the same for each test. However, the ratio α/Hbed should be the same if the model claim is valid. To test this claim, Table III presents the values of α that give the best fits for each set of experimental data, as well as the associated values of Hbed and α/Hbed. As can be seen from the table, the value of α increases steadily as the particle systems become more complex; the highest value is 2.5 times greater than the smallest value. When bed height is taken into account – by calculating α/Hbed – it is evident that the trend persists to some extent; i.e. there is, with two exceptions, an increase in the value of α/Hbed as the particle systems involve more components. The implication of this is that the model’s assumptions about the independencies of the stratification parameter are not supported by our data. However, this implication is not conclusive because it turns out that the model predictions are relatively insensitive to the value of α over the range of

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Figure 9b—Concentration plots for the six-component system shown in Figure 4 (plots for components 4 to 6)

values measured in the test; therefore the trend noted may, to some degree, be spurious. This insensitivity is demonstrated most easily by comparing the plots of recoveries and cumulative concentrations derived using ‘best-fit’ values of α with the plots derived assuming that α/Hbed was indeed The Journal of The Southern African Institute of Mining and Metallurgy

A validation study of the King stratification model

Figure 10b—Concentration plots for the seven-component system shown in Figure 4 (plots for components 5 to 7)

larger than the ‘best-fit’ value. There is also some deviation in the plots for the heavy and light components in the fivecomponent system, but the deviation is marginal. In this case the ‘averaged’ α is 23% larger than the ‘best-fit’ value. The deviations are more marked when the cumulative concentration plots are considered (Figure 12) but again, it was only with the two- and five-component data that any substantial deviation between the plots obtained using ‘bestfit’ and ‘averaged’ values of α was evident. The figure also shows plots for some of the components in the sevencomponent system. In these, the deviation is so small that the ‘best-fit’ curves fit the experimental data only slightly better than the curves derived from the ‘averaged’ α. With this data-set the ‘averaged’ value of α is 15% smaller than the best-fit value. For all the other systems, the differences between the ‘best-fit’ and ‘averaged’ values of α are less than 15% and no discernible deviation between the two sets of plots is evident. Accordingly, these plots have not been included in Figure 12.

Figure 10a—Concentration plots for the seven-component system shown in Figure 4 (plots for components 1 to 4)

invariate for the conditions that prevailed in the tests. Table III shows what the corresponding values of α would be; these ‘averaged’ values of α were calculated for each data-set using the average value of α/Hbed (1.051 L/kg.mm) and the bed height α/Hbed for each test. Figures 11 and 12 compare the plots derived using ‘best-fit’ values of α with those derived using these ‘averaged’ values. In the recovery plots shown in Figure 11, it can be seen that, with one exception, the plots obtained using the ‘averaged’ values deviate hardly at all from those obtained using the ‘best-fit’ values. The exception is with the twocomponent system. Here the ‘averaged’ value of α is 54%

Table III

Effect of the number of components on the stratification parameter No. of components Particle system Stratification parameter, α (L/kg) (‘best-fit’ values) Bed height, Hbed (mm) α/Hbed (L/kg.mm) ‘Averaged’ α (L/kg)* % Difference in α (averaged-best)/best × 100%

OYG

OYBR

OYGBR

OYGPBR

OYGPBRW

76.9 112.5 0.684 118.3 54%

108.7 116.5 0.933 122.5 13%

132.6 119.5 1.106 126.0 -5%

155.6 114.4 1.36 120.3 -23%

156.3 159 0.983 167.1 7%

190.4 153.4 1.242 161.2 -15%

Average values

1.051

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* ‘Averaged’ α = average value [α/Hbed] × Hbed for the relevant data-set

A validation study of the King stratification model What these results suggest is that the model predictions are relatively insensitive to the value of α over the range of values measured in the test. Only when the ‘averaged’ value differed from the ‘best-fit’ value by more than about 20% did the model predictions using the two different values deviate discernibly from one another. The implication of these results is that the trend noted earlier may be an artefact of experimental errors and the regression procedure, i.e. that in the regression procedure the response surfaces in the regions around the ‘best-fit’ values of α were very flat, so that equally good fits of the experimental data could be obtained for α values that were within 20% of the ‘best-fit’ value.

Conclusions

Figure 11—Component recoveries calculated using the ‘best-fit’ values of α compared with recoveries calculated using the ‘averaged’ value

Figure 12 – Comparison of component concentrations in upper and lower layers obtained using the ‘best-fit’ value of α or the ‘averaged’ value

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It is clear from the results of the validation study reported in this paper that the mathematical form of the model equation – Equation [1] – is appropriate for describing stratification in a batch jig under the ideal conditions that prevailed in the tests. For each of the six particle systems tested, this model achieved very satisfactory fits to the experimentally determined recoveries and grades of jig products that would be produced if the bed height were to be split at any particular level in the bed. The concentration profiles in the bed are inherently more sensitive to experimental error than the recovery and grade data, and here there was greater divergence between the model fits and the experimental data. However, the fits obtained were reasonable to very good in all cases. Given that a wide range of particle systems were tested – i.e. systems involving from two to seven components – the overall quality of the model fits gives very strong support to the veracity of the King model, at very least with respect to the mathematical form of its core equation. The model allows the calculation of the concentration profiles in a stratified bed as well as the recoveries and grades obtained when the bed is split. It does this requiring only the feed composition, the densities of each component in the bed, and a single experimentally determined stratification parameter, and assumes that particles are all the same shape and that size and size distribution effects can be ignored. Interestingly, the results of the study imply that the exact nature of the particle shape is not important as long as all the particles have the same shape. This can be argued from the fact that the derivation of the model assumed all particles were spheres, yet good fits were obtained for particle shapes that were all essentially squat cylinders. While the finding about the mathematical appropriateness of the model equation appears to be quite conclusive, the finding with regard to model claims about the dependencies of the stratification parameter is less certain. On the one hand, some variation was found in the ratio of the stratification parameter to bed height (α/Hbed) which, according to the model, should not happen if the particle volume and stratification dynamics are the same (which is the case in all the tests conducted). On the other hand, it was found that the model fits were relatively insensitive to the value of the stratification parameter over the range found in the tests, to the extent that the observed variation in α/Hbed may have been a direct consequence of this insensitivity. However, given that the range of particle systems tested was considerably wider and more divergent than the range likely to prevail in practical situations, it may be that for practical The Journal of The Southern African Institute of Mining and Metallurgy

A validation study of the King stratification model

Acknowledgements The generous support of Mintek in Johannesburg in making their pilot jig and associated facilities available for this work is acknowledged with thanks.

References KING, R.P. 1987. A quantitative model of gravity separation unit operations that

The Journal of The Southern African Institute of Mining and Metallurgy

rely on stratification. APCOM 87: Proceedings of the 12th International Conference on the Application of Computers and Mathematics in the Mineral Industries. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 141–151. KING, R.P. 2001. Modeling and Simulation of Mineral Processing Systems, Butterworth Heinmann, Oxford. MAYER, F.W. 1964. Fundamentals of a potential energy theory of the jigging process. Proceedings of the 7th International Mineral Processing Congress, New, York, 20-24 September 1964. Arbiter, N. (ed.). Gordon and Breach, New York. pp. 75–86. MEHROTRA, S.P. and MISHRA, B.K. 1997. Mathematical modeling of particle stratification in jigs. Proceedings of National Seminar on Processing of Fines. Institute of Minerals and Materials Technology, CSIR, Jamshedpur TAVARES, L.M. and KING, R.P. 1995. A useful model for the calculation of the performance of batch and continuous jigs. Coal Preparation, vol. 15. pp. 99–128. VETTER, D.A. 1987. Mathematical model of a fine coal batch jig. MSc thesis, University of Natal. ◆

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purposes the value of the stratification parameter may be assumed to be independent of the feed composition in an industrially operating stratification device, as the King model claims. Such a conclusion is only tentative, however, and further research is required to verify it. This and related issues are currently being investigated and will be reported in a future publication.

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a3 ISSN:2411-9717/2015/v115/n2/a3

The behaviour of free gold particles in a simulated flash flotation environment by T.D.H. McGrath*, J.J. Eksteen*, and J. Heath†

A reliable laboratory method to characterize the response of free gold particles to flash flotation conditions has been developed. The test has been performed on free milling gold ores as well as synthetic ores, using either a gravity concentrate or gold powder as the gold source, to assess the floatability of gold particles. Trends in free gold flotation kinetics, as well as size and milling effects, were identified for gold recovery based on the different feed types, reagent dosages, and residence times. It was shown that the ultimate recoveries and kinetic trends of gold particles from the gravity concentrate could be enhanced with increased dosage of collector, potassium amyl xanthate. Interestingly, in comparison to gravity-recoverable gold, recovery from pure Au powders was better in collectorless flotation, and cumulative recovery decreased with higher levels of collector addition. Improved coarse particle recovery appeared linked to increased collector additions for both the gravity concentrate and the pure gold powders. In general, coarse gold particles demonstrated slower kinetic rates thaen the fine or intermediate components in comparable tests. Keywords gold, flotation, flash flotation, natural hydrophobicity, kinetics.

Introduction The behaviour of free gold in flash flotation is currently poorly understood (Dunne 2005), especially when in competition with a gravity recovery unit in a closed-loop milling circuit, although an overlap has been identified in which both units can recover particles between 212 μm and 38 μm. This research aims to identify parameters that may determine whether free gold particles will be recovered by either unit in this competitive size range. Identifying the impact of variables such as mineralogy, reagents, mechanical factors, and physical characteristics (such as size, shape, surface area, elemental composition, etc.) on floatability will enable optimization of combined gravity and flash flotation circuits. This paper, the second in a series, is focused on the comparison of free gold and pure gold powder recoveries in laboratory flotation tests as a function of collector (potassium amyl xanthate, or PAX) addition. The first paper (McGrath et al., 2013) established the method used to study the behaviour and characterize the ultimate content of free gold recoverable by The Journal of The Southern African Institute of Mining and Metallurgy

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Background Several plants use batch centrifugal concentrators (BCCs) and flash flotation unit operations in a closed-loop milling circuit as an option for processing complex ores containing free gold as well as gold locked in a sulphide matrix. BCC circuits are used to recover the larger particles of free gold, roughly +106 μm, while a flash flotation circuit produces a sulphide concentrate encompassing smaller free gold particles (-106 μm) and gold contained in sulphides. Based on plant surveys undertaken by the Curtin University Gold Technology Group (2008), the two units will tend to compete for particles in the -212 +38 μm range, as shown in Figure 1. Because knowledge of the behaviour of free gold recovery in a closed-loop milling circuit with parallel flash flotation and gravity recovery units is limited, an improved understanding of the behaviour of gold in this situation will provide greater confidence in the application of such processes to the processing of complex gold ores.

Gravity concentration Gravity-recoverable gold (GRG) is a specific term that refers to free gold reporting to the concentrate stream with a small mass yield if separations are performed using BCCs. GRG is

* Department of Metallurgical Engineering and Mining Engineering, Western Australian School of Mines, Curtin University, Australia. † Outotec South East Asia Pacific, West Perth, Australia. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Mar. 2013; revised paper received Aug. 2014. FEBRUARY 2015

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Synopsis

flash flotation. The knowledge gained from this research contributes to a better understanding of the impact of particle size, milling effects, residence time, and collector additions upon the recovery of free gold in the milling circuit.

The behaviour of free gold particles in a simulated flash flotation environment

Figure 1—A comparison of the recoveries in flash flotation and BCC devices (Curtin University Gold Technology Group, 2008)

comprised of primarily of ‘free gold’, because the BCC units are not designed to recover bulk sulphide material. The ‘GRG test’ (Laplante and Dunne, 2002) and model generates results that can be used to model the amenability of an ore to gravity recovery by BCC in a milling circuit closed by a hydrocyclone. The GRG model developed by Laplante can also be used to estimate flash recovery (Laplante and Staunton, 2005; Laplante and Dunne, 2002). This is because the GRG test defines an ore characteristic, not BCC machine characteristics. Therefore it determines the overall gravity recovery potential of an ore and the maximum amount of GRG per size class that can be recovered. Both the GRG test and laboratory/plant-based models are available for use.

Flash flotation The SkimAir flash flotation cell was developed by Outokumpu (now Outotec) in the early 1980s to ‘flash off’ fast-floating liberated minerals of high value (Coleman, 2010). It was designed to be used ahead of conventional flotation in the circulating load of a mill in order to reduce overgrinding of sulphides (Bourke, 2002). Teague et al. (1999) have shown that flotation of free gold is affected by physical constraints such as shape and size of particles, degree of water and gangue transport to the froth, stability of the froth, and extent of sulphide bubble loading, which provides a barrier to hydrophobic bubble attachment of free gold. It has been suggested that fine gold particles are strongly hydrophobic and good candidates for flash flotation (Laplante and Dunne, 2002). Unfortunately, the effect of an industrial flash flotation cell on the recovery of free gold in Australia has been difficult to determine, as flash flotation has been incorporated at the design stage and there is little plant data available on free gold recovery before and after the introduction of the unit (MacKinnon et al., 2003). To date, Laplante’s GRG test is the only method available to approximate the expected recovery of free gold in a flash flotation cell.

The gravity/flash flotation relationship for GRG When both gravity concentration and flash flotation are

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employed in a milling circuit, flash flotation can be used in parallel, series, or cleaning arrangements with BCC units, as seen in Figure 2. In a cleaning application, the flash flotation cell creates a sulphide concentrate which is then secondarily treated by gravity recovery with removal of GRG from the bulk sulphide concentrate. In series, the BCC treats a portion of the flash tails, while in parallel, the flash and gravity units share the same feed, usually the cyclone underflow, and the tails streams are returned to the milling circuit to close the loop. In the parallel arrangement, the nature of the particles recovered to each unit and the factors affecting recovery of the GRG are not completely understood, due to the interaction of many complex factors. In general, when the GRG content is high and the size fraction is coarse, free gold is easy to recover and concentrate in gravity operations. BCC units operate ideally to concentrate coarse free gold particles larger than 106 μm. Some particles between 106 and 38 μm are recovered, but recovery is likely to depend on particle shape. BCC systems give poor recoveries of gold particles smaller than 38 μm (Laplante and Staunton, 2005). Despite the ideal particle size-gravity gold recovery curve presented for BCCs in Figure 1, Wardell-Johnson et al. (2013) have shown that the size-by-size recovery of GRG is far from an ideal monotonic function, with actual BCC devices often showing a U-shaped curve (rather than a monotonically increasing S-shape) for intermediate particle sizes. Further understanding of the factors impacting the behaviour of GRG in this type of competitive, yet complementary, parallel operation is the primary subject of this research.

Natural hydrophobicity of gold and the impact of collector (PAX) Natural hydrophobicity Tennyson (1980) demonstrated that pure metallic gold is hydrophilic. However, fine free gold will float better than gangue material without the addition of collector, and O’Connor and Dunne (1994) have also shown that untarnished gold of the appropriate size can be readily floated with only a frother in a process referred to as ‘collectorless flotation’. This hydrophobic behaviour often displayed by The Journal of The Southern African Institute of Mining and Metallurgy

The behaviour of free gold particles in a simulated flash flotation environment

Figure 2—Possible flash flotation and BCC arrangements in a simplified milling circuit

Collector Xanthates, a group of anionic collectors based on bivalent sulphur, are used in flotation plants (together with other collectors) to enhance gold recoveries (O'Connor and Dunne, 1994; Wills, 2006). PAX was the collector chosen for this investigation because it is readily available and often used in laboratory flotation test work, so comparisons may be made between these results and data produced in other studies. Furthermore, free gold floats well in the presence of xanthate collectors, but not if the particle size is too large, or if calcium salts or minerals, or Na2S are present (Teague et al., 1999). Not only is gold hydrophobicity enhanced by the addition of collectors such as xanthates, dithiophosphates, and dithiophosphinates, but untarnished gold requires even less addition of these collectors than tarnished gold to become suitably hydrophobic (Chryssoulis and Dimov, 2004). Secondary collectors, or promoters, can further increase recovery, with dithiophosphates being the most widely used promoters in gold flotation (O’Connor and Dunne, 1994). As with tarnished particles, higher collector additions may be required to float coarser particles, as demonstrated in this study. However, studies with quartz have shown that extra collector added to float coarse or tarnished material may instead be consumed by fine particles with large surface areas (Vieira and Peres, 2007).

Physical parameters Comminution As mentioned by Dunne (2005), the impact of milling on the floatability of free gold particles has been an issue of debate. It was suggested by Taggart (1945) and Pevzner et al. (1966) that milling may decrease a gold particle’s ability to be recovered by flotation because of the impregnation of The Journal of The Southern African Institute of Mining and Metallurgy

gangue material. Pevzner et al. (1966) also proposed that passivation of the gold surface during milling would lead to reduced flotation recoveries. Conversely, Allan and Woodcock (2001) hypothesized that work-hardening of gold particles during milling could activate the surfaces and improve floatability. Work-hardening will strengthen the surface of a metal by plastic deformation and can change the surface finish, thus potentially affecting the adsorption of collector. Silver content is expected to promote the flotation of GRG particles as compared to pure Au powders, and this must also be considered as the GRG concentrate particles contain between 5–20% silver (roughly 10% on average). This is because silver floats preferentially to gold in the presence of xanthates.

Particle size Particle size has a strong influence on flash flotation and BCC recovery for several reasons. Firstly, liberation is directly related to particle size and flotation will proceed only when a particle is sufficiently liberated (Zheng et al., 2010). Chalcopyrite flotation studies have shown that a coarse liberated particle floats similarly to an intermediate partially liberated particle, and a coarse particle will float slower than an intermediate particle of similar composition (Newcombe et al., 2012; Sutherland, 1989). Secondly, reagent additions and pH control have a greater influence on coarse particle flotation than other sizes (Trahar, 1981). Recovery is maximized in the 100–10 μm size fraction and drops off significantly above and below that range, with few particles greater than 300 μm able to be floated (Trahar, 1981). The literature suggests that free gold of +200 μm cannot be floated effectively (Malhotra and Harris, 1999). In most flotation plants, sulphide particles larger than 150 μm are considered too large for conventional flotation, although it is a widely held misconception in industry that flash flotation can and will recover even larger particles (Newcombe et al., 2012). It has also been shown that BCCs recover just 40% of +38 μm gold particles and only 10% of -38 μm gold particles (Chryssoulis and Dimov, 2004). With this knowledge, the size ranges of interest in this study are +212 μm, -212 + 38 μm, and -38 μm. Surveys suggest that free gold particles larger than 212 μm will preferentially report to a gravity concentrator when gravity and flash flotation are operated in a closed-loop milling circuit, while -38 μm fractions of free gold particles are usually captured in the flash flotation concentrate of the VOLUME 115

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gold particles is due to a high Hamaker constant (Drzymala, 1994), indicative of Van der Waal’s interactions resulting in a strong dispersive attraction for water (Dunne, 2005). The floatability of gold is known to be enhanced by surface coatings of some organic compounds and silver content (either as rimming or in the form of electrum), while calcium ions and some forms of sulphur can act as depressants. It has been suggested that when native gold surfaces are hydrophobic due to contamination by organics in nature, fine gold particles may be harder to recover by flotation (Aksoy and Yarar, 1989).

The behaviour of free gold particles in a simulated flash flotation environment same circuit. Particles in the size fraction -212 +38 μm are of special interest, as this is the zone of competition between the gravity and flash units, and further understanding of the response of free gold particles in this intermediate range to varying flotation conditions can be applied to optimize recovery in industrial applications. For ease of discussion, the +212, -212 +38 (or just +38), and -38 μm size fractions in the test work are referred to as coarse, intermediate, and fine, respectively throughout the remainder of this paper. Particle shape, surface area, organic and mineral coatings, and elemental rimming are also important in determining the floatability of free gold and will be addressed in a subsequent paper by the authors.

Experimental method The aim of this study is to compare trends in flotation kinetics for particles of varying size and nature as affected by the addition or absence of a collector. Gold from two sources was floated according to the free gold flash flotation test developed by McGrath et al. (2013), with PAX addition and gold type being the only variables. As noted in the results, the superficial gas velocity (volumetric gas flow per unit cross-sectional area of vessel) remained constant at 0.0074 m/s for all tests. Two synthetic ores were created for the laboratory flotation test work. One contained a BCC gravity concentrate (P100 of 600 μm), created by blending multiple concentrates, from primarily Australian sources, split into 5 g subsamples yielding head grades of about 13–16 g/t when added to silica (P100 of 600 μm, with a size distribution similar to flash feed material). A bulk assay of the BCC concentrate established that the concentrate had a gold–to-silver ratio of 9:1, although scanning electron microscopy (SEM) demonstrated that individual gold particles vary greatly in the ratio and pattern of Ag placement in the particle. Images of particles typically found in the GRG concentrate are shown in Figure 3. The second synthetic ore was created using the same silica blend as the first, but used synthesized pure gold powders (P100 250 μm, supplied by Sigma Aldrich) to obtain a head grade of 30–40 g/t. Images of typical gold powder particles can be found in Figure 4. The laboratory tests were repeated on 1 kg charges six times for each of the six conditions in order to produce enough combined concentrate mass to be screened into the three size fractions of interest. The replicate tests ensured that average masses and concentrations reported were statistically representative. Previous comparable test work demonstrates that strict adherence to the methods detailed in the standard operating procedure (SOP) yields average standard deviations of ±0.45% for mass pull and ±4.63% for gold recovery (McGrath et al., 2013). Each set of six conditions produced seven concentrates and a tails sample, all of which were screened into three size fractions (+212, +38, and -38 μm), yielding 21 samples per test or 147 for the entire data-set. Concentrate samples were fire-assayed to extinction while splits of the tails samples were subject to both fire assay and intensive cyanide leach by rolling bottle in order to better close the mass balances. Because the mass of gold in the test was either known, in the case of the powders, or calculated in the case of the concentrate, inconsistencies in the gold and mass balances are attributed

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to the nugget effect in the coarse, and to some extent, the intermediate tails samples. The nugget effect compromises the ability to achieve representative grade or concentration results due to non-uniform distribution of gold in the assayed sample as compared to the bulk material. The impact of the nugget effect is most noticeable in precious metal assays of coarse size fractions and small sample size, yielding erroneously high or low values. Previous work on this GRG concentrate has shown that the contained gold is easily leached, so cyanidation was conducted on larger splits of sized tail samples for more accurate assay values. Brezani and Zelenak (2010) describe flotation as a process that is affected by many properties, not just physicochemical and surface properties but many other chemical and mechanical factors. It is because of this complexity that flotation is often described as a simplified first-order kinetic phenomenon (Kelly and Spottiswood, 1989). Kinetic rates have been calculated for each size fraction in all data-sets of this study using the Kelsall approach, as presented in Equation [1]. Although this method was developed many years ago (Kelsall, 1961), its continued relavance has been evaluated by various authors (Kelly and Spottiswood, 1989; Kelebek and Nanthakumar, 2007; Brezani and Zelenak, 2010) throughout the years and it was recently applied to evaluate the kinetics of sulphides in flash flotation (Newcombe et al., 2012a).

C = Co[α + β exp(-ks.t) + γ exp(-kf.t)]

[1]

Figure 3—Examples of typical gold particles found in the GRG concentrate (shown with 100 μm scale bars) exhibiting platelet and crescent shapes, and demonstrating elongation and rolling as commonly created during comminution of free gold particles

Figure 4—Examples of typical gold articles found in the Au powders (shown with 100 μm scale bars) exhibiting dendritic, globular, crystalline, and spherical shapes, dependent on method of synthesis The Journal of The Southern African Institute of Mining and Metallurgy

The behaviour of free gold particles in a simulated flash flotation environment where: Co = the original concentration in the pulp C = the concentration in the pulp at time t t = elapsed time in the duration of the experiment (min) kf = rate constant for fast-floating material (min-1) ks = rate constant for slow-floating material (min-1) α, β, and γ = coefficients used to fit data for non-floating (Ø), slow-floating (s), and fast-floating (f) material, the sum of which equals unity.

Results and discussion The recoveries of particles in individual size fractions are important because they demonstrate which proportion of the material is recovered under specific conditions; this information is termed ‘fractional recovery’ in this paper. For example, 14% of coarse gold from the GRG concentrate was recovered without PAX addition, as shown in Figure 5, suggesting that 86% of the coarse GRG concentrate gold reported to tails in this test. Cumulative recovery refers the total or overall recovery, which is the sum of the recoveries for all size fractions; the cumulative recoveries are given at the end of this section. For reference, industrial flash flotation processes usually operate with around 10–25 g/t of PAX addition, with about two or three minutes’ residence time. The first two minutes of laboratory testing (which have been represented by the initial four data points in Figures 5–12 and which includes all data presented in Figure 14), roughly represent flash flotation, and have been denoted as the ‘flash period’. The floatability of coarse, +212 μm, gold is of interest in this study because this is the size fraction generally considered to be too large for flotation, with preference given to gravity recovery in this size fraction. Figure 5 demonstrates that: ➤ Coarse gold recovery and flotation kinetics were improved with the addition of PAX, although flash period recoveries are less than the ultimate recoveries. In this data-set the GRG recoveries are superior compared to the Au powders in tests involving PAX ➤ Using the free gold flash flotation test, 97% of the coarse free gold contained in the GRG concentrate was recovered with either 25 or 50 g/t PAX addition. While

the coarse Au powder particles were 90% recovered with 50 g/t PAX, only 48% recovery was achieved when the same particles were floated with 25 g/t PAX. This can be attributed PAX being available in excess of the concentration required to create the necessary monolayer on the surface of gold. The surplus PAX forms additional layers and the non-polar ends are either concealed or oriented away from the water, which effectively reduces hydrophobicity ➤ Collectorless flotation recovered less coarse free gold than tests with PAX, resulting in recovery of only 20% of Au powder and 13% of free gold from the GRG concentrate. The intermediate, -212 +38 μm, size range is of particular interest because this is the suggested area of competition between BCCs and flash flotation in parallel operation. A review of the data in Figure 6 suggests that: ➤ A moderate PAX addition improved intermediate particle recovery, although recoveries of both the GRG and the Au powders were slightly better with the lower level of PAX addition ➤ A much lower ultimate recovery was achieved for intermediate GRG material as compared to the coarse particle GRG at both levels of PAX addition. As with the coarse particle data, the intermediate Au powder particle recovery was lower than comparable GRG recoveries ➤ Collectorless flotation recovered the least amount of intermediate particles from both the Au powder and GRG concentrate, at less than 15% and just over 0%, respectively. The recoveries of -38 μm particles can be found in Figure 7. Interestingly, this is the only size range where the recoveries of Au powder particles were better than from the GRG concentrate. This is also the size range for which flash flotation is recommended for free gold recovery within the milling circuit. ➤ The fine Au powder demonstrated the highest ultimate recovery with 25 g/t PAX addition. Recovery decreased when PAX was increased to 50 g/t. Recoveries with collectorless flotation of Au powders were the same as with 50 g/t PAX addition to the GRG concentrate, with

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Figure 5—Fractional recoveries of +212 μm particles from GRG concentrate and Au powders with varying levels of PAX addition (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

The behaviour of free gold particles in a simulated flash flotation environment

Figure 6—Fractional recoveries of (-212) +38 μm particles from GRG concentrate and Au powder, with varying levels of PAX addition (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

Figure 7—Fractional recoveries of -38 μm particles from GRG concentrate and Au powders with varying levels of PAX addition (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

respective recoveries of 59% and 57% not being statistically different ➤ Again, the fine GRG particles recovered in collectorless flotation showed the poorest recovery, just as with the coarse and intermediate size fractions. Figures 8–10 allow comparison of recovery trends grouped by level of PAX addition. The recoveries of gold with 25 g/t PAX addition, given in Figure 9, are of particular interest because this level of collector is most similar to that in industrial flash flotation. In particular, opposing trends of improved recovery for some particle types with 25 g/t PAX addition are noted. For example, GRG yielded better recoveries with increasing particle size, while decreasing Au powder particle size improved recovery. Here, the highest ultimate recoveries are from the coarse fraction of GRG concentrate and the fine Au powders. Collectorless flotation results, presented in Figure 10, are important because they reveal the differences in the inherent floatability of the two types of particle under the given conditions. Without collector, recoveries from the fine Au powders were better than from all other particle size and type combinations, with reduced recoveries for the intermediate and coarse fractions. A similar trend is noticed with the GRG concentrate, where fine particle recoveries are better than the

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intermediate (which shows no appreciable recovery) or coarse particles. The effect of varying PAX additions for the two types of gold particles is given in Figures 11 and 12. This information is of interest as it gives baseline theoretical recoveries for unmilled, pure gold particles displaying various shapes in the size ranges of interest and can be used to demonstrate the influence of milling, silver content, and surface effects on the ability of PAX to float the free gold. In this test work: ➤ Fine Au powder particle recoveries were mostly better than the larger sizes, regardless of PAX addition, especially in the case of collectorless flotation. Fine and intermediate particle recovery decreased with 50 g/t PAX addition, which is probably a result of overdosing of the collector and, as a consequence, reduced hydrophobicity ➤ Despite slow kinetics, the coarse free gold from the GRG concentrate reached the highest recoveries at PAX additions of 50 g/t and 25 g/t . ➤ The fine and intermediate GRG particles showed intermediate recoveries at both the 50 g/t and the 25 g/t PAX additions. Interestingly, recoveries of intermediate particles were similar at either PAX addition level. The fine particles exhibit increased The Journal of The Southern African Institute of Mining and Metallurgy

The behaviour of free gold particles in a simulated flash flotation environment

Figure 8—Fractional recoveries of particles from GRG concentrate and Au powders at 50 g/t PAX addition (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

Figure 9—Fractional recoveries of particles from GRG concentrate and Au powders at 25 g/t PAX addition (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

Figure 10—Fractional recoveries of particles from GRG concentrate and Au powders at 0 g/t PAX addition, i.e. collectorless flotation (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s

The Journal of The Southern African Institute of Mining and Metallurgy

intermediate GRG concentrate was not recovered at all without the addition of PAX. However, with PAX additions, recoveries of intermediate and fine GRG particles were better than the coarse particles. The cumulative recoveries of each particle size for each test condition are shown in Figure 13. Although this figure does not contain any kinetic information, this is the first time that cumulative recoveries for the laboratory flash flotation VOLUME 115

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recovery with further PAX additions. This may indicate that over-ground particles have a larger surface area and require more collector to achieve maximum recovery in flotation; or that comminution has a deleterious effect on the surface of the fine particles, decreasing the capacity to adsorb collector ➤ While all GRG particles achieved poor recoveries in the flash period without the addition of PAX, the

The behaviour of free gold particles in a simulated flash flotation environment

Figure 11—Fractional recoveries of Au powders at varying PAX additions (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

Figure 12—Fractional recoveries of GRG concentrate at varying PAX additions (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

Figure 13, a few trends are evident, some of which are only a function of the particle size distribution and do not offer any suggestions about floatability.

Figure 13—Cumulative recoveries of each size fraction and their contribution to ultimate recovery in each test condition for GRG concentrate and Au powders at varying PAX additions (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

test have been documented. Previous test work (McGrath et al., 2013) has shown that differences of more than 4.6% in total gold recovery can be deemed statistically significant when using the flash flotation laboratory test for free gold. When data is reported in terms of cumulative recovery, as in

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➤ Firstly, the coarse particles float better with increasing PAX, particularly in the case of the GRG concentrate ➤ Secondly, recoveries from the intermediate Au powder are better than from the intermediate GRG concentrate by collectorless flotation; while the intermediate fractions of both powder and concentrate contribute similarly to cumulative recovery in the 25 g/t PAX trial ➤ Thirdly, the cumulative recovery from the Au powders is maximized at 25 g/t PAX, decreasing when PAX is increased to 50 g/t. Conversely, the highest cumulative recovery from GRG is obtained at the highest level tested for PAX (50 g/t), and the combined recovery drops when collector dosage is lowered to 25 g/t. Figure 14 shows ideal cumulative recoveries for the flash flotation period during the first two minutes (four data points) of the laboratory testing, as this is of specific interest in this study. Interestingly, the trend of increased recovery of fine GRG with increased residence time is evident when the flash period flotation data in Figure 14 is compared to the cumulative recoveries shown in Figure 13. Yet, this trend was not so evident for the coarse and intermediate GRG or any of the Au powder particles. The Journal of The Southern African Institute of Mining and Metallurgy

The behaviour of free gold particles in a simulated flash flotation environment ➤ The coarse fraction of GRG particles had a large proportion of slow-floating material, which was also not decreased by the addition of PAX ➤ It is important to note the high MAPE values, like those seen in the +212 μm data, show poor fit for estimation of kinetic values, which is probably due to sampling errors consistent with the nugget effect.

