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ii FEBRUARY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

OFFICE BEARERS AND COUNCIL FOR THE2014/2015 SESSION

Honorary PresidentMike TekePresident, Chamber of Mines of South Africa

Honorary Vice-PresidentsNgoako RamatlhodiMinister of Mineral Resources, South AfricaRob DaviesMinister of Trade and Industry, South AfricaNaledi PandoMinister of Science and Technology, South Africa

PresidentJ.L. Porter

President ElectR.T. Jones

Vice-PresidentsC. MusingwiniS. Ndlovu

Immediate Past PresidentM. Dworzanowski

Honorary TreasurerC. Musingwini

Ordinary Members on Council

V.G. Duke T. PegramM.F. Handley S. RupprechtA.S. Macfarlane N. SearleM. Motuku A.G. SmithM. Mthenjane M.H. SolomonD.D. Munro D. TudorG. Njowa D.J. van Niekerk

Past Presidents Serving on CouncilN.A. Barcza J.C. Ngoma R.D. Beck S.J. Ramokgopa J.A. Cruise M.H. Rogers J.R. Dixon G.L. Smith F.M.G. Egerton J.N. van der Merwe G.V.R. Landman W.H. van NiekerkR.P. Mohring

Branch ChairmenDRC S. MalebaJohannesburg I. AshmoleNamibia N. NamatePretoria N. NaudeWestern Cape C. DorflingZambia H. ZimbaZimbabwe E. MatindeZululand C. Mienie

Corresponding Members of CouncilAustralia: I.J. Corrans, R.J. Dippenaar, A. Croll,

C. Workman-DaviesAustria: H. WagnerBotswana: S.D. WilliamsUnited Kingdom: J.J.L. Cilliers, N.A. BarczaUSA: J-M.M. Rendu, P.C. Pistorius

The Southern African Institute of Mining and Metallurgy

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 AdvisersVan Hulsteyns Attorneys

AuditorsMessrs R.H. Kitching

Secretaries

The Southern African Institute of Mining and MetallurgyFifth Floor, Chamber of Mines Building5 Hollard Street, Johannesburg 2001P.O. Box 61127, Marshalltown 2107Telephone (011) 834-1273/7Fax (011) 838-5923 or (011) 833-8156E-mail: [email protected]

ContentsJournal Commentby R. Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

SANCOT Conference Announcement

President’s Corner by J.L. Porter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Effect of inorganic chloride on spontaneous combustion of coalby Y.-B. Tang, Z.-H. Li, Y.I. Yang, D.-J. Ma, and H.-J. Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

A validation study of the King stratification modelby L.C. Woollacott, M. Bwalya, and L. Mabokela. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

The behaviour of free gold particles in a simulated flash flotation environmentby T.D.H. McGrath, J.J. Eksteen, and J. Heath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

The role of gravity flow in the design and planning of large sublevel stopesby R. Castro and M. Pineda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Continuous improvement management for mining companiesby M. Vanek, K. Spakovska′, M. Mikola′s, and L. Pomothy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

A decision analysis guideline for underground bulk air heat exchanger design specificationsby M. Hooman, R.C.W. Webber-Youngman, J.J.L. du Plessis, and W.M. Marx . . . . . . . . . . . . . 125

‘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

The grate-kiln induration machine – history, advantages, and drawbacks, and outline for the futureby 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 sectorby B. Genc, C. Musingwini, and T. Celik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Thermophysical properties of rocks from the Bushveld Complexby M.Q.W. Jones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

A new preparation scheme for a difficult-to-float coking coal by column flotation following grindingby Yinfei Liaoa, Yijun Caoa, Zhongbo Hub, and Xiuxiang Taoc . . . . . . . . . . . . . . . . . . . . . . . . 161

Technological developments for spatial prediction of soil properties, and Danie Krige’s influence on itby R. Webster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

International Advisory Board

R. Dimitrakopoulos, McGill University, CanadaD. Dreisinger, University of British Columbia, CanadaE. Esterhuizen, NIOSH Research Organization, USAH. Mitri, McGill University, CanadaM.J. Nicol, Murdoch University, AustraliaH. Potgieter, Manchester Metropolitan University, United KingdomE. Topal, Curtin University, Australia

The Journal of The Southern African Institute of Mining and Metallurgy FEBRUARY 2015


▲iii

Editorial BoardR.D. BeckJ. Beukes

P. den HoedM. Dworzanowski

M.F. HandleyR.T. Jones

W.C. JoughinJ.A. LuckmannC. MusingwiniR.E. Robinson

T.R. StaceyR.J. Stewart

Editorial ConsultantD. Tudor

Typeset and Published byThe Southern African Instituteof Mining and MetallurgyP.O. Box 61127Marshalltown 2107Telephone (011) 834-1273/7Fax (011) 838-5923E-mail: [email protected]

Printed by Camera Press, Johannesburg

AdvertisingRepresentativeBarbara SpenceAvenue AdvertisingTelephone (011) 463-7940E-mail: [email protected] SecretariatThe Southern AfricanInstitute of Mining andMetallurgyISSN 2225-6253 (print)ISSN 2411-9717 (online)

THE INSTITUTE, AS A BODY, ISNOT RESPONSIBLE FOR THESTATEMENTS AND OPINIONSADVANCED IN ANY OF ITSPUBLICATIONS.Copyright© 1978 by The Southern AfricanInstitute of Mining and Metallurgy. Allrights reserved. Multiple copying of thecontents of this publication or partsthereof without permission is in breach ofcopyright, but permission is hereby givenfor the copying of titles and abstracts ofpapers and names of authors. Permissionto copy illustrations and short extractsfrom the text of individual contributions isusually given upon written application tothe Institute, provided that the source (andwhere appropriate, the copyright) isacknowledged. Apart from any fair dealingfor the purposes of review or criticismunder The Copyright Act no. 98, 1978,Section 12, of the Republic of SouthAfrica, a single copy of an article may besupplied by a library for the purposes ofresearch or private study. No part of thispublication may be reproduced, stored ina retrieval system, or transmitted in anyform or by any means without the priorpermission of the publishers. Multiplecopying of the contents of the publicationwithout permission is always illegal.

U.S. Copyright Law applicable to users Inthe U.S.A.The appearance of the statement ofcopyright at the bottom of the first page ofan article appearing in this journalindicates that the copyright holderconsents to the making of copies of thearticle for personal or internal use. Thisconsent is given on condition that thecopier pays the stated fee for each copy ofa paper beyond that permitted by Section107 or 108 of the U.S. Copyright Law. Thefee is to be paid through the CopyrightClearance Center, Inc., Operations Center,P.O. Box 765, Schenectady, New York12301, U.S.A. This consent does notextend to other kinds of copying, such ascopying for general distribution, foradvertising or promotional purposes, forcreating new collective works, or forresale.

General Papers


ˆ

T he February edition of the Journal contains 12papers that have something for everyone. For theextractive metallurgists, there are papers on jigging

and the flotation of coal and gold. For the miningengineers, there are contributions on stope design, minerefrigeration, and decision-making strategies for theselection of heat exchangers and for mine planning. Forthe physical metallurgist, there are a number of paperson steelmaking. For the geoscientist, there is a review ofsoil science. Finally, for those who are prepared to comeout of the closet and admit that they ply their trade in themurky world of ‘management’, there is a paper onmethods for continuous improvement.

It is utterly inappropriate for me to attempt tosummarize each of the 12 papers and comment on theirrelevance and impact, and I will hide under the somewhatweak excuse that this column does not provide enoughspace for me to do so. Besides, after reading the titles inthe Contents, you can simply skip to the paper’s Abstractto decide whether to delve into the detail.

So, how do I fill this column with relevant comment?I suffered sleepless nights worrying about this JournalComment when it occurred to me that every paper, insome way or other, attempts to reduce uncertainty. Whatis research for, other than to shed light on a topic, makeit make more understandable, more known, morepredictable, and more useful? As that deep thinker andwell-known political scientist (whose name is on the tipof 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 knowthat we know. There are known unknowns. That is tosay, there are things that we know we don’t know. Butthere are also unknown unknowns. There are things wedon't know we don't know.’

Quite so – I bow to his superior intellect – I could nothave put it better myself.

It seems that every generation is convinced thatuncertainty in their lifetime is increasing, that the worldis becoming more, not less, complex. It is frequently saidthat the more we know, the less we know. BuckminsterFuller, who first reported on the existence of hollowspheres 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 theuniverse. For example, there are no solids in theuniverse. There’s not even a suggestion of a solid. Thereare no absolute continuums. There are no surfaces.There are no straight lines.’

To use that great South African expression ‘Ja-well-no-fine ’ – it’s all perfectly clear to me! Whereas I identifywith these sentiments on a philosophical and emotionallevel, they are at odds with the so-called rational side of

my brain that appreciates that the more we know thebetter the engineering outcomes. Technology isadvancing at an increasing pace, only because ofincredible advances in knowledge. From a purelytechnological standpoint, the more we know, the more weknow … if you still follow my drift! (I considered deletingthe last sentence for fear that I will be placed in the samebasket category as the Donald Duck of the infamous‘Unknown Unknowns’, but I am sure that you are stillfollowing my line of clear thought).

To return to the mining industry, if you ever wantedevidence of increasing uncertainty, make the time to readthe just released ‘Mining Financial Reporting Survey2014’ prepared by KPMG, which reports that majorglobal mining companies suffered impairment losses of$70 billion in 2013/14. Most of the companies surveyedadmitted that falling commodity prices had negativelyaffected the carrying value of their underlying assets.Some coal mines are temporarily mothballed, while manyother mines are cutting back on their output. Add to thatthe inherent instability of the global economy, climatechange, and political uncertainty. Who would want thejob of preparing the first draft of any mining company’sannual business plan? – whatever you write isguaranteed to be trashed by your colleagues!

In South Africa, we can throw into the unsavourybasket of uncertainties, currency fluctuations, powerdisruptions caused by load shedding, labour relations,and the Mineral and Petroleum Resources DevelopmentAct (MPRDA) which has just been returned to Parliamentfor redrafting.

Despite the large number of ‘known unknowns’ inthe mining industry, there is obviously still value to befound and made in mining, but caution, rather thanbravado, is currently king in the short term. In themedium to long term, I have no doubt that the saviourwill be found in improved technology, as it nearly alwaysis. In South Africa, the key concept is mechanization inmining, and I anticipate that we will soon see anincreasing number of papers being published on thistopic in the Journal.

The only comments that I can add in closing to thosefeeling the heat in the boardroom is that cowpersonsdon’t cry. Or as Lt. Col. Frank Slade said: ‘If you make amistake and get all tangled up, you just tango on’ (hand-written answers as to the origin of the above quotationmust be mailed to me on a plain postcard to qualify forthe prize – googling the answer is strictly prohibited).

Happy reading!

R. Paul

Journal Comment

▲

iv FEBRUARY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

PRESENTERS AND TOPICS INCLUDE:Mechanised excavation – mining and civil industryDr Karin Bap• p• ler, HerrenknechtSea Outfalls, utility tunnellingSwen Weiner, HerrenknechtUtility tunnelling, the Durban Aqueous tunnel beneath theharbour entranceFrank Stevens, (ex Deputy Head, Water and Sanitation, eThekwiniMunicipality), President of IMESAMechanised excavation – miningDanie Roos, HerrenknechtVertical excavation utilising the V Mole SystemAllan Widlake, Murray & Roberts CementationUse of the EPB TBM on GautrainAlain Truyts, GibbPoint Road Micro TunnelMontso Lebitsa, HatchCutting in StopingRod Pickering, Sandvik Mining and ConstructionCutting Technology – Past, Present and Future TrendsProf. Jim Porter, President SAIMM, (Jim Porter Mining Consulting)Mechanised excavation in the civils industry – Past, Presentand FutureRon Tluczek, SANCOT Chairman (Executive – Geotechnical, AfricaAECOM ZA)Mechanised Sprayed ConcreteChris Viljoen, Functional Head, Hydropower, Dams, Tunnels andGeotechnics SMEC

BACKGROUNDDue to the global increase in urbanisation,pressure is being placed on governments andthe public sector to provide expandedservices such as safe and reliable publictransport, electricity, gas, water and sewagefacilities. This results in further development ofroad, rail and metro infrastructure. However,the availability of space for this necessaryinfrastructure in the urban environment isbecoming a major challenge. In order to keep upwith this increasing demand, Civil designers andContractors are having to resort to tunnelling more thanever before and, in order to deliver these services timeously,mechanised underground excavation and support installation isproving to be cost effective.

The fast, efficient and safe abstraction of raw mineral reservesis of strategic importance for leading mining companies. However,rising labour costs, coupled with labour unrest, impact heavily onthe ability of companies to achieve these goals. The South Africanmining sector needs to mechanise at a faster pace in order toremain globally competitive. This is especially true whendeveloping stopes and vertical shafts. A typical deep level minehas a life of 30 to 40 years, meaning that shafts are not sunkregularly and the specialised expertise may not be readilyavailable.

OBJECTIVESThe conference is intended to promote interaction and closercommunication between personnel and companies in the mining andcivil industries, and to create a platform where expertise and experiencegained in mechanised underground excavation can be shared.

THEMEThis conference is in response to the Civiland Mining industry being under immensepressure to deliver projects fast, efficientlyand as safely as possible. Mechanisedunderground excavation and supportinstallation is proving to be an invaluable

and cost effective tool in the executionof a project. Technology exists formechanised excavation wheretunnels can be excavated from assmall as 300mm to in excess of 18metres in order to access orebodies, build road or railwaytunnels, facilitate the installation of

utilities, construct storage cavernsfor gas and oil, etc.

It is recommended that delegatesinterested in the mining application oftunnel boring attend both days.

For further information contact:Conference Co-ordinator, Yolanda RamokgadiSAIMM, P O Box 61127, Marshalltown 2107

Tel: +27 11 834-1273/7 · Fax: +27 11 833-8156 or +27 11 838-5923E-mail: [email protected] · Website: http://www.saimm.co.za

MechanisedUndergroundExcava�on

SANCOT CONFERENCE 2015

23–24 April, 2015 - Conference25 April 2015 - Technical Visit

Elangeni Maharani, Durban

WHO SHOULD ATTENDThe 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.

Conference Announcement

The South Afican National Committee onTunnelling in affliation with

are hosting the

The Journal of The Southern African Institute of Mining and Metallurgy FEBRUARY 2015 ▲v

On 30 January 2015, the Honourable Minister of Mineral Resources, AdvocateNgoako Ramatlhodi, issued a statement announcing the 2014 health andsafety 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 twentyyears, 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 minefatalities, 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 bymany 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, rangingfrom 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 nowhaving a material and lasting effect. It caused me to cast about in my recent reading for new ideas and innovationsthat 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 controlledthrough an IP address. One drawback is how to power these devices. Enter wireless charging with sound waves. Inthis process, conceived at the University of Pennsylvania, mechanical vibrations (sound) are turned in to electricalenergy. There are plenty of vibrations in mines and we need to monitor our physical environment more efficientlywith remotely powered devices. First products are due to ship in 2017.2. Application of new materialsIBM 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 automaticallycloses 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 oneday have a lightweight, robust exoskeleton that can be worn by underground workers, providing them with theirown environmental bubble and safety cocoon? These technologies are still in the laboratory.3. Safety visionForty per cent of 40-year-olds need glasses and 8% of all men suffer from colour deficiency. This has a direct impacton how people can work – especially in hazardous or difficult conditions. The University of California and MIT havedeveloped vision correcting displays that are in the pre-production phase and could potentially be fitted to all devicesthat use digital screens (e.g. on machine dashboards) and are ‘tuned’ to the user’s specific eyesight. Most sightimpairments could be compensated for with the technology – especially in developing countries where it issometimes easier to get a mobile phone than a pair of prescription glasses.4. Energy from low-grade heatAccording to the US Environmental Protection Agency, a third of all wasted energy is ’lost’ at temperatures below100°C. It is reported that MIT has developed new efficient battery electrodes that can convert temperaturedifferentials to electricity at temperature differentials of around 50° C. This is done by exploiting the thermogalvaniceffect (look it up!). Given the temperature gradients we have in our deep gold and platinum mines, are wepotentially 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 timewhere everyone seems too busy. I spoke earlier about changing leadership styles. Taking a table at the banquet is asolid 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].