Conclusions The recovery of GRG concentrate was enhanced with increased additions of PAX, as clearly seen in the cumulative recovery data. The recoveries from Au powders were better by collectorless flotation than from the GRG concentrate but, unlike the GRG concentrate, cumulative recovery of fine and intermediate particles decreased with higher levels of PAX addition, probably due to collector overdosage. Interestingly, higher recovery of coarse particles appears to be directly linked to higher additions of PAX, for both GRG concentrate and the Au powders. Without large dosages of PAX the coarse GRG particles had slow kinetic rates, suggesting they are unlikely targets for recovery in industrial flash flotation. Unfortunately, assays for the coarse particles were skewed by problems related to the nugget effect. Therefore, absolute recovery values may shift if sufficient sample mass can be assayed to counteract the nugget effect; however, the recovery trends are expected to be similar. Recoveries of the intermediate Au powder and GRG concentrate particles were similar in the laboratory flotation test with PAX collector. Because the Au powders displayed superior potential for collectorless flotation in this size range (as well as the other two size ranges) compared to the GRG concentrate, it is suggested that milling could have a damaging effect on the natural hydrophobicity of free gold. Recoveries of fine Au powder particles were better than the fine GRG in all experiments. This is unexpected, because the literature suggests that flotation kinetics are proportional to the silver content of the GRG. Therefore, the decrease in kinetics and recovery for fine GRG particles is possibly further evidence of the deleterious effect of milling on the floatability of GRG particles. Kinetic evaluations indicate that intermediate and, to some extent, fine gold particles from both sources were either recovered in the first 30 seconds or reported to tails. The

Figure 14—Recoveries of each size fraction and their contribution to cumulative recovery in the flash flotation period of each test condition for GRG concentrate and Au powders at varying PAX additions (agitation = 1200 rpm, superficial gas velocity = 0.0074 m/s)

Kinetic values presented in Table I have been calculated using the method of least squares to fit the coefficients in Equation [1] and the Solver function in Microsoft Excel to solve for unknowns. A mean absolute percentage error (MAPE) has also been calculated as a measure of accuracy for each data-set; this is a common method for determining forecast error in timed data series. The α, β, and γ coefficients are useful in comparing the amount of material recovered in each data-set. Analysis of these coefficients and the kinetic rate data reveals that: ➤ A majority of the floatable intermediate and fine Au powder and GRG concentrate particles were recovered with the addition of PAX in the fast-floating portion, while the remainder of the gold particles contained in the pulp reported to tails, leaving hardly any material in the slow-floating category ➤ The lack of a slow-floating portion was more pronounced in the Au powders than in the GRG concentrate ➤ The fast-floating portion of GRG in the fine fraction increased with increased PAX addition and the nonfloating component decreased; however, despite changes in PAX levels the slow-floating portion of GRG in the fine fraction remained similar

Table I

Kinetic data for each type of gold particle at varying PAX concentrations GRG Concentrate

Au Powders

αφ

βs

γf

MAPE

αφ

βs

γf

MAPE

+212 +38 -38

0.48 0.30 0.73

3.43 2.96 3.70

0.00 0.43 0.44

1.00 0.06 0.12

0.00 0.51 0.44

14% 1% 0%

0.05 1.72 0.49

0.72 1.72 1.60

0.09 0.70 0.34

0.00 0.12 0.00

0.91 0.18 0.66

14% 0% 1%

+212 +38 -38

0.48 1.47 1.91

3.86 3.05 1.91

0.01 0.39 0.54

0.99 0.39 0.13

0.00 0.22 0.33

7% 1% 0%

0.39 0.14 1.10

1.01 3.23 3.73

0.51 0.43 0.15

0.07 0.10 0.18

0.42 0.47 0.67

1% 0% 0%

+212 +38 -38

0.02 1.00 0.59

0.02 101 5.65

0.00 1.00 0.85

0.66 0.00 0.12

0.34 0.00 0.03

0% 0% 0%

0.34 0.85 0.86

4.92 2.47 0.98

0.79 0.86 0.42

0.04 0.07 0.15

0.17 0.07 0.43

0% 0% 1%

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g/t PAX

The behaviour of free gold particles in a simulated flash flotation environment kinetic data, coupled with flash flotation period recoveries comparable to plant flash recoveries (with an addition of PAX), suggests that both intermediate and fine GRG particles are appropriate targets for industrial flash flotation, though cumulative recoveries are low. Further work is being conducted on samples obtained during plant surveys to help development of a size-dependent flotation model for the recovery of GRG concentrate to flash flotation or gravity recovery when these units are used in a closed-loop milling circuit. Additionally, this work focuses on identifying variations in physical parameters (shape, composition, surface area, and roughness) of gold particles found in concentrate samples as determined by QEMSCAN and Micro CT. The information gained will lead to improved understanding of the recovery of free gold in parallel flash flotation and gravity operations.

KELSALL, D.F. 1961. Application of probability in the assessment of flotation systems. Transactions of the Institution of Mining and Metallurgy, vol. 70. pp. 191–204. LAPLANTE, A. and DUNNE, R. 2002. The gravity recoverable gold test and flash flotation. Proceedings of the 34th Annual Meeting of the Canadian Mineral Processors, Ottawa, Canada, 22–24 January 2002. LAPLANTE, A. and STAUNTON, W.P. 2005. Gravity recovery of gold – an overview of recent developments. Treatment of Gold Ores: Proceedings of the International Symposium on the Treatment of Gold Ores, Calgary, Alberta, Canada, 21–24 August 2005. Canadian Institute of Mining, Metallurgy and Petroleum. MACKINNON, S., YAN, D., AND DUNNE, R. 2003. The interaction of flash flotation with closed circuit grinding. Minerals Engineering, vol. 16. pp. 1149–1160. MALHOTRA, D. and HARRIS, L. 1999. Review of plant practices of flotation of gold and silver ores. Advances In Flotation Technologies. Parekh, B.K. and

Acknowledgments The authors wish to thank the AMIRA P420E sponsors (AngloGold Ashanti, Australian Gold Reagents, Barrick Gold, Gekko Systems, Harmony, Kemix, Magotteaux, Newcrest Mining, Newmont, Norton Gold Fields, Orica Australia, Rangold, St Barbara, and Tenova) for financial and technical support, as well as their patience, expertise, and valuable input in revising this paper.

Miller, J.D. (eds.). Scociety for Mining, Metallurgy and Exploration, Littleton, Colorado. pp. 167–181. MCGRATH, T.D.H., STAUNTON, W.P., and EKSTEEN, J.J. 2013. Development of a laboratory test to characterise the behaviour of free gold for use in a combined flash flotation and gravity concentrator model. Minerals Engineering, vol. 53. pp. 276–285. NEWCOMBE, B., BRADSHAW, D., and WIGHTMAN, E. 2012. Flash flotation… and the plight of the coarse particle. Minerals Engineering, vol. 34. pp. 1–10.

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and their flotation under different conditions. Processing of Complex Ores. Dobby, G.S. and Rao, S.R. (eds.). Pergamon Press, Halifax. pp. 19-27. ALLAN, G.C. and WOODCOCK, J.T. 2001. A review of the flotation of native gold and electrum. Minerals Engineering, vol. 14, no. 9. pp. 931–961. BREZANI, I. and ZELENAK, F. 2010. MATLAB tool for determining first order flotation kinetic constants, Kosice, Slovakia. http://www.mathworks.com.au/matlabcentral/fileexchange/28583-

PEVZNER, M.L., SHCHERBAKOV, V.I., and KOSOVA, L.Y. 1966. Behaviour of gold during grinding. Tsvetnaya Metallurgia, vol. 39, no. 5, pp. 11–12. (In Russian). SUTHERLAND, D.N. 1989. Batch flotation behaviour of composite particles. Minerals Engineering, vol. 2, no. 3. pp. 351–367. TAGGART, A.F. 1945. Handbook of Mineral Dressing. Wiley, New York.

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[Accessed 24 March 2014]. BOURKE, P. 2002. Flash flotation of copper and gold. Output, vol. 3. pp. 7–11. CHRYSSOULIS, S. L. and DIMOV, S.S. 2004. Optimized conditions for selective gold flotation by TOF-SIMS and TOF-LIMS. Applied Surface Science, vol. 231–232. pp. 265–268. COLEMAN, R. 2010. Maximise your recoveries in a flash. Output Australia,

model for gold flotation. Minerals Engineering, vol. 12, no. 9. pp. 1001–1019. TENNYSON, S. 1980. The hydrophilic nature of a clean surface. Journal of Colloid and Interface Science, vol. 75. pp. 51–55. TRAHAR, W.J. 1981. A rational interpretation of the role of particle size in flotation. International Journal of Mineral Processing, vol. 8, no. 4.

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Curtin University, Perth. Unpublished work. DRZYMALA, J. 1994. Hydrophobicity and collectorless flotation of inorganic materials. Advances In Colloid and Interface Science, vol. 50. pp. 143–185. DUNNE, R. 2005. Flotation of gold and gold-bearing ores. Developments In Mineral Processing: Advances In Gold Ore Processing. Wills, B.A. (ed.). Elsiever, Amsterdam, The Netherlands. pp. 309–343. KELEBEK, S. and NANTHAKUMAR, B. 2007. Characterization of stockpile oxidation

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vol. 23, no. 15. pp. 1159–1166.

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The role of gravity flow in the design and planning of large sublevel stopes by R. Castro* and M. Pineda*

Sublevel stoping (SLS) is one of the oldest and most used methods for underground mining. It relies heavily on the use of drilling and blasting techniques to remove the rock, and gravity to transport the broken rock to drawpoints located at the base of the stope, with LHDs to transport material from the drawpoints. Current SLS operations are based on the assumption of stable geometry of the stope. Thus, the stope design includes the definition of the geometry according to the orebody shape and geomechanical constraints to avoid instability, which may cause excessive dilution. Under some circumstances, dilution could enter the stope due to geotechnical instability, especially when large stope geometries are used. A review of current design and planning practices for large SLS operations indicates that no consideration is given to the material flow and the mixing that occurs after blasting. Material flow could have a large impact on the mixing of ore when grades are heterogeneous in the stope. In this paper, we discuss the influence of gravity flow on the design and planning of large sublevel stopes with and without vertical dilution, based on laboratory experiments. The outcomes of this investigation are used to develop guidelines towards the design and planning of large SLS mines, which would complement the currently used geotechnical considerations. Keywords sublevel stoping, mine design, gravity flow, ore dilution.

Current design practices for sublevel stoping Sublevel stoping (SLS) is a method that can be implemented as sublevel open stoping (SLOS) or vertical crater retreat (VCR). The optimal conditions for the application of SLS are related to the geometry and inclination of the orebody and the stability of the walls and pillars that form the stopes (good geotechnical condition). The stability of the stope, pillar, and walls is determined by the geotechnical characteristics of the hangingwall and footwall (Potvin et al., 2001). SLS could be applied when the dip of the orebody is greater than 50°; this condition is based on the ability of the fragmented rock to flow due to gravity, when extracted at the production level (Pakalnis et al., 2011). The design of the sublevel stope includes the placement of the draw and the locations of the auxiliary and the drill levels. Figure 1 The Journal of The Southern African Institute of Mining and Metallurgy

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Synopsis

shows a schematic of the conceptual design of a large stope designed from level 1 to level 3. Note that the stope is defined as large when the height (H ) and width (W ) of the stope are greater than 30 m. In Figure 1, the dip of the footwall is 90° (vertical). The spacing of the sublevels (hi) depends on several factors, including the orebody geometry, the drilling technology, and capital costs. Figure 1 also includes a reference to a variation of ore grade with the stope height. The in situ grades are represented by horizontal layers G1, G2, and G3, where (G1>G2>G3). In current planning practices, ore grades are planned to be extracted in the order from G1 to G3 without considering the mixing that might occur during gravity flow. Figure 2 show a typical draw or production level layout for a large sublevel stope, where DD are drifts where a long drawbell is located, while PD is the production drift where, in general, mechanized equipment such as LHDs operates. In the case presented in Figure 2, drawbells are spaced at Dd metres while drawpoints are spaced at Dpe metres. Generally, for SLS, Dpe is approximately 15–18 m while Dd is 48 m (Contador et al., 2001). Figure 3 shows the application of SLS with the footwall inclined at an angle a and the hangingwall inclined at b degrees to the horizontal. In this case, the location of the drawpoints must be considered with respect to the maximum recovery of ore, especially if unplanned dilution can enter the stope due to instability. This is related to the gravity flow properties of fragmented rock.

The role of gravity flow in the design and planning of large sublevel stopes These studies have served to define the location of drawpoints for a flat or an inclined production level and the ore mixing due to flow, results that are extensively used for mine design and planning of caving methods. Guidelines for inclined drawpoint spacing in block caving consider dips from 35° to 40° and a width of the flow zone of 12 m for finelyfragmented caved rock (Laubscher, 2000). Given the differences between SLS and caving methods, the gravity flow experiments may not necessarily be applicable to SLS design and planning. We conducted research, based on experiments, on the gravity flow characteristics of fragmented rock in SLS applications. The results of the experiments were used to define the location of drawpoints for flat and inclined footwalls in SLS. Figure 1—Conceptual design of a sublevel stope. (Gi is the grade of ore in the stope, hi is the distance between levels, and wi is the distance between drawbells)

Figure 2—Plan view of a typical production level drawpoint/drawbell spacing for a large sublevel stope

Laboratory experiments To understand the effects of flow on the design of large SLS applications, controlled experiments were conducted. The objective of the laboratory experiments was to study the ore flow within a sublevel stope under inclined geometries. For the purpose of the experiments, a physical model having a typical geometry of a large stope with a footwall inclination of a = 70° and hangingwall inclination of b = 90° was built in the laboratory (Figure 4). During the design stage of the experiments, all the laws of kinematic similitude for granular materials – that is geometrical similitude, extraction rate and friction angle – were taken into consideration (Pineda, 2012). The model was built using plexiglass to enable observation of the flow and to consider an axisymmetric condition by using near-frictionless walls. The geometrical design was based on a typical drawbell spacing used in large stopes, i.e. Dd = 48 m. The dimensions of the model were 1.6 m height × 1 m length × 0.25 m width. The base of the model held an extraction system of 11 drawpoints and the drawbell geometry with a ‘shovel’ installed at each drawpoint. The ’shovels’ were linked to a servomechanism that provided an electrical impulse controlled through a software algorithm, allowing the extraction rate to be varied.

Figure 3—Conceptual design of a sublevel stope. (Gi is the ore grade, H is the height of the stope, wi is the distance between drawbells, and a is the dip of the stopes

Gravity flow studies have been extensively conducted in block and sublevel caving applications using scaled models (Kvapil 1965; Lausbscher 2000; Brown 2007) and full-scale tests (Power, 2004; Brunton et al., 2012; Viera et al., 2014).

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Figure 4—Experimental set up- physical model (left), drawpoints and apex (right) The Journal of The Southern African Institute of Mining and Metallurgy

The role of gravity flow in the design and planning of large sublevel stopes from an extra level 30 m above the first level ➤ Experiment 5—flow behaviour when dilution could enter the stope continuously from the levels above the draw level.

The size distribution of the material used in the model was based on the rock size distribution as measured at SLS mine operation, with fragment size due to blasting leaning towards fine material with a mean characteristic size or d50 of 0.3 m (Figure 5). Therefore, for the experiments, crushed gravel with a mean size of 2 mm was used, as shown in Figure 6.

Methodology

Table I

Five experiments were conducted to gain an understanding of gravitational ore flow. During the first experimental stage, all drawpoints were extracted concurrently from a horizontal level during each experiment. For the second stage, drawpoints were added at the footwall to simulate the use of more than one draw level. Table I lists the objectives and the draw strategy for each of the five experiments:

Experimental plan Experiment Draw strategy 1

Isolated draw

To determine isolated flow zone geometry for the model media

Uniform draw

To determine the flow mode when drawing from a single draw level

Uniform draw

To determine the flow mode when drawing from a single draw level

Uniform draw

A repetition of experiment 3

Uniform draw

This experiment simulates continuous dilution entry at the top of the stope. The aim is to quantify the flow mode when dilution from the back is continuous

Passing under size (%)

➤ Experiment 1—an understanding of flow under a single drawpoint ➤ Experiment 2—study of the flow under multiple drawpoints from a single production level ➤ Experiments 3 and 4—flow behaviour when drawing

Objective

0.00

0.01

Fragmentation 1

0.10 Size (m)

1.00

Fragmentation 2

10.00

Fragmentation 3

Passing under size (%)

Figure 5—Fragment size distribution for SLS operation

0.10

1.00 Size (mm)

10.00

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Figure 6—Fragment size distribution for experiments

The role of gravity flow in the design and planning of large sublevel stopes Experiment 1

Experiment 3 and 4

During the first experiment, we measured the isolated movement zone or IMZ, which is defined as the disturbed zone due to flow (Castro et al., 2007). As shown in Figure 7, the IMZ has a cylindrical shape, with a diameter d of 15 cm in the model, which corresponds to 30 m in diameter at 300 m draw height when scaled. This corresponds to the measurements of flow zones during gravity flow in granular materials. Figure 7 also shows that the angle of draw α is in the range from 69° to 76°, which should be considered when designing the location of drawpoints.

These two experiments were undertaken to determine whether the inclusion of another level is necessary to extract the ore above the footwall in SLS, under the assumption that flow of the wedge does not occur. The new level was located 60 mm above the production level (30 m when scaled to a real sublevel stope). Draw from this level started after the initial draw of material from the production level. As indicated in Figure 9, the flow zone of the drawpoint located at the footwall was connected to that developed due to the flow of Level 1 (Figure 9a). Drawing from this new level improved the early recovery of ore located at the base of the footwall, as shown in Figure 9b.

Experiment 2 In this experiment, flow was induced by drawing from the full geometry at the base of the stope. Figure 8 shows the geometry of the flow for different stages of the draw. As noted, the flow zone due to the extraction from all drawpoints did not propagate en masse but developed the shape of the isolated draw zone that propagated towards the lower column height (Figure 8a). Consequently, the flow velocity was higher in the columns with smaller column height. Subsequently, the flow reached a steady state, where the flow was mainly vertical. Subsequently, granular material at the surface moved down due to rilling (Figure 8c). This condition continued as more material was drawn, as shown in Figure 8d. This shows that the flow causes the material to mix. These phenomena should be considered when planning the extraction of a stope that continues to be stable during draw. The experimental results show that the material located at the production level is not mobilized during flow. The authors developed the force relationships using equilibrium analysis to understand the factor of safety of the wedge. The calculations indicated that failure of the wedge could be expected, which, as noted previously, did not occur during the experiment.

Figure 7—Isolated draw zone as measured during Experiment 1.at a) 33,280 ton for the drawpoint and at b) 68,224 (scaled values)

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Experiment 5 This experiment investigated the case of dilution entering the stope from above due to stope instability. The dilution was simulated by adding a granular material, which was finer in size than the initial ‘ore’ size and coloured red for contrast, on top of the stope. Figure 10 shows different stages during draw. As noted in previous experiments, the flow zone developed faster at the lower column height. Subsequently, the dilution moved down according to a flow velocity profile that was faster in a zone where the height of the column is lower. This type of flow continued until the material reached the height of interaction and rapidly appeared at a drawpoint,

Figure 8—Flow zones during experiment at different stages of draw. (a) Initial draw stage; (b) flow zones breakthrough to surface; (c) further draw; (d) rilling from surface The Journal of The Southern African Institute of Mining and Metallurgy

The role of gravity flow in the design and planning of large sublevel stopes 1. Firstly, the fragmentation due to blasting needs to be estimated. The smaller the fragments the smaller the flow zone and, therefore, the smaller the width of the IMZ. The converse also applies. It must be noted that the results provided in this paper refer to free flow conditions, where no cohesion exists in the granular material 2. Secondly, if the stope is vertical (a = 90°), a single draw level may be considered. In this case, it is necessary to calculate the height of interaction according to the angle of flow (α) and the width of the drawbell drift, and to calculate the spacing of drawpoints according to the desired height of interaction (Castro et al., 2012), that is: Figure 9—Draw from production level and level located at 30 m for different stages of draw. (a) Initial draw from upper level; (b) further draw and surface rilling

[1]

3. Thirdly, the spacing of drawbells and drawpoints should be designed for the IMZ to overlap. No reliance on extra spacing rules should be considered, as presented in block caving experiments (Trueman et al., 2008) 4. Finally, if the stope is inclined (a <90°), more than one level may be considered, vertically spaced at a height of hi metres. As shown in Figure 11, the location of the levels would depend on the width of the IMZ and the inclination of the stope. In this case, the horizontal spacing should be such that the IMZs, with diameter of d metres, overlap. In Figure 11, an example is provided where there is a main draw level (Figure 11a) and other three draw levels are located at the footwall of the stope (Figure 11b) to achieve maximum recovery of the stope. In this case the vertical distance (hi) between drawpoints is: [2]

Conclusions

as noted in block caving experiments (Laubscher, 1994). As this point, the dilution entered the drawpoints at a faster rate, as shown in Figure 10d.

Discussion: SLS design guidelines considering flow The results of the experiments described in the previous section clearly show that the design guidelines should consider the effects of material flow on the location of drawpoints and draw levels in a sublevel stope. The Journal of The Southern African Institute of Mining and Metallurgy

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Figure 10—Influence of vertical dilution on flow. (a) Initial draw; (b) flow zone breakthrough to surface; (c) dilution (in red) flow towards drawpoints; (d) fines migrate to the drawpoints with the smallest column height

SLS has been widely used in the mining industry for many years. Current design guidelines and mine planning are based on rock stability and equipment, and appear to take into consideration the flow properties of the fragmented rock under gravity. In this paper, based on laboratory experiments, we prove the importance of flow in the design and operation of large sublevel stopes. If dilution from the back of the stope is not expected to occur, it is envisaged that SLS would recover most of the ore during drawing, as the stope is emptied. In this case, the spacing of drawpoints may not be key to the success of an operation, but mine planners should consider the mixing of the ore that occurs within the fragmented column and the surface rilling to better estimate the production grades. In the case of an expected instability at the back of the stope, large amounts of dilution could mix with the ore. In this case, ore recovery would not be efficient unless the gravity flow characteristic of the fragmented rock is included in the design

The role of gravity flow in the design and planning of large sublevel stopes

Figure 11—a) Plan view and b) section of proposed draw levels for an inclined stope

and draw strategy. The design of the spacing should ensure interaction between the flow zones. This is also applicable to an inclined footwall, where an extraction level should be added in order to mobilize the ore. The mixing within the ore could be modelled using some of the latter flow simulators built to predict mixing at caving mines (see Castro et al., 2009, Pierce 2010).

CONTADOR, N. and GLAVIC, M. 2001. Sublevel open stoping at El Soldado Mine: a geomechanic challenge. Underground Mining Method. Hustrulid, W. (ed.). Society for Mining Metallurgy and Exploration Inc, Littleton, Colorado. pp. 263–270. KVAPIL, R. 1965. Gravity flow of granular material in hoppers and bins. Part 1. International Journal of Rock Mechanics and Mining Sciences, vol. 2. pp. 35–41. Laubscher, D.H. 1994. Cave mining – the state of the art. Journal of the

Acknowledgements The authors would like to acknowledge the financial support of the Chilean Government through the project Conycit FB0809 and the support of Agnico Mine, which was instrumental in delivering on the research goals. We would also like to thank Dr. Matthew Pierce for the helpful discussion during the development of this research, Mrs. Carolina Bahamondez for providing many of the schematics, and Dr. Eleonora Widzyk-Capehart for helpful feedback during the writing of this article.

Southern African Institute of Mining and Metallurgy, vol. 94, no. 10. pp. 279–93. LAUBSCHER, D.H. 2000. Block Caving Manual. Report prepared for the International Caving Study, University of Queenland, Australia. PAKALNIS, R. and HUGHES, P. 2011. Sublevel stoping. SME Mining Engineering Handbook. Darling, P. (ed.). Society for Mining Metallurgy and Exploration Inc, Littleton, Colorado. pp. 1355–1363. PIERCE, M.E. 2010. A model for gravity flow of fragmentation in block caving mines. Ph.D. Thesis, The University of Queensland, Brisbane.

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BRUNTON, I.D., SHARROCK, G.B., and LETT, J. 2012. Full scale near field material flow behavior at the Ridgeway Deeps Block Cave operation. 6th International Conference on Mass Mining, Sudbury, Ontario, Canada, 10–14 June 2012.

means of a physical model. MEng thesis, Universidad de Chile, Santiago, Chile. POTVIN, Y. and HADJIGEORGIOU, J. 2001. The stability graph method. Underground

BROWN, E.T. 2007. Block Caving Geomechanics. 2nd edn. Julius Kruttschnitt Research Center, University of Queensland, Brisbane, Australia. CASTRO, R., TRUEMAN, R., and HALIM, A. 2007. Study of isolated draw zones in block caving mines by means of a large 3D physical model. International Journal of Rock Mechanics and Mining Sciences, vol. 44. pp. 860–870. CASTRO, R., VARGAS, R., and DE LA HUERTA, F. 2012. Determination of drawpoint spacing in panel caving: a case study at the El Teniente Mine. Journal of the Southern African Institute of Mining and Metallurgy, vol. 112, no. 10.

Mining Methods. Hustrulid, W. (ed.). Society for Mining Metallurgy and Exploration Inc, Littleton Colorado. pp. 513–520. POWER, G.R. 2004. Modelling granular flow in caving mines: large scale physical modeling and full scale experiments. PhD thesis, University of Queensland, Brisbane, Australia. TRUEMAN, R. CASTRO, R., and HALIM, A. 2008. Study of multiple drawzone interaction by means of a large 3D physical model. International Journal of Rock Mechanics and Mining Sciences, vol. 45. pp. 1044–1051.

pp. 871–876. CASTRO, R., GONZALEZ, F., ARANCIBIA, E. 2009. Development of a gravity flow

VIERA, E., MONTECINO, M., AND MELENDEZ, M. 2014. First step in monitoring

numerical model for the evaluation of drawpoint spacing for block/panel

gravity flow at El Teniente mine: Installation stage in Block-2, Esmeralda

caving. Journal of the Souther African Institute of Mining and Metallurgy,

mine. Proceedings of Caving 2014, 3rd International Symposium on Block

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and Sublevel Caving, Santiago, Chile, 5–6 June 2014. pp. 348–355.

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Continuous improvement management for mining companies ˆ

by M. Vanek*, K. Spakovska′*, M. Mikola′s*, and L. Pomothy†

Keywords KAIZEN, management, continuous improvement, mining enterprise.

Introduction Mineral extraction and processing represents a common enterprise activity associated with specific risks. Mining enterprises are oriented by profit, capital valorization, maintaining market position, augmentation of company assets, and various other objectives (Vaněk et al., 2011). Attaining prominent objectives in current market conditions, characterized by competition and change, is difficult if modern managerial methods are not applied (Zaušková and Kusá, 2011). ‘Modern’ does not necessarily mean new; quality management was first introduced in the USA in about 1920 as a statistical tool for industrial production improvement (Mizuno, 1988). Furthermore, there are Deming tenets as well as PDCA principles that date back to the 1950s (Košturiak and Gregor, 1993) and are still applied by managers today. There are many methods and approaches that managers can apply, therefore they must The Journal of The Southern African Institute of Mining and Metallurgy

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Continuous improvement of processes In economic practice, two types of change or innovation are common; either a sudden, fundamental change that requires a large investment as well overcoming intrinsic

* Faculty of Mining and Geology, VSB – Technical University of Ostrava, Czech Republic. † OKD, a. s., Czech Republic. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Mar. 2013; revised paper received Sep. 2014. FEBRUARY 2015

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Enterprises are faced with increasing economic competition and managers are obliged to look for methods that will ensure a competitive edge in their companies’ markets. These methods include managerial concepts that employ common sense and are low cost. One of these approaches is the KAIZEN methodology, of Japanese origin; kaizen means continuous improvement (CI) when translated. A mining company, despite some of its idiosyncrasies, is just an enterprise where the application of KAIZEN can be advantageous. The company investigated, OKD, is the only domestic producer of hard coal in the Czech Republic and operates four mines that produce 11 Mt of coal annually. The OKD management team opted for KAIZEN as their principal method of continuous quality improvement. This paper suggests ideas for development and application of processes that provide for continuing improvement of production and management. The authors have taken the OKD Company to be their benchmark in the field of mining activities. The paper also focuses on possible difficulties accompanying the introduction of CI sustainable methods. Three years of application of CI methods has increased the income of the company by almost US$38.1 million, which provides a strong argument for continued application.

Synopsis

select wisely from all available options, despite current fashions or fads, while being open to new methods and approaches and constantly keeping their specific requirements in mind. Managers also play key roles in the implementation of continuous improvement (CI), as human resource utilization is predominantly a managerial challenge (Drucker, 1999). It is collaboration that kick-starts and sustains continuous change (Farris et al., 2009; Langbert, 2000). CI is crucial if intense market competition and low-cost operation exigency is to prevail. The global financial crisis and the related global recession have directly or indirectly afflicted the situation of many business enterprises in the Czech Republic, and the mining sector has been no exception. In many other countries worldwide, mining is a significant contributor to the economy (Nzimande and Chauke, 2012) and is subject to demand and supply inconsistencies and unfavourable price movements. No matter what the cause of decreased income, there is no doubt that managers must optimize production costs. (Erol et al., 2011; Laperche et al., 2011). In such difficult conditions, a company can keep its market position only if CI is routinely implemented (Baaij et al., 2004). It is the objective of this paper to create awareness around continuous improvement and illustrate approaches utilized by mining companies.

Continuous improvement management for mining companies apprehension, or a slow step-by-step change that is easily accepted and requires negligible investment (Bessant et al., 1994; Zaušková and Domová, 2012). A company must strive to produce better, cheaper goods and services in order to grow. In addition to this growth, it is also necessary to continuously improve (Drucker, 1999). In order to understand the CI process we return to the origin of the managerial concept named KAIZEN. In July 1950, W.E. Deming was invited to Japan to teach statistical quality control. Deming introduced the ‘Deming Cycle’, one of the crucial quality control tools for assuring CI in Japan (Imai, 1986). The meaning of the Deming Cycle (Figure 1), also known as PDCA (Plan-Do-Check-Act) cycle, is a basic principle of Total Quality Control (TQC) or Total Quality Management (TQM). KAIZEN has been accepted as a lifelong philosophy by many Japanese managers and workers. It integrates earlier approaches like consumer orientation, TQC, QC circles, Kamban, Just-in-time, zero defect, small group activities etc. (Imai, 1986; Ortiz, 2010). In simple terms, KAIZEN is the application of a common-sense and low-cost approach. The traditional Japanese approach to QC is managerially oriented (Imai, 1986) because the team is key to the process of CI and drives company culture and values (Langbert, 2000). The KAIZEN perspective requires all company staff, including workers and junior managers, to be positive that their contribution is taken seriously and their suggestions, if approved by expert opinion, are implemented (Anand et al., 2009). Only such an inclusive approach can provide the contributions required to improve existing standards, prevent stagnation, and avoid subsequent relaxing of initiative (Farris et al., 2009). In addition, KAIZEN also represents productivity improvement, TQC activities, QC circles, or labour relations (Imai, 1986). ➤ One stumbling block to implementing changes can be misgivings whether the change is acknowledged by the party involved ➤ The undisputed advantage of KAIZEN is that it builds on utilization of the standing resources and does not necessarily require investment (Bessant et al., 1994). Successful implementation requires a formal declaration and the modification of existing managerial methods. KAIZEN should not be applied uncritically as a blueprint of another experience, but should take the specific background and idiosyncrasies of the specific organization into account (Bessant et al., 2011; Recht and Wilderom 1998; Nocco, 2005). It is obvious that KAIZEN must start with the managers themselves and then continue to be applied by all staff. It is based on changing overall thinking styles and behaviours (Langbert, 2000).

company sold 11.5 Mt of coal, taking the fifth position in the list of major producers of hard coal in Europe, and the sales income reached a level of US$1.68 billion in 2010. OKD is also the biggest employer in the Moravia-Silesia region, with 18 000 employees. The company operations are divided into internal organization units that include the respective collieries and one service centre. OKD cares about its image as a socially responsible enterprise, and this is reflected in its staff care programmes, which include many employee benefits. Previously, employees could file innovation applications but there was no system in place to assess innovation potential and motivate staff. The CI system, introduced to utilize the staff innovation potential to its maximum, was instituted by a new management structure in 2008. This generated a wave of innovation proposals that could be systematically and objectively assessed, and remunerated accordingly. Management incorporated a CI department tasked to utilize the concept to its full potential. The CI management implementation was accelerated in 2009 by the establishment of a CI Manager at each of the four collieries. The CI Manager reports directly to the director of the colliery. Natural authorities with extensive practical experience were appointed and a special CI committee (consisting of the CEO, CAO, COO, CF and HRO, CI co-ordinator, and colliery director) was formed and convenes every month. An information campaign in the company’s weekly, Horník (Miner) informed the staff of the company’s CI drive and was supplemented by CI seminars and wall posters. (Brodský, 2011) The CI idea is supported by the provision of value and finance frameworks (Bessant et al., 1994). The CI structure should be realized step-by-step, especially if no similar structure is in place. Time should be allowed for staff to absorb the change (Bessant et al., 2001). Table I shows the schedule of the CI structure establishment from 2010–2012 and indicates activities that took place in 2013. In OKD, two types of activities within the CI framework were observed. On the one hand, innovation initiatives of individual employees are monitored; the other emphasizes optimizing team initiatives. To promote individual CI activity, individual employee motivation is especially necessary. The process based on individual people is quite simple, in contrast to the team

Continuous Improvement at OKD The Upper Silesia Coal District, which covers 7000 km2, is a major European coal district spanning two countries – Poland and the Czech Republic. The southern part of the district, about 1550 km2, is called Ostrava-Karvina Coal District (Dopita and Aust, 1997). The mining company, OKD a.s. (a.s. – ‘akciová společnost’ – is equivalent of the British plc), is the only producer of hard coal in the Czech Republic and runs four deep mining collieries in this district. In 2010, the

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Figure 1—Deming cycle within TQM (source: ČSN EN ISO 9001) The Journal of The Southern African Institute of Mining and Metallurgy

Continuous improvement management for mining companies Table I

CI structure establishment in the mining company OKD, 2010–2013 Stage

Year

Orientation

Specific content

2010

Individual CI

Activating creativity, individual involvement of managers and staff, employees of outsourcing companies working underground, provision of optimization teams and change alliances.

2011

Team CI

Team co-operation, filing group innovation applications, horizontal and vertical co-operation of management and staff – the project, Moje firma (My Company), internal consultancy activities.

2012

Process CI

Company macro-process autonomy (HR, material supply), removing bottlenecks of the critical macro-processes.

2013

CI custom fixation

Widening autonomy of macro-processes to safety, logistics, and coal face workings, gradual transition from volunteer to standard CI structures.

Source: OKD, original research

activity. A specialized external consultancy agency, HSG, was called in to conduct employee orientation in the newly established system and empower the teamwork activities. The project was titled ‘Moje firma’ (My Company). The results of first year’s team activities within the Moje firma project are displayed in Figure 2. The consultant organized a training programme for colliery consultants so that they could employ their own expertise. Originally, only virtual training was involved, but the outcome was subsequently put into actuality by a team of CI managers. The internal consultants chose their own project, specific to their workplace conditions (HR management, work organization, technical problem solutions, economy, alternative or theoretical solutions, etc. and subject to authorization by the CI department and colliery director. The colliery director officially nominated leaders of individual project teams, assuring managers’ support. After about six months, the results of these ‘virtual’ projects were presented to the consultants, HSG, and to all company managers. The assessment of the projects was made at a closed session. All participants were rewarded. The project outcome benefits differed – from hard financial gains through advantages difficult to express in numbers up to individual persons’ experience with project management. Some projects were chosen as items of official optimization agenda; others were nominated to be the subject of long-term support and monitoring by internal consultants. Generally the optimizing team’s activities can be initiated by an individual worker, but they consist of well-thoughtover, systematic activities in which staff participate and also include management and external consultants. Both types of activities – individual innovative initiative and the optimizing team activities – will be clearly illustrated

by taking an example of a specific CI management of a specific unit (colliery). Tables II and III provide the results of the work of the CI department and all employees involved in the activity from 2010–2011. To show financial profit and rewards paid, the current exchange rate of 19.7 Czech crowns (CZK) per US dollar was used. The results are slightly distorted due to differences of the assessment criteria provided for admission. For initial motivation the assessment criterion demands were set rather soft, and have been made stricter every year. The application of stricter criteria is substantiated because colliery resources are limited. A methodical monitoring of the financial contributions of the innovations realized is conducted every 12 months. It is then taken to be an accepted standard and is reflected in related financial planning.