J.L. PorterPresident, SAIMM

Presidentʼs

Corner

PAPERS IN THIS EDITIONThese 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 andTraining

These papers will be available on the SAIMM websitehttp://www.saimm.co.za

General PapersEffect of inorganic chloride on spontaneous combustion of coalby Y.-B. Tang, Z.-H. Li, Y.I. Yang, D.-J. Ma, and H.-J. Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87The 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 modelby L.C. Woollacott, M. Bwalya, and L. Mabokela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93This 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 environmentby T.D.H. McGrath, J.J. Eksteen, and J. Heath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103The 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 stopesby R. Castro and M. Pineda. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113This 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 companiesby M. Vanek, K. Spakovska′, M. Mikola′s, and L. Pomothy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119This 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 specificationsby M. Hooman, R.C.W. Webber-Youngman, J.J.L. du Plessis, and W.M. Marx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125A 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.

ˆ

PAPERS IN THIS EDITIONThese 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 andTraining

These papers will be available on the SAIMM websitehttp://www.saimm.co.za

‘Salem Box Test’ to predict the suitability of metallurgical coke for blast furnace ironmaking by K.B. Nagashanmugam, M.S. Pillai, and D. Ravichandar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131This 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 futureby J. Stjernberg, a, O. Isaksson and J.C. Ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137The 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 sectorby B. Genc, C. Musingwini, and T. Celik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145This 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 Complexby M.Q.W. Jones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153This 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 grindingby Yinfei Liaoa, Yijun Caoa, Zhongbo Hub, and Xiuxiang Taoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161The 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 itby R. Webster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165In 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.

IntroductionSpontaneous combustion of coal is a seriousproblem that often occurs in the coal industry(Jones and Townend, 1945; 1949. Althoughseveral theories have been proposed to accountfor the phenomenon (Wang, 2006; Li, 1996;Wang, 1999; Lopez, 1998), the definitivemechanism of coal spontaneous combustion isstill unknown. However, it is acknowledgedthat coal spontaneous combustion is a kind ofoxidizing reaction (Pis et al., 2996; Itay, Hill,and Glasser, 1989). Hence, the relevantparameters during coal oxidation at lowtemperatures can be used to indicate thetendency of coal to spontaneously combust(Jones et al., 1998; Wang, Dlugogorski, andKennedy, 2003).

Coal is a complex material consisting ofcombustible maceral and mineral components,and which contains dozens of minor elementsbesides various major elements including C, H, O, N etc. (Zhu, 2008). Furthermore, coalcontains major impurity elements such ascarbonates, sulphates, chlorides, and silicates,most of which are associated with the mineralcomponents (Tang and Zhao, 2008; Zhang,2009), and which are likely to influence thepropensity of coal to undergo spontaneouscombustion. For example, coal rich in pyrite ismore likely to undergo spontaneouscombustion, due to the presence of Fe2+ ions(Cole et al., 1987). Although the mechanism ofthis reaction has been studied extensively,there has been little work on the effect of otherelements on coal spontaneous combustion todate. Many scholars have investigated theeffect of adding different inorganic compoundsto coal as possible fire retardants in order tocontrol spontaneous combustion (Beamish andArisoy, 2008; Carras and Young, 1994). Anumber of investigations have been carried outon the effect of mineral matter on coalliquefaction, coal char combustion, and coalpyrolysis etc. (Ma, 2011; Hanzade, Reha, andAysegül, 1999; Li, Lu, and Jiao, 2009).However, there are few systematic studies ofthe influence of specific elements on coalspontaneous combustion.

Chlorine is a common trace element in coal,occurring mainly as an accompanying mineral(rock salt, potassium salt, bischofite, andhydrophilite etc.) Caswell, Holmes, and Spears,1984; Vassilev, Eskenazy, and Vassileve,2000). We therefore investigated the effects offive inorganic chlorides on coal spontaneous

Effect of inorganic chloride on spontaneouscombustion of coalby Y.-B. Tang*†, Z.-H. Li†, Y.I. Yang†, D.-J. Ma†, and H.-J. Ji†

SynopsisChlorine-containing minerals are commonly present in coal. Associatedminerals such as pyrite can undergo exothermic reactions. Consequently, itis of great significance to study the effect of inorganic chloride on thespontaneous combustion of coal. In this study, the effects of five inorganicchlorides (sodium chloride, magnesium chloride, potassium chloride,calcium chloride, and zinc chloride) on the spontaneous oxidation of coalwere investigated. Analysis of the gaseous products of coal oxidization atlow temperatures (323K to 453K) showed that the presence of inorganicchlorine in coal markedly decreases O2 consumption and the generation ofCO and CO2. Samples of raw coal and chlorine-loaded coal were oxidizedfor 36 hours under the same experimental conditions. Infrared diffusereflectance spectroscopy results showed that inorganic chloride can inhibitthe oxidative decomposition of some functional structure components(methyl, methylene, methine, and hydroxy) in the coal. The influence ofinorganic chloride on the oxidation characteristics of the functional groupsin coal during spontaneous combustion was investigated using benzylalcohol and 1-phenylpropanol as model compounds, which were testedunder the same experimental conditions as the coal samples. The oxygenconsumption of model compounds with and without the addition ofinorganic chloride further suggested that inorganic chloride may hinderthe oxygenolysis of these structures during low-temperature oxidation.This phenomenon can be attributed to the radical reaction from theperspective of radical chemistry. It can therefore be concluded thatinorganic chlorides play an inhibitory role in the spontaneous combustionof coal.

Keywordscoal, spontaneous combustion, inorganic chloride, gaseous products,model compounds, FTIR.

* College of Mining Technology, Taiyuan Universityof Technology, Taiyuan.

† School of Safety Engineering, China University ofMining and Technology, Xuzhou.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedNov. 2012; revised paper received May 2014.

87The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 FEBRUARY 2015 ▲

ISSN:2411-9717/2015/v115/n2/a1http://dx.doi.org/10.17159/2411-9717/2015/v115n2a1

Effect of inorganic chloride on spontaneous combustion of coal

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

ExperimentalThe test samples were collected from the no. 3 coal seam atXutuan, Huaibei City, Anhui Province, China. The sampleswere crushed to a grain size of between 0.180 mm andapproximately 0.250 mm for testing. The analysis (includingmacerals and minerals) of the no. 3 coal seam at Xutuan isshown in Table I. The additives, chemically pure (>99%)sodium chloride, potassium chloride, magnesium chloride,calcium chloride, and zinc chloride, were purchased fromSinopharm Chemical Reagent Co. Ltd. The samples wereprepared by dissolving 0.05 mol of each reagent in 7 ml ofdeionized water and adding the solution to 100 g of coalsample with constant stirring.

The samples were allowed to stand in sealed containersfor 24 hours in order for fully equilibrate the solution withthe coal. Before each experiment, the coal sample was driedat 40°C for 12 hours in a vacuum drying oven (ShanghaiSaiou Testing Equipment Co. Ltd) in order to exclude theinterference of moisture with spontaneous combustion.‘Control’ samples were prepared using the same procedure,but without the chloride addition. The samples used in the

experiments can be divided into two groups: coal withinorganic chloride added and coal treated with deionizedwater only.

The experimental set-up is shown in Figure 1. The coalsample (40 g) was placed into the sample tank, whichincluded two vent lines (inlet and outlet). The sample tankconsisted of a cylindrical container with the height of 105mm and diameter of 48 mm. The hole located in the centre ofthe sample tank was fitted with a temperature sensor, the topof which was in the geometric centre of the sample tank. Dryair at a flow rate of 20 ml/min was provided from acompressed gas cylinder. During the reaction, the sampletank was heated from 323K to 453K at a rate of 1K/min. Thegaseous products were analysed by gas chromatography(SP501N-type, Beijing East & West Analytical InstrumentsCo. Ltd.).

The influence of inorganic chloride on the oxidationcharacteristics of the functional groups in coal duringspontaneous combustion was investigated using modelcompounds. Each selected model compound must containonly one representative oxidative active group, which shouldbe a common structure of coal. According to the molecularstructure, coal contains aromatic structures and functionalgroups such as hydroxyls and alkanes. Therefore, benzylalcohol and 1-phenylpropanol were adopted as the modelcompounds in this experiment (Table II). Firstly, 0.02 mol ofmodel compound was mixed with 10 g acetone, 0.01 molinorganic chloride, and 40 g inert support (Figure 2). Theparameters of the inert support are shown in Table III. Thismixture was then dried for 12 hours in the vacuum dryingoven to ensure that the acetone completely evaporated andthat the model compound was uniformly attached to the inertsupport. After the abovementioned pretreatment, the modelcompounds were tested under the same experimentalconditions as the coal samples.

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88 FEBRUARY 2015 VOLUME 115 The Journal of The Southern African Institute of Mining and Metallurgy

Table I

Analysis of no. 3 coal seam, Xutuan

Moisture Ash Volatiles Calorific value Fixed carbon Sulphur Organic components Inorganic components

(%) (%) (%) (MJ/kg) (%) (%) Vitrinite (%) Inertinite (%) Liptinite (%) Carbonate (%) Oxide (%) Sulphide (%)

2.12 20.9 35.76 27.04 50.76 0.21 65.7 27.4 —— 4.1 2.6 0.2

Figure 1—Experimental set-up

In order to further elucidate the reaction principlesgoverning the effects of inorganic chloride on spontaneouscombustion of coal, the structural changes in the coalmolecule before and after low-temperature oxidation wereinvestigated using Fourier transform infrared spectroscopy(FTIR).

The coal samples were oxidized for 36 h under the above-mentioned experimental settings, then examined by infraredspectroscopy at a frequency in the range 400–4000 cm-1.Each sample was scanned 32 times.

Results and discussion

Analysis of gaseous productsAs shown in Figure 3, the proportions of the gases producedby low-temperature oxidation of coal were varied byadditions of inorganic chloride. During the entire process oflow-temperature oxidation, the oxygen concentration in airflowing through the control sample, treated with onlydeionized water, decreased from 20.46% to 5.79% between323K and 453K. For the samples loaded with inorganicchloride, the decrease was slower. Furthermore, the trends



Figure 2—The outward appearance of 6201# supporter

Table II

The model compounds of coal spontaneouscombustion

Name Active group Molecular formula

Benzyl alcohol —CH2—OH

1-phenylpropanol —CH(OH)—CH2—

Table III

Parameters of the inert support

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

6201# supporter/ Diatomite 0.180–0.250 0.4–0.55 4–5molecular sieve

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

(a) Oxygen

(b) Carbon monoxide

(c) Carbon dioxide

(d) Heating rate


for sodium chloride and potassium chloride were similar, withthe oxygen concentration falling to 15.03% and 16.08%respectively at a temperature of 453K. In the case of calciumchloride and zinc chloride, the oxygen concentration declinedslowly until 373K, and then dropped sharply to 10.67% and10.45%, respectively. Notably, the oxygen concentration withmagnesium chloride decreased only slowly up to 373K, andthen remained relatively stable at around 19.0% to 18.4%.

The CO concentration in the product gas from the low-temperature oxidation of coal increased from zero to 4056ppm between 323K and 453K. The increase was less fromcoal treated with inorganic chlorides. For sodium chloride andpotassium chloride additions, CO concentrations remained ata low level (below 200 ppm) until 383K and then rose rapidlyto 1903 ppm and 1151 ppm respectively at a temperature of453K. The upward trends for calcium chloride and zincchloride were quite similar to that for coal treated with onlydeionized water, although the CO concentrations were slightlylower, diverging only when the reaction temperature reached443K. The CO concentration for magnesium chlorideremained low at 153 ppm throughout the entire reaction.

The CO2 concentrations generally follow the same trendsas those for CO. The CO2 content of the product gas formagnesium chloride-treated coal was also lowest among thefive chloride-treated samples. The CO concentrations fromsodium chloride and potassium chloride are consistentlyhigher those from calcium chloride and zinc chloride samplesacross the entire temperature range. However, at temper-atures higher than 413K the CO2 concentrations from thecalcium chloride and zinc chloride samples exceed those fromsodium chloride and potassium chloride samples.

In summary, treatment with inorganic chloride decreasedthe oxygen consumption by the oxidation reaction at lowtemperatures, especially from 393K to 453K, and loweramounts of CO2 and CO were generated at the same reactiontemperatures. The results suggest that inorganic chloride caneffectively inhibit the low-temperature oxidation of coal.

The study shows that of the five chlorides tested,potassium chloride has a medium inhibitory effect on thelow-temperature oxidation of coal. The relevant data for

potassium chloride was therefore investigated using infraredspectroscopy and model compounds.

Infrared spectroscopy The chief characteristic of the infrared spectrum is that thefrequencies of vibration of the same types of chemical bondsare very similar and always appear within a certain range.Table IV and Figure 4 depict the attribution of the majorpeaks 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 asignificant change in the organic structure of XT coal, whichaffects the tendency to undergo spontaneous combustion.This change is most apparent at temperatures between 333Kand 453K.t At first, there is no apparent change of all theabsorption peaks in the infrared spectra induced by theheating and oxidization at 333K. However, it is clear thatafter low-temperature oxidation at 453K, the peakscorresponding to -OH,-CH2-, and -CH3 are weakened, whichsuggests that the activity of these structures (methyl,methylene, methine, and hydroxy) in coal molecule isdamaged to some extent. The peak value of thecorresponding region of C-H (aromatic ring) and C=C(aromatic ring) decreases slightly, which indicates that themain structure of the aromatic ring or fused ring has not beendestroyed during the low-temperature oxidation process.Under these conditions, apart from the absorption peak of thevibration of aromatic ring C=C and C-H bonds, all otherabsorption peaks decrease to varying degrees.

In comparison, after the addition of inorganic chloride,the functional groups on the surface of coal molecules changeslightly. In general, there is no great change in the intensityof most of the absorption peaks, including the stretchingvibrations of aromatic ring C=C and carbonyl O-H as thetemperature passes 333K; while the absorption peaks of -CH2- and C-O decrease slightly after the XT coal is heated at453K for 36 hours. This indicates that the inorganic chlorideinhibits the oxidation and decomposition of some functionalgroups in the coal during low-temperature oxidation.

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Figure 4—Infrared diffuse reflectance spectrogram (IDRS) of coal samples under different reaction conditions

Model compoundsSimilar to the coal molecular structrure (Shi, Deng, andWang, 2004), the aromatic ring in the model compounds isrelatively stable and the side chain is easily oxidized. It canbe seen from Figure 5 that potassium chloride can inhibit theoxidation of benzyl alcohol and 1-phenylpropanol. During theoxidation of benzyl alcohol, the oxygen concentration in theproduct gas decreased from 20.74% to 20.55% between 328Kand 368K. In particular, the concentration fell sharply to19.96% when the temperature rose to 428K. However, afterthe addition of potassium chloride, the oxygen concentrationdeclined more slowly, dropping to only 20.43% from 328K to428K. Similarly, potassium chloride suppressed the oxidationof 1-phenylpropanol. Between 328K and 428K, the oxygenconcentration 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 potassiumchloride. These results suggest that inorganic chloride caninhibit the oxidation of methyl, methylene, methine, andhydroxy groups in model compounds to varying degrees.

Radical reactionsSpontaneous combustion of coal produces CO, CO2, and otherproducts, which has been verified in underground andlaboratory test work. This phenomenon can be explained bythe chain-transfer of radicals. According to free radicaltheory, the initial stage of coal spontaneous combustion canbe attributed to the radical reactions (Li, 1996). The low-temperature oxidation of coal can generate numerous freeradicals, such as H*, OH*, and O*. The continuous cyclicgeneration of free radicals not only leads to spontaneouschain reactions, but also brings about heat accumulation inthe coal body, which will eventually result in spontaneouscombustion. However, with chlorine present in the coal, Cl*would be produced with increasing temperature. The reactionof Cl* and OH*, and of H* and O*, would inhibit spontaneouscombustion.