Figure 2—The results of the ‘Moje firma’ (My Company) project (source: OKD, original research)

Table II

Statistics, CI OKD, 2010

Individual employee initiative Specifically targeted projects of optimization Total

Filed

Accepted

Implemented

Financial profit, US$ million

Rewards paid, US$ million

1 075 64

742 64

558 60

5.74 5.73 11.47

0.117 0.030 0.147

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Source: OKD Annual Report

Continuous improvement management for mining companies Table III

Statistics, CI OKD, 2011

Individual employee initiative Specifically targeted projects of optimization Total

Filed

Accepted

Implemented

Financial profit, US$ million

Rewards paid, USS million

950 –

614 70

592 57

6.599 8.629 15.228

0.117 0.025 0.142

Source: OKD Annual Report

Table IV

Annual change as a chain index, 2011/2010

Individual employee initiative Specifically targeted projects of optimization Total

Filed

Accepted

Implemented

Financial profit

Rewards paid

0.88 -

0.83 1.09

1.06 0.95

1.15 1.51 1.33

1.02 0.85 0.98

Source: OKD Annual Report

Questions have been raised about the evaluation method for the innovations realized in practice. For example, innovations in the field of energy saving will generate benefits in the longer term. The annual change in performance is shown in Table IV, and it is clear that frequency of admissions decreased by 17%. At first sight this decrease may seem to be a CI failure; however, during this period income increased by 15%. The reason for this positive result is the better quality innovation proposals filed and the speed of their realization, up from 75% in 2010 to 96% in 2011. In 2012, year-on-year realization of innovation proposals decreased (500 innovations admitted) but the financial income increased by US$10.15 million. To assess the CI activities, profits were monitored (FP) per innovation realized (RN). Table V shows the development of this ratio in the period 2010–2011. A decreasing trend is noted for team project optimization, which dropped by three projects in 2011; nevertheless, financial income increased by US$2.9 million. Tables III–V present figures for the initial two years, when the CI structures were first implemented. It is obvious that many employees used the opportunity to file proposals that concerned the most critical issues of their work methods and environment (Farris et al., 2009), which resulted in a rapid increase in profit. It would not be realistic to expect a similar innovation boom in the following years. Therefore the decreasing innovation rate serves as an example of CI success (Bessant et al., 1994) as the innovations realized in the framework of KAIZEN became a standard that will be further developed by continuing improvement initiative (Glover et al., 2011). In the long run, innovation opportunities will be less frequent and the CI initiatives will have to concentrate on diagnostics (Bessant et al., 2001), and there will be less opportunity for cost optimization based on minimization. A kind of a status quo satisfaction may follow, which will require management to exert their influence to seek opportunities for further improvement or risk losing benefits gained from the CI effort.

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CI method and tools, ČSM colliery The mining company OKD consists of four collieries, each of which operates in different conditions. In KAIZEN terms, these are designated as gemba (Imai, 1997). The following section demonstrates specific tools and activities that are part of CI structures active at a specific colliery. The individual collieries differ for objective and subjective reasons, thus CI is approached differently. The global objective is dissemination of knowledge and positive staff experience. Each colliery has its idiosyncratic position and each gemba must be considered individually. Our example gemba, the ČSM colliery, has 3300 employees and is considered the third largest in OKD. This model gemba demonstrates all the processes, tools, and activities studied. ČSM colliery uses the following CI tools: ➤ Idea card (innovation proposal) ➤ Teams of optimization ➤ Analyses provided by CI department ➤ Education. The idea card (Figure 2) serves the purpose of noting down employee proposals. The innovation idea is briefly identified and specified. The idea card is not essential to the innovation proposal submission, as informal applications are also acceptable and can be e-mailed, telephoned, or verbally communicated, in either Slovak or Polish. In the case of an informal application an idea card is filled in by a CI official and authorized by the innovation initiator. The CI department registers the card, which is subsequently passed on to professional experts or supervisors of related activities. Several professionals assess the value of each innovation proposal. Table V

Development of profit increase by innovation, 2010–2012 Year FP/RN, US$

2010

2011

10 288

11 162

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Continuous improvement management for mining companies

The Journal of The Southern African Institute of Mining and Metallurgy

An independent CI unit, which uses accepted standards, provides for the timing. Their conclusions are taken as objective and obligatory, an important consideration for managers. The sense of the CI work is not repressive but provides for diagnostics. It is about identification of weak points, definition of an appropriate remedying action, and above all about ‘inflaming’ staff, persuading them about the significance of KAIZEN. The last, but not least, criterion is staff education. Tasks cannot be completed without appropriate competence. It is also important to initiate thinking centred around work improvement possibilities and ways to put innovation in practice. The colliery provides: ➤ Training – communication abilities ➤ Workshops – familiarity with analytical methods ➤ Training of internal consultants ➤ Managerial training, Manex – top management ➤ Manex practitioner – first line managers ➤ PC training courses ➤ Tailor-made courses and self-study ➤ Information and reference provision.

Conclusion There can be no doubt that current technological standards are the consequence of revolutionary or incremental innovative processes in the long history of humankind. The mining industry utilizes cutting-edge technologies characterized by developed segments of the economy. The difficult natural and market conditions in which mining enterprises operate require top-quality management that consider their own staff as sources of creativity. We are quite positive that the decision to implement the KAIZEN concept is the right one, especially if a company wants to sustain their market position. OKD’s successful operations support this argument; in three years the individual and collective optimization projects increased the company income by almost US$38 million.

Figure 3—Idea card – Colliery, ČSM: rectification of nest damage in pit (source: OKD) VOLUME 115

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After evaluation, the idea is recommended for admission or rejection. A decision is made by the CI Committee, which comprises the colliery director, his deputies, and the manager of the colliery CI department. The committee is tasked with considering ideas for admission, and once an idea has been admitted, the committee is responsible for its realization. Optimization teams are contracted by OKD or colliery management. The subject for optimization can be initiated by all parties. The CI department director nominates a team leader. An ‘expert guarantor’ and ‘sponsor’ are also nominated. The expert guarantor is a staff professional or an external consultant that coaches the team and ensures the innovation realization follows CI conventions. He warns the team if there is an emergency or cul-de-sac of the activities performed for legislative, financial, technical, or patent right reasons. The sponsor is a colliery manager. He is responsible for managing those activities at the colliery that relate to the innovation realization, and provides for organizational conditions and material means of implementing the innovation. The sponsor also plays the role of a team supervisor and communicates with the colliery management. After the innovation has been admitted for implementation, the colliery director places an order for the project. The team leader chooses his collaborators. Team membership is voluntary, and team members are rewarded on the grounds of their individual contributions and roles within the team. The sponsor assesses the work of the team according to the following criteria: ➤ Safety of work and health protection ➤ Improvement of working conditions and environment ➤ Ecological improvement ➤ Reduction in shift overheads (material and workforce cost savings) ➤ Reduced energy demands (electricity, heating, water, gas, fuel, etc.), IT and telecommunication costs, transportation costs, etc. ➤ Reduced material costs (material per se, spare parts, technical gases, oxygen, antifreeze, etc.) ➤ Improving production and functional parameters (useful economic life, breakdowns, etc.) ➤ Decreasing production losses (processing, deposition protection, etc.) ➤ Miscellaneous. The key phase of the overall improvement process is represented by the analyses that are conducted by the CI department, namely: ➤ Timing – (workplace or work process monitoring) ➤ Work map provision ➤ Mathematical analyses ➤ Critical point identification ➤ Statistical analyses ➤ Special tasks. Notwithstanding the fact that CI staff takes advantage of all analyses, the decisive diagnostics are workplace and work process time-demand monitoring. The results of time studies are visually represented by a process map (Figure 3). Feedback meetings and communication with shift-based staff are considered to be an integral part of the procedure, especially to drive the discussion around the source of problems and possible remedying action.

Continuous improvement management for mining companies BRODSKý, I. 2011. Zapojení zaměstnanců do STZ v OKD. Fórum trvalého zlepšování. Prague, 21 October 2011. DOPITA, J. and AUST, J. 1997. Geologie české části hornoslezské pánve. 1st edn. Ministerstvo životního prostředí České republiky, Prague. DRUCKER, P.F. 1999. Management challenges for the 21st century. 12th edn. Butterworth-Heinemann, Oxford . EN ISO 9001:2008 Quality Management Systems – Requirements. EROL, M., APAK, S., ATMACA, M., and ÖZTÜRK, S. 2011. Management measures to be taken for the enterprises in difficulty during times of global crisis: An empirical study. Procedia - Social and Behavioral Sciences, vol. 24. pp. 16–32. FARRIS, J.A., VAN AKEN, E.M., and DOOLEN, T.L. 2009. Critical success factors for human resource outcomes in Kaizen events: An empirical study. International Journal of Production Economics, vol. 117, no. 1. pp. 42–65. GLOVER, W.J., FARRIS, J.A., VAN AKEN, E.M., and DOOLEN, T. 2011. Critical success factors for the sustainability of Kaizen event human resource outcomes: an empirical study. International Journal of Production Economics, vol. 132, no. 2. pp. 197–213.

Figure 4—Process map example (source: CI department, ČSM colliery)

KAIZEN has a social and cultural background that is difficult for some people to adopt, but it is essential and intrinsic to the concept that all staff members participate in order to have the maximum impact. The company management of the gemba discussed here is well aware of this fact, and structures their remunerative schemes accordingly. One of their successful innovators can even win a new car. Nevertheless, some attention should be drawn to a certain disproportion that can erode innovation activities of managers and staff in future. It follows from the Annual Reports of 2010–2012 that the company spent only US$0.289 million on innovation rewards, which is only 1.08% of the company’s overall US$26.7 million income increase from improvement. ‘Penny wise and pound foolish’ policies do not work in the long run, considering the words of Peter Drucker: ’The most valuable assets of a 20th-century company were its production equipment. The most valuable asset of a 21stcentury institution, whether business or non-business, will be its knowledge, workers and their productivity.’

References ANAND, G., WARD, P.T., TATIKONDA, M.V. and SCHILLING, D.V. 2009. Dynamic capabilities through continuous improvement infrastructure. Journal of Operations Management, vol. 27, no. 6. pp. 444–461. BAAIJ, M., GREEVEN, M., and VAN DALEN, J. 2004. Persistent superior economic performance, sustainable competitive advantage, and Schumpeterian innovation: leading established computer firms, 1954–2000. European Management Journal, vol. 22, no. 5. pp. 517–531. BESSANT, J., CAFFYN, S., and MAEVE, G. 2001. An evolutionary model of continuous improvement behaviour. Technovation, vol. 21, no. 2. pp. 67–77. BESSANT, J., CAFFYN, S., GILBERT, J., HARDING, R., and WEBB, S. 1994. Rediscovering continuous improvement. Technovation, vol. 14, no. 1. pp. 17–29.

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IMAI, M. 1986. Kaizen : The Key to Japan’s Competitive Success. 1st edn. McGraw-Hill, New York. IMAI, M. 1997. Kaizen : Gemba kaizen : a common sense low-cost approach to management. 1st edn. McGraw-Hill, New York. KOšTURIAK, J. and GREGOR, M. 1993. Podnik v roce 2001: revoluce v podnikové kultuře. Grada, Prague. LANGBERT, M. 2000. Human resource management and Deming's continuous improvement concept. Journal of Quality Management, vol. 5, no. 1. pp. 85–101. LAPERCHE, B., LEFEBVRE, G., and LANGLET, D. 2011. Innovation strategies of industrial groups in the global crisis: Rationalization and new paths. Technological Forecasting and Social Change, vol. 78, no. 8. pp. 1319–1331. MIZUNO, S. 1993. Řízení jakosti. Ist edn. Victoria Publishing, Prague. NOCCO, A. 2005. The rise and fall of regional inequalities with technological differences and knowledge spillovers. Regional Science and Urban Economics, vol. 35, no. 5. pp. 542–569. NZIMANDE, Z. and CHAUKE, H. 2012. Sustainability through responsible environmental mining. Journal of the Southern African Institute of Mining and Metallurgy, vol. 112, no. 2. pp. 135–139. OKD VýROčNÍ ZPRÁVA. 2010, 2011. Dostupný z WWW. http://www.okd.cz/cs/onas/vyrocni-zpravy [Accessed 15 January 2013]. ORTIZ, CH. 2010. Kaizen vs. Lean: distinct but related. Metal Finishing, vol. 108, no. 1. pp. 50–51. RECHT, R. and WILDEROM, C. 1998. Kaizen and culture: on the transferability of Japanese suggestion systems. International Business Review, vol. 7, no. 1. pp. 7–22. VANěK, M., MIKOLÁš, M., RÙÈKOVÁ, H., BARTOÒOVÁ, J., KUÈEROVÁ, L., and OÈEK, F. 2011. Analysis of mining companies operating in the Czech Republic in the sector of non-metallic and construction minerals. Gospodarka Surowcami Mineralnymi, vol. 27, no. 4. pp. 17–32. ZAUšKOVÁ, A. and KUSÁ, A. 2011. Hodnotenie výkonnosti inovačných a marketingových procesov v malých a stredných podnikoch. Vedecká Monografia. Data Service, Zvolen. ZAUšKOVÁ, A. and DOMOVÁ, J. 2012. Inovačná schopnos a inovačná výkonnos podnikatelských subjektov. Vedecká Monografia. UCM FMK, Trnava. ◆ The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a6 ISSN:2411-9717/2015/v115/n2/a6

A decision analysis guideline for underground bulk air heat exchanger design specifications by M. Hooman*, R.C.W. Webber-Youngman*, J.J.L. du Plessis*, and W.M. Marx*

This paper discusses a study that investigated different underground bulk air heat exchanger (>100 m3/s) design criteria. A literature review found that no single document exists covering all design criteria for different heat exchangers, and therefore the need was identified to generate a guideline with decision analyser steps to arrive at a technical specification. The study investigated the factors influencing heat exchanger designs (spray chambers, towers, and indirect-contact heat exchangers) and the technical requirements for each. The decision analysers can be used to generate optimized, user-friendly fit-forpurpose designs for bulk air heat exchangers (air cooler and heat rejection). The study was tested against a constructed air cooler and heat rejection unit at a copper mine1. It was concluded that the decision analysers were used successfully. This tool (decision analysers) can be used by engineers for the efficient and cost-effectively design of heat exchangers. Keywords mine refrigeration, water-air, heat exchanger design, decision analyser.

Introduction Refrigeration systems are widely used in mines to provide air cooling for underground operations (Burrows et al., 1989). The installed refrigeration capacity is generally dictated by depth for hard rock mines where virgin rock temperature (VRT) and autocompression, among other heat sources, play a major role (Bluhm and Biffi, 2001). Refrigeration and the associated cooling installations at depth is essential to ensure that legal thermal requirements are maintained. Work done by Bluhm and Von Glehn (2010) indicates the relationship between mining depth and type of refrigeration and cooling system required (Figure 1). Figure 1 shows the VRT is a function of a surface intersects temperature in degrees Celsius (°C) and the geothermal gradient in °C/m (De Wet, 2012). The VRTs for specific

1Marx,

W., Hooman, M., Botha, P., and Meredith, G. 2010. Refrigeration and cooling design case study: Palabora Mining Company. Journal of the Mine Ventilation Society of South Africa The Journal of The Southern African Institute of Mining and Metallurgy

Background Heat exchangers can be supplied with cold water from surface or underground refrigeration plants, but energy requirements versus life-of-

* University of Pretoria. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jan. 2014; revised paper received Sep. 2014. VOLUME 115

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Synopsis

mines are calculated using the intersect temperature and geothermal gradient. The graph in Figure 1 is only a guide to determine which refrigeration option would be best suited, and selection of a suitable system depends on the mining method, heat loads, mine depth, VRT, etc. Refrigeration capacity requirements dictate the air cooling and heat rejection heat exchanger duties for surface or underground installations. Many of the design considerations for surface and underground cooling system installations are similar, but in practice there are many differences. Due to the potentially vast field of study in this regard, this study focuses on underground heat exchangers, using only bulk air heat exchanger installations exceeding 100 m3/s air flow capacity. Bulk air heat exchangers in underground operations are used either to cool air when maximum legal return temperatures from the working areas (reject temperatures) are reached, or to cool water from underground refrigeration plants (heat rejection). These heat exchangers form part of two underground water refrigeration circuits, namely the evaporator circuit where chilled water is distributed to air coolers and the condenser circuit where heat is rejected to return air (Burrows et al., 1989). Air cooling and heat rejection heat exchangers typically consist of three main types with very different design requirements and applications. Underground bulk air heat exchangers include direct-contact air-water horizontal spray chambers, direct-contact airwater cooling towers, or indirect-contact airwater banks of heat exchangers.

A decision analysis guideline for underground bulk air heat exchanger design specifications

Figure 1—Relationship between mining depth and type of refrigeration system (Bluhm and Von Glehn, 2010)

mine total cost of ownership need to be compared during project start-up. Most recent designs incorporate underground refrigeration plants as they ensure energy-efficient designs, optimized positional efficiency, and lowest total cost of ownership (Marx et al. 2010). This study provides a comprehensive summary of different heat exchangers from many resources. It further provides the mine ventilation and refrigeration design engineer with a userfriendly guideline for underground bulk air heat exchanger designs. All aspects pertaining to the design will be found in a single document to save the user time in finding solutions for the specific application. This document will be a technical specification that can be used to contact specialist engineers to complete ‘for-construction’ designs. Du Plessis et al. (2013) indicated that energy-efficient designs are imperative with increasing electrical costs. This work further indicated that energy consumption will have a direct impact on carbon usage and need to be managed as carbon emission tax legislation is planned for the start of 2015. Design constraints may impact the energy consumption of a mine and are an important part of the design process to consider. Poor designs can result in the safety and health of personnel being compromised, and this cannot be tolerated since mines are legally responsible for employee health and safety. The objective of the study was to determine the requirements of underground heat exchanger designs. The study included: ➤ A detailed literature review ➤ Identification of the factors influencing the selection criteria for underground bulk air heat exchangers ➤ Quantifying the engineering and technical requirements. Quick and easy decision analyser steps were then compiled to assist mine operating engineers to write design specifications for a specific heat exchanger type. These decision factors were tested on an existing bulk air heat exchanger design as part of a case study.

Factors influencing selection criteria for bulk air heat exchangers The factors that influence the selection criteria for bulk air heat exchangers need to be identified and the effect on the design process quantified. If done correctly, this will ensure that the design is optimized and that an energy-efficient solution is derived. The following criteria must be considered:

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➤ Reject temperature design criteria for the mine will determine how much cooling is required (DMR, 2001; 2002) ➤ Mine working depth (VRT) and effect of autocompression (Karsten and MacKay, 2012) ➤ Heat load, including influence of mining method, equipment, water used, etc. (Burrows et al., 1989) ➤ Excavation size and location to establish the number of heat exchanger locations and the size of new or existing tunnels ➤ Inlet conditions of heat exchangers, which determine the duty and ultimately the outlet temperature of the air to the working air (for coolers) and the outlet water temperature for condenser heat exchangers (heat rejection units) (Burrows et al., 1989) ➤ Environmental conditions, including air and water quality, define how many treatment facilities will be required and the maintenance schedule (Whillier, 1977) ➤ Rock engineering sign-off on the proposed excavations ➤ Surrounding activities such as blasting, crushing, conveying, etc. need to be considered, as these will impact on the heat exchanger design and maintenance frequencies ➤ Construction logistics are important to ensure that installations are in fresh air, with on-time delivered equipment and in a safe environment.

Engineering and technical requirements The engineering and technical requirements for each of the heat exchangers were identified. Furthermore, the water reticulation system and positional efficiency for each type was calculated. The technical requirements associated with each of the types of heat exchanger are listed in the following paragraphs. Indirect-contact heat exchanger banks are mainly used for cooling. The following selection criteria were considered: ➤ Log-mean temperature difference (LMTD) (Incropera and De Witt, 2002) versus number of transfer unit(s) (NTU) method (Ramsden, 1980; Kays and London, 1964) ➤ Air and water energy balances (heat capacities, and air and water inlet and outlet temperatures (CabezasGomez et al., 2006) ➤ Air and water efficiencies (Burrows et al., 1989) ➤ Heat transfer coefficient (McPherson, 2007) ➤ Tube water velocity (Hendy Coils, 2008) ➤ Water design pressures (Burrows et al., 1989). Direct-contact spray towers are mainly used for heat rejection. The following criteria were considered: ➤ Packed or unpacked alternatives (Burrows et al., 1989) ➤ Mechanical or natural draught towers (Perry and Green, 1997) ➤ Type of packing (Energy Efficiency United Nations Environmental Programme, 2006) ➤ Air and water mass flow rate (Burrows et al., 1989) ➤ Inlet air and water temperature (Mine Ventilation Society of South Africa, 2008) ➤ Air velocity through tower (Burrows et al., 1989) ➤ Height to diameter (McPherson, 2007) ➤ Water to air ratio (Stroh, 1982) The Journal of The Southern African Institute of Mining and Metallurgy

A decision analysis guideline for underground bulk air heat exchanger design specifications ➤ Factor of merit (Burrows et al., 1989). Direct-contact spray chamber can be used for air cooling and heat rejection. The following criteria were considered: ➤ Type of nozzle (Bluhm, 1983) ➤ Air and water mass flow rate (Burrows et al., 1989) ➤ Inlet air and water temperature (Mine Ventilation Society of South Africa, 2008) ➤ Water loading (Bluhm, 1983) ➤ Air velocity through chamber (Burrows et al., 1989) ➤ Water to air ratio (Reuther, 1987) ➤ Factor of merit (Burrows et al., 1989). The listed engineering and technical requirements, together with the factors identified from the previous two paragraphs, were used to generate simple decision analysing steps to use during the heat exchanger selection criteria process. This selection criteria process will improve the technical specification(s) of bulk air heat exchangers.

excavations will be required. This is important for determining the most cost-effective option – whether to distribute smaller heat exchangers or fewer large heat exchangers in the available or new excavations. Air coolers are generally placed adjacent to main intake airways and heat rejection units in or adjacent to the main return airways. Once the number of heat exchangers and their locations are determined, proceed to Step 3.

Step 3: inlet conditions and water loading Once the number of locations to place air coolers and heat rejection units has been determined, the inlet conditions at each unit need to be confirmed (Figure 4). The inlet conditions

Decision analyser steps Step 1: determine heat exchanger requirement The first step is to determine the thermal design criteria of the mine (Figure 2). Ventilation and cooling requirements need to be determined so as to establish whether more air is available to cool the underground environment to below the thermal design criteria. If this is possible, then no underground heat exchanger is required. In addition, the working depth of underground workings is required. A rule of thumb suggested that surface cooling is best utilized at critical depths up to 1 200 m and VRTs up to 50°C (Figure 1). The applicable mine’s VRT may result in cooling being repaired at a particular mining depth. This decision will be dependent on the ore being mined and mining method utilized. If the critical depth does not necessitate an underground heat exchanger, then other heat source considerations, such as heat from diesel vehicles and fragmented rock, need to be identified and included in this step. The additional heat sources may necessitate an underground air cooler. In the event that underground heat exchangers are required, it needs to be established whether a bulk air flow of more than 100 m3/s is available for heat transfer. If this is true, continue to Step 2.

Figure 2—Heat exchanger requirements

Step 2: determine heat exchanger size and location

The Journal of The Southern African Institute of Mining and Metallurgy

Figure 3—Heat exchanger size and location VOLUME 115

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The second decision analyser step starts by calculating the underground heat load (Figure 3). Heat load components include autocompression, the contribution from the VRT, vehicles and other artificial sources. If the air temperature is below the reject temperature the air carries a certain air cooling capacity (heat removal capacity). An additional cooling source is service water, and the capacities of both of these need to be determined. Mass and energy balances for the mine include the heat load and air and service cooling capacity, which determine the additional underground cooling required. Air flow and thermodynamic software simulation models (VUMA3D-network, Ventsim, or fully equivalent) can be used to confirm heat exchanger requirements. The required air cooling and heat rejection heat exchangers can be divided into a number of units and the location for each needs to be determined in order to determine how many new

A decision analysis guideline for underground bulk air heat exchanger design specifications The excavation size for the heat rejection units needs to be known in order for the designer to determine the water loading factor. The area of the available or new excavations will determine the required length of each unit. In this manner the number of heat rejection units can be confirmed and correctly selected.

Step 4: environmental conditions and surrounding activities The quality of air and water are important factors to evaluate when designing heat exchangers (Figure 5). Both parameters will impact on the maintenance frequency. In the event that fresh air is used, no problem should be anticipated. When air is re-used from certain working areas the quality of the air need to be investigated with respect to blasting fumes, dust, radiation, and other pollutants. These factors will impact on technical maintenance and design requirements of the heat exchangers in terms of conductivity of the water, blow-down rate, maintenance and cleaning frequency, make-up water rate, equipment selection, etc. The location of the heat exchangers needs to be verified by the rock engineers to ensure stability of the excavation and surrounding strata. Maintenance frequencies need to be determined based on the quality of the air and water, materials of construction, and equipment life. The constructability logistics include all activities involving installing the equipment, from shaft time, crane installation to offload equipment, and actually commissioning the process system. A constructability manager is usually appointed for this purpose to assist with on-time delivery and installation.

Figure 4—Heat exchanger inlet conditions

Step 5: types of bulk air heat exchangers

Figure 5—Environmental conditions and surrounding activities

will determine how much cooling or heat rejection can be achieved. For air coolers, the outlet air temperature needs to be within the minimum allowable air temperature. For heat rejection units, the maximum allowable water temperature that can return to the condenser heat exchanger need to be confirmed with suppliers. The input parameters of the air and water and, depending on whether air coolers or heat rejection units are used, the design criteria, need to be satisfied. When the design criteria are not met for these heat exchangers, the inlet air quantity needs be increased where possible.

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In this step the type of heat exchanger will be determined. As stated earlier, three types of coolers – indirect-contact heat exchangers, spray towers, and spray chambers – are available to cool the air. Heat rejection can be achieved in spray towers and spray chambers. Banks of indirect-contact heat exchangers will be used mainly in closed-circuit water systems in clean air. For indirect-contact heat exchangers two design options exist, namely the LMTD method and the NTU method. For direct-contact (open-circuit) heat exchangers, spray towers and spray chambers are possible options. Spray towers are classified under filled or unpacked units and the selection under each type is unique. Design processes for spray chambers, together with spray towers and indirect-contact heat exchangers, are discussed in a decision process. Figure 6 is a typical example of a decision analyser step as applied to various types of heat exchanger. The factor of merit (FOM) and positional efficiency are two of the major contributors to the final selection of heat exchanger type. Indirect-contact heat exchangers, spray towers, and spray chamber heat exchangers can be selected for heat rejection and cooling. The number of units, stages, and/or cells will give an indication of the size of the excavation required to install the heat exchanger. After this process, the heat exchanger type, location(s), quantities, air distribution, water distribution, etc. need to be verified to ensure that the correct unit will be installed. The Journal of The Southern African Institute of Mining and Metallurgy

A decision analysis guideline for underground bulk air heat exchanger design specifications BLUHM, S.J. and BIFFI, M. 2001. Variations in ultra-deep, narrow reef stoping configurations and the effects on cooling and ventilation. Journal of the Southern African Institute of Mining and Metallurgy, vol. 101, no 3. pp. 127–134. BLUHM, S.J. and von GLEHN, F. 2010. Refrigeration and Cooling Concepts for UltraDeep Operations, BBE Report No. 0310. BURROWS, J., HEMP, R., HOLDING, W., and STROH, R.M. 1989. Environmental Engineering in South African Mines. Mine Ventilation Society of South Africa, Johannesburg. CABEZAS-GÓMEZ, L, NAVARRO, H.A., and SAIZ-JABARDO, J.M. 2006. Thermal performance of multipass parallel and counter-cross-flow heat exchangers. Journal of Heat Transfer, vol. 129, no. 3. pp. 282–290. http://heattransfer.asmedigitalcollection.asme.org/article.aspx?articleid=144 8635 [Accessed 5 September 2013]. DEPARTMENT OF MINERAL RESOURCES (DMR). 2002. Guideline for the compilation of a mandatory code of practice for an occupational health programme on thermal stress. Reference no. DMR 16/3/2/4-A2. Pretoria, South Africa. DEPARTMENT OF MINERAL RESOURCES (DMR). 2001. Guideline for the compilation of a mandatory code of practice on minimum standards of fitness to perform work at a mine. Reference no. DMR 16/3/2/3-A1. Pretoria, South Africa. DE WET, J.R. 2012. Ventilation and refrigeration of a deep platinum mine. BEng hons thesis, University of Pretoria. DU PLESSIS, J.L.L, HOFFMAN, D, MARX W.M., and VAN DER WESTHUIZEN, R. 2013. Optimising ventilation and cooling systems for an operating mine using network simulation models. Association of Mine Managers South Africa, Johannesburg. HENDY COILS. 2008. Cooling Coil Specifications. http://www.hendycoils.com.au/documents/Cooling_Coil_Design.pdf INCROPERA, F.P. and DE WITT, D.P. 2002. Fundamentals of Mass and Heat Transfer. 3rd edn. Wiley, Hoboken, NJ.

Figure 6—Types of heat exchangers

KARSTEN, M. and MACKAY, L. 2012. underground environmental challenges in deep platinum mining and some suggested solutions. Platinum 2012. Fifth International Platinum Conference ‘A Catalyst for Change’ Sun City, South Africa, 17-21 September 2012. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 177–192. KAYS, W.M. and LONDON, A.L. 1964. Compact Heat Exchangers. 2nd edn. Mc Graw-Hill, New York.

A step-by-step guide to all factors contributing to underground bulk air heat exchanger technical specifications was developed. This guide was generated from environmental factors (including thermal design criteria, heat loads, excavation requirements, environmental conditions, and rock mechanics) and engineering and technical requirements (including types of heat exchangers, factor of merit, and hydraulics). The step-by-step guide makes it possible to design a quick and easy fit-for-purpose technical specification for underground heat exchangers. This specification can be used by manufacturers and construction companies to design optimized ‘for-construction’ heat exchangers. It can be concluded that the decision analysers do provide a guideline of the criteria required for technical specifications for bulk air heat exchangers. The overall aim was to provide operational engineers with a tool to quickly and easily specify optimized technical specifications for fit-for-purpose heat exchangers. The factors and technical requirements identified in this study could be applied to a real mine with the used of the decision analysers.

References BLUHM, S.J. 1983. Spot cooling of air in direct contact heat exchangers. Report of Environmental Engineering Laboratory, Chamber of Mines Research Organization Johannesburg. The Journal of The Southern African Institute of Mining and Metallurgy

MARX, W., HOOMAN, M., BOTHA, P., and MEREDITH, G. 2010. Refrigeration and cooling design case study: Palabora Mining Company. Journal of the Mine Ventilation Society of South Africa. MCPHERSON, M.J. 2007. Refrigeration plant and mine air conditioning systems. Subsurface Ventilation and Environmental Engineering. Chapman and Hall, London. MCPHERSON, M.J. 2007. The aerodynamics, sources and control of airborne dust. Subsurface Ventilation and Environmental Engineering. Chapman and Hall, London. MINE VENTILATION SOCIETY OF SOUTH AFRICA. 2008. Mine Environmental Control (MEC). Workbook 2: Thermal Engineering, . PERRY, R.H. and GREEN, D.W. 1997. Perry’s Chemical Engineers’ Handbook. 7th edn, McGraw-Hill, New York. RAMSDEN, R 1980. The Performance of Cooling Coils (Part 1 and Part 2). Environmental Engineering Laboratory, Chamber of Mines of South Africa Research Organisation. REUTHER, E.U., UNRUH, J., and DOHMEN, A. 1987. Simulation techniques for the optimization of high capacity refrigeration in German coal mines. APCOM 87. Proceedings of the Twentieth International Symposium on the Application of Computers and Mathematics in the Mineral Industries. Volume 1. South African Institute of Mining and Metallurgy, Johannesburg, pp. 307–317. STROH, R.M. 1982. Environmental Engineering in South African Mines. Chapter 24 (Refrigeration Practice) and chapter 25 (Chilled Water Reticulation. Cape and Transvaal Printers, Cape Town. UNITED NATIONS ENVIRONMENT PROGRAMME. 2006. Energy Efficiency Guide for Industry in Asia. www.energyefficiencyasia.org [Accessed 14 March 2012]. WHILLIER, A. 1977. Predicting the performance forced-draught cooling towers. Journal of the Mine Ventilation Society of South Africa, vol. 30. pp. 2–25. ◆ VOLUME 115

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Conclusions

MARX, W, HOOMAN, M, BOTHA, P., and MEREDITH G. 2010. Cooling system design for a block cave mine. Mine Ventilation Society of South Africa Conference, 2010.

BACKGROUND The evolving nature of the current mining environment suggests that there be strict environmental and social considerations as key components in determining mine profitability. Recent research on environmental and social risks and business costs in the extractive industry found that environmental issues were the most common cause of disputes, resulting in lost productivity. These environmental issues were centred on the pollution of, competition over and access to natural resources. International best practices and compliance standards have set the benchmark for mining companies together with national legislation. However, over time, the essence of these benchmarks loses meaning when they become ‘tick boxes’ for the industry to show sustainability. This appears to be the case currently. There is a need to take stock of what has been achieved thus far, recognise the changing nature of environmental and social impacts and consider ways of building resilient socio-ecological systems that include mining.

Mintek, Randburg

Mining, Environment and Society Conference

Beyond sustainability—Building resilience 12–13 May 2015 KEYNOTE SPEAKER:

OBJECTIVES The key objective of the conference is to get the relevant stakeholders within the mining sector together to: Re-invigorate the debate around mining and the environment Clarify and understand the evolving nature of new mining practices and approaches Investigate whether there is alignment of national legislation with international best practices and compliance standards as it relates to social and environmental concerns Explore the interactions of the various stakeholders in mining transactions Develop a better understanding of effective stakeholder relations Understand mining’s role in society and the development challenge it poses Consider the role of education in contributing to the environmental and social sustainability of mines Highlight leading-edge innovations in environmental and social impact quantification Share information

For further information contact: Conference Co-ordinator, Yolanda Ramokgadi, SAIMM P O Box 61127, Marshalltown 2107 · Tel: +27 (0) 11 834-1273/7 E-mail: yolanda@saimm.co.za · Website: http://www.saimm.co.za

Rohitesh Dhawan, KPMG’s Global Mining Leader for Climate Change & Sustainability. Currently co-located between Johannesburg and London, he has spent time in head offices and down mining shafts working on issues related to strategy, social performance, environmental sustainability and governance primarily in the coal, gold and platinum sectors. The issues that he enjoys working and researching on include calculating social return on investment, decision-making under conditions of uncertainty, the role of business in society, corporate purpose and managing environmental impacts. An Economist by background, he holds a Masters degree from the University of Oxford and is a fellow of the inaugural class of the Young African Leadership Initiative. Rohitesh was named one of Mail & Guardian’s 40 Climate change Leaders and the South African Rising Star in the Professional Services Category.

WHO SHOULD ATTEND The conference should be of interest to anyone working in or with the mining sector, including government and civil society organisations. It would be of particular relevance to advisors, consultants, practitioners, researchers, organised labour, government officials and specialists working in the following: Environmental Management Sustainability Stakeholder Engagement Local and Regional Development Planning Mining Legislation.

Conference Announcement

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a7 ISSN:2411-9717/2015/v115/n2/a7

‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking by K.B. Nagashanmugam, M.S. Pillai, and D. Ravichandar*

Blast furnace performance depends strongly on the coke reactivity index (CRI) and coke strength after reaction (CSR) properties. An innovative and cost-effective method, known as the Salem Box Test, has been developed to prevent the mass production of inferior coke unsuitable for blast furnace use. This method consists of coal carbonization on a microscale and involves charging approximately 18 kg of coal blend in a stainless steel box, carbonizing it together with coal cake in the plant coke ovens, and testing the coke produced for CRI and CSR to determine its suitability for blast furnace use. Only coal blends that yield coke with CRI <25% and CSR >64% are permitted for mass production, and other coal blends are either rejected or the blending ratios adjusted in an attempt to upgrade them. The experimental results reveal that, for a given coal blend, the quality of coke produced by the Salem Box Test is comparable with that produced by bulk production, indicating that the test is acceptable as a screening tool for regular use. The present paper describes the methodology and application of Salem Box Test to predict the suitability of coke for blast furnace use at JSW Steel Limited, Salem Works (JSWSL), and illustrates its advantages in adjusting the coal blending ratio to produce superior coke, in detecting coal contamination, and in preventing bulk production of inferior coke. Keywords coke-making, blast furnace, metallurgical coke, Salem Box Test, coke reactivity index (CRI), coke strength after reaction (CSR).

Introduction The dominant ironmaking process in the world today is the blast furnace process, and the most important raw material fed into the blast furnace in terms of operational efficiency and hot metal quality is metallurgical coke. Inside the blast furnace, coke performs three functions: ➤ Thermal: as a fuel providing the energy required for endothermic chemical reactions and for melting of iron and slag ➤ Chemical: as a reductant by producing reducing gases for iron oxide reduction ➤ Mechanical: as a permeable medium providing passage for liquids and gases in the furnace, particularly in the lower part of the furnace. The Journal of The Southern African Institute of Mining and Metallurgy

* R & D Centre & Coke Ovens, JSW Steel Ltd, Salem Works, Salem, India. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jan. 2014; revised paper received Oct. 2014. VOLUME 115

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Synopsis

When coke passes through a blast furnace, it degrades and generates fines which affect bed permeability and the process efficiency. Coke quality is often characterized by the hot and cold strength, ash composition (Mayaleeke et al., 2009; Nagashanmugam and Reji Mathai, 2012), and chemistry, which are largely dictated by the coal properties (Grosspietsh et al., 2000). Unfortunately, cold strength and hot strength are not clearly defined, and the methods used for quantifying these parameters by experimental measurement vary (Van Niekerk and Dippenaar, 1991). A range of laboratory tests and procedures have been developed to characterize the physical and chemical properties of coke and gauge their potential effects in blast furnaces. The most commonly used and well-known tests are the coke reactivity index (CRI) and coke strength after reaction (CSR) developed by Nippon Steel Corporation in Japan in early 1970s to assess the effect of CO2 reactions on coke. Generally, a high CSR is believed to prevent the coke from breaking down, improve the permeability, and increase the productivity of the ironmaking process as well as decrease the specific coke consumption (Grosspietsh et al., 2000). However, there is no international consensus on an ideal way to determine the quality of coke, as each industry relies on empirical experience for its interpretation. These laboratory tests are designed to test coke properties under a specific set of conditions,

‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking which might not be universally suitable. The reproducibility of CRI and CSR values also varies considerably between different laboratories (Arendt et al., 2001). Charging an inferior coke to the blast furnace poses problems such as high dust generation, poor permeability, hanging, slips, high fuel consumption, reduction in the quantity of coal injection, low productivity etc. To avoid such a scenario, a novel technique, known as the ‘Salem Box Test’, which employs micro-level carbonization of the coal blend in a stainless steel box, was developed at JSW Steel Limited’s Salem Works (JSWSL). The coke obtained by the box test is then tested for CRI and CSR to determine its suitability for blast furnace operation. Only those coal blends that produce coke with a maximum of 25% CRI and a minimum of 64% CSR, are used for mass production in the coke oven plant. This method avoids the mass production of coke unsuitable for blast furnace operation and ensures the manufacture and consistent supply of suitable coke. Coke with a maximum of 25% CRI and a minimum value of 64% CSR was found to be suitable for the JSWSL blast furnaces. This paper describes the use of the Salem Box Test to predict the suitability of coke for ironmaking in a blast furnace.

Material and method At JSWSL, it is a normal practice to blend various types of coal, viz. hard coking coal, semi-hard coking coal, and noncoking coal in the required proportions such that the volatile matter and ash content of the blended coal are less than 26% and 9% respectively. These coals are blended so as to minimize the use of scarce hard coking coal and also to minimize the cost of coke production, as the profitability of a steelmaking operation depends directly on the cost of coke.

Procedure for Salem Box Test The Salem Box Test (patent under application) is a costeffective method for evaluating the suitability of different coal blends before mass production of coke. At JSWSL, this method is used for selection / optimization of coal blends that would produce coke with the required CRI and CSR in the real operating environment. Samples of various coals, which constitute the coal blend, are collected and crushed to below 3 mm in size. The coal samples are mixed in the required proportions to prepare the coal blend, the required quantity of water is added to maintain approximately 10% moisture, and the sample is then homogenized by manual mixing. The coal blend placed inside a stainless steel box (size 250 mm × 250 mm × 250 mm, wall thickness 10 mm) in three to four increments and stamped with a metal stamper until approximately 18 kg of coal blend is compacted. The sample is now ready for carbonization. As the box needs to remain uncovered, a lid is not provided. The box is placed over the ‘ready-to-charge’ stamped coal cake by removing a portion of the cake at the center of its width to accommodate the box. The coal cake is

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then charged into the oven for carbonization, together with the box of test blend (Nagashanmugam et al., 2012). After carbonization, the coke cake is pushed onto the quenching car and is water-quenched. The box is then removed and the coke obtained is air-cooled, crushed, and sieved to 19–21 mm size. The sized coke is analysed for CRI and CSR (ASTM D5341-99, 2004) using equipment from M/s. Naskar Instruments Ltd, Kolkata, India. The test results determine the suitability of the coal blend for mass production in coke ovens. Only those coals / coal blends that yield coke (by the Salem Box Test) having CSR >64% and CRI <25% are be considered for mass production at JSWSL.

Determination of CRI and CSR About 10 kg of coke is crushed and screened to 20 ±1 mm. A 200 g sample of coke is placed in the reaction tube and heated to 1100°C in an inert atmosphere of nitrogen. Pure carbon dioxide gas is then passed through the sample. The reaction is sustained for 2 hours. The reacted coke is then cooled and weighed to determine the CRI (Nagashanmugam and Reji Mathai, 2012). The strength test is performed by placing the sample in an I-shaped drum and rotating it at 20 r/min for 30 minutes. The coke is then weighed to determine the CSR (Nagashanmugam and Reji Mathai, 2012).

Results and discussion Before the introduction of the Salem Box Test, testing of coals/coal blends for bulk manufacture of coke was carried out in large-scale oven trials. Although such a method has obvious advantages, it also has several disadvantages. For example, oven trials must not interfere with the usual routine of the plant, but must await a convenient time. Sometimes, due to a considerable time lag between the arrival of the coal at the plant and its use at the ovens, serious deterioration of the coking properties occurs through weathering. Furthermore, the full oven test becomes quite expensive (assuming it results in an inferior coke), hence obtaining the required information by simpler means leads to cost savings. Large-scale tests are needed only to confirm the result obtained by the Salem Box Test. Initially, the box tests were performed in cube-shaped mild steel box with a thickness of 7 mm and dimensions of 300 mm × 300 mm × 300 mm. This was later optimized to 250 mm × 250 mm × 250 mm and 10 mm thickness to facilitate manual handling and increase the service life of the boxes. As boxes of both dimensions were found to give consistent results, trials with other dimensions were not conducted. Although the size of the box is not critical, it is better to have the box dimension at least 10 times the size of coke (19-21 mm) used for CRI and CSR analysis. Although boxes made of mild steel gave satisfactory results, the results were occasionally found to be inconsistent. Subsequently, this was found to be due to corrosion of the mild steel. It was suspected that iron oxide, the product of corrosion, might The Journal of The Southern African Institute of Mining and Metallurgy

‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking Table I

CRI and CSR values obtained in presence and absence of iron oxide Coal blend / mix Name of coal

Australian coal A Australian coal B Australian coal C Russian anthracite coal

Test condition Fraction in blend (%)

Iron oxide present

Iron oxide absent

CRI (%)

CSR (%)

CRI (%)

CSR (%)

32.0

58.0

24.0

65.88

60 25 5 10

Figure 1–Stainless steel box used in the Salem Box Test

The Journal of The Southern African Institute of Mining and Metallurgy

diagram depicting various steps in performing the test. A large number of box tests have been carried out to date, the results of which have proved to be highly satisfactory for the prediction of the coke quality that would be obtained by mass production in coke ovens. Table II presents few examples of box tests conducted with various coals / coal blends. Those coal blends that yielded CRI values below 25% and CSR values above 64%, were passed for mass production, and other coal blends were either rejected or further tested by varying the blending ratios. In order to determine the applicability and suitability of the Salem Box Test, the coal blends that were found to yield the required CRI and CSR were carbonized in coke ovens for mass production as per normal procedure and the resultant coke was subjected to CRI and CSR analysis. The CRI and CSR values of coke obtained from box tests and those obtained from coke ovens using same coal blends are compared in Table III. It can be seen from Table III that the CRI and CSR values of coke obtained from box tests and bulk tests for the same coal blends correlate well, and the variation is insignificant. This indicates that the coke obtained from the box test could VOLUME 115

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initiate the gasification reaction at low temperature and hence affects the CRI and CSR indices of the coke. In order to confirm this supposition, two box tests were carried out with the same coal blend, one in presence of iron oxide and the other in its absence, and the resultant cokes were tested for CRI and CSR. The results of the test are presented in Table I. Table I reveals that the CRI and CSR values of coke obtained by carbonization in the absence of iron oxides were suitable for blast furnace, as they meet the JSWSL specification of CRI <25% and CSR >64%. The coke obtained from the same coal blend in the presence of iron oxides was found to have deteriorated in quality to a larger extent with respect to CRI and CSR, thus becoming unsuitable for blast furnace use. It is also clear that, even though the coal blend is good, the presence of iron oxide has spoiled the quality of the coke. The coal blend, being of very good quality, would have yielded better coke (suitable for the blast furnace) in actual ovens, had it not been affected by corrosion of the mild steel box. The stainless steel boxes were found to yield highly consistent results, and therefore boxes made of other materials were not tested. Figure 1 shows a schematic of the stainless steel box used in the Salem Box Test, and Figure 2 illustrates the flow

‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking

Figure 2–Steps involved in performing the Salem Box Test

Table II

CRI and CSR of coke obtained by the Salem Box Test Sl. no.

Coal blend / coal mix

CRI (%)

CSR (%)

45 25 30

26.0

63.0

Australian hard coking coal D Australian hard coking coal E Australian semi-hard coking coal B Australian non-coking coal A Russian non-coking coal

45 25 25 2.5 2.5

27.0

62.0

Australian hard coking coal B Australian hard coking coal E Australian semi-hard coking coal B Australian hard coking coal C

45 20 30 5

25.0

65.33

Australian hard coking coal B Australian hard coking coal E Australian hard coking coal F Australian semi-hard coking coal C Australian semi-hard coking coal A Australian hard coking coal A

45 5 15 25 5 5

25.0

65.33

Australian hard coking coal C Australian semi-hard coking coal C Australian hard coking coal E Russian non-coking coal

50 25 10 15

26.5

63.94

South African hard coking coal Australian hard coking coal B Australian semi-hard coking coal B Australian semi-hard coking coal A Indonesian hard coking coal Australian non-coking coal B US non-coking coal

27 20 20 10 5 10 8

25.0

65.0

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Source and category

Fraction in blend (%)

Australian hard coking coal D Australian semi hard coking coal B Australian hard coking coal E

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The Journal of The Southern African Institute of Mining and Metallurgy

‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking Table III

Comparison of coke quality obtained by Salem Box and bulk tests Coal blend / coal mix Name of coal

Test condition Fraction in blend (%)

Box test

Bulk test

CRI (%)

CSR (%)

CRI (%)

CSR (%)

Canadian hard coking coal Australian hard coking coal E Australian semi-hard coking coal C

35 30 35

20.5

71.0

21.0

70.8

Australian hard coking coal D Australian hard coking coal E Australian semi-hard coking coal B Australian non-coking coal-A Russian non-coking coal

45 25 25 2.5 2.5

27.0

62.0

27.5

62.4

be considered to represent coke that would be obtained by bulk production using the same coal blends. Thus, the Salem Box Test becomes an effective screening tool for selecting or rejecting a given coal blend for bulk coke manufacture. Prior to the introduction of the Salem Box Test, there were numerous instances where coke was rejected for use in the blast furnace owing to poor quality. After the introduction of box tests, such instances have been avoided as the box test gives a clear indication as to the suitability of a particular coal blend for producing blast-furnace quality coke. Furthermore, a continuous supply of suitable coke is also ensured. Salem Box tests are also helpful in making minor adjustments in the coal blend compostion in order to produce better coke. There are instances where coal blends / coal mix were changed based on the results of box tests. Table IV illustrates typical examples where the proportions of the coal blend were changed or modified suitably such that box tests yielded better CRI and CSR results. Table IV illustrates three cases in which the designated coal blends produced coke with inferior CRI and CSR values. However, slight modification in the weight percentage of individual coals (especially Indonesian coal) led to an improvement in coke quality, making it suitable for blast furnace use. It can be seen that, even by maintaining the total weight fractions of hard coking, semi-hard, and non-coking coals constant, it becomes possible to obtain coke of the required quality. Subsequent investigation revealed that high volatile matter and alkali content (Na2O + K2O) in Indonesian hard coking coal in examples 1 and 2 and Australian semihard coking coal B in example 3 were responsible for the poor coke quality. The results in Table IV reveal that by suitably changing the blending ratio of individual coals it is at times possible to obtain the required coke quality. The Salem Box Test also serves to reject / screen out an individual coal or coal blends that are unsuitable for bulk coke manufacture. Table V illustrates few typical instances where the bulk production of an inferior coke was averted by using box tests. The Journal of The Southern African Institute of Mining and Metallurgy

Based on the composition and properties of these coals, it was expected that both the coal blends should yield coke with the required CRI and CSR properties, but the results obtained negated the expectation. Detailed investigation and analysis revealed that Australian hard coking coal E was contaminated at the loading port (example 1) and Australian semihard coking coal C was contaminated with other non-coking coals at the unloading port (example 2). This contamination was found to be the root cause for the poor quality of coke. Thus, based on the results of box tests, the mass production of inferior coke was averted.

Conclusions ➤ The quality of coke produced by the Salem Box Test is comparable with that produced by bulk production, indicating that the test is acceptable as a screening tool for regular use ➤ The Salem Box Test serves to reject an individual coal or coal blend as unsuitable for coke production and prevents bulk manufacture of inferior coke ➤ The Salem Box Test is also an effective method for detecting contamination in coal, and can be used to indicate where coal blending ratios can be adjusted to yield a coke product with suitable CRI and CSR properties. ➤ The Salem Box Test has been successfully used for more than 7 years at JSWSL to avoid the mass production of unsuitable coke and ensure the manufacture, availability, and consistent supply of coke suitable for blast furnace operation. This has resulted in better productivity and cost savings in blast furnace operation.

Acknowledgement The authors are thankful to the management of M/s. JSW Steel Limited, Salem Works, for granting permission and for providing facilities to carry out the research work in JSWSL, R&D centre and coke ovens. VOLUME 115

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‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking Table IV

Details of changes made in blending ratio to obtain suitable coke Sl. no.

Coal blend / mix

CRI %

CSR %

Coal blend /mix (%)

Name of coal

Fraction in blend %

Fraction in blend%

Australian hard coking coal B South African hard coking coal Indonesian hard coking coal Australian semi-hard coking coal B Australian semi-hard coking coal A US non-coking coal Australian non-coking coal B

17 25 10 20 10 12 6

20 27 5 25 5 10 8

Australian hard coking coal B South African hard coking coal Indonesian hard coking coal Australian semi-hard coking coal B Canadian semi-hard coking coal Australian semi-hard coking coal A US non-coking coal Australian non-coking coal C

15 30 15 5 10 5 8 12

Australian hard coking coal South African hard coking coal Australian semi-hard coking coal A Australian semi-hard coking coal B Australian semi-hard coking coal C US non-coking coal

25 35 15 5 5 15

28.0

60.27

28.0

20 30 10 5 10 5 10 10

61.8

27.0

25 35 15 0 10 15

61.3

CRI (%)

CSR (%)

24.2

66.2

23.5

65.0

24.0

64.8

Table V

Typical instances where the results of box tests were useful in detecting coal contamination S. no.

Coal blend / mix Name of coal

Fraction in blend (%)

Australian hard coking coal E Australian semi-hard coking coal B US hard coking coal Australian hard coking coal A

40 30 20 10

Australian hard coking coal B Australian semi-hard coking coal C Russian non-coking

50 30 20

CRI (%)

CSR (%)

Remarks

31.0

60.86

Contamination of Australian hard coking coal E

30.0

60.0

Contamination of Australian semihard coking coal C

coke making. Pacific Journal of Science and Technology, vol. 10, no. 2. pp.

References

782–787. ARENDT, P., HUHN, F., and KUHL, H. 2001. CRI and CSR - a survey of international round robins. Coke Making International, vol. 2. pp. 50–53. ASTM D-5341-99, Reapproved 2004. Standard test method for measuring Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR). pp. 1–4. GROSSPIETSH, K.H., LYNGEN, H.B., DAUWELS, G., KARJALAHTI, T., VAN DER VELDEN, B., and WILLMERS, R. 2000. Coke quality requirements by European blast

NAGASHANMUGAM, K.B. and REJI MATHAI. 2012. The influence of coal ash chemistry on the quality of metallurgical coke, Coromandal Journal of Science, vol. 1, no. 1. pp. 60–64.

NAGASHANMUGAM, K.B., SATHAYE, J.M., PILLAI, M.S., and BHATTACHARYA, H. 2012. A method for testing / screening the suitability of coke for blast

furnace operators in the turn of the millennium. Proceedings of the 4th

furnace iron making (Salem Box test), complete specification. Application.

European Coke and Ironmaking Congress, Paris La Défense, 19–21 June

no.811/CHE/2012, www.ipindia.nic.in 30 August 2013. pp. 1–13.

2000. Vol. 1. pp. 1–11. VAN NIEKERK, W.H. and DIPPENAAR, R.J. 1991. Blast-furnace coke: a coalMAYALEEKE, A.H., ADLLEKE, A.O., and DASHAK., D.A. 2009, Studies on the ash chemistry of Nigerial Enugu Coal as a blend component in metallurgical

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blending model. Journal of the South African Institute of Minerals and Metallurgy, vol. 91, no. 2. pp. 53–61.

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The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a8 ISSN:2411-9717/2015/v115/n2/a8

The grate-kiln induration machine — history, advantages, and drawbacks, and outline for the future by J. Stjernberg*†, O. Isaksson† and J.C. Ion‡

Iron ore pellets are a preferred feedstock for ironmaking. One method used for pelletizing is the grate-kiln process, first established in 1960. During the past decade, the establishment of new grate-kiln plants has increased rapidly, especially in China, and new constructors of pellet plants have started to operate in the market. It is well known that the grate-kiln method yields a superior and more consistent pellet quality compared with the straight-grate process. However, certain issues exist with the grate-kiln plant, which are discussed here, together with proposed practical solutions. Keywords grate-kiln, pelletizing, iron ore.

Introduction In 2013, 1.600 Mt of steel were produced worldwide, approximately 77% of which was produced via ironmaking by the blast furnace (BF) and basic oxygen furnace (BOF) routes or by direct reduction (DR) and electric arc furnace (EAF), while direct melting of scrap by EAF accounted for approximately 23% (World Steel, 2014). Iron ore pellets are a preferred feedstock in ironmaking by both the BF and DR routes, and the demand for pellets is predicted to increase markedly until at least 2025 (Huerta et al., 2013). Of all the iron ore mined in 2012, 23% was converted into pellets (Ericsson et al., 2013). Pelletizing is preferred because the chemical, physical, and metallurgical characteristics of pellets make them a more desirable feed for the ironmaking processes (Mbele, 2012). Moreover, because of their high strength and suitability for storage, pellets can be transported easily over long distances, with repeated transhipments if necessary. During pelletizing, iron ore is crushed and milled to a fine concentrate, mixed with additives and a binder, and balled into pellets prior to sintering and induration (hardening) in the furnace. In the past, the size and form of the pellets varied markedly. Figure 1a shows iron ore pellets produced in Persberg, Sweden, during the 1970s. Today, pellets are fabricated into a more uniform shape, with sizes typically 9–15 mm (Forsmo et al., 2008). Figure 1b The Journal of The Southern African Institute of Mining and Metallurgy

* Division of Materials Science, Luleå University of Technology, Sweden. † Loussavaara-Kiirunavaara Limited (LKAB), Sweden. ‡ Malmö University, Materials Science, Sweden. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Aug. 2014; revised paper received Oct. 2014. VOLUME 115

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Synopsis

shows iron ore pellets produced in Kiruna, Sweden, during the 2000s. Magnetite is a preferred feed in pelletmaking because of the exothermal energy released during oxidation. The most common method of pelletizing is the travelling-grate process (Huerta et al., 2013). This travelling grate uses a stationary bed of pellets, which are transported through the entire process, consisting of zones of drying, oxidation, sintering, and cooling (Potts, 1991). The travelling-grate process is often also used for pelletizing of haematite ores. The second most common pelletizing processes is the grate-kilncooler (GKC) or just grate-kiln process, which is often used for pelletizing magnetite ores. This process uses a shorter grate, with part of the oxidation (when using magnetite) and sintering taking place in the kiln, which is a rotating furnace that achieves more homogenous induration (Zhang et al., 2011). A third system used for pelletizing is the shaft furnace, the most traditional of the facilities. However, very few plants use this system today because of the limiting scale (Yamaguchi et al., 2010). A pelletizing process consist of four consecutive steps (Yamaguchi et al., 2010): ➤ Reception of raw material ➤ Pre-treatment ➤ Balling ➤ Induration. This paper deals with the fourth of these steps, the induration, as performed in the grate-kiln process. A short description and history is given, the benefits and drawbacks are discussed, typical problems are raised, and an outline for the future of the process is given.

The grate-kiln induration machine

a) Figure 1a—Indurated iron ore pellets, produced in Persberg, Sweden, during the 1970s

The annular cooler is functionally the same as the traveling grate, except for its annular configuration. The pellets fall from the kiln, down in the circular cooler carousel where they travel lying on the conveying elements (pallet grids). Ambient air is blown through the pallets and the temperature of the pellets drops from approximately 1200°C to 100°C during an orbit; the heated process gas is transported back to the grate for heat exchange. Cooled pellets discharge through the cooler’s discharge hopper to a product load-out system. The typical outer diameter of an annular cooler is between 15 m and 30 m. There are also a few grate-kiln plants with straight coolers. Figure 2 shows the outline of a modern GKC plant according to Metso’s design (Metso, 2014). Figure 3 shows the LKAB (Loussavaara Kiirunavaara Limited) kiln No. 4 in Kiruna, Sweden

History Rotary kilns were originally developed in the late 19th century for Portland cement production, and the cement industry is still the largest user (Boateng, 2008). To improve energy efficiency in cement plants, a pre-heater in the form of a Lepol grate was used for the first time in 1927 (invented by Otto Lellep, marketed by Polysius), and it is from this system that the grate-kiln for iron ore pelletizing originated (Trescot et al., 2000). Today, rotary kilns have been adopted for processing several different metal ores (besides iron ore), e.g. nickel (Tsuji and Tachino, 2012) and titanium (Folmo and Rierson, 1992), as well as for direct reduction of iron ore (Tsweleng, 2013).

b) Figure 1b—Indurated iron ore pellets, produced in Kiruna, Sweden, during the 2000s

The grate-kiln-cooler process The main function of the grate (in the GKC process) is drying and pre-heating of the green pellets (Feng et al., 2012). The grate is divided into three (Katsuyoshi et al., 1984), four (Forsmo et al., 2003), or five (Metso, 2014) different zones. These zones are normally: updraft drying (UDD), downdraft drying (DDD), temperature pre-heat (TPH), and pre-heat (PH). The typical length of a full grate furnace in the GKC is approximately 60 m, with a width of about 5 m. A typical rotary kiln used in iron ore pellet production usually has a length between 30–50 m, and a diameter of 5–7.5 m (normally not more than 7.2 m, as difficulties with the refractory lining may occur at larger diameters), and is fired by coal or natural gas. The highest temperatures in the process are achieved in the kiln, up to about 1400°C. The refractory lining in the kiln normally comprises bricks based on Al2O3 and SiO2. There are also kilns lined with castables. A burner is located in the outlet of the kiln, where the pellets falls down in the cooler. In the case of pelletizing of haematite, burners are also located in the grate because of the lack of exothermal energy released by magnetite oxidation. The burner fuel is normally coal or gas, with fuel oil used as secondary fuel.

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Figure 2—Outline of a grate-kiln plant

Figure 3—Kiln at the LKAB plant No. 4 in Kiruna, Sweden (view looking upstream) The Journal of The Southern African Institute of Mining and Metallurgy

The grate-kiln induration machine

The grate-kiln process: benefits and drawbacks The grate-kiln vs the travelling grate The GKC process possesses both advantages and drawbacks compared with the travelling-grate process. A general comparison between the two (Sgouris and Oja, 2008; Zhang et al., 2011; Huerta et al., 2013) shows that: ➤ The grate-kiln yields a superior and more consistent pellet quality, and consumes less electrical energy. Since the speed of the grate, kiln, and cooler can be controlled independently, it provides process flexibility, allowing adjustment to changes in concentrate feed. The grate-kiln is more flexible regarding choice of fuel: cheaper fuels can be used. Moreover, less expensive high-temperature-resistant steel alloys are used in construction. Significant drawbacks are the low

Figure 4—Grate-kiln plants for iron ore pelletizing built since 1960, and their geographical distribution. Asia* excludes India, China, Japan, and the Arabian Peninsula The Journal of The Southern African Institute of Mining and Metallurgy

suitability for pelletizing of pure haematite feedstock, higher generation of fines in the process, and lower energy efficiency ➤ The travelling grate has a lower fuel consumption, as there is less radiated heat loss and a better heat exchange between the solids and the air because of the deeper bed of pellets. The maintenance and refractory costs are lower, and the cold start-up time is shorter. It is suitable for pelletizing of both magnetite and haematite burden (and magnetite-haematite mixtures), and fewer fines are formed in the final product. Significant drawbacks are the higher electricity consumption, and coal (or other solid fuels) cannot be used as primary fuel.

Typical problem issues with the grate-kiln machine There are some typical problem issues and symptoms that can arise with the grate-kiln, summarized here (based on the literature and drift-logs from LKAB).

Deposition of material on the refractory lining Coal always contains inclusions of mineral matter that remain as fly-ash after combustion (Reid, 1984). Disintegrated pellets can, together with fly-ash from the coal burned to heat the kiln, form accretions on the lining, sometimes as ringforms in the kiln (Jiang et al., 2009; Xu et al., 2009). This phenomenon is also common in lime kilns (Potgieter and Wirth, 1996) and cement kilns (Recio Dominguez et al., 2010). This material can also be deposited as stalagmite structures in the kiln (Figure 5) or as accretions in the transfer chute. Air flows tend to be turbulent, especially in the transfer chute (Burström et al., 2010), as this is the geometrical bottleneck of the induration machine. A thin layer of deposit on the surface can act as protection for the lining, but when these deposits increase in thickness they contribute to mechanical strain. A fallout of such deposit causes a rapid increase in the temperature of the lining at the new hot face, which may lead to thermal shock and spalling (Stjernberg et al., 2012). The Allis-Chalmers Corporation investigated fuel

Figure 5—Accreted chunks in a rotary kiln (Svappavaara, Sweden) during a maintenance stop in 2013 VOLUME 115

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Pelletizing of iron ore is a method of Swedish origin, patented in 1912 by A.G. Andersson (Yamaguchi et al., 2010). The process was developed in the USA in the 1940s, and the first commercial plant started operation in Babbitt, Minnesota in 1952 (Chernyshev, 1962). The first iron ore pellet plant of the grate-kiln type was established at Humboldt Mine, Michigan in 1960 (Sgouris and Oja, 2008). Allis-Chalmers (a predecessor company to Metso) have since built around 50 such plants. However, very few of the older plants built before 1975 are still in use. Another constructor of grate-kiln plants is Kobe Steel, who built their first plant in 1966 at Kobe Works, Nadahama, and have since then constructed more than ten plants, most of which are still in use (Yamaguchi et al., 2010). Since 2000, the grate-kiln process developed by the Shougang Group has been rapidly adopted in China (Zhang and Yu, 2009). The establishment of new grate-kiln plants in China has been very prominent in the last decade (Zhang et al., 2011), with the rise of new fabricators such as Jiangsu Hongda and Citic. Figure 4 shows the growth in grate-kiln plants for iron ore pelletizing since 1960, and their geographical distribution. An exponential increase can be seen since 2000, driven mainly by installations in China. Moreover, very few plants were built anywhere in the world between 1985 and 2000.

The grate-kiln induration machine combustion in grate-kiln plants in the 1970s (Cnare, 1977). It was observed that the key factor for deposition in the kiln was the presence of dust, and that deposition of fly ash on the lining could be minimized through correct selection of coal and optimization of overall process control. Tests with combustion of natural gas showed that in addition to the absence of fly ash deposits, another benefit was that the lining temperature could be maintained more than 50ºC lower, because the radiant heat of natural gas combustion is lower than that of coal or oil. Kobe Steel noted in 1981 that when the burner fuel was changed from heavy oil to coal (because of the sharp increase in the price of fuel), adhesion of deposits on the lining was enhanced (Uenaka et al., 1983). Adhesion tests were carried out on a water-cooled specimen inserted through a hole situated at the end of the pre-heat chamber, just before the starting point of the process. Uenaka et al. investigated the amount of ash adhering to the detection bar, the density of the deposit, coal properties such as fine particle size, ash content, the proportion of pulverized ore in the pre-heated pellets, and operating conditions such as kiln off-gas temperature. It was observed that even if the same type of coal was fired, the amount of deposit varied with operating conditions. Similar tests with water-cooled probes were carried out at LKAB in Kiruna, Sweden (Jonsson et al., 2013; Stjernberg et al., 2013). It was found that inertial impaction was the dominant deposition mechanism, and that the fluegas flow direction determines the texture and formation of the deposits. The deposits were mainly haematite particles embedded in a bonding phase, comprising mainly calciumaluminium-iron silicate. In addition to haematite and the bonding phase, the minerals anorthite, mullite, cristobalite, quartz, forsterite, and apatite were also observed in the deposits after cooling to room temperature. Lining problems in rotary kilns in Wuhan, China have been reported by Xu et al. (2009). These kilns also had problems with rapid accumulation of deposits on the lining that were hard to remove. One of the main reasons for this was the use of burner coal with a high fly-ash content and low ash melting temperature. Another important factor was the compressional strength of the iron ore pellets, which was observed to depend on the qualities of the ore and bentonite, mixing method, and moisture content during mixing. Pressure and air flow were observed to be important parameters in one of the plants, since these dictate where the fly ash falls, and also affect the extent of pellet disintegration, contributing to deposits on the lining. At some production sites an additive based on magnesia (MgO) is added to the coal, mainly to increase the melting temperature of the slag phase in the deposits, in order to decrease the adhesiveness of particles on the lining. Another method is to add silicon carbide (SiC) or some other carbonbonded phase to the lining, in order to decrease the wettability of the slag.

Refractory failures The refractory lining in the grate and in the cooler seldom causes failures that lead to urgent shutdowns, as are caused by kiln failures. Upon heating, the lining in a rotary kiln expands with temperature in proportion to the coefficient of thermal expansion (CTE) of the refractory (Shubin, 2001a).

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This aids in securing the bricks, but stresses from the liners in the ring brickwork arise in the metal casing (Shubin, 2001b). Thermal shock induced by temperature gradients at starts and stops in the operation, together with mechanical strain, may cause urgent stops in production because of brick spallation or fallouts of bricks. Moreover, an even larger thermal stress acts on the lining at starts and stops during production, giving rise to several stress states in the form of longitudinal and lateral bending, brickwork twisting, vibration, and torsion (Shubin, 2001c, 2001d). The cold crushing strength of refractory bricks is often given in datasheets However, it is more important that the material has a low Young’s modulus at high temperatures, such that the lining becomes more flexible during operation. Otherwise the bricks suffer thermomechanical strains far above the cold or hot crushing strength (Saxena, 2009). Even during steady-state conditions, the lining is exposed to temperature oscillations as it rotates (Shubin, 2001a). In rotary kilns used for iron ore pellet production the lining can be assumed to be exposed to different temperatures during each revolution of the kiln. When the pellet bed covers the lining, it is exposed to less radiation from the flame, but is exposed to heat from the energy liberated from the oxidation of the pellets (when using magnetite). If the kiln rotates at approximately 2 r/min (which is common for rotary kilns in this application) it revolves 3000 times a day, with a temperature oscillation during each revolution. The temperature oscillation range varies from kiln to kiln, depending on operating conditions, but the temperature variation can be as high as 100ºC in the lining, down to a depth of 30 to 40 mm beneath the hot face (Shubin, 2001a). This cycle gives rise to thermal fatigue. Kingery (1955) showed that thermal expansion hysteresis is associated with microcracks in ceramic materials. A study carried out in a grate-kiln plant in Kiruna, Sweden (Stjernberg et al., 2012), showed that migration of potassium through the hot face of the lining caused the formation of feldspathoid minerals, leading to spallation. Moreover, haematite was found to migrate into the lining. This phenomenon was also found in rotary kilns for pellet production in China (Zhang et al., 2009). Although these are relatively slow phenomena, they contribute to the overall degradation of the lining. Zhu et al. (2003) reported lining problems in a rotary kiln in Qian’an, China. Urgent production stops caused by fallouts of bricks occurred only a few months apart. Different types of brickwork and lining material (e.g. chamotte, high alumina, and phosphate-bonded alumina) were tested in the kiln to avoid fallouts and rapid deterioration of the lining. In 2000 a mullite brick of an increased size was tested, which was still in service two years later.