The reactions are as follows:

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

HCl* + H* → H2 + Cl*

The newly generated Cl* can further react withcombustible materials. The continuous reaction is capable ofremoving considerable amounts of OH*, H*, and O*. As aresult, chlorides can inhibit the spontaneous combustion ofcoal.


The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 115 FEBRUARY 2015 91 ▲

Table IV

Infrared spectrum absorption peaks of coal sample

Number Peak position,cm-1 Functional groups Attribution

A 3697–3625 -OH Free -OH bondB 3624–3610 -OH -OH (self-association hydrogen bonds)C 3550–3200 -OH Stretching vibration of –OH (phenols, alcohols, carboxylic acids, peroxides)D 3056–3032 -CH Stretching vibration of -CH in aromaticsE 2935–2915 -CH2- Antisymmetric stretching vibration of –CH2- in naphthenic or aliphaticF 2857–2851 -CH2- Symmetric stretching vibration of –CH2 in naphthenic or aliphaticG 1910–1900 Vibration of C-C, C-H in benzeneH 1605–1595 C=C Stretching vibration of C=C in aromatic ring or fused ringI 1460–1405 -CH3 Antisymmetric deformation vibration of -CH3

J 1384–1367 -CH3 Shear vibration of -CH3

K 1330–1110 C-O Stretching vibration of C-OL 1060–1020 R-O Stretching vibration of R-OM 900–850 Plane deformation vibration of CH in aromatics where H atoms are substituted.N 540–475 S-S or S-H Characteristic peaks of S-S or S-H

Figure 5—Oxygen concentration change in experiments with the modelcompounds


ConclusionThis study revealed that inorganic chloride can inhibit thespontaneous combustion of coal. The important indicators ofthe spontaneous combustion process, such as O2consumption and production of CO and CO2, decreased signif-icantly when inorganic chlorides were added to coal samples.Of the five reagents used in the investigation, magnesiumchloride has the best inhibiting effect on the low-temperatureoxidation of coal. We conclude that inorganic chloride caninhibit the oxidation of methyl, methylene, methine, andhydroxy groups in the low-temperature oxidation process ofmodel compounds of coal. This phenomenon can beexplained to some extent by the process of radical reaction.This may play an important role in coal spontaneouscombustion if the raw coal has a high chlorine content. Thereis plenty of scope for extending this work to investigate theeffect of other elements on coal spontaneous combustion.

AcknowledgementThis work was supported by the Project of China NationalNatural Science Foundation (No. 51074158) and theFundamental Research Funds for the Central Universities(No. 2012LWBZ10).

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IntroductionSeveral mineral beneficiation processes arebased on stratifying particles in a bedaccording to their density. Although thisapproach to separating minerals has a longhistory, it has proved quite difficult to developreliable models that can describe or predictdensity stratification in a satisfactory way.Mehrotra and Mishra (1997) provide anexcellent review of the different modellingapproaches that have been used.

One of the most promising models ofstratification currently available is that due toKing (King, 1987; Tavares and King, 1995;King, 2001). The model has been shown togive very good agreement with experimentaldata for systems of PVC cubes, simple coalsystems, and coal and marble mixtures (Vetter,1987; King, 1987), and reasonable agreementfor stratification of an iron ore (King, 1987). Ineach case the systems were binary in nature.Tavares and King (1995) extended the rangeof experimental validation by showing goodfits of the model with Vetter’s (1987) data forternary systems of PVC cubes, and of coal-marble mixtures. They also reported additionalvalidation work that involved multi-componentcoal systems and continuous jigging. Thatwork focused on testing a model of continuousjigging based on the King stratification modelcombined with models for the splitting

efficiency and for flow patterns in a jig.Although that work constituted a test of themodel of continuous jigging, as opposed to atest of the stratification model, it is significantthat good fits of the experimental data wereobtained – a result that provides furthersupport for the veracity of King’s stratificationmodel.

Since the model was first published in1987 and then extended in 1995 it hasreceived little attention in the literature. This israther surprising given, firstly, the consid-erable potential which the abovementionedvalidation work demonstrates, and secondly,that the model has some inherent restrictionswhich suggest that further development isworthwhile given its demonstrated potential.This paper makes a contribution in this regardby presenting additional validation work. Itbegins with a summary of the model andthereafter presents the findings from avalidation study based on stratification in abatch jig.

The King stratification model: asummaryThe King model envisages that density stratifi-cation of particles in a bed is the result of adynamic equilibrium between two verticallyopposing fluxes of particles – a stratificationflux and a diffusive flux. The stratification fluxis driven by the reduction in potential energythat occurs when particles of differentdensities stratify (Mayer, 1964). The diffusiveflux is driven by ‘random walk’ diffusionprocesses that are considered to be Fickian innature. When particle motion has reached astate of dynamic equilibrium, the two opposingfluxes are equal and the concentration profiles

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

SynopsisThis paper presents a study on the ability of the King stratification modelto describe density stratification patterns that are achieved under idealizedconditions. Tests were conducted in a batch jig using artificial particles inseven density classes. All particles in a density class had essentially thesame density, size, and shape. Tests were conducted for particle systemsinvolving from two to seven components. Good agreement was obtainedbetween measured and modelled data to an extent that gave strongendorsement of the mathematical appropriateness of the core equation inthe King model. Somewhat ambiguous results were found with regard toclaims about the independencies of the single experimentally-determinedparameter required by the model.

Keywordsmineral jigs, jigging, batch jigging, stratification, mineral beneficiation.

* 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 receivedJul. 2014; revised paper received Sep. 2014.



A validation study of the King stratification model

of particles of different density stabilize. King developedexpressions for the two fluxes in a bed of mono-sizedspheres. Equating expressions for the two fluxes leads to thedifferential 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 ofparticles having a density ρj in the very thin horizontal layerin the bed located at a relative height h to h+dh from thebottom of the bed, i.e. h=H/Hbed where H is the actual heightof the bottom of the thin layer and Hbed is the height of thebed. The variation of Cj(h) with h is termed the concentrationprofile.

In Equation [1], ρ–(h) is the average density of theparticles in the thin layer at h and can be calculated fromEquation [2]

[2]

The stratification parameter, α, is a composite parameterwhich, as Equation [3] indicates, takes into account thenature of the diffusive flux as described by the diffusioncoefficient, D, and the nature of the stratification flux asdescribed by the ‘specific penetration velocity’ u–. The otherterms in α are the gravitational acceleration, g, the volume ofeach particle, v, and the depth of the bed, Hbed. The stratifi-cation constant has the units of inverse density and, signifi-cantly, is not a function of particle density or of thecomposition of the feed to the jig.

[3]

To determine the concentration profile, Equation [1] mustbe integrated to give Equation [4]. (Note that in thisequation, k is used to represent the relative height instead ofh within the integral, i.e. ∫0

hρ–(k) dk)

[4]

Cj0

is the concentration of component j at the bottom ofthe bed (i.e. h = 0) and is related, through Equation [5], toCj

f, the concentration of component j in the feed to the jig.

[5]

Equation [4] can be solved analytically for binarysystems (King, 1987; 2001) but a numerical procedure isrequired for multicomponent systems involving more thantwo components (Tavares and King, 1995; King, 2001). Theysuggest an iterative procedure beginning with an estimate ofρ–(u), integrating numerically, and normalizing successiveestimates of Cj

0to satisfy the constraint that ∑all j Cj

0= 1.

Application to batch jigging

In batch jigging, a density separation is performed bysplitting the stratified bed horizontally at some height hsplit toform two layers – the upper layer consisting predominantly ofless dense particles and the lower layer consisting predomi-nantly of denser particles. However, for the purposes ofexperimentation and parameter estimation of parameter the

bed can be split into a number of slices, where any slice iconsists of the particles found between h=bi and h=ti. Here bi

and ti are respectively the relative height of the bottom andtop of slice i. The concentrations of the various componentsin each slice can be determined experimentally and the valueof the stratification parameter that leads to the best fit of theexperimental data can be established by an appropriateregression procedure. In such a procedure, the experimentaldata can be expressed in several forms: namely as Cj

i, theconcentration of component j in slice i; or as C

→j, the

cumulative concentration of component j up to h (i.e. theconcentration 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 toh=hsplit); or as R

→jior R

←ji, the recovery of the component j to

the lower or upper layers respectively. The correspondingmodel values relating to the upper and lower layers in the bedcan be calculated from the concentration profile, Cj, usingEquation [6] or [7] and appropriate values for bi and ti.

[6]

[7]

An investigation into the veracity of the King modelThe rationale behind the study reported in this paper is that,at very least, the model should be able to describe stratifi-cation behaviour under ideal conditions. Accordingly, testswere conducted in a batch jig in which reproducible andtightly controlled conditions could be maintained. A pilot-scale jig was kindly made available by Mintek inJohannesburg. Wall effects were minimized by using areasonably large, cylindrical jigging chamber. The diameter ofthe chamber was 300 mm, which was well over 17 times thediameter of the particles used in the study. In addition, theparticles selected for the study were ideal in nature in thatthey were all essentially the same size and the particles ineach density class all had essentially the same density, asindicated in Table I. The particles used were colour-coded‘density tracers’ which had been manufactured for conductinginvestigations on the performance of DMS separators in thecoal industry. Although the particles were not the idealspherical shape assumed by the King model, all particles hadthe same shape. As shown in Figure 2, this shape wasessentially that of a squat cylinder with a domed top surfaceand an indented bottom surface. The diameter of the particleswas 17 mm and the height between 7 and 8.5 mm. The smalldegree of variation in the density and the dimensions of thetracers is shown in Table I.

Experimental details As shown in Figure 1, the jig chamber was made up of ringsthat were 25 mm in height and had an internal diameter of300 mm. The rings were mounted on top of each other on thejig support screen to make up a cylindrical jigging chamberabout 0.3 m high. After the rings had been clamped together,the sample to be tested was poured into the chamber, which

▲


was then flooded with water and subjected to the jiggingcycle shown in Figure 2. The jig cycle was generatedpneumatically and was controlled by a PLC so that the jiggingconditions in every test were identical. In each test, a hutchwater flow of 500 ml/min was maintained. The duration ofeach test was 20 minutes, which had been shown to be wellin excess of the time required for the concentration profiles inthe bed to reach a state of equilibrium. After each test the bedheight was measured, the rings were unclamped, and sliceswere removed progressively from the top of the bed byinserting a slicing device between the rings. Except for thetop slice, each slice constituted a layer of the bed 25 mmthick.

Six tests were undertaken to investigate how well themodel fitted the experimental data for particle systemsinvolving from two to seven components. About 3000particles were used for each test which amounted to between7 and 11 kg per sample, depending on the density of theparticles. Preliminary tests had indicated that this wassufficient to produce a bed height of around 100 to 150 mm,which would allow the bed to be sliced horizontally into 4–6fractions. This had been shown to give a satisfactorydefinition of the concentration profiles within the constraintsof the experimental conditions, namely that the bed could besliced only in 25 mm increments, that the particles wererelatively large (i.e. around 8×17 mm), and that testsinvolving only the seven densities indicated in Table I couldbe undertaken.

The compositions of the particle systems tested areshown in Table II. In most cases the proportions of eachcomponent were approximately similar, but arbitraryadjustments were made to obtain round numbers. Thisapproach was violated for the two-component systembecause insufficient red tracers were available to make up a50:50 mixture of the required bed volume. The woodycoloured tracers were used only when the seven-componentsystem was investigated because they were slightly smallerthan the other tracers and because, being slightly porous,their effective density in a jigging environment wasuncertain; measured SGs varied between 1.28 whenmeasured dry and 1.35 after they had been soaked in water.The SG value used for this component in the analysis of theseven-component system was 1.3, the nominal value givenby the manufacturers of the tracers. The shape of the tracersis shown in Figure 3.

Results

Stratification patterns in the jig

The experimental data obtained, together with the best modelfits to that data, is shown in Figures 4 to 10. Plots ofcomponent recoveries as a function of split height are themost compact way to present the data, and these arepresented first (Figure 4). The plots show R

←ji, the component

recoveries to the top (lighter) fraction when the bed is split ata relative height h. The curves in the plots represent the



Table I

Specific gravities and dimensions of the density tracers

Tracer colour Orange Yellow Green Pink Blue Red Woody

Code O Y G P B R W

No. of tracers measured 6 6 5 5 4 5 5Specific gravity (SG)Nominal SG 2.1 1.9 1.7 1.6 1.5 1.4 1.3Measured SG 2.130 1.916 1.714 1.579 1.520 1.417 *Std deviation 0.0039 0.0332 0.0014 0.0013 0.0021 0.0107 *% std deviation 0.18 1.73 0.08 0.08 0.14 0.76 *

Tracer volumeAverage (ml) 1.854 1.809 1.693 1.687 1.605 1.807 1.416Std deviation (ml) 0.0291 0.0468 0.0296 0.0247 0.0196 0.0529 0.0196% std deviation 1.57 2.59 1.75 1.47 1.22 2.93 1.36

Tracer dimensions (mm)Diameter 17 17 17 17 17 17 16Average height 8.2 8.0 7.5 7.4 7.1 8.0 7.3

Figure 1—Details of construction of the jig chamber

Figure 2—The jig cycle used in the tests

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


recoveries calculated using the model while the pointsrepresent the experimental data. It should be noted that foreach particle system, only a single experimentally determinedparameter value (for α) was used to generate the recoverycurves for each component in that system. The actual valuesof the parameters for the different systems are presented anddiscussed in the following section. If the model provides agood description of the stratification patterns achieved, thenthe experimentally determined recoveries and the modelvalues should coincide. As can be seen from Figure 4, thelevel of agreement between the model and experimentalvalues is remarkably good.

Figure 5 to 10 compare experimentally determinedconcentrations in the jig bed with the data generated by themodel. In each figure, the right-hand column of plots showsthe variation of the cumulative concentrations, C

←j, of the

upper layer of the bed as a function of h. Each curve indicateswhat the model predicts the concentration of component jwould be in the top (lighter) fraction of the bed if it were tobe split at a relative height hsplit=h. The filled circles representexperimental points. These should fall on the relevant curve ifthe model is providing an accurate description of stratificationbehaviour. As can be seen, the alignment of the experimentaland modelled values is remarkably good. Only with twopoints in the four-component system is there somesubstantive misalignment, and even there the experimentaland modelled values differ by no more than 5%.

The left-hand columns of the plots in Figures 5 to 10compare the measured component concentrations in eachslice, Cj

i, shown as filled circles, with the model-predictedvalues shown as unfilled circles. To provide perspective when

comparing these values, the concentration profile, Cj, for eachcomponent is shown as a dotted curve. This curve refers tothe concentration of component j in the very thin layer at h,whereas the filled and unfilled points refer to the muchthicker slices removed experimentally from the particle bed,the vertical centres of these slices being located at h.Accordingly, at the relevant value of h, the Cj

i points for thethick slices should have a value somewhere near the value ofCj (for very thin layers), but these values should notnecessarily coincide, because they refer to layers of differentthicknesses.

As can be seen from the figures, there is a generally goodagreement between the experimentally measured andmodelled values and, in most cases, these values are close tothe curve. However, the agreement is not always as good asis the case with the cumulative concentrations (the right-

▲


Table II

Compositions of the six particle systems investigated

Volumetric composition (%)

Tracer SG 2.130 1.916 1.714 1.579 1.520 1.417 1.3Tracer colour* Orange Yellow Green Pink Blue Red Woody2 components (BR) 70 303 components (OYG) 38 25 374 components (OYBR) 25 25 25 255 components (OYGBR) 21 18 20 20 216 components (OYGPBR) 15 12 17 11 30 147 components (OYGBPBRW) 15 13 11 12 14 14 21

Figure 3—Shape of the density tracers

Figure 4—Component recoveries to the upper layer of the stratified bed

* 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

hand plots in the figures) and some significant differencesoccur; in eight cases, the difference is as large as 10% and intwo cases it is 16%.

It is to be expected that the coincidence of measured andmodelled data for the concentration profiles would not be asgood as with the cumulative concentration data because theformer is inherently more sensitive to experimental error.When slicing the particle bed, the slicer is forced through thebed and particles that lie in the path of the slicer are forcedupwards or downwards into the layer above or below theslicer. In this process, particles can be misplaced to the wronglayer. In addition, because the particles are relatively large,misplacement errors can also occur because the particle

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 thedata in the study, the error is doubled when determining theconcentrations of slices removed from the bed. This isbecause the slicing error occurs at both the top and bottom ofthe slice. When the data is cumulated from the top of the bed,the error is associated only with the bottom of the slice andthe effect of the error diminishes as the data is cumulateddownwards through the bed. The situation is similar whencumulating from the bottom of the bed.

Considering all results taken together, it can be concludedthat the model was able to give reasonable to gooddescriptions of the concentrations of slices taken from thebed, and remarkably good descriptions of the cumulativeconcentrations and recoveries associated with the upperfraction of the bed when it is split at any particular height h.With regard to the lower fractions split from the bed, asimilar positive conclusion is reached both by implication andby examining the relevant plots (not shown in this paper).The implication of these conclusions is that given the feedcomposition to a batch jig, the density of each component inthe feed, and a single experimentally determined parameter,α, the model is able to give reliable descriptions of the strati-fication that would be achieved in a batch jig underequilibrium conditions.



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

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

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


The value of the stratification parameterA significant feature of the King stratification model is itsclaim that the value of the stratification parameter, α, isindependent of feed composition and the densities of thecomponents in the feed. According to Equation [3], α is afunction 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 ofα is not expected to be the same for each test. However, theratio α/Hbed should be the same if the model claim is valid.To test this claim, Table III presents the values of α that givethe best fits for each set of experimental data, as well as theassociated values of Hbed and α/Hbed.

As can be seen from the table, the value of α increasessteadily as the particle systems become more complex; thehighest value is 2.5 times greater than the smallest value.When bed height is taken into account – by calculatingα/Hbed – it is evident that the trend persists to some extent;i.e. there is, with two exceptions, an increase in the value ofα/Hbed as the particle systems involve more components. Theimplication of this is that the model’s assumptions about theindependencies of the stratification parameter are notsupported by our data. However, this implication is notconclusive because it turns out that the model predictions arerelatively insensitive to the value of α over the range of

values measured in the test; therefore the trend noted may, tosome degree, be spurious. This insensitivity is demonstratedmost easily by comparing the plots of recoveries andcumulative concentrations derived using ‘best-fit’ values of αwith the plots derived assuming that α/Hbed was indeed

▲


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

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

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

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-setusing the average value of α/Hbed (1.051 L/kg.mm) and thebed height α/Hbed for each test. Figures 11 and 12 comparethe plots derived using ‘best-fit’ values of α with thosederived using these ‘averaged’ values.

In the recovery plots shown in Figure 11, it can be seenthat, with one exception, the plots obtained using the‘averaged’ values deviate hardly at all from those obtainedusing the ‘best-fit’ values. The exception is with the two-component system. Here the ‘averaged’ value of α is 54%

larger than the ‘best-fit’ value. There is also some deviationin the plots for the heavy and light components in the five-component system, but the deviation is marginal. In this casethe ‘averaged’ α is 23% larger than the ‘best-fit’ value.

The deviations are more marked when the cumulativeconcentration plots are considered (Figure 12) but again, itwas only with the two- and five-component data that anysubstantial deviation between the plots obtained using ‘best-fit’ and ‘averaged’ values of α was evident. The figure alsoshows plots for some of the components in the seven-component system. In these, the deviation is so small that the‘best-fit’ curves fit the experimental data only slightly betterthan the curves derived from the ‘averaged’ α. With thisdata-set the ‘averaged’ value of α is 15% smaller than thebest-fit value. For all the other systems, the differencesbetween the ‘best-fit’ and ‘averaged’ values of α are less than15% and no discernible deviation between the two sets ofplots is evident. Accordingly, these plots have not beenincluded in Figure 12.



Table III

Effect of the number of components on the stratification parameter

No. of components 2 3 4 5 6 7 Average values

Particle system BR OYG OYBR OYGBR OYGPBR OYGPBRW

Stratification parameter, α (L/kg) (‘best-fit’ values) 76.9 108.7 132.6 155.6 156.3 190.4Bed height, Hbed (mm) 112.5 116.5 119.5 114.4 159 153.4α/Hbed (L/kg.mm) 0.684 0.933 1.106 1.36 0.983 1.242 1.051‘Averaged’ α (L/kg)* 118.3 122.5 126.0 120.3 167.1 161.2% Difference in α (averaged-best)/best × 100% 54% 13% -5% -23% 7% -15%

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

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

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


What these results suggest is that the model predictionsare relatively insensitive to the value of α over the range ofvalues measured in the test. Only when the ‘averaged’ valuediffered from the ‘best-fit’ value by more than about 20% didthe model predictions using the two different values deviatediscernibly from one another. The implication of these resultsis that the trend noted earlier may be an artefact of experi-mental errors and the regression procedure, i.e. that in theregression procedure the response surfaces in the regionsaround the ‘best-fit’ values of α were very flat, so thatequally good fits of the experimental data could be obtainedfor α values that were within 20% of the ‘best-fit’ value.

ConclusionsIt is clear from the results of the validation study reported inthis paper that the mathematical form of the model equation –Equation [1] – is appropriate for describing stratification in abatch jig under the ideal conditions that prevailed in the tests.For each of the six particle systems tested, this modelachieved very satisfactory fits to the experimentallydetermined recoveries and grades of jig products that wouldbe produced if the bed height were to be split at anyparticular level in the bed. The concentration profiles in thebed are inherently more sensitive to experimental error thanthe recovery and grade data, and here there was greaterdivergence between the model fits and the experimental data.However, the fits obtained were reasonable to very good inall cases. Given that a wide range of particle systems weretested – i.e. systems involving from two to seven components– the overall quality of the model fits gives very strongsupport to the veracity of the King model, at very least withrespect to the mathematical form of its core equation. Themodel allows the calculation of the concentration profiles in astratified bed as well as the recoveries and grades obtainedwhen the bed is split. It does this requiring only the feedcomposition, the densities of each component in the bed, anda single experimentally determined stratification parameter,and assumes that particles are all the same shape and thatsize and size distribution effects can be ignored.Interestingly, the results of the study imply that the exactnature of the particle shape is not important as long as all theparticles have the same shape. This can be argued from thefact that the derivation of the model assumed all particleswere spheres, yet good fits were obtained for particle shapesthat were all essentially squat cylinders.

While the finding about the mathematical appropriatenessof the model equation appears to be quite conclusive, thefinding with regard to model claims about the dependenciesof the stratification parameter is less certain. On the onehand, some variation was found in the ratio of the stratifi-cation parameter to bed height (α/Hbed) which, according tothe model, should not happen if the particle volume andstratification dynamics are the same (which is the case in allthe tests conducted). On the other hand, it was found that themodel fits were relatively insensitive to the value of thestratification parameter over the range found in the tests, tothe extent that the observed variation in α/Hbed may havebeen a direct consequence of this insensitivity. However,given that the range of particle systems tested was consid-erably wider and more divergent than the range likely toprevail in practical situations, it may be that for practical

▲


Figure 12 – Comparison of component concentrations in upper andlower layers obtained using the ‘best-fit’ value of αor the ‘averaged’value

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

purposes the value of the stratification parameter may beassumed to be independent of the feed composition in anindustrially operating stratification device, as the King modelclaims. Such a conclusion is only tentative, however, andfurther research is required to verify it. This and relatedissues are currently being investigated and will be reported ina future publication.

AcknowledgementsThe generous support of Mintek in Johannesburg in makingtheir pilot jig and associated facilities available for this workis acknowledged with thanks.

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

rely on stratification. APCOM 87: Proceedings of the 12th InternationalConference on the Application of Computers and Mathematics in theMineral 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 jiggingprocess. 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 particlestratification in jigs. Proceedings of National Seminar on Processing ofFines. Institute of Minerals and Materials Technology, CSIR, Jamshedpur

TAVARES, L.M. and KING, R.P. 1995. A useful model for the calculation of theperformance 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. ◆



IntroductionThe behaviour of free gold in flash flotation iscurrently poorly understood (Dunne 2005),especially when in competition with a gravityrecovery unit in a closed-loop milling circuit,although an overlap has been identified inwhich both units can recover particles between212 μm and 38 μm. This research aims toidentify parameters that may determinewhether free gold particles will be recovered byeither unit in this competitive size range.Identifying the impact of variables such asmineralogy, reagents, mechanical factors, andphysical characteristics (such as size, shape,surface area, elemental composition, etc.) onfloatability will enable optimization ofcombined gravity and flash flotation circuits.This paper, the second in a series, is focusedon the comparison of free gold and pure goldpowder recoveries in laboratory flotation testsas a function of collector (potassium amylxanthate, or PAX) addition. The first paper(McGrath et al., 2013) established the methodused to study the behaviour and characterizethe ultimate content of free gold recoverable by

flash flotation. The knowledge gained fromthis research contributes to a betterunderstanding of the impact of particle size,milling effects, residence time, and collectoradditions upon the recovery of free gold in themilling circuit.

BackgroundSeveral plants use batch centrifugal concen-trators (BCCs) and flash flotation unitoperations in a closed-loop milling circuit as anoption for processing complex ores containingfree gold as well as gold locked in a sulphidematrix. BCC circuits are used to recover thelarger particles of free gold, roughly +106 μm,while a flash flotation circuit produces asulphide concentrate encompassing smallerfree gold particles (-106 μm) and goldcontained in sulphides. Based on plant surveysundertaken by the Curtin University GoldTechnology Group (2008), the two units willtend to compete for particles in the -212 +38 μm range, as shown in Figure 1.

Because knowledge of the behaviour offree gold recovery in a closed-loop millingcircuit with parallel flash flotation and gravityrecovery units is limited, an improvedunderstanding of the behaviour of gold in thissituation will provide greater confidence in theapplication of such processes to the processingof complex gold ores.

Gravity concentrationGravity-recoverable gold (GRG) is a specificterm that refers to free gold reporting to theconcentrate stream with a small mass yield ifseparations are performed using BCCs. GRG is

The behaviour of free gold particles in asimulated flash flotation environmentby T.D.H. McGrath*, J.J. Eksteen*, and J. Heath†

SynopsisA reliable laboratory method to characterize the response of free goldparticles to flash flotation conditions has been developed. The test hasbeen performed on free milling gold ores as well as synthetic ores, usingeither a gravity concentrate or gold powder as the gold source, to assessthe floatability of gold particles. Trends in free gold flotation kinetics, aswell as size and milling effects, were identified for gold recovery based onthe different feed types, reagent dosages, and residence times. It wasshown that the ultimate recoveries and kinetic trends of gold particlesfrom the gravity concentrate could be enhanced with increased dosage ofcollector, potassium amyl xanthate. Interestingly, in comparison togravity-recoverable gold, recovery from pure Au powders was better incollectorless flotation, and cumulative recovery decreased with higherlevels of collector addition. Improved coarse particle recovery appearedlinked to increased collector additions for both the gravity concentrate andthe pure gold powders. In general, coarse gold particles demonstratedslower kinetic rates thaen the fine or intermediate components incomparable tests.

Keywordsgold, flotation, flash flotation, natural hydrophobicity, kinetics.

* Department of Metallurgical Engineering andMining Engineering, Western Australian School ofMines, Curtin University, Australia.

† Outotec South East Asia Pacific, West Perth,Australia.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedMar. 2013; revised paper received Aug. 2014.



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

comprised of primarily of ‘free gold’, because the BCC unitsare not designed to recover bulk sulphide material. The ‘GRGtest’ (Laplante and Dunne, 2002) and model generatesresults that can be used to model the amenability of an ore togravity recovery by BCC in a milling circuit closed by ahydrocyclone. The GRG model developed by Laplante can alsobe used to estimate flash recovery (Laplante and Staunton,2005; Laplante and Dunne, 2002). This is because the GRGtest defines an ore characteristic, not BCC machine character-istics. Therefore it determines the overall gravity recoverypotential of an ore and the maximum amount of GRG per sizeclass that can be recovered. Both the GRG test andlaboratory/plant-based models are available for use.

Flash flotationThe SkimAir flash flotation cell was developed by Outokumpu(now Outotec) in the early 1980s to ‘flash off’ fast-floatingliberated minerals of high value (Coleman, 2010). It wasdesigned to be used ahead of conventional flotation in thecirculating load of a mill in order to reduce overgrinding ofsulphides (Bourke, 2002). Teague et al. (1999) have shownthat flotation of free gold is affected by physical constraintssuch as shape and size of particles, degree of water andgangue transport to the froth, stability of the froth, andextent of sulphide bubble loading, which provides a barrier tohydrophobic bubble attachment of free gold. It has beensuggested that fine gold particles are strongly hydrophobicand good candidates for flash flotation (Laplante and Dunne,2002). Unfortunately, the effect of an industrial flashflotation cell on the recovery of free gold in Australia hasbeen difficult to determine, as flash flotation has beenincorporated at the design stage and there is little plant dataavailable on free gold recovery before and after theintroduction of the unit (MacKinnon et al., 2003). To date,Laplante’s GRG test is the only method available toapproximate the expected recovery of free gold in a flashflotation cell.

The gravity/flash flotation relationship for GRGWhen both gravity concentration and flash flotation are

employed in a milling circuit, flash flotation can be used inparallel, series, or cleaning arrangements with BCC units, asseen in Figure 2. In a cleaning application, the flash flotationcell creates a sulphide concentrate which is then secondarilytreated by gravity recovery with removal of GRG from thebulk sulphide concentrate. In series, the BCC treats a portionof the flash tails, while in parallel, the flash and gravity unitsshare the same feed, usually the cyclone underflow, and thetails streams are returned to the milling circuit to close theloop. In the parallel arrangement, the nature of the particlesrecovered to each unit and the factors affecting recovery ofthe GRG are not completely understood, due to the interactionof many complex factors.

In general, when the GRG content is high and the sizefraction is coarse, free gold is easy to recover and concentratein gravity operations. BCC units operate ideally to concentratecoarse free gold particles larger than 106 μm. Some particlesbetween 106 and 38 μm are recovered, but recovery is likelyto depend on particle shape. BCC systems give poor recoveriesof gold particles smaller than 38 μm (Laplante and Staunton,2005). Despite the ideal particle size-gravity gold recoverycurve presented for BCCs in Figure 1, Wardell-Johnson et al.(2013) have shown that the size-by-size recovery of GRG isfar from an ideal monotonic function, with actual BCC devicesoften showing a U-shaped curve (rather than a monotonicallyincreasing S-shape) for intermediate particle sizes. Furtherunderstanding of the factors impacting the behaviour of GRGin this type of competitive, yet complementary, paralleloperation is the primary subject of this research.

Natural hydrophobicity of gold and the impact ofcollector (PAX)

Natural hydrophobicityTennyson (1980) demonstrated that pure metallic gold ishydrophilic. However, fine free gold will float better thangangue material without the addition of collector, andO’Connor and Dunne (1994) have also shown thatuntarnished gold of the appropriate size can be readily floatedwith only a frother in a process referred to as ‘collectorlessflotation’. This hydrophobic behaviour often displayed by

▲


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

gold particles is due to a high Hamaker constant (Drzymala,1994), indicative of Van der Waal’s interactions resulting in astrong dispersive attraction for water (Dunne, 2005). Thefloatability of gold is known to be enhanced by surfacecoatings of some organic compounds and silver content(either as rimming or in the form of electrum), while calciumions and some forms of sulphur can act as depressants. It hasbeen suggested that when native gold surfaces arehydrophobic due to contamination by organics in nature, finegold particles may be harder to recover by flotation (Aksoyand Yarar, 1989).