Problems with fuel supply Rapid changes in the fuel supply cause rapid thermal changes that affect the pellet quality, but more critically, cause thermal shocks in the refractory lining. Problems with coal quality at different production sites arise occasionally, and constitute a threat for uniform heat supply. This can arise from, for example, wet coal. Problems with sensors or automation may also disturb the fuel supply. Grate-kiln plants using coal as their primary fuel switch to a secondary fuel (oil or gas) The Journal of The Southern African Institute of Mining and Metallurgy

The grate-kiln induration machine when problems with the coal supply arise. Even if this transfer appears perfunctory, the temperature profile in the plant changes markedly. An even worse scenario is when the secondary fuel does not initiate. If the electricity supply to the production site is disrupted for any reason, it usually some time before the backup power comes into operation, causing unwanted temperature variations. The alignment of the burner is also important to avoid overheating of the lining. Some of these incidents can lead to serious failures of the lining.

Wear of grate plates As the flue gas passes vertically through the pellet bed and the grate plates, it carries particles of iron oxide that have an abrasive effect on the grate plates, leading to deterioration of the plates. Figure 6a shows a plate as produced, and Figure 6b a worn-out plate. When the ribs wear down (as in Figure 6b), pellets may become stuck in the intermediate columns, affecting the air flow and the pellet quality, or pellets may even fall through the columns. The plates are usually made from a high nickel-chromium steel alloy (austenitic stainless steel), due to the requirement for

Figure 6a—Grate plate, as produced

refractoriness and resistance to wear and corrosion (Nilsson et al., 2013). Process parameters in the grate, such as the time of drying and pre-heating, air quantity, blast temperature, and blast velocity, affect the pellet temperature and strength. However, they may also cause burnout of the grate plate, shaft bending, chain breaking, and deviation of grate motion (Feng et al., 2012). An investigation of a grate-link plate that had served in a grate-kiln plant for 8 months (Nilsson et al., 2013) showed that alkaline vapours from warmer parts of the indurator condense on the plates, forming chlorides and sulphates, which promote hot corrosion and intergranular attack (IGA) of the material along the grain boundaries.

Riding ring fatigue The riding rings (tyres) of rotary kilns are subjected to static and dynamic stresses caused by mechanical forces and temperature gradients, of which only the stresses caused by mechanical forces can be influenced by the dimensions of the ring. The initiation of a crack can be caused by either the static strength or the fatigue strength being exceeded. Hertzian pressures between the ring and the rollers reach their maximum beneath the surface, and consequently cracks are usually not visible until they reach an advanced stage. Riding ring cracks are in general not a consequence of poor dimensioning, but of unfavourable running conditions and/or material defects (Bowen and Saxer, 1985). Unfavourable running conditions are: ➤ High temperature gradients ➤ Inadequate guidance of the riding ring (wobbling) ➤ Insufficient contact area between riding ring and rollers ➤ Badly aligned kiln axis ➤ Badly adjusted rollers. Structural material defects are: ➤ Cavities and/or nonmetallic inclusions ➤ Repair welding spots. Riding cracks do not occur as frequently as some of other issues stated here. However, failures of the riding ring that necessitate replacement and the associated actions are timeconsuming. A replacement of the riding ring involves cutting of the kiln on both sides of the ring, a heavy lift, and repair welding. It is therefore important that the riding ring satisfies the requirements of high rigidity or stiffness, high surface durability, and high static and fatigue strength, to achieve the longest possible lifetime (Bowen and Saxer, 1985). Figures 7a and 7b are from a riding ring replacement at LKAB’s grate-kiln plant in Svappavaara, Sweden, in 2009.

Deformation of steel casing

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Figure 6b—Grate plate, worn-out

The casing (shell) of the kiln deforms elastically owing to the pressure exerted by gravitational forces through the tyre towards the support rollers. This dynamic action can be detrimental to the refractory lining, especially if the kiln is rotated in a cooled state. However, there are other factors that create permanent deformation of the casing. At locations in the kiln where bricks have fallen out, and the kiln has not been cooled down quickly enough, permanent deformations may occur that are difficult to rectify. It is therefore important to monitor the casing with e.g. a kiln scanner, in order to

The grate-kiln induration machine realigned three times over a five-year period. During this time the identical gear and pinions on the comparison kiln remained as good as new. Analysis of the problem showed that the uphill face of the thrust tyres comprised several noticeable discontinuities. When the tyre was cast there were probably voids in the cast, which were repair-welded and machined. The welded portions of the tyre were much harder than the surrounding areas, and high spots were developed at these locations. As the kiln revolved, these high spots on the side of the tyre created a sharp impact load towards the discharge end. This pressure caused pitting and wear of the pinion and gear. The problem was resolved by resurfacing the thrust tyre and eliminating the vertical load between the thrust rollers and tyres. Cleveland Cliffs reported (Rosten, 1980) that they improved the lifetime of wear parts by using a nitriding hard surfacing process on chrome-type spare parts, and carburization of the surface of mild steels, used as fan wear liner applications.

Process fans The fans that are used to transport flue gas through the system sometimes fail due to power failures, or when the blades become eroded by particles carried by the flue gas, if bearings are worn out, or due to problems with the frequency modulation. Failures of the fans can lead to the pellets not being sufficiently dried before they enter the warmer parts of the furnace, where the sintering usually takes part. Erosion of the blades of the fan can be prevented by installing electrostatic precipitators prior to the fan in the flue gas circuit. b)

Flue gas scrubbing

Figure 7â&#x20AC;&#x201D;(a) Crane lifting a riding ring in Svappavarra, and (b) installation in the gap of the casing

identify heat abnormalities as soon as they occur (Zakharenko and Nikonenko, 2002). Permanent deformation can also result when the temperature is increased too rapidly and the riding ring does not expand as fast as the casing, leading to plastic deformation (Chapman and Yann, 1989).

Wear of mechanical parts Gearboxes, sliding and rolling bearings, kiln girth gears, pinions etc. require continuous maintenance. In addition to the slow operating speeds of many of these parts, there are thermal, alignment, and cleanliness issues that need to be considered. Safe operation relies on a hydrodynamic oil film to avoid metal-to-metal contact (Singhal, 2008). Use of inadequate lubricants may decrease the service life of these mechanical parts markedly. Hankes (2013) reviewed the selection and application of lubricants for rotary kiln girth gears and pinions. He stressed the importance of not only using a correct lubricant, but also of using it correctly. Monitoring is the key to avoiding catastrophic tooth damage. Lovas (2003) reported the performance of two identical cement kilns: one that ran without problems; the other that was plagued with drive-related failures. On the problem kiln, the pinion had to be replaced three times and the gear

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Flue gas scrubbing using e.g. electrostatic precipitators and NOx and SOx removers occasionally cause failures in pellet plants. There is the possibility to bypass such equipment; however, environmental regulations in many countries do not permit this. Moreover, more stringent regulations motivate companies to evaluate alternatives to current combustion technologies (Fredriksson et al. 2011).

Alternative methods Several improvements have been made at different production sites, and other methods can be adopted from related processes.

Cooling system In 2001 the Minntac division of US Steel introduced a ported kiln in their plant (Trescot et al., 2004). It was a well-proven design that had been in use for more than ten years at the Tinfos direct reduction kiln for ilmenite in Norway. However, this was the first ported grate-kiln plant for iron ore pelletizing. This system injects air under the bed of pellets in the rotary kiln through slots in the joints between the specially designed refractory bricks. This design results in more rapid oxidation of the pellets. The company noticed several benefits. As the magnetite oxidizes more rapidly, a lower kiln temperature can be used. With more energy liberated in the kiln, the heat load in the annular cooler is reduced, and therefore a higher tonnage can be produced. An improved pellet quality was also observed. The Journal of The Southern African Institute of Mining and Metallurgy

The grate-kiln induration machine In the Tinfos direct reduction kiln for ilmenite in Norway, the lining consists of a monolithic castable applied by shotcreting (Folmo and Rierson, 1992). The air slots present for cooling complicate the installation of bricks, and shotcreting is a quick method. This could be the method of choice for the linings of rotary kilns for iron ore pelletizing in the future. Shotcreting is a fast installation method, and the lining does not have to be replaced as often as a brick lining. The drawback is that removal of the lining for maintenance is more complex.

Development of burners Many burners used in the grate-kiln plants today are basically a lined steel pipe through which milled coal powder is blown. The combustion equipment used for the heat supply in rotary kilns for cement production is often far more complex than the burners used in the grate-kiln. The use of multi-channel burners for different fuels and different air channels allows adjustment of the flame shape during operation and ensures a stable flame front (Vaccaro, 2006).

Outlook for the grate-kiln process Most of the grate-kiln plants for iron ore pelletizing built over the last 30 years have been built in China (around 40 plants over the last 10 years). China will likely continue to build grate-kiln plants; however, one significant characteristic of the Chinese growth cycle is the relatively direct influence of governmental policies (Becker, 2013). The previous trend was to build larger plants (5–6 Mt/a), while most plants built in China today have a capacity below 3 Mt/a. Several plants with lower capacity are less dependent on the economic situation and access to raw material, compared with one large plant, when plants are used on an on-and-off basis. However, Metso has designed a plant with 7 Mt/a capacity and Kobelco an 8 Mt/a plant, and many existing plants are continuously being upgraded to cater for the increasing demand for iron ore pellets on the market. Oxidation of magnetite in iron ore pellets occurs fastest between 1100°C and 1200°C. At higher temperatures the oxidation rate decreases as a result of increasing dissociation pressure and severe sintering in both the oxidized haematite shell (which becomes denser) and the magnetite core (Forsmo, 2008). As fully oxidized pellets already in the grate are desired when using magnetite (Niiniskorpi, 2002), a grate of increased length and increased dwell time in a zone where the temperature is optimal for oxidation, this, in combination with oxygen injection to further improve the oxidation rate, may be an alternative in the future. This could be combined with a shorter kiln in a machine comparable with a straight-grate plant completed with a short kiln, as the rotary kiln is the more sensitive part in the grate-kiln construction. Coal will continue to be the major fossil energy source in China, at least in the coming decade (Zhou et al., 2013). However, with increasing energy prices, limited coal reserves, and environmental issues, biofuels (e.g. biogas and wood pellets) will in coming years be burned in pellet plants. With this innovation, new techniques may have to be developed, and plant outlines may have to change. A wood-based fuel The Journal of The Southern African Institute of Mining and Metallurgy

has a lower energy density (heating value of approx. 20 MJ/kg compared with approx. 30 MJ/kg for coal), and therefore the feeding rate of fuel has to increase by some 50%. Moreover, wood-based fuels have in general higher friction coefficients (compared with coal) during transportation through pipes, which may cause bridging and hold-ups in the fuel feeding system. Co-combustion of blended coal (approx. 90%) and a wood-based fuel (approx. 10%) is realistic already today. Possibly, it might be beneficial to subject a wood-based fuel to pyrolysis prior to combustion. The advantages of wood-based fuels compared with coal are lower CO2 emissions and a lower ash content (Nordgren, 2013). The use of waste material as an energy supply is required in cement production to make it economically possible, and burners are developed for this (Vaccaro, 2006). However, iron ore pellet makers can possibly be more selective in their choice of fuels, but will in the future need to use more alternative fuels. The iron ore price has decreased rapidly over the last three years (2011–2014). However, the demand for iron is unlikely to decrease in the medium term, and predictions are that around 80 new pellet plants will need to be built in the coming decade (Huerta et al., 2013). Many are likely to be of the grate-kiln type.

Acknowledgement The authors are grateful to Johan Sandberg, Kent Tano, and Henrik Wiinikka for fruitful discussions.

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ZHOU, H., ZHOU, B., LI, L., and ZHANG, H. 2013. Experimental measurement of the effective thermal conductivity of ash deposit for high sodium coal (Zhun Dong coal) in a 300 KW test furnace. Energy Fuels, vol. 27, no. 11. pp. 7008–7022. ZHU, K. and ZHAO, C. 2003. On increasing the lining lifetime of rotary kiln. Sintering and Pelletizing, vol. 28, no. 1. pp. 24–25 (in Chinese). ◆ The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a9 ISSN:2411-9717/2015/v115/n2/a9

Estimating mine planning software utilization for decision-making strategies in the South African gold mining sector by B. Genc*, C. Musingwini*, and T. Celik†

Synopsis This paper discusses a new methodology for defining and measuring mine planning software utilization in the South African gold mining sector within an evolving data-set framework. An initial data-set showing the mine planning software providers, their corresponding software solutions, as well as the software capabilities and information on the number of licences was collected and compiled in 2012 in an online database for software utilized in the South African mining industry. Details of the database development and implementation were published in the Journal of the Southern African Institute of Mining and Metallurgy in 2013. In 2014 the data-set was updated with additional and new information. Using the 2012 and 2014 timestamps, a methodology for estimating the software utilization was developed. In this methodology, the three variables of commodity, functionality, and time factor were used to define and measure the software utilization in order to ultimately inform decision-making strategies for optimal software utilization. Using six different functionalities, namely Geological Data Management, Geological Modelling and Resource Estimation, Design and Layout, Scheduling, Financial Valuation, and Optimization, utilization in the gold sector was measured. This paper presents the methodology employed for measuring the mine planning software utilization. The methodology is useful for stakeholders reviewing existing software combinations or intending to purchase new software in the near future and who want to estimate the comparative attractiveness of a certain software package. These stakeholders include mining companies, consulting companies, educational institutions, and software providers. The work presented in this paper is part of a PhD research study in the School of Mining Engineering at the University of the Witwatersrand. Keywords gold sector, mine planning software, software utilization, database, South African mining industry.

An initial data-set showing the mine planning software providers, their corresponding software solutions, as well as the software capabilities and information on the number of licences was collected and compiled in 2012 in an online database. Details of the database development and implementation were published in the Journal of the Southern African Institute of Mining and Metallurgy in 2013 (Katakwa, et al., 2013). In 2014 the data-set was updated with additional and new information. Using the updated data-set, a methodology was developed to measure mine planning software utilization in the gold sector in order to ultimately inform decision-making strategies for optimal utilization. Utilization is a well-known concept within the mining industry because of its ties with the level of productivity. Higher utilization often leads to higher productivity, hence better profit margins. From this point of view, utilization is an important factor regardless of the size of any operation, including those in the gold sector. The root of the word of utilization comes from the word ‘utilize’ meaning ‘make practical and effective use of’ (Oxford English Dictionaries, 2014). By using this definition, software utilization can be defined as the effective use of mine planning software in South Africa; but in general, utilization is associated with the overall equipment effectiveness, which is one of the key performance-based metrics. It is important to understand the fundamentals behind these metrics.

Introduction

Overall equipment effectiveness

This paper outlines the development of a new methodology to define and measure mine planning software utilization in the South African gold mining sector. Although the calculations can be done for any commodity, in this paper calculations were done only for gold, as gold is not only used in a variety of different fields such as electronics, engineering, and health care, but also gold generated almost 13% of South Africa’s mining income during 2013 (Statistics South Africa, 2014).

In the literature, utilization is associated with time in a way such that it can be defined as

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* School of Mining Engineering, University of Witwatersrand, Johannesburg, South Africa. † School of Computer Science, University of Witwatersrand, Johannesburg, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jul. 2014; revised paper received Oct. 2014.

Estimating mine planning software utilization for decision-making strategies the measurement of the time used to perform effective work. In the mining industry, regardless of whether it is a surface or underground operation, better equipment utilization often leads to higher level of productivity, hence profitability. Although there are a number of ways to measure performance against the various metrics, the most widely used measure to determine performance against capability of the equipment is Overall Equipment Effectiveness (OEE). OEE measurement is also commonly used as a key performance indicator (KPI) in Total Productive Maintenance (TPM) and Lean Manufacturing programmes for measuring production efficiency (Vorne Industries, 2008). There are six factors, also known as the ‘Six Big Losses’, which are the main causes of production losses. TPM and OEE programmes aim to control these six factors. Nakajima (1998) listed these six factors affecting equipment utilization as: ➤ Breakdown loss ➤ Setup and adjustment loss ➤ Idling and minor stoppage ➤ Reduced speed loss ➤ Quality defects and re-work ➤ Startup loss. In the TPM model, Nakajima (1998) furthermore formulated utilization using availability, performance rate, and quality rate as shown in the following formula: Equipment effectiveness = Availability × Performance rate × Quality rate In this formula, equipment effectiveness defines the meaning of equipment utilization and is calculated by multiplying equipment availability by performance rate and quality rate. Figure 1 shows time factors effecting equipment utilization. In Figure 1, operation time is associated with the total available time for a given period, as this can be anything from a single shift to a whole month. As shown in Figure 1, loading time can be calculated by deducting downtime from the operation time. Availability can be calculated by dividing loading time by operation time. As the loading time calculation is already given, the availability formula is then (Shirose, 2013):

Furthermore, speed loss time is the lost time caused by operating below the planned speed, and can be calculated by using the actual time to make the production quantity minus the design time to make the same quantity, as formulated below (Shirose, 2013): Speed loss time = Parts produced × (Design cycle time − Actual cycle time) Cycle time is the time taken to produce one part. Design cycle time is used to calculate the equipment’s designed production rate, and actual cycle time used to calculate the

Figure 1—Time factors affecting equipment utilization (after Shirose, 2013)

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equipment’s actual production rate. Design operating time is the time the equipment should have taken to produce the parts and is the difference between the loading time and the speed loss time. Performance rate is the ratio of the design operating time to loading time, as shown below (Shirose, 2013):

Quality loss time is the time lost making nonconforming material. Valuable operating time is the time the equipment spends making conforming material. Quality rate is the ratio of conforming parts produced to total parts produced, as shown below (Shirose, 2013): Quality loss time = Nonconforming parts × Actual cycle time Although OEE is a very powerful tool to measure efficiency, hence utilization; it is fundamentally designed for equipment utilization, which can be defined as hardware utilization. The aim of this study is to define strategic software utilization in the South African mining industry, which can be defined as software utilization. Although OEE gives some ideas regarding utilization, it is not designed to establish a framework that can bring a new approach towards strategic mine planning software utilization. El-Ramly and Stroulia (2004) stated that there are a number of techniques available to understand how often software is being used, as well as to what extent it is being used. Many software systems collect, or can be set up to collect, data about how users employ them, i.e., system-user interaction data. Such data can be of great value for program understanding and re-engineering purposes. Sequential data mining methods can be applied to identify patterns of user activities from system-user interaction traces (El-Ramly and Stroulia (2004). Despite the fact that user data may be available in some instances, using the data mining methods based on user behaviour to measure mine planning software utilization is inappropriate when considering the size of the South African gold sector and user privacy. By selecting the number of targeted mining sites, limited research output could be possible, but most probably would not be sufficient to satisfy the entire gold mining industry in South Africa. To achieve a successful research initiative which covers the whole South African gold mining sector, a methodology was developed in such a way that optimal utilization of the various mine planning software packages that are used in gold mining sector could be measured. The next section defines this measurement framework.

Strategic software utilization By using an analogy to the one given earlier, strategic software utilization can also be defined by associating manyto-many, one-to-many, and many-to-one relationships between entity types. In this association, the relationships between software vendors, commodity, functionality, and time factor were used to develop the following terminology: {Ci, Fl} → Sk={i, l} The Journal of The Southern African Institute of Mining and Metallurgy

Estimating mine planning software utilization for decision-making strategies {Ci, Fl} → Sk = {i,l} where ‘Ci ’ denotes commodity (i ) and ‘Fl ’ denotes functionality (l ). Furthermore, Sk is the software that performs tasks on commodity (i) and functionality (l). In the market, there is usually more than one software solution specifically designed for commodity (i) and functionality (l). In order to identify and evaluate each particular software solution, a new index (m) (m) is used so that Sk is defined to represent a unique software solution, whereas k={i,l} is an index which is a specific combination of {i,l}, and m=1, 2, 3…M where M is the total number of software solutions. For example, assume there are three software companies, X, Y, and Z. Each of these three companies might have a number of software solutions, i.e. software company X has three types of software, namely X1, X2, and X3; company Y has only one type, namely Y1; and company Z has two types of software, namely Z1 and Z2. Table I displays how to find M. From Table I, the total number of available software solutions, M, is 6. Using a similar approach, the utilization of the software can also be defined. Although there is no rigid definition of software utilization, it can be defined as a numeric value that falls in to the range between 0 and 1 inclusive, i.e. (m)

where ui,l is the utilization of the software that performs task on commodity (i) and functionality (l) by using software (m). Thus, further analytic development on the software utilisation can be accomplished. Furthermore, the utilization formula can be extended by considering the time factor (t) as follows: (m,t)

where fi,l is a quantity factor that relates to the software that performs a specific task on commodity (i) and functionality (l) using software (m) at a specific time (t), and (m,t) wi,l is the weighing factor, which will handle the missing data-related issues and/or other factors such as market (m,t) capitalization of the companies. For instance fi,l can be defined as the total number of sites. For example, if the market capitalizations of the software companies X and Y are US$1 million and US$100 million respectively, but both companies have a software solution with the same functionality, then the weighing factor for the small company will be higher than that for the larger company. Furthermore, the price of the mine planning software as well as support availability plays an important role when considering the weighing factor. Software utilization is already defined in a generic way. However, the software utilization can also be defined in a specific way, i.e. the relative utilization (r). Relative utilization can be considered as a weighted software utilization and can be formulated as:

(n,t)

where - ui,l n=1

is total utilisation of all software which is used

for normalization. Calculating relative utilization leads to weighted market impact of the software utilization. However, calculating relative utilization, three variables were used to generate the results, namely: ➤ Commodity (i) ➤ Functionality (l) ➤ Time factor (t). For example, the following results were calculated for only one commodity (i), namely gold, using six different functionalities (l) (Katakwa et al., 2013): 1. Geological Data Management 2. Geological Modelling and Resource Estimation 3. Design and Layout 4. Scheduling 5. Financial Valuation 6. Optimization. The six functionalities listed by Katakwa et al. originated from the Open Group’s Business Reference Model, which categorizes not only the functionalities of mine planning software, but also mine value chain stages and mining methods (The Open Group, 2010). The Open Group’s Business Reference Model illustrates how the various software solutions interact with each other, although this classification can be debateable. For example, Mine 2-4D software, which is used in mine scheduling, is often used in conjunction with Enhanced Production Scheduler (EPS) as it cannot produce a schedule without the use of EPS. Figure 2 shows the names of available mine planning software solutions and their functionalities along the mining value chain. The time (t) factor has two timestamp indicators showing different data collection dates, namely: ➤ September 2012, t=1 ➤ April 2014, t=2 By using all three variables, the weighted software utilization, hence the market impact of each participating mine planning software solution, was calculated. The dataset was extracted from the updated database and the programming language GNU Octave was used for the data analysis and the calculation of the software utilization per functionality for the selected commodity (gold) using two different timestamps as mentioned previously. (m,t) It is important to note that if fi,l is 0, the subject software either does not support the specific functionality or does not support the specific commodity. Furthermore, when (m,t) (m,t) calculating ui,l and wi,l , the value is set to 1 as at this stage of calculation it was decided that the weighted software

Table I

Number of software solutions by company X1 1

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Company X X2 2

X3 3

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Company Z Z1 5

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Name of software company Name of software solution m

Estimating mine planning software utilization for decision-making strategies Geological data management software results for gold Table II shows the market impact of the individual software solutions for gold using the functionality Geological Data Management as at September 2012 while Table III shows the same results using the second timestamp, i.e. April 2014. (m,t) Figure 3 illustrates both tables graphically. Note that fi,l , (m,t) (m,t) (m,t) wi,l , ui,l , ri,l and column headings in Tables II to XII were defined previously. When comparing the diagrams in Figure 3, there is a significant difference between the two pie charts; CAE Mining’s Geological Data Management Solution software with a 31% market impact in the April 2014 chart is clearly visible. Pegs Lite and MRM have each a 23% market impact in this field.

Geological modelling and resource estimation software results for gold Table IV shows the market impact of the individual software for gold using the Geological Modelling and Resource Estimation functionality as at September, 2012, while Table V shows the same results using the second timestamp, April 2014. Figure 4 illustrates both tables graphically.

Table II

Geological Data Management functionality software utilization for gold as of September 2012 Figure 2—Available mine planning software solutions and their functionalities along the mining value chain

utilization did not have any impact on the calculation of the relative software utilization. Unique identifiers of each particular software solution are not named in this research work, and the identifiers (ids) have been numbered randomly.

Results for the gold sector In this section, strategic mine planning software utilization for commodity (i) gold is calculated. Six functionalities (l) with two timestamps (t) were used for the calculations and the results for each functionality with two timestamps are presented as tables and figures, respectively. Accordingly, a total number of {6(l) × 2(t) = 12} tables were created for each commodity. According to the functionality list provided earlier, the first functionality, ‘Geological Data Management’ was used with two different timestamps to produce the first sets of two tables. After generating the tables, pie charts were created for each table for easy interpretation of the results. Consequently, using the functionality list, the remaining tables and figures were created in a similar manner. The following software providers participated in this study: Geovia, MineRP Solutions, Sable, RungePincockMinarco, Maptek, Cyest Technology, and CAE Mining. Note that the data on CAE Mining was only made available in the April 2014 data-set. The results presented here do not cater for either the mining methods or the type of mine (surface or an underground operation).

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Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13

9 10 13 38 68 72 83 93 95 97 98 100 113

(m,t)

fi,l

(m,t)

wi,l

4 12 2 1 1 0 9 0 0 32 31 0 0

(m,t)

ui,l

1 1 1 1 1 1 1 1 1 1 1 1 1

4 12 2 1 1 0 9 0 0 32 31 0 0

(m,t)

ri,l

0.0435 0.1304 0.0217 0.0109 0.0109 0 0.0978 0 0 0.3478 0.337 0 0

Table III

Geological Data Management functionality software utilization for gold as of April 2014 m

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13

9 10 13 38 68 72 83 93 95 97 98 100 113

(m,t)

fi,l

4 12 2 1 1 0 9 2 81 32 31 2 0

(m,t)

wi,l

1 1 1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

4 12 2 1 1 0 9 2 81 32 31 2 0

(m,t)

ri,l

0.0226 0.0678 0.0113 0.0056 0.0056 0 0.0508 0.0113 0.4576 0.1808 0.1751 0.0113 0

The Journal of The Southern African Institute of Mining and Metallurgy

Estimating mine planning software utilization for decision-making strategies

Figure 3 – Geological Data Management functionality software utiliszation for gold

When comparing the diagrams in Figure 4, similar to the previous results, there is huge difference between the two pie charts; Studio 3 - Geology is the leading software with a 62% market impact in the April 2014 chart. Pegs Lite and MRM have each a 14% market impact in this field.

Table IV

Geological Modelling and Resource Estimation functionality software utilization for gold as of September 2012 m

Software_id

1 2 3 4 5 6 7 8 9 10 11

9 10 13 48 68 72 84 93 94 98 99

(m,t)

fi,l

(m,t)

wi,l

4 12 2 0 1 0 0 0 0 31 31

(m,t)

ui,l

1 1 1 1 1 1 1 1 1 1 1

4 12 2 0 1 0 0 0 0 31 31

ri,l

0.0494 0.1481 0.0247 0 0.0123 0 0 0 0 0.3827 0.3827

Design and layout software results for gold Table VI shows the market impact of the individual software solutions for gold using the Design and Layout functionality as at September 2012, while Table VII shows the same results using the second timestamp, April 2014. Figure 5 illustrates both tables graphically. When comparing the diagrams in Figure 5, similar to the previous results, there is significant difference between the two pie charts; Studio 3 - Engineering is the leading software with a 26% market impact in the April 2014 chart. MRM and CADSMine have each a 20% market impact in this field.

Scheduling software results for gold

Table V

Geological Modelling and Resource Estimation functionality software utilization for gold as of April 2014 m

Software_id

1 2 3 4 5 6 7 8 9 10 11

9 10 13 48 68 72 84 93 94 98 99

(m,t)

fi,l

4 12 2 0 1 0 134 2 0 31 31

(m,t)

wi,l

1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

4 12 2 0 1 0 134 2 0 31 31

ri,l

0.0184 0.0553 0.0092 0 0.0046 0 0.6175 0.0092 0 0.1429 0.1429

Table VIII shows the market impact of the individual software solutions for gold using the Scheduling functionality as at September 2012, while Table IX shows the same results using the second timestamp, April 2014. Figure 6 is a graphical representation of Table VIII, while Figure 7 shows the graphical representation of Table IX. There is not much difference between Figure 6 and Figure 7; MRM and CADSMine software still have the biggest market impact, both with 20%, in the Scheduling software field. Enhanced Production Scheduler (CAE) has 15% market impact in this field.

Financial valuation software results for gold Table X shows the market impact of the individual software solutions for gold using the Financial Valuation software

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Figure 4—Geological Modelling and Resource Estimation functionality software utilization for gold

Estimating mine planning software utilization for decision-making strategies Table VI

Table VII

Design and Layout functionality software utilization for gold as of September 2012

Design and Layout functionality software utilization for gold as of April 2014

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

2 5 9 10 13 31 32 46 48 49 68 70 85 86 88 89 90 96 98 99 101 102

(m,t)

fi,l

8 3 4 12 2 0 0 0 0 0 1 2 0 0 0 0 0 0 31 31 1 0

(m,t)

wi,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

8 3 4 12 2 0 0 0 0 0 1 2 0 0 0 0 0 0 31 31 1 0

(m,t)

ri,l

0.0842 0.0316 0.0421 0.1263 0.0211 0 0 0 0 0 0.0105 0.0211 0 0 0 0 0 0 0.3263 0.3263 0.0105 0

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

2 5 9 10 13 31 32 46 48 49 68 70 85 86 88 89 90 96 98 99 101 102

(m,t)

fi,l

8 3 4 12 2 0 0 0 0 0 1 2 40 0 0 1 0 20 31 31 1 0

(m,t)

wi,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

8 3 4 12 2 0 0 0 0 0 1 2 40 0 0 1 0 20 31 31 1 0

(m,t)

ri,l

0.0513 0.0192 0.0256 0.0769 0.0128 0 0 0 0 0 0.0064 0.0128 0.2564 0 0 0.0064 0 0.1282 0.1987 0.1987 0.0064 0

Figure 5—Design and Layout functionality software utilization for gold

functionality as at September 2012, while Table XI shows the same results using the second timestamp, April 2014. Figure 8 illustrates both tables graphically. Figure 8 indicates that MRM is the leading software, with a 51% market impact in the gold sector when it comes to the Financial Valuation software. Carbon Economics is in second place with a 21% market impact in this field.

Optimization software results for gold Table XII shows the market impact of the individual software solutions for the commodity gold using the Optimization functionality as at September, 2012, while Table XIII shows the same results using the second timestamp, April 2014. Figure 9 is a graphical representation of both tables. When comparing the diagrams in Figure 9, there is a noteworthy difference between the two pie charts; Studio 3 – Geology has emerged as a new leader with a 62% market impact in April 2014, followed by MRM with a 14% market impact in the Optimization software field.

Conclusion In this paper, a methodology for the evaluation of mine planning software for measuring utilization in the South

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African gold mining sector was developed. Three variables, namely, commodity (i), functionality (l), and time factor (t) were used to calculate the results. Although the calculations can be done for any commodity in a similar manner, in this paper, calculations were done only for gold; six functionalities namely Geological Data Management, Geological Modelling and Resource Estimation, Design and Layout, Scheduling, Financial Valuation, and Optimization were applied using two different timestamps (September 2012 and April 2014). It is important to note that data on CAE Mining was only made available in the April 2014 data-set. When comparing the results, the CAE Mining market impact is clearly visible in the gold sector, especially in the fields of Geological Data Management, Geological Modelling and Resource Estimation, Design and Layout, and Optimization. By using this newly developed framework, utilization of the various mine planning software solutions was measured. This methodology provides an opportunity for software users to review existing software combinations, or for those intending to purchase new software, a tool for estimating the comparative attractiveness of certain software packages. For example, mining companies can position themselves better by The Journal of The Southern African Institute of Mining and Metallurgy

Estimating mine planning software utilization for decision-making strategies Table VII

Table IX

Scheduling functionality software utilization for gold as of September 2012

Scheduling functionality software utilization for gold as of April 2014

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

2 4 7 12 14 20 21 33 69 71 74 75 76 80 81 86 87 88 89 91 96 98 99 101 102 108 109 111 112 113 114

(m,t)

fi,l

8 9 0 6 2 1 0 1 1 4 0 0 9 1 2 0 0 0 0 0 0 31 31 1 0 0 0 0 0 0 0

(m,t)

wi,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

8 9 0 6 2 1 0 1 1 4 0 0 9 1 2 0 0 0 0 0 0 31 31 1 0 0 0 0 0 0 0

ri,l

0.0748 0.0841 0 0.0561 0.0187 0.0093 0 0.0093 0.0093 0.0374 0 0 0.0841 0.0093 0.0187 0 0 0 0 0 0 0.2897 0.2897 0.0093 0 0 0 0 0 0 0

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

2 4 7 12 14 20 21 33 69 71 74 75 76 80 81 86 87 88 89 91 96 98 99 101 102 108 109 111 112 113 114

(m,t)

fi,l

(m,t)

wi,l

8 9 0 6 2 1 0 1 1 4 0 0 9 1 2 0 23 0 1 2 20 31 31 1 0 0 0 0 0 0 0

(m,t)

ui,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

8 9 0 6 2 1 0 1 1 4 0 0 9 1 2 0 23 0 1 2 20 31 31 1 0 0 0 0 0 0 0

(m,t)

ri,l

0.0523 0.0588 0 0.0392 0.0131 0.0065 0 0.0065 0.0065 0.0261 0 0 0.0588 0.0065 0.0131 0 0.1503 0 0.0065 0.0131 0.1307 0.2026 0.2026 0.0065 0 0 0 0 0 0 0

acquiring optimal combinations of mine planning software; consulting companies can advise their clients more effectively to make the right choices of software solutions; tertiary education institutions offering mining-related qualifications can strategically choose which software to expose their students to; and software providers can strategically position themselves within the mine planning software market.

Table X

Figure 7—Scheduling functionality software utilization for gold as of April 2014 The Journal of The Southern African Institute of Mining and Metallurgy

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

7 15 73 77 78 79 80 91 92 98 103 104 105 106 109 110

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(m,t)

fi,l

0 4 0 13 1 0 1 0 0 31 3 0 0 0 0 0

(m,t)

wi,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

0 4 0 13 1 0 1 0 0 31 3 0 0 0 0 0

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(m,t)

ri,l

0 0.0755 0 0.2453 0.0189 0 0.0189 0 0 0.5849 0.0566 0 0 0 0 0

151

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Figure 6—Scheduling functionality software utilization for gold as of September 2012

Financial Valuation functionality software utilization for gold as of September 2012

Estimating mine planning software utilization for decision-making strategies Table XI

Table XII

Financial Valuation functionality software utilization for gold as of April 2014

Optimization functionality software utilization for gold as of September 2012

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

7 15 73 77 78 79 80 91 92 98 103 104 105 106 109 110

(m,t)

fi,l

(m,t)

wi,l

0 4 0 13 1 0 1 2 5 31 3 0 0 0 0 0

(m,t)

ui,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 4 0 13 1 0 1 2 5 31 3 0 0 0 0 0

ri,l

0 0.0667 0 0.2167 0.0167 0 0.0167 0.0333 0.0833 0.5167 0.05 0 0 0 0 0

Table XIII

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 15 21 73 74 77 79 82 84 87 88 91 92 98 102 103 105 106 107 110

(m,t)

fi,l

0 4 0 0 0 13 0 0 0 0 0 0 0 31 0 3 0 0 0 0

(m,t)

wi,l

(m,t)

ui,l

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 4 0 0 0 13 0 0 0 0 0 0 0 31 0 3 0 0 0 0

(m,t)

ri,l

0 0.0784 0 0 0 0.2549 0 0 0 0 0 0 0 0.6078 0 0.0588 0 0 0 0

Optimization functionality software utilization for gold as of April 2014 m

Software_id

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 15 21 73 74 77 79 82 84 87 88 91 92 98 102 103 105 106 107 110

(m,t)

fi,l

(m,t)

wi,l

3 4 0 0 0 13 0 0 134 23 0 2 5 31 0 3 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

(m,t)

ui,l

3 4 0 0 0 13 0 0 134 23 0 2 5 31 0 3 0 0 0 0

ri,l

0.0138 0.0183 0 0 0 0.0596 0 0 0.6147 0.1055 0 0.0092 0.0229 0.1422 0 0.0138 0 0 0 0

References

Figure 8 —Financial Valuation functionality software utilization for gold

Figure 9 —Optimization functionality software utilization for gold

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Engineering, 25 May 2004, Edinburgh, UK. Vol. 2004, no. 917. pp. 64–68. KATAKWA, T.P., MUSINGWINI, C., and GENC, B. 2013. Online database of mine planning and peripheral software used in the South African mining industry. Journal of the Southern African Institute of Mining and Metallurgy, vol. 113, no. 6. pp. 497–504.

STATISTICS SOUTH AFRICA. 2014. Publications. http://beta2.statssa.gov.za/ publications/P2041/P2041January2014.pdf [Accessed 24 July 2014]. THE OPEN GROUP. 2010. The exploration and mining business reference model. https://collaboration.opengroup.org/emmmv/documents/22706/Getting_st arted_with_the_EM_Business_Model_v_01.00.pdf [Accessed 21 July

NAKAJIMA, S. 1998. Introduction to Total Productive Maintenance. Productivity Press, Cambridge, MA.

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fast-guide-to-oee.pdf [Accessed 15 June 2014].

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http://dx.doi.org/10.17159/2411-9717/2015/v115n2a10 ISSN:2411-9717/2015/v115/n2/a10

Thermophysical properties of rocks from the Bushveld Complex by M.Q.W. Jones*

This paper presents a compilation of physical properties of rocks from the Bushveld Complex. The database consists of more than 900 measurements each of thermal conductivity and density. The data are well distributed from localities around the Complex and most rock types are well represented. Thermal conductivity and density are shown to vary widely in the ranges 1.8–4.2 W m-1 K-1 and 2600–4200 kg m-3 respectively. Although only 190 heat capacity measurements are available, this parameter is quite uniform for most rock types present, 800–900 J kg-1 K-1, except for chromitite, which has a lower average, 750 J kg-1 K-1. Rocks encountered in deep platinum mines are particularly well characterized and this has important implications for prediction of mine refrigeration requirements. The heat flux into a semi-infinite region with properties typical of the Bushveld Complex as a function of time is substantially lower than an equivalent model for the Witwatersrand Basin. Keywords thermophysical rock properties, platinum mines, mine cooling.

Introduction Geothermal research provides insights into a wide variety of geological problems, including tectonic studies (Jones, 1988, 1992, 1998), maturation of oil and natural gas (Royden et al., 1980), exploration for geothermal energy (e.g. Martinelli et al., 1995), and even climatology (e.g. Jones et al., 1999). In South Africa, the most important application is in mine engineering. South Africa leads the world in deep mining, and knowledge of virgin rock temperatures, geothermal gradients, and thermal properties of rocks is essential for planning refrigeration and ventilation requirements of deep mines (Jones, 1988; Jones and Rawlins, 2002; Rawlins et al., 2002; Jones, 2003a, 2003b). One of the most important challenges of mining is to control the environmental conditions, particularly temperature, of deep underground workings. The primary cause of elevated temperatures is heat derived from exposed rock surfaces, whose temperature is determined by the natural increase of virgin rock temperature (VRT) with depth (Rawlins et al., 2002; Jones 2003a). Knowledge of VRT is therefore fundamental for calculating refrigThe Journal of The Southern African Institute of Mining and Metallurgy

* School of Geosciences, University of the Witwatersrand, Johannesburg. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Feb. 2013; revised paper received Oct. 2014. VOLUME 115

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Synopsis

eration requirements. Knowledge of rock thermal properties is also fundamental for refrigeration studies because (1) they determine the rate at which heat flows from the rock into underground excavations, and (2) they control the thermal gradient in strata overlying and underlying these excavations and hence their VRT (Jones, 2003a, 2003b). Because of its long history of gold mining, which has progressively approached greater depths, the Witwatersrand Basin has been the subject of geothermal research for more than 75 years. Measurements of rock temperature and thermal conductivity dating back to 1938 (Weiss 1938; Bullard, 1939; Krige, 1939) provided the foundation for later studies and eventually an intensified collaborative research programme (from about 1980–2000) between the University of the Witwatersrand (Wits) and the Chamber of Mines Research Organisation (COMRO), and later, CSIR Mining Technology. The net product was a database of VRT, thermal gradient, and rock properties that probably surpasses any in the world (Jones, 1988, 2003a, 2003b). In the latter period, platinum mining depths in the Bushveld Complex (to the north of the Witwatersrand Basin) increased significantly. Earlier measurements at two localities showed that the geothermal gradient in the Bushveld Complex is substantially higher than that in the Witwatersrand Basin (Carte and van Rooyen, 1969), and it was obvious that there was a need for a detailed geothermal investigation of the Complex. Subsequent research collaboration between Wits, COMRO, and other key stakeholders from industry ensued. New measurements of the geothermal gradient were made in many of the existing and potential platinum mining areas, and these measurements confirmed that the

Thermophysical properties of rocks from the Bushveld Complex thermal gradients are high (generally 20–25 K km-1) (M.Q.W. Jones, unpublished data, 2014). These data will be presented for publication elsewhere. During the past 30 years, the number of measurements of thermal conductivity, density, and heat capacity on Bushveld rocks gradually grew and the database currently rivals that from the Witwatersrand Basin in size and coverage of rock types. It is the purpose of this paper to present a summary of the Bushveld database, characterize the thermal properties of various rock units constituting the Bushveld Complex, and discuss the implications for deep mining.

Geological background The Bushveld Complex is an enormous igneous province occupying an area of approximately 65 000 km2 to the north, east, and west of Pretoria (Figure 1). The Complex hosts the world’s largest reserves of platinum, chromium, and vanadium, and consequently there is a vast literature. This brief summary is largely based on reviews provided by SACS (1980) and Cawthorn et al. (2007). For ease of reference, Table I contains a glossary of major Bushveld rock types discussed below. The Bushveld Complex consists of coarse-grained igneous rocks with a wide range of composition from ultramafic to felsic. It is temporally, and possibly genetically, related to volcanic rocks of the Rooiberg Group (Cawthorn et al., 2007) (Figure 1). However, this paper deals solely with the plutonic rocks that were intruded into the Transvaal Supergroup and older rocks 2060 Ma ago. The Complex is formally classified into three distinct units: the ultramafic to mafic Rustenburg Layered Suite and the felsic Rashoop Granophyre and Lebowa Granite Suites (Figure 2). The average thickness of the Rustenburg Layered Suite is approximately 6 km, and it crops out in four main ’limbs’. The geology of the eastern limb (northwest to southwest of Burgersfort) and the western limb (Thabazimbi to Pretoria) is reasonably well known, whereas large parts of the southern

(or Bethal) limb and northern (or Potgietersrus/Mokopane) limb are covered by younger Karoo and Waterberg strata. Different rock units can be traced for hundreds of kilometres and, although the succession is seldom complete, geophysical evidence suggests that at least the eastern and western limbs are connected at depth (Cawthorn et al., 1998). The Rustenburg Layered Suite consists of five main ‘zones’, which is the most convenient classification for discussing similar rocks from similar levels (Figure 2). Although specific rock types are listed below and in Table I, it should be noted that there is often a gradational change from one rock type to another. For example, there can be an almost continuous variation from anorthosite to pyroxenite and, as will be seen, this results in a gradation of thermal properties.

Table I

Glossary of major rock types in the Bushveld Complex Rock type

Major minerals

Felsic rocks Granite Granophyre Mafic rocks Anorthosite Norite Gabbro Gabbronorite Diorite Ultramafic rocks Pyroxenite Peridotite Harzburgite Wehrlite Dunite Ore-bearing units Magnetitite layers Merensky Reef Chromitite layers

Quartz, feldspar Quartz, plagioclase/alkali feldspar (mica) Quartz, alkali feldspar Feldspar, pyroxene, olivine Plagioclase feldspar Plagioclase feldspar, orthopyroxene Plagioclase feldspar, clinopyroxene Undifferentiated gabbro/norite Plagioclase feldspar, pyroxene, olivine Pyroxene, olivine Orthopyroxene and/or clinopyroxene Olivine, pyroxene Olivine, orthopyroxene Olivine, clinopyroxene Olivine Magnetite Pyroxene Chromite (pyroxene)

Figure 1—Suboutcrop map of the Bushveld Complex showing the distribution of major geological units after artificially removing overlying Transvaal Supergroup strata and younger Karoo Supergroup and Waterberg Group strata (after Cairncross and Dixon, 1995). Yellow, Rooiberg Group; green, Rustenburg Layered Suite; pink, Rashoop Granophyre Suite and Lebowa Granite Suite. Red dots indicate localities from which samples were collected for rock property analysis

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The Journal of The Southern African Institute of Mining and Metallurgy

Thermophysical properties of rocks from the Bushveld Complex The last stage of Bushveld magmatism involved the emplacement of large volumes of granophyric and granitic rocks of the Rashoop Granophyre Suite and the Lebowa Granite Suite, which occupy the interior of the Complex (Figure 1). Although they are often referred to collectively as ‘Bushveld granite’, there are significant variations in the proportions of major minerals (quartz and feldspar) as well as the content of minor mafic phases (mainly hornblende, biotite, and pyroxene). This results in subtle variations of thermal properties.

Measurement of thermophysical properties The methods used to measure the thermophysical properties are essentially the same as those reported in this journal by Jones 10 years ago (Jones, 2003b). The reader is referred to that paper for details, and only the most salient points will be repeated here. The measured parameters are thermal conductivity (K, units W m-1 K-1), density (ρ, kg m-3), and heat capacity (C, J kg-1 K-1). A fourth parameter, thermal diffusivity (κ, m2 s-1) is calculated from the relation κ=K/Cρ. All measurements reported here were made in the heat flow laboratory at Wits.

Thermal conductivity

The lowermost ‘Marginal Zone’ consists predominantly of uniform, relatively fine-grained norite with minor amounts of pyroxenite. The overlying ‘Lower Zone’ is ultramafic in composition and dominated by pyroxenite, but includes thick layers of harzburgite with some dunite. From a mining point of view, the ‘Critical Zone’ is the most important because it is the host of chromium-rich and platiniferous ores. The lower part of the Critical Zone is essentially pyroxenite with some olivine-bearing rocks, whereas the upper section is represented by cyclic layers involving pyroxenite, norite, and anorthosite. Numerous chromitite layers may be well developed in the Critical Zone, and these are divided into the Lower Group (LG1-7), Middle Group (MG1-4), and Upper Group (UG1-3). The LG6 and MG1 layers have been extensively mined for chromium. The main sources of platinum and associated platinum group elements (PGEs) are the UG2 chromitite layer and pyroxenites within the Merensky Reef (Figure 2). The ‘Main Zone’ is a thick uniform sequence of norite and gabbronorite with a few layers of anorthosite and pyroxenite. It is overlain by the ‘Upper Zone’ in which magnetite appears as an accessory mineral in many rocks, which are predominantly anorthosite, gabbronorite, magnetite gabbro, and olivine diorite towards the top. Magnetite may accumulate in up to 24 layers, some of which exceed 2 m in thickness. Vanadium is associated with all the magnetitite layers and can reach concentrations of up to 2%. The Main Magnetite Layer in the lower part of the Upper Zone (Figure 2) is mined for vanadium. The Journal of The Southern African Institute of Mining and Metallurgy

Density Most densities were determined from accurately measured masses (using a precision balance) and volumes (using a micrometer and digital vernier) of samples prepared for thermal conductivity measurement. Measurements, first dry and then saturated with water, on 30 samples across the spectrum of rocks analysed showed no significant difference (<5 kg m-3 on average); this is to be expected because the VOLUME 115

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Figure 2—Generalized stratigraphic column for the Bushveld Complex indicating predominant rock types (after SACS, 1980). Red arrows schematically show the relative position of major ore-bearing layers

Thermal conductivity measurements were made using a divided bar apparatus that is specifically designed for precise analysis of competent rocks such as those encountered in the Bushveld Complex. This is a steady-state method in which the conductivity of a rock sample is measured relative to that of a material of known conductivity (the ‘substandard’). Precisely machined rock discs (30 or 38 mm in diameter and approximately 20 mm in length) and discs of the substandard are placed in the divided bar. Heat is supplied and abstracted at either end of the divided bar using temperature-controlled water circulators. When a condition of linear heat flow along the stack of discs is established, the conductivity of the sample disc relative to that of the substandard is determined by measuring the temperature gradient across each disc and applying Fourier’s Law of heat conduction, q=KdT/dx, where q is heat flux (W m-2) and dT/dx is thermal gradient (K m-1). The contact resistance between discs constituting the divided bar is minimized by applying an axial pressure of 5 MPa, and radial heat loss is minimized by carefully insulating the stack. All samples were saturated with water prior to measurement and average sample temperatures were close to 25°C in all experiments. The thermal conductivity of the substandard was calibrated against gem-quality quartz cored perpendicular to the c-crystallographic axis, for which an international standard conductivity was used. The overall error in determining conductivity, including reproducibility and calibration errors, using this method is estimated to be less than 0.1 W m-1 K-1.

Thermophysical properties of rocks from the Bushveld Complex porosity of the rocks is essentially zero. Repeat measurements on the same 30 specimens also yielded differences averaging at approximately 5 kg m-3, and the maximum uncertainty in measuring density is estimated at 10 kg m-3. The densities of damaged conductivity samples and other irregular specimens were determined from their measured masses while suspended in air and water and applying Archimedes’ Principle. Measurements were made on 10 control samples using both methods and the average difference was found to be less than the above overall uncertainty.

essentially controlled by their constituent minerals (Table I), which have distinct thermal properties (Table IV). As noted previously, the distinction between different rock types is not always clear-cut, and there is continuous gradation from one rock type to another. This is particularly relevant in the Bushveld Complex, where rock density variations may reveal the relative proportions of the major rock-forming minerals (Cawthorn and Spies, 2003; Davis et al., 2007). It was not possible to generate conductivity-density plots with the same horizontal (density) scale, so data for the Critical Zone and Main Zone (Figure 6a) are reproduced in all such diagrams (Figures 4, 7, 9, and 11) to facilitate comparison.

Heat capacity Heat capacities of samples, crushed and sieved to sand-sized grains, were determined by calorimetry using the method of mixtures. The temperature of measurement in all experiments was as close as possible to ambient temperature, but in most cases a small correction for Newtonian cooling was necessary. The uncertainty in heat capacity measurement, based on repeat measurements on 10 samples, is estimated at approximately 25 J kg-1 K-1.

Database overview During the past 30 years, routine thermal conductivity measurements have been made for both mine engineering applications and for tectonic studies of the Bushveld Complex. Initially, density measurements reported here were made primarily for mine refrigeration studies, but these were supplemented during the past year with measurements made on specimens originally prepared for conductivity analysis. The total number of measured values for each parameter exceeds 900. The distribution of boreholes and mines from which samples were derived is indicated in Figure 1. Average thermal conductivities and densities of different rock types in different stratigraphic units of the Bushveld Complex are listed in Table II. It is clear that the database is representative of the Complex as a whole and that all important rock types constituting the Complex are well characterized. Conductivity and density variations are discussed in the next section. Additional detailed density measurements are available from boreholes and mines in the Complex (Cawthorn and Spies, 2003; Ashwal et al., 2005; Davis et al., 2007). Heat capacity measurements were made specifically for mine refrigeration investigations. The data are summarized in Table III. Although only 190 values are available, most rock types encountered in the deep platinum mines are reasonably well represented. Fortunately, heat capacity is a relatively uniform rock parameter, and the data in Table III suffice for most purposes. Heat capacity data are discussed in the last section of this paper.

Thermal conductivity and density Variations of thermal conductivity and density of rocks within different stratigraphic units of the Bushveld Complex are illustrated in the form of histograms and plots of conductivity versus density (Figures 3–11). Histograms for different rock types, identified from hand specimens, are all drawn to the same horizontal and vertical scales so that the results can be compared directly. Conductivity and density of rocks are

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Table II

Thermal conductivity and density of rocks from the Bushveld Complex Rock unit and type

GRANITIC ROCKS Granite (undifferentiated) MAFIC ROCKS UPPER ZONE Diorite Anorthosite Gabbronorite Gabbronorite (magnetite-bearing) Pyroxenite Pyroxenite (magnetite-bearing) MAIN ZONE and CRITICAL ZONE Anorthosite Norite Pyroxenite Chromitite LOWER ZONE Pyroxenite, harzburgite, dunite

ρ±s.d., kg m-3

K±s.d., W m-1 K-1

3.43±0.41

103

2660±60

100

2.17±0.07 2.09±0.08 2.40±0.20 2.34±0.24

8 7 73 48

3100±250 2790±40 2980±120 3200±200

8 7 70 48

3.39±0.37 3.35±0.15

23 6

3170±70 3310±30

23 6

1.92±0.12 2.28±0.15 3.45±0.35 2.45±0.18

144 360 89 16

2780±40 2910±50 3190±70 3970±120

144 359 89 16

4.13±0.21

3280±40

K, thermal conductivity; ρ, density; s.d., standard deviation; N, number of observations.

Table III

Heat capacity and thermal diffusivity Rock unit and type

MAFIC ROCKS MAIN ZONE and CRITICAL ZONE Anorthosite Norite Pyroxenite Chromitite LOWER ZONE Pyroxenite, harzburgite, dunite

κ±s.d., 10-6 m2 s-1

820±30 840±40 870±60 750±50

36 46 82 16

0.83±0.04 0.95±0.09 1.27±0.16 0.84±0.11

36 46 82 16

870±20

1.28±0.14

C±s.d., J kg-1 K-1

C, heat capacity; κ, thermal diffusivity; s.d., standard deviation; N, number of observations. The Journal of The Southern African Institute of Mining and Metallurgy

Thermophysical properties of rocks from the Bushveld Complex Lower Zone

Critical Zone and Main Zone

Rocks sampled from the Lower Zone are all ultramafic, consisting essentially of pyroxene with at least small amounts of olivine. Because both of these minerals have a high thermal conductivity and density (Table IV), the respective values for the rocks are the highest in the Complex (Table II, Figures 3 and 4), with the exception of chromitite from the Critical Zone and magnetitite from the Upper Zone.

Thermal characterization of the Critical Zone and Main Zone (particularly its lower part) is crucial for investigations of refrigeration requirements of platinum mines and potential deep-level chromium mines. These zones are the best studied and are discussed together here for convenience. The most important rocks vary in composition from anorthosite (almost pure plagioclase feldspar) through norite and gabbro to pyroxenite (almost pure pyroxene) (Table I). Table III, the histograms (Figure 5), and the conductivity-density plot (Figure 6) show the effect of the increasing pyroxene content. Figure 6a shows that the gradation of thermal properties is not quite complete, as reflected in the paucity of data at approximately 3000 kg m-3. Nor is the relationship between the parameters purely linear. Average conductivities for successive 50 kg m-3 density intervals suggest that a relationship is better defined by two lines, one dominated by plagioclase and the other by pyroxene (Figure 6b). This may be understood by the fact that density is controlled simply by the percentages of plagioclase and pyroxene, whereas conductivity is controlled by more or less conductive paths associated with connectivity of these minerals, as well as their relative abundance. Measuring density is quick and easy, and the correlation between conductivity and density is potentially useful for estimating conductivity where measurements of this parameter are not available. Results for chromitite (Table II, Figure 7) are shifted substantially toward much higher density. All data are from the UG2 Reef. The data adequately characterize this important platinum-bearing horizon and serve as best estimates for Lower and Middle Group layers exploited for

Table IV

Thermal conductivity and density of important minerals found in rocks listed in Table I (Clark, 1966; Deer, Howie, and Zussman, 1966; Horai, 1971; Cermak and Rybach, 1982) Mineral Quartz Feldspar Alkali feldspar Plagioclase feldspar Pyroxene Orthopyroxene Clinopyroxene Olivine Chromite Magnetite

K, W m-1 K-1

ρ, kg m-3

7.69

2650

2.31-2.49 1.53-2.14

2560-2630 2620-2760

4.16-4.47 3.82-4.94 3.45-5.16 2.52 5.10

3210-3960 3220-3560 3220-4390 4800 5150

Figure 3—Histograms showing the distribution of thermal conductivity (a) and density (b) for rocks constituting the Lower Zone

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Figure 5—Histograms showing the distribution of thermal conductivity (a-c) and density (d-f) for rocks constituting the Critical and Main Zones VOLUME 115

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Figure 4—Thermal conductivity plotted against density for pyroxenite, harzburgite, and dunite from the Lower Zone (red dots). Blue dots indicate data for anorthosite, norite, and pyroxenite from the Critical and Main Zones (Figure 6), and are included in all similar diagrams for ease of comparison

Thermophysical properties of rocks from the Bushveld Complex samples of anorthosite and pyroxenite, and all data are plotted in the same histograms, the only distinction being between rocks that are apparently depleted in magnetite (Figure 8a and Figure 8c) and those that are obviously magnetite-bearing (Figure 8b and Figure 8d). Approximately half of the results fall close to the trend defined by anorthosites-pyroxenites from the Critical and Main Zones (Figure 6), but the other half suggests a different trend (Figure 9a). The latter samples are all magnetite-bearing and are displaced to higher densities as indicated by the leastsquares line fitted to successive 50 kg m-3 density intervals for this subset of the data Figure (9b). Although magnetite has a high conductivity (Figure 9b) (Table IV), its relatively low abundance in most rocks means that the connectivity

Figure 6—(a) Thermal conductivity plotted against density for individual samples of anorthosite, norite and pyroxenite from the Critical Zone and Main Zone; these data are reproduced in Figures 4, 7, 9, and 11 to facilitate comparison. (b) Average thermal conductivity and standard deviation for 50 kg m-3 density intervals derived from data in Figure 6a plotted against density; the two lines are least-squares fits to the averaged data for values with density less than and greater than 3000 kg m-3

Figure 8—Histograms showing the distribution of thermal conductivity (a and b) and density (c and d) for rocks constituting the Upper Zone

Figure 7—Thermal conductivity plotted against density for UG2 chromitite from the Critical Zone (red dots). Blue dots indicate data for anorthosite, norite, and pyroxenite from the Critical and Main Zones (Figure 6)

chromium. Few samples are known to be derived from the Merensky Reef, but this layer consists essentially of pyroxenite, and the data from the Critical and Main Zones in Table II are appropriate for mine engineering purposes.

Upper Zone Thermal characterization of the Upper Zone is complicated by the presence of significant amounts of magnetite in many of the rocks. Most samples are norite and/or gabbro, with fewer

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Figure 9—(a) Thermal conductivity plotted against density for all rocks from the Upper Zone (red dots). ( b) Average thermal conductivity and standard deviation for 50 kg m-3 density intervals derived from magnetite-bearing gabbronorites and two magnetitites from the Upper Zone (green dots); the line is the least-squares fit to the averaged data. Blue dots indicate data for anorthosite, norite, and pyroxenite from the Critical and Main Zones (Figure 6) The Journal of The Southern African Institute of Mining and Metallurgy

Thermophysical properties of rocks from the Bushveld Complex between magnetite grains is incomplete and apparently not efficient enough to substantially enhance thermal conductivity. Density, on the other hand, is purely a function of the amount of magnetite present. Vanadium mining on the Main Magnetite Layer is shallow at present, but the data are potentially useful for engineering purposes if deeper levels are exploited in the future.

Bushveld granites and granophyres Granitic rocks of the Rashoop Granophyre Suite and Lebowa Granite Suite have not been differentiated. Table II and Figures 10 and 11 indicate that these rocks are thermally quite distinct from the mafic and ultramafic rocks of the Rustenburg Layered Suite. The thermal parameters are typical of granites, except for a few magnetite-bearing specimens that have higher densities (Figure 11a). Although there is no strong relationship between conductivity and density in Figure 11a, a plot of average conductivity versus average density for different sample localities suggests a negative correlation (Figure 11b).

Heat capacity and thermal diffusivity Heat capacity measurements were made on 190 samples and results are summarized in Table III. Most of the samples were derived from the Critical Zone and lower part of the Main Zone because data on rocks from these levels is most important for applications in mine refrigeration. Although heat capacity increases with increasing pyroxene content (Table I and Table III) this effect is small, and an overall average value for the Critical and Main Zones (850 ±50 J kg-1 K-1) will suffice (in the absence of measured values) for calculating thermal diffusivity for mine refrigeration purposes. Chromitite has a lower average heat capacity (750 J kg-1 K-1, Table III), which should be used for calculations involving mining of the UG2 chromitite layer. The value for Main Zone pyroxenite is recommended for similar analyses involving the Merensky Reef. The calculated parameter, thermal diffusivity, varies from 0.8 × 10-6 to 1.4 × 10-6 m2 s-1. Estimating the heat load from the country rock in underground workings is a complicated process because it depends on several factors including mine geometry, VRT, rock properties, and boundary conditions at newly exposed rock surfaces. This is beyond the scope of this paper, but the effect of thermal properties alone reported here can be illustrated by calculating the heat flux at the surface of a semi-infinite region initially at a temperature of Ti and instantaneously exposed to a constant surface temperature of T0 at time t=0:

Figure 10—Histograms showing the distribution of thermal conductivity (a) and density (b) for granitic rocks from the Bushveld Complex

Figure 12 shows the surface heat flux as a function of time for Ti=50°C, T0=25°C, and various values for the thermal properties. The heat flux is up to 50% lower if the half-space is characterized by thermal parameters appropriate to the Bushveld Complex (blue) compared with an equivalent

Figure 12—Heat flux as a function of time into the surface of a cooling half-space with initial temperature Ti=50°C and surface temperature T0=25°C. Blue, calculated using thermal properties typical of the Bushveld Complex (Critical-Main Zone anorthosite, K=1.9 W m-1 K-1, κ=0.8×10-6 m2 s-1; Critical-Main Zone pyroxenite, K=3.5 W m-1 K-1, κ=1.3×10-6 m2 s-1). Red, calculated using thermal properties typical of rocks in the Witwatersrand Basin (Jones, 2003b) (Ventersdorp lava, K=3.5 W m-1 K-1, κ=1.4×10-6 m2 s-1; Witwatersrand quartzite, K=6.4 W m-1 K-1, κ=2.9×10-6 m2 s-1)

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Figure 11—(a) Thermal conductivity plotted against density for granitic rocks (red dots) and magnetite-bearing granitic rocks (green dots) from the Bushveld Complex. (b) Average thermal conductivity plotted against average density for granite from different localities in the Complex (red dots); bars represent standard deviations and the red line is a leastsquares fit to the averaged data. Blue dots indicate data for anorthosite, norite, and pyroxenite from the Critical and Main Zones (Figure 6)

Thermophysical properties of rocks from the Bushveld Complex situation in gold mines of the Witwatersrand Basin (red). Estimation of the controlling effect of rock properties on heat flux into underground workings obviously requires specific and in-depth calculations (C.A. Rawlins, personal communication, 2014).

Conclusions The extensive thermophysical rock property database from the Bushveld Complex permits reliable estimation of the average thermal conductivity, density, heat capacity, and thermal diffusivity of most rock types in various stratigraphic units constituting the Complex. The results provide important inputs for calculations of the heat load on deep platinum mines and would be potentially important in chromium and vanadium mines if such mining proceeds to deeper levels. A positive correlation between conductivity and density of rocks in the Critical Zone and Main Zone may be useful as a conductivity estimator where conductivity data are not available. The high magnetite content of many rocks in the Upper Zone results in a correlation that differs from the Critical and Main Zones and the rest of the Upper Zone; the density is substantially higher but the conductivity is largely unaffected. Illustrative calculations indicate that the generally lower thermal conductivity and thermal diffusivity of rocks in Bushveld platinum mines results in a lower heat flux into underground workings compared with gold mines in the Witwatersrand Basin.

Acknowledgements The research leading to this publication was made possible by financial and logistical support from Bluhm Burton Engineering (Pty) Ltd, the Chamber of Mines Research Organisation (later CSIR Mining Technology), General Mining Union Corporation Ltd, the Geological Survey of South Africa (now Council for Geoscience), Gold Fields of South Africa Ltd, Johannesburg Consolidated Investment Company Ltd, and Impala Platinum Ltd. Individual geologists who assisted in field surveys and sample collection are too numerous to list, but their efforts have not been forgotten. John Sorour’s assistance in measuring thermal conductivity is greatly appreciated. The author gratefully acknowledges reviews by Grant Cawthorn, Alex Rawlins, and Russel Ramsden.

References ASHWAL, L.D., WEBB, S.J., and KNOPER, M.W. 2005. Magmatic stratigraphy in the Bushveld northern lobe: continuous geophysical and mineralogical data from the 2950 m Bellevue drillcore. South African Journal of Geology, vol. 108. pp 199–132. BULLARD, E.C. 1939. Heat flow in South Africa. Proceedings of the Royal Society of London, Series A, vol. 173. pp. 474–502. CAIRNCROSS, B. and DIXON, R. 1995. Minerals of South Africa. Geological Society of South Africa. Johannesburg. CARTE, A.E. and VAN ROOYEN, A.I.M. 1969. Further measurements of heat flow in South Africa. Special Publication of the Geological Society of South Africa, vol 2. Johannesburg. pp. 445–448. CAWTHORN, R.G., COOPER, G.R.J., and WEBB, S.J. 1998. Connectivity between the western and eastern limbs of the Bushveld Complex. South African Journal of Geology, vol. 101. pp. 291–298. CAWTHORN, R.G., EALES, H.V., WALRAVEN, F., UKEN, R., and WATKEYS, M.K. 2007. The Bushveld Complex. The Geology of South Africa. Johnson, M.R.,

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Anhaeusser, C.R., and Thomas, R.J. (eds). Geological Society of South Africa/Council for Geoscience, Johannesburg/Pretoria. pp. 261–281. CAWTHORN, R.G. and SPIES, L. 2003. Plagioclase content of cyclic units in the Bushveld Complex, South Africa. Contributions to Mineralogy and Petrology, vol. 145. pp. 47–60. CERMAK, V. and RYBACH, L. 1982. Thermal conductivity and specific heat of minerals and rocks. Landholt-Bornstein Numerical Data and Functional Relationships in Science and Technology. New Series, Group V, vol. 16. Angenheister, G. (ed.). Springer-Verlag. Berlin. pp. 305–343. CLARK, S.P. 1966. Thermal conductivity. Handbook of Physical Constants. Memoir 97. Clark, S.P. (ed.). Geological Society of America, New York. pp. 459–482. DAVIS, M. D., WILSON, A. H., and VAN DER MERWE, A. J. 2007. The use of density as a stratigraphic and correlative tool for the Bushveld Complex, South Africa. Proceedings of Exploration 07: Fifth Decennial International Conference on Mineral Exploration. Milkereit, B. (ed.). Decennial Mineral Exploration Conferences, Toronto. pp. 1193–1197. DEER, W.A., HOWIE, R.A., and ZUSSMAN, J. 1966. An Introduction to the RockForming Minerals. Longmans, London. HORAI, K. 1971. Thermal conductivity of rock forming minerals. Journal of Geophysical Research, vol. 76. pp. 1278–1308. JONES, M.Q.W. 1988. Heat flow in the Witwatersrand Basin and environs and its significance for the South African shield geotherm and lithosphere thickness. Journal of Geophysical Research, vol. 93. pp. 3234–3260. JONES, M.Q.W. 1992. Heat flow in South Africa. Handbook 14. Geological Survey of South Africa. Pretoria. JONES, M.Q.W. 1998. A review of heat flow in southern Africa and the thermal structure of the lithosphere. South African Geophysical Reviews, vol. 2. pp. 115–122. JONES, M.Q.W. 2003a. An update in virgin rock temperature analysis of the Witwatersrand Basin. Journal of the Mine Ventilation Society of South Africa, vol. 56. pp. 107–112. JONES, M.Q.W. 2003b. Thermal properties of stratified rocks from Witwatersrand gold mining areas. Journal of the South African Institute of Mining and Metallurgy, vol. 101. pp. 173–185. JONES, M.Q.W. and RAWLINS, C.A. 2002. Thermal properties of backfill from a deep South African gold mine. Journal of the Mine Ventilation Society of South Africa, vol. 54. pp. 100–106. JONES, M.Q.W., TYSON, P.D., and COOPER, G.R.J. 1999. Modelling climatic change in South Africa from perturbed borehole temperature profiles. Quaternary International, vol. 57/58. pp. 185–192. KRIGE, L.J. 1939. Borehole temperature measurements in the Transvaal and Orange Free State. Proceedings of the Royal Society of London, Series A, vol. 173. pp. 450–474. MARTINELLI, G., DONGARRÀ, D., JONES, M.Q.W, and RODRIGUES, A. 1995. Geothermal features of Mozambique - country update. Proceedings of the World Geothermal Congress 1995, vol. 1. Barbier, E., Frye, G., Iglesias, E., and Palmason, G. (eds). International Geothermal Association. Auckland. pp. 251–273. RAWLINS, C.A., PHILLIPS, H.R., and JONES, M.Q.W. 2002. The use of backfill to control heat ingress in deep level mining. Mine Ventilation. Proceeding of the North American 9th US Mine Ventilation Symposium, Kingston, Ontario, Canada. De Souza, E. (ed.). Balkema, Lisse. pp. 355–361. ROYDEN, L., SCLATER, J.G., and VON HERZEN, R.P. 1980. Continental margin subsidence and heat flow: important parameters in formation of petroleum hydrocarbons. Bulletin of the American Association of Petroleum Geologists, vol. 64. pp. 173–187. SACS (SOUTH AFRICAN COMMITTEE FOR STRATIGRAPHY). 1980. Stratigraphy of South Africa. Part 1. Lithostratigraphy of the Republic of South Africa, South West Africa/Namibia, and the Republics of Bophuthatswana, Transkei and Venda. Kent., L.E. (compiler). Handbook 8. Geological Survey of South Africa. Pretoria. WEISS, O. 1938. Temperature measurements with an electrical resistance thermometer in a deep borehole on the East Rand. Journal of the Chemical, Metallurgical and Mining Society of South Africa, vol. 39. pp. 149–166.◆ The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a11 ISSN:2411-9717/2015/v115/n2/a11

A new preparation scheme for a difficult-tofloat coking coal by column flotation following grinding by Yinfei Liaoa*, Yijun Caoa*, Zhongbo Hub†, and Xiuxiang Taoc‡

A new preparation scheme for a difficult-to-float coking coal from the Kailuan Mine, Tangshan, China was investigated. The results showed that grinding followed by column flotation was beneficial for obtaining products with low ash content. The positive effect of grinding on the coal floatability is attributed to the liberation of intergrowths and coal surface improvement. Tests indicated that 10 minutes was the optimum grinding time, and overgrinding resulted in a deterioration in flotation performance. With a grinding time of 10 minutes, conventional flotation had potential to yield a product with around 12.42% ash content and 69.15% combustible recovery. Column flotation can reduce the product ash content to 11.15% and increase combustible recovery to 74.47%. Consistently better flotation results reveal that column flotation is more efficient than conventional flotation for such fines. Keywords coal flotation, column flotation, grinding, difficult-to-float, liberation.