CollectorXanthates, a group of anionic collectors based on bivalentsulphur, are used in flotation plants (together with othercollectors) to enhance gold recoveries (O'Connor and Dunne,1994; Wills, 2006). PAX was the collector chosen for thisinvestigation because it is readily available and often used inlaboratory flotation test work, so comparisons may be madebetween these results and data produced in other studies.Furthermore, free gold floats well in the presence of xanthatecollectors, but not if the particle size is too large, or if calciumsalts or minerals, or Na2S are present (Teague et al., 1999).Not only is gold hydrophobicity enhanced by the addition ofcollectors such as xanthates, dithiophosphates, and dithio-phosphinates, but untarnished gold requires even lessaddition of these collectors than tarnished gold to becomesuitably hydrophobic (Chryssoulis and Dimov, 2004).Secondary collectors, or promoters, can further increaserecovery, with dithiophosphates being the most widely usedpromoters in gold flotation (O’Connor and Dunne, 1994). Aswith tarnished particles, higher collector additions may berequired to float coarser particles, as demonstrated in thisstudy. However, studies with quartz have shown that extracollector added to float coarse or tarnished material mayinstead be consumed by fine particles with large surfaceareas (Vieira and Peres, 2007).

Physical parameters

ComminutionAs mentioned by Dunne (2005), the impact of milling on thefloatability 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 tobe recovered by flotation because of the impregnation of

gangue material. Pevzner et al. (1966) also proposed thatpassivation of the gold surface during milling would lead toreduced flotation recoveries. Conversely, Allan and Woodcock(2001) hypothesized that work-hardening of gold particlesduring milling could activate the surfaces and improve floata-bility. Work-hardening will strengthen the surface of a metalby plastic deformation and can change the surface finish,thus potentially affecting the adsorption of collector. Silvercontent is expected to promote the flotation of GRG particles as compared to pure Au powders, and this must also beconsidered as the GRG concentrate particles contain between5–20% silver (roughly 10% on average). This is becausesilver floats preferentially to gold in the presence ofxanthates.

Particle sizeParticle size has a strong influence on flash flotation and BCCrecovery for several reasons. Firstly, liberation is directlyrelated to particle size and flotation will proceed only when aparticle is sufficiently liberated (Zheng et al., 2010).Chalcopyrite flotation studies have shown that a coarseliberated particle floats similarly to an intermediate partiallyliberated particle, and a coarse particle will float slower thanan intermediate particle of similar composition (Newcombe etal., 2012; Sutherland, 1989). Secondly, reagent additions andpH control have a greater influence on coarse particleflotation than other sizes (Trahar, 1981). Recovery ismaximized in the 100–10 μm size fraction and drops offsignificantly above and below that range, with few particlesgreater than 300 μm able to be floated (Trahar, 1981). Theliterature suggests that free gold of +200 μm cannot befloated effectively (Malhotra and Harris, 1999). In mostflotation plants, sulphide particles larger than 150 μm areconsidered too large for conventional flotation, although it isa widely held misconception in industry that flash flotationcan 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, thesize 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 concentratorwhen gravity and flash flotation are operated in a closed-loopmilling circuit, while -38 μm fractions of free gold particlesare usually captured in the flash flotation concentrate of the



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


same circuit. Particles in the size fraction -212 +38 μm are ofspecial interest, as this is the zone of competition between thegravity and flash units, and further understanding of theresponse of free gold particles in this intermediate range tovarying flotation conditions can be applied to optimizerecovery in industrial applications. For ease of discussion, the+212, -212 +38 (or just +38), and -38 μm size fractions inthe test work are referred to as coarse, intermediate, and fine,respectively throughout the remainder of this paper. Particleshape, surface area, organic and mineral coatings, andelemental rimming are also important in determining thefloatability of free gold and will be addressed in a subsequentpaper by the authors.

Experimental methodThe aim of this study is to compare trends in flotationkinetics for particles of varying size and nature as affected bythe addition or absence of a collector. Gold from two sourceswas floated according to the free gold flash flotation testdeveloped by McGrath et al. (2013), with PAX addition andgold type being the only variables. As noted in the results,the superficial gas velocity (volumetric gas flow per unitcross-sectional area of vessel) remained constant at 0.0074m/s for all tests.

Two synthetic ores were created for the laboratoryflotation 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 subsamplesyielding head grades of about 13–16 g/t when added to silica(P100 of 600 μm, with a size distribution similar to flash feedmaterial). A bulk assay of the BCC concentrate establishedthat the concentrate had a gold–to-silver ratio of 9:1,although scanning electron microscopy (SEM) demonstratedthat individual gold particles vary greatly in the ratio andpattern of Ag placement in the particle. Images of particlestypically found in the GRG concentrate are shown in Figure 3.The second synthetic ore was created using the same silicablend as the first, but used synthesized pure gold powders(P100 250 μm, supplied by Sigma Aldrich) to obtain a headgrade of 30–40 g/t. Images of typical gold powder particlescan be found in Figure 4.

The laboratory tests were repeated on 1 kg charges sixtimes for each of the six conditions in order to produceenough combined concentrate mass to be screened into thethree size fractions of interest. The replicate tests ensuredthat average masses and concentrations reported were statis-tically representative. Previous comparable test workdemonstrates that strict adherence to the methods detailed inthe standard operating procedure (SOP) yields averagestandard deviations of ±0.45% for mass pull and ±4.63% forgold recovery (McGrath et al., 2013). Each set of sixconditions 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 theentire data-set. Concentrate samples were fire-assayed toextinction while splits of the tails samples were subject toboth fire assay and intensive cyanide leach by rolling bottlein order to better close the mass balances. Because the massof gold in the test was either known, in the case of thepowders, or calculated in the case of the concentrate,inconsistencies in the gold and mass balances are attributed

to the nugget effect in the coarse, and to some extent, theintermediate tails samples. The nugget effect compromisesthe ability to achieve representative grade or concentrationresults due to non-uniform distribution of gold in the assayedsample as compared to the bulk material. The impact of thenugget effect is most noticeable in precious metal assays ofcoarse size fractions and small sample size, yieldingerroneously high or low values. Previous work on this GRGconcentrate has shown that the contained gold is easilyleached, so cyanidation was conducted on larger splits ofsized tail samples for more accurate assay values.

Brezani and Zelenak (2010) describe flotation as aprocess that is affected by many properties, not just physico-chemical and surface properties but many other chemical andmechanical factors. It is because of this complexity thatflotation is often described as a simplified first-order kineticphenomenon (Kelly and Spottiswood, 1989). Kinetic rateshave been calculated for each size fraction in all data-sets ofthis study using the Kelsall approach, as presented inEquation [1]. Although this method was developed manyyears ago (Kelsall, 1961), its continued relavance has beenevaluated by various authors (Kelly and Spottiswood, 1989;Kelebek and Nanthakumar, 2007; Brezani and Zelenak,2010) throughout the years and it was recently applied toevaluate 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 GRGconcentrate (shown with 100 μm scale bars) exhibiting platelet andcrescent shapes, and demonstrating elongation and rolling ascommonly 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

where:Co = the original concentration in the pulpC = the concentration in the pulp at time tt = 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 ofwhich equals unity.

Results and discussionThe recoveries of particles in individual size fractions areimportant because they demonstrate which proportion of thematerial is recovered under specific conditions; thisinformation is termed ‘fractional recovery’ in this paper. Forexample, 14% of coarse gold from the GRG concentrate wasrecovered without PAX addition, as shown in Figure 5,suggesting that 86% of the coarse GRG concentrate goldreported to tails in this test. Cumulative recovery refers thetotal or overall recovery, which is the sum of the recoveriesfor all size fractions; the cumulative recoveries are given atthe end of this section. For reference, industrial flashflotation processes usually operate with around 10–25 g/t ofPAX addition, with about two or three minutes’ residencetime. The first two minutes of laboratory testing (which havebeen represented by the initial four data points in Figures5–12 and which includes all data presented in Figure 14),roughly represent flash flotation, and have been denoted asthe ‘flash period’.

The floatability of coarse, +212 μm, gold is of interest inthis study because this is the size fraction generallyconsidered to be too large for flotation, with preference givento gravity recovery in this size fraction. Figure 5demonstrates that:

➤ Coarse gold recovery and flotation kinetics wereimproved with the addition of PAX, although flashperiod recoveries are less than the ultimate recoveries.In this data-set the GRG recoveries are superiorcompared to the Au powders in tests involving PAX

➤ Using the free gold flash flotation test, 97% of thecoarse free gold contained in the GRG concentrate wasrecovered with either 25 or 50 g/t PAX addition. While

the coarse Au powder particles were 90% recoveredwith 50 g/t PAX, only 48% recovery was achievedwhen the same particles were floated with 25 g/t PAX.This can be attributed PAX being available in excess ofthe concentration required to create the necessarymonolayer on the surface of gold. The surplus PAXforms additional layers and the non-polar ends areeither concealed or oriented away from the water,which effectively reduces hydrophobicity

➤ Collectorless flotation recovered less coarse free goldthan tests with PAX, resulting in recovery of only 20%of Au powder and 13% of free gold from the GRGconcentrate.

The intermediate, -212 +38 μm, size range is of particularinterest because this is the suggested area of competitionbetween BCCs and flash flotation in parallel operation. Areview of the data in Figure 6 suggests that:

➤ A moderate PAX addition improved intermediateparticle recovery, although recoveries of both the GRGand the Au powders were slightly better with the lowerlevel of PAX addition

➤ A much lower ultimate recovery was achieved forintermediate GRG material as compared to the coarseparticle GRG at both levels of PAX addition. As withthe coarse particle data, the intermediate Au powderparticle recovery was lower than comparable GRGrecoveries

➤ Collectorless flotation recovered the least amount ofintermediate particles from both the Au powder andGRG 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 therecoveries of Au powder particles were better than from theGRG concentrate. This is also the size range for which flashflotation is recommended for free gold recovery within themilling circuit.

➤ The fine Au powder demonstrated the highest ultimaterecovery with 25 g/t PAX addition. Recovery decreasedwhen PAX was increased to 50 g/t. Recoveries withcollectorless flotation of Au powders were the same aswith 50 g/t PAX addition to the GRG concentrate, with



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)


respective recoveries of 59% and 57% not being statis-tically different

➤ Again, the fine GRG particles recovered in collectorlessflotation showed the poorest recovery, just as with thecoarse and intermediate size fractions.

Figures 8–10 allow comparison of recovery trendsgrouped by level of PAX addition. The recoveries of gold with25 g/t PAX addition, given in Figure 9, are of particularinterest because this level of collector is most similar to thatin industrial flash flotation. In particular, opposing trends ofimproved recovery for some particle types with 25 g/t PAXaddition are noted. For example, GRG yielded betterrecoveries with increasing particle size, while decreasing Aupowder particle size improved recovery. Here, the highestultimate recoveries are from the coarse fraction of GRGconcentrate and the fine Au powders.

Collectorless flotation results, presented in Figure 10, areimportant because they reveal the differences in the inherentfloatability of the two types of particle under the givenconditions. Without collector, recoveries from the fine Aupowders were better than from all other particle size and typecombinations, with reduced recoveries for the intermediateand coarse fractions. A similar trend is noticed with the GRGconcentrate, where fine particle recoveries are better than the

intermediate (which shows no appreciable recovery) or coarseparticles.

The effect of varying PAX additions for the two types ofgold particles is given in Figures 11 and 12. This informationis of interest as it gives baseline theoretical recoveries forunmilled, pure gold particles displaying various shapes in thesize ranges of interest and can be used to demonstrate theinfluence of milling, silver content, and surface effects on theability of PAX to float the free gold. In this test work:

➤ Fine Au powder particle recoveries were mostly betterthan the larger sizes, regardless of PAX addition,especially in the case of collectorless flotation. Fine andintermediate particle recovery decreased with 50 g/tPAX addition, which is probably a result of overdosingof the collector and, as a consequence, reducedhydrophobicity

➤ Despite slow kinetics, the coarse free gold from theGRG concentrate reached the highest recoveries at PAXadditions of 50 g/t and 25 g/t .

➤ The fine and intermediate GRG particles showedintermediate recoveries at both the 50 g/t and the 25 g/t PAX additions. Interestingly, recoveries ofintermediate particles were similar at either PAXaddition level. The fine particles exhibit increased

▲


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)

recovery with further PAX additions. This may indicatethat over-ground particles have a larger surface areaand require more collector to achieve maximumrecovery in flotation; or that comminution has adeleterious effect on the surface of the fine particles,decreasing the capacity to adsorb collector

➤ While all GRG particles achieved poor recoveries in theflash period without the addition of PAX, the

intermediate GRG concentrate was not recovered at allwithout the addition of PAX. However, with PAXadditions, recoveries of intermediate and fine GRGparticles were better than the coarse particles.

The cumulative recoveries of each particle size for eachtest condition are shown in Figure 13. Although this figuredoes not contain any kinetic information, this is the first timethat cumulative recoveries for the laboratory flash flotation



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


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

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

➤ Firstly, the coarse particles float better with increasingPAX, particularly in the case of the GRG concentrate

➤ Secondly, recoveries from the intermediate Au powderare better than from the intermediate GRG concentrateby collectorless flotation; while the intermediatefractions of both powder and concentrate contributesimilarly to cumulative recovery in the 25 g/t PAX trial

➤ Thirdly, the cumulative recovery from the Au powdersis maximized at 25 g/t PAX, decreasing when PAX isincreased to 50 g/t. Conversely, the highest cumulativerecovery from GRG is obtained at the highest leveltested for PAX (50 g/t), and the combined recoverydrops when collector dosage is lowered to 25 g/t.

Figure 14 shows ideal cumulative recoveries for the flashflotation period during the first two minutes (four datapoints) of the laboratory testing, as this is of specific interestin this study. Interestingly, the trend of increased recovery offine GRG with increased residence time is evident when theflash period flotation data in Figure 14 is compared to thecumulative recoveries shown in Figure 13. Yet, this trend wasnot so evident for the coarse and intermediate GRG or any ofthe Au powder particles.

▲


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

Kinetic values presented in Table I have been calculatedusing the method of least squares to fit the coefficients inEquation [1] and the Solver function in Microsoft Excel tosolve for unknowns. A mean absolute percentage error(MAPE) has also been calculated as a measure of accuracyfor each data-set; this is a common method for determiningforecast error in timed data series. The α, β, and γ coefficientsare useful in comparing the amount of material recovered ineach data-set. Analysis of these coefficients and the kineticrate data reveals that:

➤ A majority of the floatable intermediate and fine Aupowder and GRG concentrate particles were recoveredwith the addition of PAX in the fast-floating portion,while the remainder of the gold particles contained inthe pulp reported to tails, leaving hardly any materialin the slow-floating category

➤ The lack of a slow-floating portion was morepronounced in the Au powders than in the GRGconcentrate

➤ The fast-floating portion of GRG in the fine fractionincreased with increased PAX addition and the non-floating component decreased; however, despitechanges in PAX levels the slow-floating portion of GRGin the fine fraction remained similar

➤ The coarse fraction of GRG particles had a largeproportion of slow-floating material, which was alsonot decreased by the addition of PAX

➤ It is important to note the high MAPE values, like thoseseen in the +212 μm data, show poor fit for estimationof kinetic values, which is probably due to samplingerrors consistent with the nugget effect.

ConclusionsThe recovery of GRG concentrate was enhanced withincreased additions of PAX, as clearly seen in the cumulativerecovery data. The recoveries from Au powders were betterby collectorless flotation than from the GRG concentrate but,unlike the GRG concentrate, cumulative recovery of fine andintermediate particles decreased with higher levels of PAXaddition, probably due to collector overdosage.

Interestingly, higher recovery of coarse particles appearsto be directly linked to higher additions of PAX, for both GRGconcentrate and the Au powders. Without large dosages ofPAX the coarse GRG particles had slow kinetic rates,suggesting they are unlikely targets for recovery in industrialflash flotation. Unfortunately, assays for the coarse particleswere skewed by problems related to the nugget effect.Therefore, absolute recovery values may shift if sufficientsample 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 GRGconcentrate particles were similar in the laboratory flotationtest with PAX collector. Because the Au powders displayedsuperior potential for collectorless flotation in this size range(as well as the other two size ranges) compared to the GRGconcentrate, it is suggested that milling could have adamaging effect on the natural hydrophobicity of free gold.Recoveries of fine Au powder particles were better than thefine GRG in all experiments. This is unexpected, because theliterature suggests that flotation kinetics are proportional tothe silver content of the GRG. Therefore, the decrease inkinetics and recovery for fine GRG particles is possiblyfurther evidence of the deleterious effect of milling on thefloatability of GRG particles.

Kinetic evaluations indicate that intermediate and, tosome extent, fine gold particles from both sources were eitherrecovered in the first 30 seconds or reported to tails. The



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

Table I

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

GRG Concentrate Au Powders

g/t PAX μm ks kf αφ βs γf MAPE ks kf αφ βs γf MAPE

50 +212 0.48 3.43 0.00 1.00 0.00 14% 0.05 0.72 0.09 0.00 0.91 14%+38 0.30 2.96 0.43 0.06 0.51 1% 1.72 1.72 0.70 0.12 0.18 0%-38 0.73 3.70 0.44 0.12 0.44 0% 0.49 1.60 0.34 0.00 0.66 1%

25 +212 0.48 3.86 0.01 0.99 0.00 7% 0.39 1.01 0.51 0.07 0.42 1%+38 1.47 3.05 0.39 0.39 0.22 1% 0.14 3.23 0.43 0.10 0.47 0%-38 1.91 1.91 0.54 0.13 0.33 0% 1.10 3.73 0.15 0.18 0.67 0%

0 +212 0.02 0.02 0.00 0.66 0.34 0% 0.34 4.92 0.79 0.04 0.17 0%+38 1.00 101 1.00 0.00 0.00 0% 0.85 2.47 0.86 0.07 0.07 0%-38 0.59 5.65 0.85 0.12 0.03 0% 0.86 0.98 0.42 0.15 0.43 1%


kinetic data, coupled with flash flotation period recoveriescomparable to plant flash recoveries (with an addition ofPAX), suggests that both intermediate and fine GRG particlesare appropriate targets for industrial flash flotation, thoughcumulative recoveries are low.

Further work is being conducted on samples obtainedduring plant surveys to help development of a size-dependentflotation model for the recovery of GRG concentrate to flashflotation or gravity recovery when these units are used in aclosed-loop milling circuit. Additionally, this work focuses onidentifying variations in physical parameters (shape,composition, surface area, and roughness) of gold particlesfound in concentrate samples as determined by QEMSCANand Micro CT. The information gained will lead to improvedunderstanding of the recovery of free gold in parallel flashflotation and gravity operations.

AcknowledgmentsThe authors wish to thank the AMIRA P420E sponsors(AngloGold Ashanti, Australian Gold Reagents, Barrick Gold,Gekko Systems, Harmony, Kemix, Magotteaux, NewcrestMining, Newmont, Norton Gold Fields, Orica Australia,Rangold, St Barbara, and Tenova) for financial and technicalsupport, as well as their patience, expertise, and valuableinput in revising this paper.

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▲


Current design practices for sublevelstopingSublevel stoping (SLS) is a method that can beimplemented as sublevel open stoping (SLOS)or vertical crater retreat (VCR). The optimalconditions for the application of SLS arerelated to the geometry and inclination of theorebody and the stability of the walls andpillars that form the stopes (good geotechnicalcondition). The stability of the stope, pillar,and walls is determined by the geotechnicalcharacteristics of the hangingwall and footwall(Potvin et al., 2001). SLS could be appliedwhen the dip of the orebody is greater than50°; this condition is based on the ability ofthe fragmented rock to flow due to gravity,when extracted at the production level(Pakalnis et al., 2011).

The design of the sublevel stope includesthe placement of the draw and the locations ofthe auxiliary and the drill levels. Figure 1

shows a schematic of the conceptual design ofa large stope designed from level 1 to level 3.Note that the stope is defined as large whenthe height (H) and width (W) of the stope aregreater than 30 m. In Figure 1, the dip of thefootwall is 90° (vertical). The spacing of thesublevels (hi) depends on several factors,including the orebody geometry, the drillingtechnology, and capital costs.

Figure 1 also includes a reference to avariation of ore grade with the stope height.The in situ grades are represented byhorizontal layers G1, G2, and G3, where(G1>G2>G3). In current planning practices, oregrades are planned to be extracted in the orderfrom G1 to G3 without considering the mixingthat might occur during gravity flow. Figure 2show a typical draw or production level layoutfor a large sublevel stope, where DD are driftswhere a long drawbell is located, while PD isthe production drift where, in general,mechanized equipment such as LHDs operates.In the case presented in Figure 2, drawbells arespaced at Dd metres while drawpoints arespaced at Dpe metres. Generally, for SLS, Dpeis approximately 15–18 m while Dd is 48 m(Contador et al., 2001).

Figure 3 shows the application of SLS withthe footwall inclined at an angle a and thehangingwall inclined at b degrees to thehorizontal. In this case, the location of thedrawpoints must be considered with respect tothe maximum recovery of ore, especially ifunplanned dilution can enter the stope due toinstability. This is related to the gravity flowproperties of fragmented rock.

The role of gravity flow in the design andplanning of large sublevel stopesby R. Castro* and M. Pineda*

SynopsisSublevel stoping (SLS) is one of the oldest and most used methods forunderground mining. It relies heavily on the use of drilling and blastingtechniques to remove the rock, and gravity to transport the broken rock todrawpoints located at the base of the stope, with LHDs to transportmaterial from the drawpoints. Current SLS operations are based on theassumption of stable geometry of the stope. Thus, the stope designincludes the definition of the geometry according to the orebody shape andgeomechanical constraints to avoid instability, which may cause excessivedilution. Under some circumstances, dilution could enter the stope due togeotechnical instability, especially when large stope geometries are used.A review of current design and planning practices for large SLS operationsindicates that no consideration is given to the material flow and themixing that occurs after blasting. Material flow could have a large impacton the mixing of ore when grades are heterogeneous in the stope. In thispaper, we discuss the influence of gravity flow on the design and planningof large sublevel stopes with and without vertical dilution, based onlaboratory experiments. The outcomes of this investigation are used todevelop guidelines towards the design and planning of large SLS mines,which would complement the currently used geotechnical considerations.

Keywordssublevel stoping, mine design, gravity flow, ore dilution.

* Advanced Mining Technology Center, Universidadde Chile, Santiago, Chile.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedJun. 2014; revised paper received Aug. 2014.



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

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

These studies have served to define the location ofdrawpoints for a flat or an inclined production level and theore mixing due to flow, results that are extensively used formine design and planning of caving methods. Guidelines forinclined drawpoint spacing in block caving consider dips from35° to 40° and a width of the flow zone of 12 m for finely-fragmented caved rock (Laubscher, 2000). Given thedifferences between SLS and caving methods, the gravityflow experiments may not necessarily be applicable to SLSdesign and planning.

We conducted research, based on experiments, on thegravity flow characteristics of fragmented rock in SLSapplications. The results of the experiments were used todefine the location of drawpoints for flat and inclinedfootwalls in SLS.

Laboratory experimentsTo understand the effects of flow on the design of large SLSapplications, controlled experiments were conducted. Theobjective of the laboratory experiments was to study the oreflow within a sublevel stope under inclined geometries. Forthe purpose of the experiments, a physical model having atypical geometry of a large stope with a footwall inclination ofa = 70° and hangingwall inclination of b = 90° was built inthe laboratory (Figure 4). During the design stage of theexperiments, all the laws of kinematic similitude for granularmaterials – that is geometrical similitude, extraction rate andfriction angle – were taken into consideration (Pineda, 2012).

The model was built using plexiglass to enableobservation of the flow and to consider an axisymmetriccondition by using near-frictionless walls. The geometricaldesign was based on a typical drawbell spacing used in largestopes, 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 themodel held an extraction system of 11 drawpoints and thedrawbell geometry with a ‘shovel’ installed at eachdrawpoint. The ’shovels’ were linked to a servomechanismthat provided an electrical impulse controlled through asoftware algorithm, allowing the extraction rate to be varied.

▲


Figure 1—Conceptual design of a sublevel stope. (Gi is the grade of orein the stope, hi is the distance between levels, and wi is the distancebetween drawbells)

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

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

Figure 4—Experimental set up- physical model (left), drawpoints andapex (right)

The size distribution of the material used in the modelwas based on the rock size distribution as measured at SLSmine operation, with fragment size due to blasting leaningtowards fine material with a mean characteristic size or d50 of0.3 m (Figure 5). Therefore, for the experiments, crushedgravel with a mean size of 2 mm was used, as shown inFigure 6.

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

➤ Experiment 1—an understanding of flow under a singledrawpoint

➤ Experiment 2—study of the flow under multipledrawpoints from a single production level

➤ Experiments 3 and 4—flow behaviour when drawing

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 thedraw level.



Table I

Experimental plan

Experiment Draw strategy Objective

1 Isolated draw To determine isolated flow zone geometry for the model media

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

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

4 Uniform draw A repetition of experiment 3

5 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

Figure 5—Fragment size distribution for SLS operation

Figure 6—Fragment size distribution for experiments

Pas

sing

und

er s

ize

(%)

0.10 1.00 10.00Size (mm)

0.00 0.01 0.10 1.00 10.00Size (m)

Fragmentation 1 Fragmentation 2 Fragmentation 3

Pas

sing

und

er s

ize

(%)


Experiment 1

During the first experiment, we measured the isolatedmovement zone or IMZ, which is defined as the disturbedzone 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 inthe model, which corresponds to 30 m in diameter at 300 m draw height when scaled. This corresponds to themeasurements of flow zones during gravity flow in granularmaterials. Figure 7 also shows that the angle of draw α is inthe range from 69° to 76°, which should be considered whendesigning the location of drawpoints.

Experiment 2

In this experiment, flow was induced by drawing from the fullgeometry at the base of the stope. Figure 8 shows thegeometry of the flow for different stages of the draw. Asnoted, the flow zone due to the extraction from all drawpointsdid not propagate en masse but developed the shape of theisolated draw zone that propagated towards the lower columnheight (Figure 8a). Consequently, the flow velocity washigher in the columns with smaller column height.Subsequently, the flow reached a steady state, where the flowwas mainly vertical. Subsequently, granular material at thesurface moved down due to rilling (Figure 8c). This conditioncontinued as more material was drawn, as shown in Figure 8d. This shows that the flow causes the material tomix. These phenomena should be considered when planningthe extraction of a stope that continues to be stable duringdraw.

The experimental results show that the material located atthe production level is not mobilized during flow. The authorsdeveloped the force relationships using equilibrium analysisto understand the factor of safety of the wedge. Thecalculations indicated that failure of the wedge could beexpected, which, as noted previously, did not occur duringthe experiment.

Experiment 3 and 4

These two experiments were undertaken to determinewhether the inclusion of another level is necessary to extractthe ore above the footwall in SLS, under the assumption thatflow of the wedge does not occur. The new level was located60 mm above the production level (30 m when scaled to areal sublevel stope). Draw from this level started after theinitial draw of material from the production level. Asindicated in Figure 9, the flow zone of the drawpoint locatedat the footwall was connected to that developed due to theflow of Level 1 (Figure 9a). Drawing from this new levelimproved the early recovery of ore located at the base of thefootwall, as shown in Figure 9b.

Experiment 5

This experiment investigated the case of dilution entering thestope from above due to stope instability. The dilution wassimulated by adding a granular material, which was finer insize than the initial ‘ore’ size and coloured red for contrast,on top of the stope. Figure 10 shows different stages duringdraw. As noted in previous experiments, the flow zonedeveloped faster at the lower column height. Subsequently,the dilution moved down according to a flow velocity profilethat was faster in a zone where the height of the column islower. This type of flow continued until the material reachedthe 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) furtherdraw; (d) rilling from surface

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)

as noted in block caving experiments (Laubscher, 1994). Asthis 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 previoussection clearly show that the design guidelines shouldconsider the effects of material flow on the location ofdrawpoints and draw levels in a sublevel stope.

1. Firstly, the fragmentation due to blasting needs to beestimated. The smaller the fragments the smaller theflow zone and, therefore, the smaller the width of theIMZ. The converse also applies. It must be noted thatthe results provided in this paper refer to free flowconditions, where no cohesion exists in the granularmaterial

2. Secondly, if the stope is vertical (a = 90°), a singledraw level may be considered. In this case, it isnecessary to calculate the height of interactionaccording to the angle of flow (α) and the width of thedrawbell drift, and to calculate the spacing ofdrawpoints according to the desired height ofinteraction (Castro et al., 2012), that is:

[1]

3. Thirdly, the spacing of drawbells and drawpointsshould be designed for the IMZ to overlap. No relianceon extra spacing rules should be considered, aspresented in block caving experiments (Trueman etal., 2008)

4. Finally, if the stope is inclined (a <90°), more thanone level may be considered, vertically spaced at aheight of hi metres. As shown in Figure 11, thelocation of the levels would depend on the width of theIMZ and the inclination of the stope. In this case, thehorizontal spacing should be such that the IMZs, withdiameter of d metres, overlap. In Figure 11, anexample is provided where there is a main draw level(Figure 11a) and other three draw levels are located atthe footwall of the stope (Figure 11b) to achievemaximum recovery of the stope. In this case thevertical distance (hi) between drawpoints is:

[2]

ConclusionsSLS has been widely used in the mining industry for manyyears. Current design guidelines and mine planning are basedon rock stability and equipment, and appear to take intoconsideration the flow properties of the fragmented rockunder gravity.

In this paper, based on laboratory experiments, we provethe importance of flow in the design and operation of largesublevel stopes.

If dilution from the back of the stope is not expected tooccur, it is envisaged that SLS would recover most of the oreduring drawing, as the stope is emptied. In this case, thespacing of drawpoints may not be key to the success of anoperation, but mine planners should consider the mixing ofthe ore that occurs within the fragmented column and thesurface rilling to better estimate the production grades. In thecase of an expected instability at the back of the stope, largeamounts of dilution could mix with the ore. In this case, orerecovery would not be efficient unless the gravity flowcharacteristic of the fragmented rock is included in the design



Figure 9—Draw from production level and level located at 30 m fordifferent stages of draw. (a) Initial draw from upper level; (b) furtherdraw and surface rilling

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


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

Acknowledgements The authors would like to acknowledge the financial supportof the Chilean Government through the project ConycitFB0809 and the support of Agnico Mine, which was instru-mental in delivering on the research goals. We would alsolike to thank Dr. Matthew Pierce for the helpful discussionduring the development of this research, Mrs. CarolinaBahamondez for providing many of the schematics, and Dr. Eleonora Widzyk-Capehart for helpful feedback duringthe writing of this article.

References

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CASTRO, R., TRUEMAN, R., and HALIM, A. 2007. Study of isolated draw zones inblock caving mines by means of a large 3D physical model. InternationalJournal of Rock Mechanics and Mining Sciences, vol. 44. pp. 860–870.

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

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

IntroductionMineral extraction and processing represents acommon enterprise activity associated withspecific risks. Mining enterprises are orientedby profit, capital valorization, maintainingmarket position, augmentation of companyassets, and various other objectives (Vaněk etal., 2011). Attaining prominent objectives incurrent market conditions, characterized bycompetition and change, is difficult if modernmanagerial methods are not applied (Zauškováand Kusá, 2011). ‘Modern’ does notnecessarily mean new; quality managementwas first introduced in the USA in about 1920as a statistical tool for industrial productionimprovement (Mizuno, 1988). Furthermore,there are Deming tenets as well as PDCAprinciples that date back to the 1950s(Košturiak and Gregor, 1993) and are stillapplied by managers today.

There are many methods and approachesthat managers can apply, therefore they must

select wisely from all available options, despitecurrent fashions or fads, while being open tonew methods and approaches and constantlykeeping their specific requirements in mind.Managers also play key roles in the implemen-tation of continuous improvement (CI), ashuman resource utilization is predominantly amanagerial challenge (Drucker, 1999). It iscollaboration that kick-starts and sustainscontinuous change (Farris et al., 2009;Langbert, 2000).

CI is crucial if intense market competitionand low-cost operation exigency is to prevail.The global financial crisis and the relatedglobal recession have directly or indirectlyafflicted the situation of many businessenterprises in the Czech Republic, and themining sector has been no exception. In manyother countries worldwide, mining is asignificant contributor to the economy(Nzimande and Chauke, 2012) and is subjectto demand and supply inconsistencies andunfavourable price movements. No matterwhat the cause of decreased income, there isno doubt that managers must optimizeproduction costs. (Erol et al., 2011; Lapercheet al., 2011).