Introduction Coking coal is scarce in China, accounting for less than 10% of total coal reserves (Tao et al., 2009). Most of these valuable resources are difficult to wash, with high ash content in the product and low recoveries. Reduced availability of good quality coking coal has resulted in Chinese steel plants using low-ash imported coal as a sweetener in coal blends (Liu et al., 2009). Ash has a highly adverse effect on blast furnace productivity and coke consumption (Dey and Bhattacharyya, 2007). Thus it is necessary to develop an efficient preparation scheme to produce coking coal with a low ash content. Froth flotation, which is the most common separation technique used for cleaning fine coals, has been widely applied since the 1920s (Hacifazlioglu and Toroglu, 2007; Hacifazlioglu and Sutcu, 2007). Column flotation has been developed into an efficient technology in the past few decades. In many studies, it has been claimed that column flotation can give a higher recovery with lower ash content (Jena et al., 2008; Jena, Biswal, and Rudramuniyappa, 2008; Tao Luttrell, and Yoon, 2000). Furthermore, recent work has proved that coal floatability can be improved considerably by grinding (Sokolovic, Stanojlovic, and Markovic, 2012; Xia et al., The Journal of The Southern African Institute of Mining and Metallurgy

Experimental procedure Materials A coking coal sample of -0.5 mm size fraction was collected from Kailuan Mine, Tangshan, China. An SPB200 vibrating Taylor screen was used for size analysis. Size fractions of +0.5, -

* National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology, China. † Station of Coal Quality Supervision and Inspection, Anhui Province Coal Science Research Institute, China. ‡ School of Chemical Engineering and Technology, China University of Mining and Technology, China. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jun. 2014; revised paper received Nov 2014. VOLUME 115

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Synopsis

2012; Feng and Aldrich, 2000). The floatability of low-rank coal is enhanced through grinding in the presence of bituminous coal pitch (Atesok and Celik, 2000). Grinding has also been adopted to improve the floatability of oxidized coal (Xia, Yang, and Zhu, 2012). Clean coals have been successfully produced from Mecsek bituminous coal by flotation following ultrafine liberation (Bokanyi and Csoke, 2003). Thus by combining the two processes of grinding pretreatment and column flotation, the separation efficiency may be greatly improved. In this investigation, we compar the ash content and combustible matter recovery by conventional flotation with that by column flotation for different grinding times. A cyclonic-static microbubble flotation column (Cao et al., 2012; Li et al, 2010), a novel column developed by China University of Mining and Technology, was used for the flotation tests. Size analysis, density analysis, and contact angle measurements were used to investigate the effects of grinding pretreatment. The efficiency of combined grinding pretreatment and column flotation is also discussed.

A new preparation scheme for a difficult-to-float coking coal by column flotation following grinding 0.5+0.25, -0.25+0.125, -0.125+0.074, -0.074+0.045, and 0.045 mm were generated. Each size fraction was analysed for ash. Ash content (%) = Wt. of baked ash/Wt. of unbaked coal. A GL-21 M high-speed centrifuge was used for density analysis, with a centrifuge speed of 3000 r/min. The organic solutions with densities of 1.3, 1.4, 1.5, 1.6, and 1.8 g/cm3 were prepared. Each density fraction was washed, filtered, dried, weighed, and analysed.

Grinding pretreatment A cylindrical laboratory ball mill with a diameter of 160 mm and a length of 200 mm was used for wet grinding in all the experiments. The ball mill was run at a constant speed of 120 r/min and a media filling of 20%. A 500 g coal sample was added to the ball mill at a pulp concentration of 40%. The grinding times were 10, 20, 30, and 40 minutes. Size analysis, density analysis, and contact angle measurements were carried out on the grinding products. Contact angles were determined by the sessile drop method using a digital goniometer (Drop Shape Analysis System, DSA100, KrĂźss GmbH, Hamburg, Germany). The measurements were repeated three times for every sample.

Results and discussion Characterization of coal samples The particle size characterization data is given in Table II. It can be seen that the ash content increases with decreasing coal particle size. The lowest ash content was found in the +0.5 mm fraction. The majority of the material falls in the size range -0.074 +0.045 mm, with 34.27% yield. The fines and ash contents of the fine-grained samples are both high. The -0.074 mm fraction accounted for 39.78% of yield at an ash content of 26.27%, which is significantly higher than the other fractions. The fine particles with high ash content float into clean coals easily through mechanical entrainment, thus generating a high-ash concentrate. The selective recovery of fine fractions has thus become the key to preparation of these types of coal. The density analysis results for this fine coal are shown in Table III. It can be observed that the major yield of the coal is in the density range of -1.5 g/cm3. At a theoretical separation density of 1.4 g/cm3 and 1.5 g/cm3, the content of

Flotation tests The flotation tests were carried out in both a conventional flotation cell and a laboratory flotation column. Diesel oil was used as collector and 2-octanol as frother. Sodium hexametaphosphate was used as silica depressant as well as dispersant. About 100 g of coal was taken for conventional flotation experiments using a 1.5 L XFD flotation cell with an impeller speed of 1590 r/min and an air flow rate of 2 L/min. The slurry was prepared with 6.25% solid concentration and conditioned with sodium hexametaphosphate (1.5 kg/t) for 2 minutes. It was then treated with the required amount of diesel oil (310 g/t) for an additional 2 minutes. 2-octanol (120 g/t) was then added and the slurry further conditioned for 1 minute. The flotation was carried out by introducing air and the froth was collected for 3 minutes. The flotation products were filtered, dried, weighed, and analysed for ash. The organic solution was comprised by carbon tetrachloride, benzene and tribromethane. The column flotation study was carried out employing a 100 mm diameter by 2000 mm tall laboratory flotation column. A schematic diagram of the experimental set-up is shown in Figure 1. The slurry was treated with sodium hexametaphosphate (1.5 kg/t) at 6.25% solid concentration in the conditioner for 2 minutes. It was then agitated with diesel oil (310 g/t) for an additional 2 minutes, after which 2octanol (120 g/t) was then added and the slurry further conditioned for 1 minute. The slurry was fed with a peristaltic slurry pump at a specified rate to the column. The required air rate was monitored by flow meter. The slurry flow rates of feed and tailings were checked, and when both remained more or less constant, the concentrate and tailings were collected simultaneously at a certain time interval. The operating parameters for column flotation are presented in Table I. The samples were analysed using a similar procedure to that followed for the conventional flotation products. Each experiment was replicated to ensure the reproducible of data within the acceptable experimental error.

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Figure 1â&#x20AC;&#x201D;Schematic diagram of the flotation column. 1. Conditioner 2. Feed peristaltic pump 3. Flotation column 4. Tailing peristaltic pump 5. Circulation pump 6. Bubble generato; 7. Flow meter

Table I

Operating parameters of column flotation Operating parameters

Index

Collector Frother Dispersant Solid concentration Feed rate Air rate Froth depth Circulating pressure

Diesel oil (310 g/t) 2-octanol (120 g/t) Sodium hexametaphosphate (1.5 kg/t) 6.25% 3 l/min 5-6 l/min 400 mm 0.20 Mpa

Table II

Size analysis results Size fraction (mm)

Weight (%)

Ash content (%)

Combustible recovery (%)

+0.5 -0.5+0.25 -0.25+0.125 -0.125+0.074 -0.074+0.045 -0.045 Total

3.21 15.73 24.75 16.53 34.27 5.51 100.00

15.31 16.66 17.05 19.47 26.06 27.58 21.00

2.34 12.48 20.09 15.33 42.53 7.24 100.00

The Journal of The Southern African Institute of Mining and Metallurgy

A new preparation scheme for a difficult-to-float coking coal by column flotation following grinding Table III

Density analysis results Density

Yield (%)

Ash (%)

(g/cm3) -1.3 1.3-1.4 1.4-1.5 1.5-1.6 1.6-1.8 +1.8 Total

8.56 31.81 36.55 9.94 7.21 5.93 100

4.32 11.36 19.62 24.35 41.94 72.05 20.87

Float

Content of δ±0.1

Sink

Yield (%)

Ash (%)

Yield (%)

Ash (%)

Density (g/cm3)

Yield (%)

8.56 40.37 76.92 86.86 94.07 100 —

4.32 9.87 14.5 15.63 17.64 20.87 —

100 91.44 59.63 23.08 13.14 5.93 —

20.87 22.42 28.32 42.1 55.53 72.05 —

1.3 1.4 1.5 1.6 1.7 1.8

40.37 68.36 46.49 13.55 7.21 9.54

δ±0.1 is 68.36% and 46.49%, respectively. This demonstrates that the washability of such fine coal is poor. The ash content in the density range of 1.6 g/cm3 to 1.8 g/cm3 is relatively low. It can be indicated that there are numerous nonliberated intergrowth particles formed by gangue minerals and coals. It was supposed by the above density analysis result that a microscopic investigation will be done for further verification. It is therefore difficult to obtain low-ash clean coals and high-ash tailings by direct conventional flotation.

both cases, the concentrate ash content at first decreases with grinding time and then increases when the grinding time is more than 10 minutes. The ash content of the column flotation product is lower than that of conventional flotation at all grinding times. These results indicate that the scheme of column flotation following grinding is beneficial for obtaining products with low ash content.

Grinding properties Figure 2 shows that the yield of the +0.074 mm size fraction decreases with the grinding time. However, the yield of the -0.074 mm size fraction increases with grinding time. Moreover, it is interesting to note that the yield changes quickly during the first 10 minutes, and changes slowly when the grinding time is more than 20 minutes. This indicates that the grinding efficiency is high during the first 10 minutes and then declines. Figure 3 presents the relationship between cumulative yield and ash content, according to the density analysis of grinding products, for different grinding times. It can be seen that the yield increases with ash content. Moreover, the yield increases with the grinding time for a given ash content. This indicates that intergrowth particles are liberated in the grinding process and some coals are enriched to a certain extent. Contact angle has been extensively used to characterize the hydrophobicity and floatability of coal samples. Figure 4 shows that the contact angle initially increases with grinding time, then decreases slowly, decreasing more rapidly when the grinding time is more than 30 minutes. This phenomenon indicates that the hydrophobicity of this fine coal can be improved by appropriate grinding, but will deteriorate with overgrinding. Some coals are liberated and enriched through grinding. The coal floatability is improved in the attrition process. However, if the grinding time is too long, the coal will be overground and the coal surface will be covered by high-ash slime.

Figure 2—Effect of grinding time on size composition of coal samples

Figure 3—Effect of grinding time on the relationship between cumulative yield and ash content

Flotation results

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Figure 4—Effect of grinding time on contact angle of coal samples VOLUME 115

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Figure 5 shows that combustible matter recovery by both column flotation and conventional flotation increases initially with grinding time and then decreases when the grinding time is more than 10 minutes. However, the combustible matter recovery by column flotation is consistently higher than that by conventional flotation at all grinding times. In

A new preparation scheme for a difficult-to-float coking coal by column flotation following grinding grinding is beneficial for obtaining products with low ash content.

Acknowledgments This research was supported by the National Key Basic Research Program of China (Grant no. 2012CB214905) and the Fundamental Research Funds for the Central Universities (Grant no. 2014XT05). The authors also acknowledge the assistance of Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. Figure 5—Effect of grinding time on flotation performance

References ATESOK, G. and CELIK, M.S. 2000. A new flotation scheme for a difficult-to-float coal using pitch additive in dry grinding. Fuel, vol. 79. pp. 1509–1513.

It is worth mentioning that both column flotation and conventional flotation achieve the best performance at a grinding time of 10 minutes. The contact angle at first increases with the grinding time, and then decreases as shown in Figure. 4. Appropriate grinding can be used to improve flotation performance, but overgrinding will exacerbate mechanical entrainment, leading to a deterioration in flotation performance. Therefore, these results demonstrate that 10 minutes is the optimum grinding time. Conventional flotation reduces the ash content from 21.36% to 12.42%, with 69.15% combustible recovery. Using column flotation, the ash content of the clean coals is reduced to 11.15%, with 74.47% combustible recovery. It is obvious that column flotation is superior to conventional flotation, producing cleaner coals in terms of lower ash content and higher combustible matter recovery. Column flotation has advantages in recovering valuable fines at a better grade due to the minimization or prevention of hydraulic entrainment of undesirable fines (Li et al., 2012; Demir et al., 2008; Finch, 1995). The fine fractions increase after grinding pretreatment. Column flotation can selectively separate these fines to obtain clean coals of lower ash content. Moreover, the cyclonic-static microbubble flotation column features multiple mineralization steps, including countercurrent mineralization, cyclone mineralization, and pipe flow mineralization, which provide sufficient retention time to ensure fines recovery (Zhang et al., 2013).

Conclusions Investigations carried out on coking coal collected from Kailuan Mine indicate that it is difficult to obtain low-ash clean coals and high-ash tailings through direct conventional flotation. Improved hydrophobicity and floatability can be achieved by appropriate grinding. The effect of grinding on coal floatability is attributed to the liberation of intergrowths and improvement of the coal surface properties. It is concluded that 10 minutes is the optimum grinding time, and overgrinding results in a deterioration in flotation performance. With a grinding time of 10 minutes, conventional flotation has the potential to yield a product with approximately 12.42% ash content and 69.15% combustible recovery, while the product ash content can be further reduced to 11.15% with 74.47% combustible recovery in case of column flotation. Flotation tests results show that column flotation is more efficient than conventional flotation for such coking coal fines. The scheme of column flotation following

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BOKANYI, L. and CSOKE, B. 2003. Preparation of clean coal by flotation following ultra fine liberation. Applied Energy, vol. 74. pp. 349–358. CAO, Y.J., LI, G.S., LIU, J.T., ZHANG, H.J., and ZHAI, X. 2012. Removal of unburned carbon from fly ash using a cyclonic-static microbubble flotation column. Journal of the Southern African Institute of Mining and Metallurgy, vol. 112, no. 10. pp. 891–896. DEMIR, U., YAMIK, A., KELEBEK, S., OTEYAKA, B., UCAR, A., and SAHBAZ, O. 2008. Characterization and column flotation of bottom ashes from Tuncbilek power plant. Fuel, vol. 87, no. 6. pp. 666–672. DEY, S. and BHATTACHARYYA, K.K. 2007. Split and collectorless flotation to medium coking coal fines for multi-product zero waste concept. Fuel Processing Technology, vol. 88. pp. 585–590. FENG, D. and ALDRICH, C. 2000. A comparison of the flotation of ore from the Merensky Reef after wet and dry grinding. International Journal of Mineral Processing, vol. 60. pp. 115–129. FINCH, J.A. 1995. Column flotation: a selected review-part IV: novel flotation devices. Minerals Engineering, vol 8, no. 6. pp. 587–602. HACIFAZLIOGLU, H. and SUTCU, H. 2007. Optimization of some parameters in column flotation and a comparison of conventional cell and column cell in terms of flotation performance. Journal of the Chinese Institute of Chemical Engineers, vol. 38.2 pp. 87–293. HACIFAZLIOGLU, H. and TOROGLU, I. 2007. Optimization of design and operating parameters in a pilot scale Jameson cell for slime coal cleaning. Fuel Processing Technology, vol. 88. pp. 731–736. JENA, M.S., BISWAL, S.K., and RUDRAMUNIYAPPA, M.V. 2008. Study on flotation characteristics of oxidised Indian high ash sub-bituminous coal. International Journal of Mineral Processing, vol. 87. pp. 42–50. JENA, M.S., BISWAL, S.K., DAS, S.P., and REDDY, P.S.R. 2008. Comparative study of the performance of conventional and column flotation when treating coking coal fines. Fuel Processing Technology, vol. 89. pp. 1409–1415. LI, G.S., CAO, Y.J., LIU, J.T., and WANG, D.P. 2012. Cyclonic flotation column of siliceous phosphate ore. International Journal of Mineral Processing, vol. 110. pp. 11. LI, L., LIU, J.T., WANG, L.J., and YU, H.S. 2010. Numerical simulation of a selfabsorbing microbubble generator for a cyclonic-static microbubble flotation column. Mining Science and Technology, vol. 20, no. 1. pp. 88–92. LIU L.J., ZHENG, G.S., LIU, J.T., and WANG, Y.T. 2009. Technologic optimization for separation of scarce coal sludge. Procedia Earth and Planetary Science, vol. 1. pp. 785–790. SOKOLOVIC, J.M., STANOJLOVIC, R.D., and MARKOVIC, Z.S. 2012. Activation of oxidized surface of anthracite waste coal by attrition. Physicochemical Problems of Mineral Processing, vol 48, no. 1. pp. 5–18. TAO, D., LUTTRELL, G.H., and YOON, R.H. 2000. An experimental investigation on column flotation circuit configuration. International Journal of Mineral Processing, vol. 60. pp. 37–56. TAO, X.X., CAO, Y.J., LIU, J., SHI, K.Y., LIU, J.Y., and FAN, M.M. 2009. Studies on characteristics and flotation of a hard-to-float high-ash fine coal. Procedia Earth and Planetary Science, vol. 1. pp. 799–806. Xia, W.C., Yang, J.G., and Zhu, B. 2012. Flotation of oxidized coal dry-ground with collector. Powder Technology, vol. 228. pp. 324–326. XIA, W., YANG, J., ZHAO, Y., ZHU, B., and WANG, Y. 2012. Improving floatability of Taixi anthracite coal of mild oxidation by grinding. Physicochemical Problems of Mineral Processing, vol. 48, no. 2. pp. 393–401. ZHANG, H.J., LIU, J.T., WANG, Y.T., CAO, Y.J., MA, Z.L., and LI, X.B. 2013. Cyclonic-static micro-bubble flotation column. Minerals Engineering, vol. 45. pp. 3. ◆ The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n2a12 ISSN:2411-9717/2015/v115/n2/a12

Technological developments for spatial prediction of soil properties, and Danie Krige’s influence on it by R. Webster*

Daniel Krige’s influence on soil science, and on soil survey in particular, has been profound. From the 1920s onwards soil surveyors made their maps by classifying the soils and drawing boundaries between the classes they recognized. By the 1960s many influential pedologists were convinced that if one knew to which class of soil a site belonged then one would be able to predict the soil’s properties there. At the same time, engineers began to realize that prediction from such maps was essentially a statistical matter and to apply classical sampling theory. Such methods, though sound, proved inefficient because they failed to take account of the spatial dependence within the classes. Matters changed dramatically in the 1970s when soil scientists learned of the work of Daniel Krige and Georges Matheron’s theory of regionalized variables. Statistical pedologists (pedometricians) first linked R.A. Fisher’s analysis of variance to regionalized variables via spatial hierarchical designs to estimate spatial components of variance. They then applied the mainstream geostatistical methods of spatial analysis and kriging to map plant nutrients, trace elements, pollutants, salt, and agricultural pests in soil, which has led to advances in modern precision agriculture. They were among the first Earth scientists to use nonlinear statistical estimation for modelling variograms and to make the programmed algorithms publicly available. More recently, pedometricians have turned to likelihood methods, specifically residual maximum likelihood (REML), to combine fixed effects, such as trend and external variables, with spatially correlated variables in linear mixed models for spatial prediction. They have also explored nonstationary variances with wavelets and by spectral tempering, although it is not clear how the results should be used for prediction. This paper illustrates the most significant advances, with results from research projects. Keywords soil, variogram, spectral analysis, gilgai, drift, mixed models, REML, nonstationary, variance.

Prologue Daniel Krige’s influence on soil science, and on soil survey in particular, has been profound. Yet, I suspect that Daniel did not realize it. Some 25 years ago I found myself close to his holiday home in the Cape Province and visited him there. We chatted over coffee. I told him how we were adapting and developing geostatistics for mapping soil. He was encouraging, but he was not surprised: of course the technology was applicable in our domain. What he did not know was the base from which we were starting. He could not know how Aristotelian logic, with its emphasis on classification, had constrained both soil scientists’ thought and their mapping practices for more than half a century; it was not his field. So before I describe some of our achievements, I provide a little history. The Journal of The Southern African Institute of Mining and Metallurgy

I began my professional career in Africa. The year was 1957. The British Colonial Office, with the fiasco of the East African Groundnut Scheme fresh in its collective mind, recruited me to evaluate the suitability of land for agricultural development in what was then Northern Rhodesia, now Zambia. It became my job to survey and map soils for that purpose. My training in Britain, based on the identification of distinct classes of soil in a way similar to that of much geological survey at the time, ill-equipped me for what I should find. There were no obvious boundaries between one kind of soil and another on the deeply weathered rocks of the Zambian plateaux. The soil seemed to vary gradually over the landscape, though in repetitive patterns on a grand scale in sequences for which Milne (1936) had earlier coined the term ‘catena’. Furthermore, the fairly dense miombo woodland in the north of country meant that one could rarely see for more than a few tens of metres, and air-photo interpretation had so far been of little help. Surveys had to be done almost entirely by sampling, and mapping by interpolation from the point observations. How was I to interpolate? There must be some method better than by hand and eye. I was no nearer to answering my question when in 1960 I was invited by Philip Beckett to pursue the matter at Oxford University alongside the Royal Engineers. The aim was to predict soil conditions at unvisited places. Many influential pedologists at the time were convinced that if one knew to which class of soil a site belonged then one would be able to predict the soil’s properties there. Beckett and I were far from convinced. A few engineers had begun to realize that the problem was essentially statistical and were toying with a combination of classical soil maps and prediction statistics based on stratified random sampling in which the classes of the

* Rothamsted Research, Great Britain. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jun. 2013; revised paper received Jun. 2014. VOLUME 115

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Synopsis

Technological developments for spatial prediction of soil properties maps were the strata. Morse and Thornburn (1961) published statistics obtained by sampling agricultural soil maps in Illinois, and a year later Kantey and Williams (1962) reported results of sampling engineering soil maps in South Africa. We planned a thorough study along similar lines. We classified and mapped a large part of the Oxfordshire landscape, sampled it to a stratified random design, and measured properties of the soil. Then by analysis of variance we assessed our classification for its effectiveness in (a) diminishing the variances within the classes and (b) predicting values with acceptable precision. We also assessed maps made and sampled by several of our collaborators. We had mixed success. Our map of the Oxford region enabled us to predict the mechanical properties of the soil reasonably well. It predicted relatively poorly the soil’s pH and organic matter content, and it was useless for predicting the plant nutrients in the soil. Table I from Webster and Beckett (1970) summarizes our results.

First steps Despite our partial success, even in the most favourable situations there was substantial residual variation for which we could not account. Some we might treat as white noise, but much was evidently structural. We had not solved the problem of the catena or any other form of gradual change or trend. If we simply drew boundaries in those situations then the residuals would be spatially correlated. At about the same time, trend-surface analysis was becoming fashionable in geography and petroleum exploration, but it was unsatisfactory because (a) fluctuation in one part of a region affected the fit of the surface elsewhere and (b) the residuals were correlated so that calculated prediction variances were biased. I was joined from Mexico by H.E. Cuanalo in 1968. He pointed out that time-series analysts have similar problems, and they treat actuality as realizations of stochastic processes to describe quantitatively fluctuations in time. Could we not do the same for soil? So we switched our thinking from the classical mode and took a leap of imagination; we should treat the soil as if it were random – against all the tenets of the day!

and Cuanalo, 1975). Correlograms computed from the data showed strong spatial correlation extending to 200–250 m. This distance corresponded approximately to the average width of the outcrops and to the evident changes in soil. If we filtered out the variation due to the presence of the distinct underlying Jurassic strata we discovered that there was still spatial correlation in the residuals, though with a range of only about 80 m. Figure 1 shows an example, here with variograms rather than correlograms, for the clay content in the subsoil (±65 cm). The points on the graphs, the experimental semivariances, are computed by the usual method of moments at 10 m intervals, and the spherical functions are fitted by weighted least squares with the ‘fitnonlinear’ command in GenStat (Payne, 2013) with weights proportional to the numbers of paired comparisons – see also below. Table II summarizes the statistics and Table III lists the fitting parameters. From today’s viewpoint the situation seems obvious. We had two sources of variation, one from class to class, which we might treat as a fixed effect; and the other within classes, which we should treat as random. We needed a mixed model to describe it. I return to the matter below.

Nested sampling and analysis I spent the year 1973 working with B.E. Butler, doyen of Australian pedology in the CSIRO. Our first task was to

The Sandford transect I To test the feasibility of the approach we sampled the soil at 10 m intervals on a transect 3.2 km long across the Jurassic scarplands of north Oxfordshire, near Sandford St Martin, and measured several properties of the soil at each point (Webster

Table I

Components of variance and intra-class correlations for a soil classification in Oxfordshire (from Webster and Beckett, 1970) Soil property

Mean

Strength (cone index) Clay content, % Plastic limit, % pH Organic matter, % Available P, % Available K, %

138 37.2 38.8 7.1 9.8 0.031 0.013

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Variance components Between Within classes classes 1248 112.3 125.6 0.161 3.96 0.000113 6.0×10-6

510 90.2 111.4 0.326 9.48 0.00114 93.9×10-6

Figure 1—Variograms of the percentage of clay in the subsoil (65 cm) along a transect in north Oxfordshire. The upper sequence of points is of the raw data; the lower sequence is the variogram of the data after the means of the individual stratigraphic outcrops have been filtered out. The curve between the two is the model fitted by REML. The models fitted, shown by the lines, are spherical with nugget:

0.71 0.61 0.53 0.40 0.28 0.09 0.06

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and exponential with nugget:

The values of the parameters c0, c, r, and a are listed in Table III The Journal of The Southern African Institute of Mining and Metallurgy

Technological developments for spatial prediction of soil properties

Statistical summary of data on the clay content (percentage by mass) in the subsoil (±65 cm) of north Oxfordshire Raw data Mean Median Variance Class means Sharp’s Hill Beds (clay) Great Oolite (limestone) Lower Estuarine Beds (silt) Chipping Norton Limestone (limestone) Chipping Norton Limestone (sand) Upper Lias (clay) Pleistocene and Recent (silt and clay) Middle Lias (ironstone) Middle and Lower Lias (clay)

39.1 36.0 936.81 OLS estimates 61.3 33.4 12.9 9.5 15.1 69.9 55.8 41.4 68.6

REML estimates 75.4 42.3 15.1 10.8 25.2 50.3 51.0 43.4 67.5

discover the spatial scale(s) on which soil properties varied on the Southern Tablelands of Australia. We sampled to a balanced spatially nested design and estimated the components of variance from a hierarchical analysis of variance (ANOVA), quite unaware that the technique had been proposed 36 years earlier by Youden and Mehlich (1937) and published in their house journal, or that it had been used by geologists Olsen and Potter (1954) and Krumbein and Slack (1956) in the intervening years, and then forgotten. We summed the components to form rough variograms and discovered that different soil properties varied on disparate scales in that complex landscape (Webster and Butler, 1976). Figure 2 shows such variograms of two of the variables. Notice that the spacings in the design increase in logarithmic progression. At about the same time Miesch (1975) had the idea of applying the same techniques for geochemistry and ore evaluation. Along the bottom of the graph are the degrees of freedom with which the components are estimated. You will see that as one moves from right to left on the graph, i.e. as the scale becomes increasingly fine, the number of degrees of freedom increases twofold with each step after the first. If one wants more steps on the graph for a more refined picture, then maintaining balance by doubling the sampling soon becomes unaffordable. The increased precision at the shorter lag distances is also unnecessary. Margaret Oliver recognized the problem and sacrificed balance and analytical elegance for greater efficiency for studying the soil in the Wyre Forest of England. She and I designed a five-stage hierarchy but without doubling all branches of the hierarchy at the lowest stage, and we programmed Gower’s (1962) algorithm to estimate the components of variance (Oliver and Webster, 1987). Shortly afterwards Boag and I devised an extreme form of unbalanced hierarchy with equal degrees of freedom at all stages, apart from the first, in a study of the distribution of cereal cyst nematodes in soil, and again we estimated the components of variance by Gower’s method (Webster and Boag, 1992). We have since replaced Gower’s method, which though unbiased is not unique, by the more efficient residual maximum likelihood method (REML) of Patterson and Thompson (1971). A full account of the procedures and guide to computer code can be found in Webster et al. (2006). The Journal of The Southern African Institute of Mining and Metallurgy

That paper, however, is not the last word; since then Lark (2011) has sought to optimize hierarchical spatial sampling. If he assumed that the variances contributed at spacings incremented in a logarithmic progression were equal, then neither the fully balanced design nor the one that distributed the degrees of freedom equally was best; the optimal design was intermediate between the two. Webster and Lark (2013) summarize the search mechanism using simulated annealing to find the optimum and the results. The last three publications cited above should ensure that this efficient, economical way of obtaining a first rough estimate of the variogram in unknown territory will not be forgotten. It should be a part of any geostatistician’s toolkit.

Gilgai and spectral analysis A second topic in my Australian research was to investigate the repetitive spatial patterns of gilgais. Gilgais are typically shallow wet depressions a few metres across in otherwise flat plains, and their patterns seemed to be regular. The question was: is there some regularity? and if so what are its characteristics? As in north Oxfordshire, I sampled the soil at regular intervals on a transect on the Bland Plain of New South Wales (Webster, 1977). The transect was almost 1.5 km long and was sampled at 4 m intervals. Table IV summarizes the statistics. The correlograms of several properties appeared wavy, and I transformed them to their corresponding power spectra. I illustrate the outcome with results for just one variable, the electrical conductivity in the subsoil (30–40 cm) converted to logarithms to stabilize the variance and with the variogram instead of the correlogram (Figure 3). The function fitted to the experimental variogram comprises four components, namely nugget, spherical, linear, and periodic. Figure 4 shows the spectrum computed with a Parzen lag window of width 60 sampling intervals. Notice the strong peak at approximately 0.12 cycles corresponding to the wavelength of 34 m in the model fitted to the variogram.

Figure 2—Accumulated variances as proportions of the total variances forsoluble potassium in the soil and the water held at –10 kPa estimated by hi-erarchical analysis of variance from nested spatial sampling at Ginninderra, Australia. The numbers immediately above the abscissa are the degrees of freedom at the spacings (data from Webster and Butler, 1976) VOLUME 115

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Table II

Technological developments for spatial prediction of soil properties

Figure 3—Experimental variogram of log10 (electrical conductivity) in the subsoil (30–40 cm) of the soil on the Bland Plain of New South Wales showing points and the four-component model fitted to it

Table III

Estimated parameters of variogram of the clay content in the subsoil (≈65 cm) of north Oxfordshire Model type

Raw data OLS residuals REML residuals

Spherical c

r/m

120.6 108.4

580.3 296.4

207.0 79.2

77.5

Exponential c a/m

505.3

67.3

But how could we use this intelligence to interpolate? Perhaps the most significant event during my sojourn in Australia occurred a week or so before I was due to leave. A complete stranger breezed into my office without a by-yourleave and asked me bluntly, ‘What’s this kriging?’. I had never heard the term before, and rather than plead complete ignorance I played for time. Who was this intruder? and why did he ask? He was Daniel Sampey, a mining geologist. I let him talk, which he did for about 20 minutes. He told me of a certain Professor Krige and Georges Matheron, of the theory of regionalized variables and of its application in geostatistics. Then, clearly disappointed that I knew even less than he did, he left as abruptly as he had arrived. His parting shot was that as I was about to return to Britain I should visit Leeds University, where mining engineers knew a thing or two. In those 20 minutes I realized that my problem of spatial prediction of soil conditions at unvisited places had been solved, at least in principle, and in general terms I understood how. On my return to Britain I contacted Anthony Royle at Leeds. He amplified what Daniel Sampey had told me, and he generously gave me a copy of his lecture notes on the subject and some references to the literature, including Matheron’s (1965) seminal thesis. Back in Oxford I was joined by Trevor Burgess, a young mathematician. Together we turned Matheron’s equations into algorithms and the algorithms into computer code. Our first scientific papers appeared in 1980 (Burgess and Webster, 1980a, 1980b; Webster and Burgess, 1980). They were the first to describe for soil scientists the variogram as we know it today and the first to display maps of soil properties made by kriging. It is from there that geostatistics in soil science burgeoned to become a branch of science with its own identity, pedometrics, and its magazine Pedometron (http://pedometrics.org/?page id=33 for the latest issue).

Soil: an ideal medium for geostatistics

Figure 4—Power spectrum log10 (electrical conductivity) in the subsoil (30 – 40 cm) of the soil on the Bland Plain of New South Wales derived from the correlogram and smoothed with a Parzen lag window of width 60 sampling intervals. Note that frequency is the reciprocal of sampling interval

The patterns are two-dimensional, of course, and one would like to extend the above analysis in two dimensions. Despite the remark above, sampling the soil at sufficient places for such analysis was prohibitively expensive. An alternative was to analyse aerial photographic images of the Bland Plain, on which the gilgais appear typically as roughly circular dark patches on a paler background. Milne et al. (2010) took this approach.