In such difficult conditions, a company cankeep its market position only if CI is routinelyimplemented (Baaij et al., 2004). It is theobjective of this paper to create awarenessaround continuous improvement and illustrateapproaches utilized by mining companies.

Continuous improvement of processesIn economic practice, two types of change orinnovation are common; either a sudden,fundamental change that requires a largeinvestment as well overcoming intrinsic

Continuous improvement management formining companiesby M. Vanek*, K. Spakovska′*, M. Mikola′s*, and L. Pomothy†

SynopsisEnterprises are faced with increasing economic competition and managersare obliged to look for methods that will ensure a competitive edge in theircompanies’ markets. These methods include managerial concepts thatemploy common sense and are low cost. One of these approaches is theKAIZEN methodology, of Japanese origin; kaizen means continuousimprovement (CI) when translated. A mining company, despite some of itsidiosyncrasies, is just an enterprise where the application of KAIZEN canbe advantageous.

The company investigated, OKD, is the only domestic producer of hardcoal in the Czech Republic and operates four mines that produce 11 Mt ofcoal annually. The OKD management team opted for KAIZEN as theirprincipal method of continuous quality improvement.

This paper suggests ideas for development and application ofprocesses that provide for continuing improvement of production andmanagement. The authors have taken the OKD Company to be theirbenchmark in the field of mining activities. The paper also focuses onpossible difficulties accompanying the introduction of CI sustainablemethods. Three years of application of CI methods has increased theincome of the company by almost US$38.1 million, which provides astrong argument for continued application.

KeywordsKAIZEN, management, continuous improvement, mining enterprise.

* Faculty of Mining and Geology, VSB – TechnicalUniversity of Ostrava, Czech Republic.

† OKD, a. s., Czech Republic.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. Paper receivedMar. 2013; revised paper received Sep. 2014.


ˆ ˆ ˆ

ˆ


Continuous improvement management for mining companies

apprehension, or a slow step-by-step change that is easilyaccepted and requires negligible investment (Bessant et al.,1994; Zaušková and Domová, 2012).

A company must strive to produce better, cheaper goodsand services in order to grow. In addition to this growth, it isalso necessary to continuously improve (Drucker, 1999). Inorder to understand the CI process we return to the origin ofthe managerial concept named KAIZEN. In July 1950, W.E.Deming was invited to Japan to teach statistical qualitycontrol. Deming introduced the ‘Deming Cycle’, one of thecrucial quality control tools for assuring CI in Japan (Imai,1986). The meaning of the Deming Cycle (Figure 1), alsoknown as PDCA (Plan-Do-Check-Act) cycle, is a basicprinciple of Total Quality Control (TQC) or Total QualityManagement (TQM).

KAIZEN has been accepted as a lifelong philosophy bymany Japanese managers and workers. It integrates earlierapproaches 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 theapplication of a common-sense and low-cost approach.

The traditional Japanese approach to QC is manageriallyoriented (Imai, 1986) because the team is key to the processof CI and drives company culture and values (Langbert,2000). The KAIZEN perspective requires all company staff,including workers and junior managers, to be positive thattheir contribution is taken seriously and their suggestions, ifapproved by expert opinion, are implemented (Anand et al.,2009). Only such an inclusive approach can provide thecontributions required to improve existing standards, preventstagnation, and avoid subsequent relaxing of initiative(Farris et al., 2009). In addition, KAIZEN also representsproductivity improvement, TQC activities, QC circles, or labourrelations (Imai, 1986).

➤ One stumbling block to implementing changes can bemisgivings whether the change is acknowledged by theparty involved

➤ The undisputed advantage of KAIZEN is that it buildson utilization of the standing resources and does notnecessarily require investment (Bessant et al., 1994).

Successful implementation requires a formal declarationand the modification of existing managerial methods.KAIZEN should not be applied uncritically as a blueprint ofanother experience, but should take the specific backgroundand 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 themanagers themselves and then continue to be applied by allstaff. It is based on changing overall thinking styles andbehaviours (Langbert, 2000).

Continuous Improvement at OKD The Upper Silesia Coal District, which covers 7000 km2, is amajor European coal district spanning two countries – Polandand 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 andruns four deep mining collieries in this district. In 2010, the

company sold 11.5 Mt of coal, taking the fifth position in thelist of major producers of hard coal in Europe, and the salesincome reached a level of US$1.68 billion in 2010. OKD isalso the biggest employer in the Moravia-Silesia region, with18 000 employees. The company operations are divided intointernal organization units that include the respectivecollieries and one service centre.

OKD cares about its image as a socially responsibleenterprise, and this is reflected in its staff care programmes,which include many employee benefits. Previously,employees could file innovation applications but there was nosystem in place to assess innovation potential and motivatestaff.

The CI system, introduced to utilize the staff innovationpotential to its maximum, was instituted by a newmanagement structure in 2008. This generated a wave ofinnovation proposals that could be systematically andobjectively assessed, and remunerated accordingly.

Management incorporated a CI department tasked toutilize the concept to its full potential. The CI managementimplementation was accelerated in 2009 by the establishmentof a CI Manager at each of the four collieries. The CI Managerreports directly to the director of the colliery. Naturalauthorities with extensive practical experience wereappointed 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 driveand was supplemented by CI seminars and wall posters.(Brodský, 2011)

The CI idea is supported by the provision of value andfinance frameworks (Bessant et al., 1994). The CI structureshould be realized step-by-step, especially if no similarstructure is in place. Time should be allowed for staff toabsorb the change (Bessant et al., 2001). Table I shows theschedule of the CI structure establishment from 2010–2012and indicates activities that took place in 2013.

In OKD, two types of activities within the CI frameworkwere observed. On the one hand, innovation initiatives ofindividual employees are monitored; the other emphasizesoptimizing team initiatives.

To promote individual CI activity, individual employeemotivation is especially necessary. The process based onindividual people is quite simple, in contrast to the team

▲


Figure 1—Deming cycle within TQM (source: ČSN EN ISO 9001)

activity. A specialized external consultancy agency, HSG, wascalled in to conduct employee orientation in the newlyestablished system and empower the teamwork activities.The project was titled ‘Moje firma’ (My Company). Theresults of first year’s team activities within the Moje firmaproject are displayed in Figure 2.

The consultant organized a training programme forcolliery consultants so that they could employ their ownexpertise. Originally, only virtual training was involved, butthe outcome was subsequently put into actuality by a team ofCI managers. The internal consultants chose their ownproject, specific to their workplace conditions (HRmanagement, work organization, technical problem solutions,economy, alternative or theoretical solutions, etc. and subjectto authorization by the CI department and colliery director.The colliery director officially nominated leaders of individualproject teams, assuring managers’ support. After about sixmonths, the results of these ‘virtual’ projects were presentedto the consultants, HSG, and to all company managers. Theassessment of the projects was made at a closed session. Allparticipants were rewarded. The project outcome benefitsdiffered – from hard financial gains through advantagesdifficult to express in numbers up to individual persons’experience with project management. Some projects werechosen as items of official optimization agenda; others werenominated to be the subject of long-term support andmonitoring by internal consultants.

Generally the optimizing team’s activities can be initiatedby an individual worker, but they consist of well-thought-over, systematic activities in which staff participate and alsoinclude management and external consultants.

Both types of activities – individual innovative initiativeand the optimizing team activities – will be clearly illustrated

by taking an example of a specific CI management of aspecific unit (colliery).

Tables II and III provide the results of the work of the CIdepartment and all employees involved in the activity from2010–2011. To show financial profit and rewards paid, thecurrent exchange rate of 19.7 Czech crowns (CZK) per USdollar was used.

The results are slightly distorted due to differences of theassessment criteria provided for admission. For initialmotivation the assessment criterion demands were set rathersoft, and have been made stricter every year. The applicationof stricter criteria is substantiated because colliery resourcesare limited.

A methodical monitoring of the financial contributions ofthe innovations realized is conducted every 12 months. It isthen taken to be an accepted standard and is reflected inrelated financial planning.



Table I

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

Stage Year Orientation Specific content

1 2010 Individual CI Activating creativity, individual involvement of managers and staff, employees of outsourcing companies working underground, provision of optimization teams and change alliances.

2 2011 Team CI Team co-operation, filing group innovation applications, horizontal and vertical co-operation ofmanagement and staff – the project, Moje firma (My Company), internal consultancy activities.

3 2012 Process CI Company macro-process autonomy (HR, material supply), removing bottlenecks of the critical macro-processes.

4 2013 CI custom fixation Widening autonomy of macro-processes to safety, logistics, and coal face workings, gradual transitionfrom volunteer to standard CI structures.

Table II

Statistics, CI OKD, 2010

Filed Accepted Implemented Financial profit, US$ million Rewards paid, US$ million

Individual employee initiative 1 075 742 558 5.74 0.117Specifically targeted projects of optimization 64 64 60 5.73 0.030Total 11.47 0.147

Source: OKD, original research

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

Source: OKD Annual Report


Questions have been raised about the evaluation methodfor the innovations realized in practice. For example,innovations in the field of energy saving will generatebenefits in the longer term.

The annual change in performance is shown in Table IV,and it is clear that frequency of admissions decreased by17%. At first sight this decrease may seem to be a CI failure;however, during this period income increased by 15%. Thereason for this positive result is the better quality innovationproposals filed and the speed of their realization, up from75% in 2010 to 96% in 2011. In 2012, year-on-yearrealization of innovation proposals decreased (500innovations admitted) but the financial income increased byUS$10.15 million.

To assess the CI activities, profits were monitored (FP)per innovation realized (RN). Table V shows the developmentof 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–Vpresent figures for the initial two years, when the CIstructures were first implemented. It is obvious that manyemployees used the opportunity to file proposals thatconcerned the most critical issues of their work methods andenvironment (Farris et al., 2009), which resulted in a rapidincrease in profit. It would not be realistic to expect a similarinnovation boom in the following years. Therefore thedecreasing innovation rate serves as an example of CI success(Bessant et al., 1994) as the innovations realized in theframework of KAIZEN became a standard that will be furtherdeveloped by continuing improvement initiative (Glover et al., 2011).

In the long run, innovation opportunities will be lessfrequent and the CI initiatives will have to concentrate ondiagnostics (Bessant et al., 2001), and there will be lessopportunity for cost optimization based on minimization. Akind of a status quo satisfaction may follow, which willrequire management to exert their influence to seek opportu-nities for further improvement or risk losing benefits gainedfrom the CI effort.

CI method and tools, ČSM collieryThe mining company OKD consists of four collieries, each ofwhich operates in different conditions. In KAIZEN terms,these are designated as gemba (Imai, 1997). The followingsection demonstrates specific tools and activities that are partof CI structures active at a specific colliery. The individualcollieries differ for objective and subjective reasons, thus CI isapproached differently. The global objective is disseminationof knowledge and positive staff experience.

Each colliery has its idiosyncratic position and eachgemba must be considered individually. Our example gemba,the ČSM colliery, has 3300 employees and is considered thethird largest in OKD. This model gemba demonstrates all theprocesses, 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 notingdown employee proposals. The innovation idea is brieflyidentified and specified. The idea card is not essential to theinnovation proposal submission, as informal applications arealso acceptable and can be e-mailed, telephoned, or verballycommunicated, in either Slovak or Polish. In the case of aninformal application an idea card is filled in by a CI officialand authorized by the innovation initiator.

The CI department registers the card, which issubsequently passed on to professional experts orsupervisors of related activities. Several professionals assessthe value of each innovation proposal.

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Table V

Development of profit increase by innovation,2010–2012

Year 2010 2011

FP/RN, US$ 10 288 11 162

Table III

Statistics, CI OKD, 2011

Filed Accepted Implemented Financial profit, US$ million Rewards paid, USS million

Individual employee initiative 950 614 592 6.599 0.117Specifically targeted projects of optimization – 70 57 8.629 0.025Total 15.228 0.142


Table IV

Annual change as a chain index, 2011/2010

Filed Accepted Implemented Financial profit Rewards paid

Individual employee initiative 0.88 0.83 1.06 1.15 1.02Specifically targeted projects of optimization - 1.09 0.95 1.51 0.85Total 1.33 0.98


After evaluation, the idea is recommended for admissionor rejection. A decision is made by the CI Committee, whichcomprises the colliery director, his deputies, and the managerof the colliery CI department. The committee is tasked withconsidering ideas for admission, and once an idea has beenadmitted, the committee is responsible for its realization.

Optimization teams are contracted by OKD or collierymanagement. The subject for optimization can be initiated byall parties. The CI department director nominates a teamleader. An ‘expert guarantor’ and ‘sponsor’ are alsonominated. The expert guarantor is a staff professional or anexternal consultant that coaches the team and ensures theinnovation realization follows CI conventions. He warns theteam if there is an emergency or cul-de-sac of the activitiesperformed for legislative, financial, technical, or patent rightreasons. The sponsor is a colliery manager. He is responsiblefor managing those activities at the colliery that relate to theinnovation realization, and provides for organizationalconditions and material means of implementing theinnovation. The sponsor also plays the role of a teamsupervisor and communicates with the colliery management.

After the innovation has been admitted for implemen-tation, the colliery director places an order for the project. Theteam leader chooses his collaborators. Team membership isvoluntary, and team members are rewarded on the groundsof their individual contributions and roles within the team.

The sponsor assesses the work of the team according tothe 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, depositionprotection, etc.)

➤ Miscellaneous.The key phase of the overall improvement process is

represented by the analyses that are conducted by the CIdepartment, 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 ofall analyses, the decisive diagnostics are workplace and workprocess time-demand monitoring. The results of time studiesare visually represented by a process map (Figure 3).Feedback meetings and communication with shift-based staffare considered to be an integral part of the procedure,especially to drive the discussion around the source ofproblems and possible remedying action.

An independent CI unit, which uses accepted standards,provides for the timing. Their conclusions are taken asobjective and obligatory, an important consideration formanagers. The sense of the CI work is not repressive butprovides for diagnostics. It is about identification of weakpoints, definition of an appropriate remedying action, andabove all about ‘inflaming’ staff, persuading them about thesignificance of KAIZEN.

The last, but not least, criterion is staff education. Taskscannot be completed without appropriate competence. It isalso important to initiate thinking centred around workimprovement possibilities and ways to put innovation inpractice.

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.

ConclusionThere can be no doubt that current technological standardsare the consequence of revolutionary or incrementalinnovative processes in the long history of humankind.

The mining industry utilizes cutting-edge technologiescharacterized by developed segments of the economy. Thedifficult natural and market conditions in which miningenterprises operate require top-quality management thatconsider their own staff as sources of creativity.

We are quite positive that the decision to implement theKAIZEN concept is the right one, especially if a companywants to sustain their market position. OKD’s successfuloperations support this argument; in three years theindividual and collective optimization projects increased thecompany income by almost US$38 million.



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


KAIZEN has a social and cultural background that isdifficult for some people to adopt, but it is essential andintrinsic to the concept that all staff members participate inorder to have the maximum impact. The companymanagement of the gemba discussed here is well aware ofthis fact, and structures their remunerative schemesaccordingly. One of their successful innovators can even wina new car.

Nevertheless, some attention should be drawn to a certaindisproportion that can erode innovation activities ofmanagers and staff in future. It follows from the AnnualReports of 2010–2012 that the company spent only US$0.289million on innovation rewards, which is only 1.08% of thecompany’s overall US$26.7 million income increase fromimprovement.

‘Penny wise and pound foolish’ policies do not work inthe long run, considering the words of Peter Drucker: ’Themost valuable assets of a 20th-century company were itsproduction equipment. The most valuable asset of a 21st-century institution, whether business or non-business, willbe its knowledge, workers and their productivity.’

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BAAIJ, M., GREEVEN, M., and VAN DALEN, J. 2004. Persistent superior economicperformance, sustainable competitive advantage, and Schumpeterianinnovation: leading established computer firms, 1954–2000. EuropeanManagement Journal, vol. 22, no. 5. pp. 517–531.

BESSANT, J., CAFFYN, S., and MAEVE, G. 2001. An evolutionary model ofcontinuous 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.