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Soil is almost the ideal medium for practical geostatistics. It forms a continuous mantle over large parts of the Earth’s land surface. Access is easy over much of that, so that sampling at the working scale of the individual field, farm, or estate can be cheap. Some of soil’s most important properties, such as pH, concentrations of the major plant nutrients and trace elements, salinity, and pollutant heavy metals are also cheap to measure nowadays: pedometricians need not be short of data for these variables, and from large databases they can estimate spatial covariance functions or variograms accurately. The statistical Table IV

Summary statistics of electrical conductivity in the soil at 30–40 cm on the Bland Plain of New South Wales Electrical conductivity

Minimum Maximum Mean Median Variance Skewness

mS cm-1

log10(mS cm-1)

0.06 5.10 0.958 0.54 0.95948 1.64

–1:214 0.707 –0:2298 –0:2668 0.19205 0.10

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Technological developments for spatial prediction of soil properties

E[Z(x) – Z(x + h)] = 0 for all x

[1]

and E[{Z(x) – Z(x + h)}2 ] = 2γ(h) for all x ;

[2]

where Z(x) and Z(x + h) denote random variables at places x and x + h, and vector h is the separation, or lag, between them in the two dimensions of soil survey. Ordinary kriging follows. From the 1980s onwards it has become the workhorse of geostatistics in surveys, not only of soil itself but also in the related fields of agronomy, pest infestation, and pollution; there are hundreds of examples of its application described in the literature. It is proving to be valuable in modern precision agriculture in particular – see Oliver (2010) and the topics therein. Disjunctive kriging required a somewhat larger advance in technique. Matheron (1976) formulated it for selection in mining. Pedologists saw in it the means of estimating and mapping the probabilities of nutrient deficiencies over land grazed by cattle and sheep (Webster and Oliver, 1989; Webster and Rivoirard, 1991) and of contamination by potentially toxic metals (von Steiger et al., 1996). In both situations thresholds are specified to trigger action. In the first, the thresholds are minimum concentrations of available trace elements such as copper and cobalt. If the soil contains less than these thresholds then stock farmers are advised to supplement their animals’ feed or to add salts of the elements to their fertilizers. However, kriged estimates of concentrations are subject to error, and farmers do not want to risk deficiencies by taking those estimates at face value. They want in addition estimates of the probabilities, given the data, that patches of ground are deficient. In the second situation the thresholds are maxima, in excess of which authorities must clean up or restrict access. Again, estimated concentrations are more or less erroneous, and an authority will wish to have estimates of the probabilities of excess before spending taxpayers’ money on unnecessary remediation or risking poisoning people or grazing animals by doing nothing.

Nonlinear modelling of variograms Throughout the 1980s one of the most serious stumbling blocks in practical geostatistics was the lack of software in the public domain for fitting models to sample variograms. Many practitioners fitted models by eye, and they defended their practice with vigour. In some instances, estimated semivariances fell neatly on smooth curves that matched one or other of the standard valid variogram functions, and in those circumstances the practice was reasonable. But in many other situations, choosing and fitting functions to experimental The Journal of The Southern African Institute of Mining and Metallurgy

variograms were, and remain, problematic. Some experimental variograms are erratic, usually because they are derived from rather sparse data. In some the numbers of paired comparisons vary greatly so that it is hard to know how to weight the points on graphs. The variation can be strongly anisotropic, so that again one cannot see what models might fit. And there can be combinations of these. All of the popular functions, apart from the unbounded linear model, contain nonlinear distance parameters, and these cannot be estimated by ordinary least-squares regression. Some, such as the exponential and power functions, can be reparameterized so that they are linear. Others, such as the spherical and related functions, cannot; they must be estimated by numerical approximation, and doing that requires expertise in numerical analysis. Rothamsted had that expertise; Ross (1987) had written his program, MLP, for nonlinear estimation, and we soil scientists used it to advantage for fitting models to experimental variograms (McBratney and Webster, 1986). The algorithms were incorporated in GenStat, Rothamsted’s general statistical program, now in its 16th release (Payne, 2013), to provide the facilities for estimating and modelling spatial covariances completely under the control of the practitioner and with transparent monitoring of the processes. These facilities include the choice of steps, bins and maximum lags; the robust estimates of Cressie and Hawkins (1980), Dowd (1984), and Genton (1998) in addition to the usual method of moments; and variable weighting according to the expected values, as suggested by Cressie (1985). They include also the linear model of coregionalization for two or more random variables. The facilities are readily called into play from menus. Alternatively they can be built into programs in the GenStat language, so that one can proceed from raw data, via their screening, distributions, and transformation, variography, kriging and cross-validation, to final output as gridded predictions, back-transformed if necessary, and their variances ready for mapping. GenStat (http://www.vsni.co.uk/ software/genstat) is immensely powerful and is available in the public domain for a modest price. Statistical modelling is no longer novel, and it has largely replaced fitting models by eye. Unfortunately, the pendulum has swung too far towards automation. Too often, modelling is now a blinkered push-button exercise in a geographic information system applied with little understanding or control and no facilities for monitoring.

Where are we now? Mixed models incorporating trend and other knowledge Although the assumption of intrinsic stationarity has seemed, and continues to seem, easily satisfied there are two situations in which it is not satisfactory. The first is where there is geographic trend – ‘drift’ in the geostatistical jargon – and for which Matheron (1969) devised universal kriging. Universal kriging itself requires no more than an augmentation of the ordinary kriging system. Obtaining a valid estimate of the variogram of the random component of variation is more difficult, because what is required is the variogram of the residuals from the drift, and one cannot know what the residuals are until one has correctly identified the drift. Webster and Burgess (1980) recognized the situation, and to VOLUME 115

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distributions of these properties are in most instances ‘wellbehaved’ in that they are either close to normal (Gaussian) or to lognormal, so that in the latter situation a simple transformation to logarithms makes analysis straightforward and efficient. Furthermore, although the laws of physics must be obeyed as soil is formed, the numerous processes that operate and have operated in combination over many millennia to form the present-day soil have produced a complexity that is indistinguishable from random (Webster, 2000). So, we can often treat soil as the outcome of random processes without harming our professional reputations. It is a small step from there to assume that soil variables are intrinsically stationary. In the conventional notation

Technological developments for spatial prediction of soil properties map the soil’s electrical resistivity over an archaeological site that had been sampled on a dense grid they programmed the algorithms set out by Olea (1975) for the purpose. Trend is a kind of knowledge in addition to the sample data. There are other kinds of additional knowledge about soil. The variable of interest, the target variable, might be related to one or more other variables that the pedologist knows or can measure cheaply at the prediction points. Or, as at Sandford mentioned above, there might already be a classification of the region that could partition the variance. How should one take this knowledge into account? For some years pedologists answered with a pragmatic approach; they called it ‘regression kriging’. They regressed by ordinary least squares (OLS) the target variables on the predictors, which could be the geographic coordinates for trend, the ancillary variables they measured, or the classes of the regions being mapped. They estimated variograms of the OLS residuals, kriged the residuals, and then added back the estimates from the regression equations at the prediction sites – see, for example, Odeh et al. (1995). The predictions are unbiased, but the prediction variances are underestimated, partly because the variograms are biased (Cressie, 1993) and partly because there is no valid way of combining the errors from the kriging and the OLS regressions. The problem was to estimate simultaneously the regression or deterministic component with minimum variance and the random residuals from the regression without bias, and then to sum the predictions of the deterministic and random variation at unsampled sites with known minimized variance. The solution, pointed out by Stein (1999), was to use maximum likelihood methods and to obtain what he called the empirical best linear unbiased predictor (E-BLUP). The basic maximum likelihood technique can be biased; residual maximum likelihood (REML) is not, and soil scientists, especially my colleague Murray Lark and co-workers, are now at the forefront among geostatisticians in its application. The basic model underlying the E-BLUP is the linear mixed model: Z(x) = wβ + ε(x);

[3]

in which the vector w, with K+1 columns, contains the K+1 elements 1; x1; … ; xK of the regression function and β contains the coefficients. The quantity ε(x) is the random residual from the regression. It is assumed to be second-order stationary with mean zero and covariance function C(h), which, because of that assumption, has the equivalent variogram ϒ(h) = C(0) – C(h). Its parameters are typically a nugget variance, c0, a structural variance, c, and a distance parameter, a, which we may denote in short by θ ≡ (c0, c, a}. One finds values for the parameters in θ numerically by maximizing the log-likelihood of the residuals given the data: L [θ|z (Xd)]. Having found them, one then estimates the coefficients in β by generalized least squares, and with both sets of values known one can proceed to the kriging for prediction with its variances. Webster and Oliver (2007, pp. 200–202) provide the details. In 2006 Lark et al. (2006) called the technique ‘state of the art’, and though their solution by simulated annealing was still in the research phase, they showed the way forward. Now, with facilities in the public domain in SAS (http://support.sas.com/documen-tation), GenStat (Payne, 2013), and R (R Core Development Team, 2010; Pinheiro et al., 2013), for example, it can be applied as best practice.

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Lark et al. (2006) illustrated their solution by kriging the soil’s water content in a field with a strong trend. Later, Webster and Oliver (2007) incorporated both trend and an external drift variable – the apparent electrical conductivity of the soil – to predict and map the soil’s sand content in another field. The comparisons they made of the variograms obtained using REML with those of the raw data and the OLS residuals are instructive, and I summarize them below.

Drift at Yattendon Oliver and Carroll (2004) sampled a 23 ha field on the Chalk downland of southern England, and they measured, among other variables, the percentage of sand in the topsoil (0–15 cm) at 230 places. Their map in pixel form showed a strong regional trend. In addition they measured the apparent electrical conductivity, ECa, of the soil. In this field the ECa and the sand content are linearly related, and so one can treat the ECa as an external drift variable when predicting the proportion of sand. Webster and Oliver (2007) compared four treatments of the data: (a) Analysis of the raw data (b) Ordinary least-squares regression on the spatial coordinates (c) REML to incorporate spatial trend (d) REML to incorporate both spatial trend and ECa as external drift. Figure 5 shows the variograms from the four treatments, and Table V lists the values of parameters of the spherical models fitted to them. The variogram of the raw data, Figure 5(a), increases to an apparent sill at a lag distance of approximately 200 m, and then increases beyond with ever-increasing gradient. The latter increase is characteristic of long-range trend. If one fits a quadratic trend surface by OLS regression and then models the variogram of the residuals one obtains Figure 5(b). Evidently the residuals are much less variable than the raw data, but they are also autocorrelated, and so the OLS regression gives a faulty representation of the truth. Figure 5(c) shows the REML variogram of the residuals with the quadratic trend fitted as a deterministic (fixed) effect. Its sill at 176.4%2 is substantially larger than that of the OLS residuals in Figure 5(b). By taking into account the additional knowledge of the relation between sand content and ECa as external drift in the REML analysis one obtains the variogram shown in Figure 5(d), now with a sill of only 151.6%2. Webster and Oliver (2007) went on to map the sand content by punctual kriging from the data at the nodes of a fine grid of 5 m × 5 m using these variograms. The maps of the predicted values were similar, as might be expected because kriging is so robust. The kriging variances differed substantially. Those for ordinary kriging from the raw data were quite the largest; they had a mean of 63.2%2. The mean variance for the OLS residuals was 52.0%2, which we know to be an underestimate. That for the universal kriging, i.e. incorporating the spatial coordinates only, item (c) above, was 53.5%2, and the mean for kriging with the external ECa in addition, item (d), was 48.2(%)2. The example shows that the more pertinent information we have the more accurate are our predictions.

The Sandford transect II The analysis above of the Sandford transect also used what we know about the environment, in that instance, the stratigraphy The Journal of The Southern African Institute of Mining and Metallurgy

Technological developments for spatial prediction of soil properties with parameter values listed in Table III. I have plotted it on the same graph as the variograms of the raw data and OLS residuals to show the comparisons in Figure 2. Its sill (c0 + c = 582.4%2) is substantially larger than that (c0 + c = 404:8%2) of the OLS residuals. It has a different shape from the first two, and its effective range (3 x 67.3 = 201.9 m) is comparable to the range (207.0 m) of the variogram of the raw data and much larger than that of the OLS residuals (79.2 m).

Nonstationary variances

Figure 5—Variograms of the sand content of the topsoil at Yattendon: (a) of the raw data; (b) of the ordinary least-squares residuals from quadratic trend; (c) of the residuals from REML with quadratic trend; (d) of the residuals from REML with quadratic trend and ECa. The parameters are listed in Table V

Table V

Model parameters of spherical variogram models* fitted to sand content (percentage) at Yattendon, England Analysis

Raw data 26.1 OLS residuals from quadratic trend 10.4 REML with quadratic trend 16.6 1 REML with quadratic trend and ECa 21.7

c0 + c

r/m

208.7 104.3 59.8 129.9

234.8 114.7 176.4 151.6

252.4 101.5 175.8 208.7

* The symbols c0, c, and r are the conventions for nugget, sill of the correlated variance, and the range, respectively, for the model as given at the foot of Figure 1

A more intractable stumbling block in the spatial prediction of soil properties is that of nonstationary variances. The field pedologist knows well that some parts of a landscape are more variable than others: for example, the flood plain of a braided river does not vary in the same way or on the same spatial scale as the river’s higher catchment. At Sandford the soil seemed not to vary equally on all sedimentary outcrops, and Lark and Webster (1999) introduced wavelets into soil science to investigate the matter. A wavelet is a function that varies within a narrow window and is constant at zero outside. The window is moved step by step, i.e. translated, over the field of data, and at each position it is convolved with the data in the window to obtain its coefficient. The window can be also be dilated and again translated so that a new set of coefficients are obtained at a different scale. By plotting the results against spatial position one can see where and on what spatial scales the variance changes (e.g. Lark and Webster, 2001; Milne et al., 2011). Milne et al. (2010) also analysed aerial photographs of the gilgai patterns on the Bland Plain. Their results accorded well with, and augmented, those from the earlier spectral analysis above. What is not yet clear is how we should take into account nonstationary variance in prediction, unless we have abundant data and can segment fields of data into zones of stationary variance. Some progress has been made by Paciorek and Schervish (2006) using a new class of nonstationary covariance functions, and by Haskard et al. (2010), who combined the linear mixed model with spectral tempering. This should be a matter of further research in the years to come.

Epilogue

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of the rocks beneath the soil as represented by the geological map. We may criticize it for the same reasons as we now criticize the early regression kriging based on the simple OLS residuals: the sampling was at regular intervals, not random, and so the within-class variances would be biased and perhaps the mean values also. Murray Lark, whom I thank, has kindly re-analysed the data based on the model of Equation [3], but now with β containing the mean value of the stratigraphic class to which position x belongs and w taking the value 1 if x belongs to that class. So again we use REML to find both the class means and the variogram of the residuals. Table II lists the mean values as estimated both by ordinary least squares and by REML. The differences among the latter are highly significant statistically; the Wald statistic is 33.12, which, with 8 degrees of freedom, gives a probability of < 0:0001 for the null hypothesis. The best-fitting variogram of the residuals estimated by REML turns out to be exponential

We soil scientists are greatly indebted to Daniel Krige. Perhaps without his realizing, it was on his pioneering technology that we built and advanced in our own field; the technology and the ideas behind it released us from the constraining mind-set of earlier years and opened up a whole new field of endeavour – pedometrics. We should thank also Daniel Sampey; I shall not forget him. Who knows how much longer we should have groped at snail’s pace towards the solution of our problem of spatial prediction had he not burst into my office in Australia that day 40 years ago? Finally, we should recognize the recent achievements of Murray Lark. His penetrating study of drift in its various forms, its estimation as part of the linear mixed model, and his clear and convincing writing (e.g. Lark 2012) have set soil scientists on a new and sound course in regional prediction and mapping – see, for example, the papers by Philippot et al. (2009) and Villanneau et al. (2011). Furthermore, with packages for the analysis now in the public domain we have no excuse for inferior practice.

Technological developments for spatial prediction of soil properties References BURGESS, T.M. and WEBSTER, R. 1980a. Optimal interpolation and isarithmic mapping of soil properties. I. The semivariogram and punctual kriging. Journal of Soil Science, vol. 31. pp. 315–331.

OLEA, R.A. 1975. Optimum Mapping Techniques using Regionalized Variable Theory. Series on Spatial Analysis, no 2. Kansas Geological Survey, Lawrence, Kansas. OLIVER, M.A. (ed.) 2010. Geostatistical Applications for Precision Agriculture. Springer, Dordrecht.

BURGESS, T.M. and WEBSTER, R. 1980b. Optimal interpolation and isarithmic mapping of soil properties. II. Block kriging. Journal of Soil Science, vol. 31. pp. 333–341.

OLIVER, M.A. and CARROLL, Z.L. 2004. Description of Spatial Variation in Soil to Optimize Cereal Management. Project Report 330. Home-Grown Cereals Authority, London.

CRESSIE, N. 1985. Fitting variogram models by least squares. Journal of the International Association of Mathematical Geology, vol. 17. pp. 563–575.

OLIVER, M.A. and WEBSTER, R. 1987. The elucidation of soil pattern in the Wyre Forest of the West Midlands, England. II. Spatial distribution. Journal of Soil Science, vol. 38. pp. 293–307.

CRESSIE, N.A.C. 1993. Statistics for Spatial Data. Revised edition. John Wiley & Sons, New York. CRESSIE, N. and HAWKINS, D.M. 1980. Robust estimation of the variogram. Journal of the International Association of Mathematical Geology, vol. 12. pp. 115–125. DOWD, P.A. 1984.The variogram and kriging: robust and resistent estimators. Geostatistics for Natural Resources Characterization. Verly, G., David, M., Journel, A.G., and Marechal, A. (eds). D. Reidel, Dordrecht. pp. 91–106. GENTON, M.G. 1998. Highly robust variogram estimation. Mathematical Geology, vol. 30. pp. 213–221. GOWER, J.C. 1962. Variance component estimation for unbalanced hierarchical classification. Biometrics, vol. 18. pp. 168–182.

OLSEN, J.S. and POTTER, P.E. 1954. Variance components of cross-bedding direction in some basal Pennsylvanian sandstones of the Eastern Interior Basin: statistical methods. Journal of Geology, vol. 62. pp. 26–49. PACIOREK, C.J. and SCHERVISH, M.J. 2006. Spatial modelling using a new class of nonstationary covariance functions. Environmetrics, vol. 17. pp. 483–506. PATTERSON, H.D. and THOMPSON, R. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika, vol. 58. pp. 545–554. PAYNE, R.W. (ed.). 2013. The Guide to GenStat Release 16 – Part 2 Statistics. VSN International, Hemel Hempstead, UK. PHILIPPOT, L., BRU, D., SABY, N.P.A., CUHEL, J., ARROUAYS, D., SIMEK, M., and HALLIN, S. 2009. Spatial patterns of bacterial taxa in nature reflect ecological traits of deep branches of the 16S rRNA bacterial tree. Environmental Microbiology, vol. 11. pp. 3096–3104.

HASKARD, K.A., WELHAM, S.J., and LARK, R.M. 2010. A linear mixed model with spectral tempering of the variance parameters for nitrous oxide emission rates from soil across an agricultural landscape. Geoderma, vol. 159. pp. 358–370.

PINHEIRO, J., BATES, D., DEBROY, S., SARKAR, D., and THE R DEVELOPMENT CORE TEAM. 2013. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-110.

KANTEY, B.A. and WILLIAMS, A.A.B. 1962. The use of engineering soil maps for road projects. Transactions of the South African Institute of Civil Engineers, vol. 4. pp. 149–159.

R DEVELOPMENT CORE TEAM. 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Wien. ISBN 3-90005107-0. http://www.R-project.org/

KRUMBEIN, W.C. and SLACK, H.A. 1956. Statistical analysis of low-level radioactivity of Pennsylvanian back fissile shale in Illinois. Bulletin of the Geological Society of America, vol. 67. pp. 739–762.

ROSS, G.J.S. 1987. MLP User Manual. Numerical Algorithms Group, Oxford.

LARK, R.M. 2011. Spatially nested sampling schemes for spatial variance components: scope for their optimization. Computers and Geosciences, vol. 37. pp. 1633–1641. LARK, R.M. 2012. Towards soil geostatistics. Spatial Statistics, vol. 1. pp. 92–99. LARK, R.M., CULLIS, B.R., and WELHAM, S.J. 2006. On the prediction of soil properties in the presence of spatial trend: the empirical best linear unbiased predictor (E-BLUP) with REML. European Journal of Soil Science, vol. 57. pp. 787–799. LARK, R.M. and WEBSTER, R. 1999. Analysis and elucidation of soil variation using wavelets. European Journal of Soil Science, vol. 50. pp. 185–206. LARK, R.M. and WEBSTER, R. 2001. Changes in variance and correlation of soil properties with scale and location: analysis using an adapted maximal overlap discrete wavelet transform. European Journal of Soil Science, vol. 52. pp. 547–562. MATHERON, G. 1965. Les variables régionalisées et leur estimation. Masson, Paris. MATHERON, G. 1969. Le krigeage universel. Cahiers du Centre de Morphologie Mathématique, No 1. Ecole des Mines de Paris, Fontainebleau. MCBRATNEY, A.B. and WEBSTER, R. 1986. Choosing functions for semi-variograms of soil properties and fitting them to sampling estimates. Journal of Soil Science, vol. 37. pp. 617–639. MIESCH, A.T. 1975. Variograms and variance components in geochemistry and ore evaluation. Geological Society of America Memoir, vol. 142. pp. 192–207.

VILLANNEAU, E.J., SABY, N.P.A., MARCHANT, B.P., JOLIVET, C.C., BOULONNE. L., CARIA, G., BARRIUSO, E., BISPO, A., BRIAND, O. and ARROUAYS, D. 2011. Which persistent organic pollutants can we map in soil using a large spacing systematic soil monitoring design? A case study in Northern France. Science of the Total Environment, vol. 409. pp. 3719–3731. VON STEIGER, B., WEBSTER, R., SCHULIN, R., and LEHMANN, R. 1996. Mapping heavy metals in polluted soil by disjunctive kriging. Environmental Pollution, vol. 94. pp. 205–215. WEBSTER, R. 1977. Spectral analysis of gilgai soil. Australian Journal of Soil Research, vol. 15. pp. 191–204. WEBSTER, R. 2000. Is soil variation random? Geoderma, vol. 97. pp. 149–163. WEBSTER, R. and BECKETT, P.H.T. 1970. Terrain classification and evaluation using air photography: a review of recent work at Oxford. Photogrammetria, vol. 26. pp. 51–75. WEBSTER, R. and BOAG, B. 1992. Geostatistical analysis of cyst nematodes in soil. Journal of Soil Science, vol. 43. pp. 583–595. WEBSTER, R. and BURGESS, T.M. 1980. Optimal interpolation and isarithmic mapping of soil properties. III. Changing drift and universal kriging. Journal of Soil Science, vol. 31. pp. 505–524. WEBSTER, R. and BUTLER, B.E. 1976. Soil survey and classification studies at Ginninderra. Australian Journal of Soil Research, vol. 14. pp. 1–26. WEBSTER, R. and CUANALO DE LA C., H.E. 1975. Soil transect correlograms of north Oxfordshire and their interpretation. Journal of Soil Science, vol. 26. pp. 176–194. WEBSTER, R. and LARK, R.M. 2013. Field Sampling for Environmental Science and Management. Routledge, London.

MILNE, G. 1936. Normal erosion as a factor in soil profile development. Nature (London), vol. 138. pp. 548–549.

WEBSTER, R. and OLIVER, M.A. 2007. Geostatistics for Environmental Scientists. 2nd edn. John Wiley & Sons, Chichester.

MILNE, A.E., WEBSTER, R., and LARK, R.M. 2010. Spectral and wavelet analysis of gilgai patterns from air photography. Australian Journal of Soil Research, vol. 48. pp. 309–325.

WEBSTER, R. and RIVOIRARD, J. 1991. Copper and cobalt deficiency in soil: a study using disjunctive kriging. Cahiers de Géostatistique, Compte-rendu des Journées de Géostatistique. Volume 1. Ecole des Mines de Paris, Fontainebleau, pp. 205–223.

MILNE, A.E., HASKARD, K.A., WEBSTER, C.P., TRUAN, I.A., GOULDING, K.W.T., and LARK, R.M. 2011. Wavelet analysis of the correlations between soil properties and potential nitrous oxide emission at farm and landscape scales European Journal of Soil Science, vol. 62. pp. 467–478.

WEBSTER, R., WELHAM, S.J., POTTS, J.M., and OLIVER, M.A. 2006. Estimating the spatial scale of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood. Computers and Geosciences, vol. 32. pp. 1320–1333.

MORSE, R.K. and THORNBURN, T.H. 1961. Reliability of soil units. Proceedings of the 5th International Conference on Soil Mechanics and Foundation Engineering, vol. 1. Dunod, Paris. pp. 259–262.

YOUDEN, W.J. and MEHLICH, A. 1937. Selection of efficient methods for soil sampling. Contributions of the Boyce Thompson Institute for Plant Research, vol. 9. pp. 59–70. ◆

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The Journal of The Southern African Institute of Mining and Metallurgy

Copper Cobalt Africa

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For further information contact: Head of Conferencing Raymond van der Berg, SAIMM P O Box 61127, Marshalltown 2107 Tel: +27 (0) 11 834-1273/7 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za

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Join us for the inaugural Copper Cobalt Africa Conference in the heart of Africa. To be held at Victoria Falls, one of the Seven Natural Wonders of the World, this prestigious event will provide a unique forum for discussion, sharing of experience and knowledge, and networking for all those interested in the processing of copper and cobalt in an African context, in one of the worldʼs most spectacular settings. The African Copper Belt has experienced a huge resurgence of activity in recent years following many years of political and economic instability. Today, a significant proportion of capital spending, project development, operational expansions, and metal value production in the Southern African mining industry are occurring in this region. The geology and mineralogy of the ores are significantly different from those in other major copper-producing regions of the world, often having very high grades as well as the presence of cobalt. Both mining and metallurgy present some unique challenges, not only in the technical arena, but also with respect to logistics and supply chain, human capital, community engagement, and legislative issues. This conference provides a platform for discussion of these topics, spanning the value chain from exploration, projects, through mining and processing. For international participants, this conference offers an ideal opportunity to gain in-depth knowledge of and exposure to the Southern African base metals industry, and to better understand SPONSORS: the various facets of mining and processing in this part of the world that both excite and frustrate the industry. Premium A limited number of places are available for post-conference tours to Zambiaʼs most important commercial operations, including Kansanshi, the largest mine in Zambia, with 340 kt/y copper production and its soon-to-be-completed 300 kt/y smelter, and Chambishi Metals. Jointly hosted by the mining and metallurgy technical committees of the Southern African Institute of Mining and Metallurgy (SAIMM), this conference aims to: • Promote dialogue between the mining and metallurgical disciplines on common challenges facing the industry, • Encourage participation and build capacity amongst young and emerging professionals from the Copper Belt region, • Improve understanding of new and existing technologies, leading to safe and optimal resource utilisation. The organising committee looks forward to your participation.

INTERNATIONAL ACTIVITIES 2015

8–10 April 2015 — 5th Sulphur and Sulphuric Acid 2015 Conference Southern Sun Elangeni Maharani KwaZulu-Natal, 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 23–25 April 2015 — SANCOT Conference 2015 Mechanised Underground Excavation Elangeni Maharani, Durban 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 12–13 May 2015 — Mining, Environment and Society Conference: Beyond sustainability—Building resilience Mintek, Randburg, 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 10-11 June 2015 — Risks in Mining 2015 Conference 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 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 24–25 June 2015 — Mine to Market Conference 2015 Emperors Palace 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 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 13–14 July 2015 — School production of Clean Steel Emperors Palace, 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 15–17 July 2015 — Virtual Reality and spatial information applications in the mining industry Conference 2015 University of Pretoria Contact: Camielah Jardine The Journal of The Southern African Institute of Mining and Metallurgy

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 11–14 August 2015 — 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.z 19–20 August 2015 — The Danie Krige Geostatistical Conference Geostatistical geovalue —rewards and returns for spatial modelling 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 15–17 September 2015 — Formability, microstructure and texture in metal alloys Conference 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 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.z 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 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 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 FEBRUARY 2015

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11 – 12 March 2015 — Mining Business Optimisation Conference 2015 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

Company Affiliates The following organizations have been admitted to the Institute as Company Affiliates AECOM SA (Pty) Ltd

Engineering and Project Company Ltd

Namakwa Sands (Pty) Ltd

AEL Mining Services Limited

eThekwini Municipality

New Concept Mining (Pty) Limited

Air Liquide (PTY) Ltd

Evraz Highveld Steel and Vanadium Corp Ltd

Northam Platinum Ltd - Zondereinde

AMEC Mining and Metals AMIRA International Africa (Pty) Ltd

Exxaro Coal (Pty) Ltd

Outotec (RSA) (Proprietary) Limited

Exxaro Resources Limited

ANDRITZ Delkor(Pty) Ltd

PANalytical (Pty) Ltd

Fasken Martineau

Anglo Platinum Management Services (Pty) Ltd

Osborn Engineered Products SA (Pty) Ltd

Paterson and Cooke Consulting Engineers (Pty) Ltd

FLSmidth Minerals (Pty) Ltd

Anglo Operations Ltd

Fluor Daniel SA (Pty) Ltd

Anglogold Ashanti Ltd

Franki Africa (Pty) Ltd Johannesburg

Polysius A Division Of Thyssenkrupp Industrial Solutions (Pty) Ltd

Atlas Copco Holdings South Africa (Pty) Limited

Fraser Alexander Group

Precious Metals Refiners

Glencore

Rand Refinery Limited

Aurecon South Africa (Pty) Ltd

Goba (Pty) Ltd

Redpath Mining (South Africa) (Pty) Ltd

Aveng Moolmans (Pty) Ltd

Hall Core Drilling (Pty) Ltd

Rosond (Pty) Ltd

Axis House (Pty) Ltd

Hatch (Pty) Ltd

Royal Bafokeng Platinum

Bafokeng Rasimone Platinum Mine

Herrenknecht AG

Roymec Tecvhnologies (Pty) Ltd

Barloworld Equipment -Mining

HPE Hydro Power Equipment (Pty) Ltd

RSV Misym Engineering Services (Pty) Ltd

BASF Holdings SA (Pty) Ltd

Impala Platinum Limited

Rustenburg Platinum Mines Limited

Bateman Minerals and Metals (Pty) Ltd

IMS Engineering (Pty) Ltd

SAIEG

BCL Limited

JENNMAR South Africa

Salene Mining (Pty) Ltd

Becker Mining (Pty) Ltd

Joy Global Inc. (Africa)

BedRock Mining Support (Pty) Ltd

Leco Africa (Pty) Limited

Sandvik Mining and Construction Delmas (Pty) Ltd

Bell Equipment Company (Pty) Ltd

Longyear South Africa (Pty) Ltd

BHP Billiton Energy Coal SA Ltd

Lonmin Plc

Blue Cube Systems (Pty) Ltd

Ludowici Africa

Bluhm Burton Engineering (Pty) Ltd Blyvooruitzicht Gold Mining Company Ltd

Lull Storm Trading (PTY)Ltd T/A Wekaba Engineering

BSC Resources

Magnetech (Pty) Ltd

Sebilo Resources (Pty) Ltd

CAE Mining (Pty) Limited

Magotteaux(PTY) LTD

SENET

Caledonia Mining Corporation

MBE Minerals SA Pty Ltd

Senmin International (Pty) Ltd

CDM Group

MCC Contracts (Pty) Ltd

Shaft Sinkers (Pty) Limited

CGG Services SA

MDM Technical Africa (Pty) Ltd

Sibanye Gold (Pty) Ltd

Chamber of Mines

Metalock Industrial Services Africa (Pty)Ltd

Smec SA

Concor Mining

Metorex Limited

SMS Siemag South Africa (Pty) Ltd

Concor Technicrete

Metso Minerals (South Africa) (Pty) Ltd

SNC Lavalin (Pty) Ltd

Council for Geoscience Library

Minerals Operations Executive (Pty) Ltd

Sound Mining Solutions (Pty) Ltd

CSIR-Natural Resources and the Environment

MineRP Holding (Pty) Ltd

SRK Consulting SA (Pty) Ltd

Mintek

Time Mining and Processing (Pty) Ltd

Department of Water Affairs and Forestry

MIP Process Technologies

Tomra Sorting Solutions Mining (Pty) Ltd

Deutsche Securities (Pty) Ltd

Modular Mining Systems Africa (Pty) Ltd

TWP Projects (Pty) Ltd

Digby Wells and Associates

Runge Pincock Minarco Limited

Ukwazi Mining Solutions (Pty) Ltd

Downer EDI Mining

MSA Group (Pty) Ltd

Umgeni Water

DRA Mineral Projects (Pty) Ltd

Multotec (Pty) Ltd

VBKOM Consulting Engineers

Duraset

Murray and Roberts Cementation

Webber Wentzel

Elbroc Mining Products (Pty) Ltd

Nalco Africa (Pty) Ltd

Weir Minerals Africa

â&#x2013;˛

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FEBRUARY 2015

Sandvik Mining and Construction RSA(Pty) Ltd SANIRE Sasol Mining(Pty) Ltd Scanmin Africa (Pty) Ltd

The Journal of The Southern African Institute of Mining and Metallurgy

IP PONSORSH EXHIBITS/S ng to sponsor

Forthcoming SAIMM events...

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

2015

◆ CONFERENCE Mining Business Optimisation Conference 2015 11–12 March 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

◆

5th Sulphur and Sulphuric Acid 2015 Conference 8–10 April 2015, Southern Sun Elangeni Maharani KwaZulu-Natal SANCOT Conference 2015 Mechanised Underground Excavation 23–25 April 2015, Elangeni Maharani, Durban Mining, Environment and Society Conference 12–13 May 2015, Mintek, Randburg, Johannesburg Risks in Mining 2015 Conference 10–11 June 2015, Johannesburg Mine to Market Conference 2015 24–25 June 2015, Emperors Palace, Johannesburg Copper Cobalt Africa Incorporating The 8th Southern African Base Metals Conference 6–8 July 2015, Zambezi Sun Hotel, Victoria Falls, Livingstone, Zambia Production of Clean Steel 13–14 July 2015, Emperors Palace, Johannesburg Virtual Reality and spatial information applications in the mining industry Conference 2015 15–17 July 2015, University of Pretoria, Pretoria The Tenth International Heavy Minerals Conference 11–14 August 2015, Sun City, South Africa The Danie Krige Geostatistical Conference 2015 19–20 August 2015, Johannesburg Formability, microstructure and texture in metal alloys Conference 2015 15–17 September 2015 World Gold Conference 2015 28 September–2 October 2015, Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Muldersdrift International Symposium on slope stability in open pit mining and civil engineering 12–14– October 2015 In association with the

Website: http://www.saimm.co.za

Surface Blasting School 15–16 October 2015, Cape Town Convention Centre, Cape Town

Solid advantage RCS® technology maximizes flotation performance Proven at more than 600 installations worldwide, Metso’s RCS® flotation technology combines a circular tank design with our patented DV™ deep-vane impeller. The result is a powerful radial flow and slurry recirculation that maximizes flotation performance for all roughing, cleaning and scavenging duties. With improved performance, simplified maintenance and reduced operating costs, you can rely on Metso’s equipment and service. They’re both rock solid.

BEE Ownership Compliant metso.com – email: mmsa.info.za@metso.com