BRODSKý, I. 2011. Zapojení zaměstnanců do STZ v OKD. Fórum trvaléhozlepšování. 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 .

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FARRIS, J.A., VAN AKEN, E.M., and DOOLEN, T.L. 2009. Critical success factors forhuman 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 successfactors for the sustainability of Kaizen event human resource outcomes:an empirical study. International Journal of Production Economics,vol. 132, no. 2. pp. 197–213.

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 tomanagement. 1st edn. McGraw-Hill, New York.

KOšTURIAK, J. and GREGOR, M. 1993. Podnik v roce 2001: revoluce v podnikovékultuře. Grada, Prague.

LANGBERT, M. 2000. Human resource management and Deming's continuousimprovement concept. Journal of Quality Management, vol. 5, no. 1. pp. 85–101.

LAPERCHE, B., LEFEBVRE, G., and LANGLET, D. 2011. Innovation strategies ofindustrial 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 technologicaldifferences and knowledge spillovers. Regional Science and UrbanEconomics, vol. 35, no. 5. pp. 542–569.

NZIMANDE, Z. and CHAUKE, H. 2012. Sustainability through responsible environ-mental mining. Journal of the Southern African Institute of Mining andMetallurgy, vol. 112, no. 2. pp. 135–139.

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ZAUšKOVÁ, A. and KUSÁ, A. 2011. Hodnotenie výkonnosti inovačných amarketingových procesov v malých a stredných podnikoch. VedeckáMonografia. Data Service, Zvolen.

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

Introduction

Refrigeration systems are widely used in minesto provide air cooling for undergroundoperations (Burrows et al., 1989). The installedrefrigeration capacity is generally dictated bydepth for hard rock mines where virgin rocktemperature (VRT) and autocompression,among other heat sources, play a major role(Bluhm and Biffi, 2001). Refrigeration and theassociated cooling installations at depth isessential to ensure that legal thermalrequirements are maintained. Work done byBluhm and Von Glehn (2010) indicates therelationship between mining depth and type ofrefrigeration and cooling system required(Figure 1).

Figure 1 shows the VRT is a function of asurface intersects temperature in degreesCelsius (°C) and the geothermal gradient in°C/m (De Wet, 2012). The VRTs for specific

mines are calculated using the intersecttemperature and geothermal gradient. Thegraph in Figure 1 is only a guide to determinewhich refrigeration option would be best suited,and selection of a suitable system depends onthe mining method, heat loads, mine depth,VRT, etc.

Refrigeration capacity requirements dictatethe air cooling and heat rejection heatexchanger duties for surface or undergroundinstallations. Many of the design considerationsfor surface and underground cooling systeminstallations are similar, but in practice there aremany differences. Due to the potentially vastfield of study in this regard, this study focuseson underground heat exchangers, using onlybulk air heat exchanger installations exceeding100 m3/s air flow capacity.

Bulk air heat exchangers in undergroundoperations are used either to cool air whenmaximum legal return temperatures from theworking areas (reject temperatures) arereached, or to cool water from undergroundrefrigeration plants (heat rejection). These heatexchangers form part of two underground waterrefrigeration circuits, namely the evaporatorcircuit where chilled water is distributed to aircoolers and the condenser circuit where heat isrejected to return air (Burrows et al., 1989).

Air cooling and heat rejection heatexchangers typically consist of three main typeswith very different design requirements andapplications. Underground bulk air heatexchangers include direct-contact air-waterhorizontal spray chambers, direct-contact air-water cooling towers, or indirect-contact air-water banks of heat exchangers.

Background

Heat exchangers can be supplied with coldwater from surface or underground refrigerationplants, but energy requirements versus life-of-

A decision analysis guideline for undergroundbulk air heat exchanger design specificationsby M. Hooman*, R.C.W. Webber-Youngman*, J.J.L. du Plessis*, andW.M. Marx*

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

Keywordsmine refrigeration, water-air, heat exchanger design, decision analyser.

* University of Pretoria.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. Paper receivedJan. 2014; revised paper received Sep. 2014.


1Marx, W., Hooman, M., Botha, P., and Meredith, G.2010. Refrigeration and cooling design case study:Palabora Mining Company. Journal of the MineVentilation Society of South Africa


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

mine total cost of ownership need to be compared duringproject start-up. Most recent designs incorporate undergroundrefrigeration plants as they ensure energy-efficient designs,optimized positional efficiency, and lowest total cost ofownership (Marx et al. 2010).

This study provides a comprehensive summary of differentheat exchangers from many resources. It further provides themine ventilation and refrigeration design engineer with a user-friendly guideline for underground bulk air heat exchangerdesigns. All aspects pertaining to the design will be found in asingle document to save the user time in finding solutions forthe specific application. This document will be a technicalspecification that can be used to contact specialist engineers tocomplete ‘for-construction’ designs.

Du Plessis et al. (2013) indicated that energy-efficientdesigns are imperative with increasing electrical costs. Thiswork further indicated that energy consumption will have adirect impact on carbon usage and need to be managed ascarbon emission tax legislation is planned for the start of 2015.

Design constraints may impact the energy consumption ofa mine and are an important part of the design process toconsider. Poor designs can result in the safety and health ofpersonnel being compromised, and this cannot be toleratedsince mines are legally responsible for employee health andsafety.

The objective of the study was to determine therequirements of underground heat exchanger designs. Thestudy 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 specifi-cations for a specific heat exchanger type. These decisionfactors were tested on an existing bulk air heat exchangerdesign as part of a case study.

Factors influencing selection criteria for bulk air heatexchangers

The factors that influence the selection criteria for bulk air heatexchangers need to be identified and the effect on the designprocess quantified. If done correctly, this will ensure that thedesign is optimized and that an energy-efficient solution isderived. The following criteria must be considered:

➤ Reject temperature design criteria for the mine willdetermine how much cooling is required (DMR, 2001;2002)

➤ Mine working depth (VRT) and effect of autocom-pression (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 ofheat exchanger locations and the size of new orexisting tunnels

➤ Inlet conditions of heat exchangers, which determinethe duty and ultimately the outlet temperature of theair to the working air (for coolers) and the outlet watertemperature for condenser heat exchangers (heatrejection units) (Burrows et al., 1989)

➤ Environmental conditions, including air and waterquality, define how many treatment facilities will berequired 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 willimpact on the heat exchanger design and maintenancefrequencies

➤ Construction logistics are important to ensure thatinstallations are in fresh air, with on-time deliveredequipment and in a safe environment.

Engineering and technical requirements

The engineering and technical requirements for each of theheat exchangers were identified. Furthermore, the water reticu-lation system and positional efficiency for each type wascalculated. The technical requirements associated with each ofthe types of heat exchanger are listed in the followingparagraphs.

Indirect-contact heat exchanger banks are mainly used forcooling. The following selection criteria were considered:

➤ Log-mean temperature difference (LMTD) (Incroperaand 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 airand water inlet and outlet temperatures (Cabezas-Gomez 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)

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

➤ 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 useduring the heat exchanger selection criteria process. Thisselection criteria process will improve the technical specifi-cation(s) of bulk air heat exchangers.

Decision analyser steps

Step 1: determine heat exchanger requirement

The first step is to determine the thermal design criteria of themine (Figure 2). Ventilation and cooling requirements need tobe determined so as to establish whether more air is availableto cool the underground environment to below the thermaldesign criteria. If this is possible, then no underground heatexchanger is required. In addition, the working depth ofunderground workings is required. A rule of thumb suggestedthat surface cooling is best utilized at critical depths up to 1200 m and VRTs up to 50°C (Figure 1). The applicable mine’sVRT may result in cooling being repaired at a particular miningdepth. This decision will be dependent on the ore being minedand mining method utilized.

If the critical depth does not necessitate an undergroundheat exchanger, then other heat source considerations, such asheat from diesel vehicles and fragmented rock, need to beidentified and included in this step. The additional heat sourcesmay necessitate an underground air cooler.

In the event that underground heat exchangers arerequired, it needs to be established whether a bulk air flow ofmore than 100 m3/s is available for heat transfer. If this istrue, continue to Step 2.

Step 2: determine heat exchanger size and location

The second decision analyser step starts by calculating theunderground heat load (Figure 3). Heat load componentsinclude autocompression, the contribution from the VRT,vehicles and other artificial sources. If the air temperature isbelow the reject temperature the air carries a certain air coolingcapacity (heat removal capacity). An additional cooling sourceis service water, and the capacities of both of these need to bedetermined.

Mass and energy balances for the mine include the heatload and air and service cooling capacity, which determine theadditional underground cooling required. Air flow and thermo-dynamic software simulation models (VUMA3D-network,Ventsim, or fully equivalent) can be used to confirm heatexchanger requirements.

The required air cooling and heat rejection heat exchangerscan be divided into a number of units and the location for eachneeds to be determined in order to determine how many new

excavations will be required. This is important for determiningthe most cost-effective option – whether to distribute smallerheat exchangers or fewer large heat exchangers in theavailable or new excavations.

Air coolers are generally placed adjacent to main intakeairways and heat rejection units in or adjacent to the mainreturn airways. Once the number of heat exchangers and theirlocations are determined, proceed to Step 3.

Step 3: inlet conditions and water loading

Once the number of locations to place air coolers and heatrejection units has been determined, the inlet conditions ateach unit need to be confirmed (Figure 4). The inlet conditions



Figure 2—Heat exchanger requirements

Figure 3—Heat exchanger size and location

NO


will determine how much cooling or heat rejection can beachieved. For air coolers, the outlet air temperature needs to bewithin the minimum allowable air temperature. For heatrejection units, the maximum allowable water temperature thatcan return to the condenser heat exchanger need to beconfirmed with suppliers. The input parameters of the air andwater and, depending on whether air coolers or heat rejectionunits are used, the design criteria, need to be satisfied. Whenthe design criteria are not met for these heat exchangers, theinlet air quantity needs be increased where possible.

The excavation size for the heat rejection units needs to beknown in order for the designer to determine the water loadingfactor. The area of the available or new excavations willdetermine the required length of each unit. In this manner thenumber of heat rejection units can be confirmed and correctlyselected.

Step 4: environmental conditions and surroundingactivities

The quality of air and water are important factors to evaluatewhen designing heat exchangers (Figure 5). Both parameterswill impact on the maintenance frequency. In the event thatfresh air is used, no problem should be anticipated. When airis re-used from certain working areas the quality of the airneed to be investigated with respect to blasting fumes, dust,radiation, and other pollutants. These factors will impact ontechnical maintenance and design requirements of the heatexchangers in terms of conductivity of the water, blow-downrate, maintenance and cleaning frequency, make-up waterrate, equipment selection, etc.

The location of the heat exchangers needs to be verifiedby the rock engineers to ensure stability of the excavationand surrounding strata.

Maintenance frequencies need to be determined based onthe quality of the air and water, materials of construction,and equipment life.

The constructability logistics include all activitiesinvolving installing the equipment, from shaft time, craneinstallation to offload equipment, and actually commissioningthe process system. A constructability manager is usuallyappointed for this purpose to assist with on-time delivery andinstallation.

Step 5: types of bulk air heat exchangers

In this step the type of heat exchanger will be determined. Asstated earlier, three types of coolers – indirect-contact heatexchangers, spray towers, and spray chambers – are availableto cool the air. Heat rejection can be achieved in spray towersand spray chambers. Banks of indirect-contact heat exchangerswill be used mainly in closed-circuit water systems in clean air.

For indirect-contact heat exchangers two design optionsexist, namely the LMTD method and the NTU method. Fordirect-contact (open-circuit) heat exchangers, spray towers andspray chambers are possible options. Spray towers areclassified under filled or unpacked units and the selectionunder each type is unique. Design processes for spraychambers, together with spray towers and indirect-contact heatexchangers, are discussed in a decision process. Figure 6 is atypical example of a decision analyser step as applied tovarious types of heat exchanger.

The factor of merit (FOM) and positional efficiency are twoof the major contributors to the final selection of heatexchanger type. Indirect-contact heat exchangers, spraytowers, and spray chamber heat exchangers can be selected forheat rejection and cooling.

The number of units, stages, and/or cells will give anindication of the size of the excavation required to install theheat 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 beinstalled.

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Figure 4—Heat exchanger inlet conditions

Figure 5—Environmental conditions and surrounding activities

Conclusions

A step-by-step guide to all factors contributing to undergroundbulk air heat exchanger technical specifications was developed.This guide was generated from environmental factors(including thermal design criteria, heat loads, excavationrequirements, environmental conditions, and rock mechanics)and engineering and technical requirements (including types ofheat exchangers, factor of merit, and hydraulics).

The step-by-step guide makes it possible to design a quickand easy fit-for-purpose technical specification forunderground heat exchangers. This specification can be usedby manufacturers and construction companies to designoptimized ‘for-construction’ heat exchangers.

It can be concluded that the decision analysers do provide aguideline of the criteria required for technical specifications forbulk air heat exchangers. The overall aim was to provideoperational engineers with a tool to quickly and easily specifyoptimized technical specifications for fit-for-purpose heatexchangers. The factors and technical requirements identifiedin this study could be applied to a real mine with the used ofthe decision analysers.

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DEPARTMENT OF MINERAL RESOURCES (DMR). 2001. Guideline for the compilation ofa mandatory code of practice on minimum standards of fitness to performwork 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. BEnghons 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 usingnetwork 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

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KARSTEN, M. and MACKAY, L. 2012. underground environmental challenges indeep platinum mining and some suggested solutions. Platinum 2012. FifthInternational Platinum Conference ‘A Catalyst for Change’ Sun City, SouthAfrica, 17-21 September 2012. Southern African Institute of Mining andMetallurgy, Johannesburg. pp. 177–192.

KAYS, W.M. and LONDON, A.L. 1964. Compact Heat Exchangers. 2nd edn. McGraw-Hill, New York.

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

MARX, W., HOOMAN, M., BOTHA, P., and MEREDITH, G. 2010. Refrigeration andcooling design case study: Palabora Mining Company. Journal of the MineVentilation 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. 7thedn, 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 AfricaResearch Organisation.

REUTHER, E.U., UNRUH, J., and DOHMEN, A. 1987. Simulation techniques for theoptimization of high capacity refrigeration in German coal mines. APCOM87. Proceedings of the Twentieth International Symposium on theApplication 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. Chapter24 (Refrigeration Practice) and chapter 25 (Chilled Water Reticulation. Capeand Transvaal Printers, Cape Town.

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WHILLIER, A. 1977. Predicting the performance forced-draught cooling towers.Journal of the Mine Ventilation Society of South Africa, vol. 30. pp. 2–25. ◆



Figure 6—Types of heat exchangers

WHO SHOULD ATTENDThe conference should be of interest to anyone working in orwith the mining sector, including government and civil societyorganisations. 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.

BACKGROUNDThe evolving nature of the current mining environment suggests thatthere be strict environmental and social considerations as keycomponents in determining mine profitability. Recent research onenvironmental and social risks and business costs in the extractiveindustry found that environmental issues were the most commoncause of disputes, resulting in lost productivity. These environmentalissues were centred on the pollution of, competition over and accessto natural resources. International best practices and compliancestandards have set the benchmark for mining companies togetherwith national legislation. However, over time, the essence of thesebenchmarks loses meaning when they become ‘tick boxes’ for theindustry 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 impactsand consider ways of building resilient socio-ecological systems thatinclude mining.

OBJECTIVESThe key objective of the conference is to get the relevantstakeholders 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 itrelates to social and environmental concerns

� Explore the interactions of the various stakeholders in miningtransactions

� 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

Mining, Environment and Society ConferenceBeyond sustainability—Building resilience

12–13 May 2015

Conference AnnouncementFor further information contact:Conference Co-ordinator, Yolanda Ramokgadi, SAIMM

P O Box 61127, Marshalltown 2107 · Tel: +27 (0) 11 834-1273/7E-mail: [email protected] · Website: http://www.saimm.co.za

Mintek, Randburg

KEYNOTE SPEAKER:Rohitesh Dhawan, KPMG’s Global Mining Leader forClimate Change & Sustainability. Currently co-locatedbetween Johannesburg and London, he has spent time inhead offices and down mining shafts working on issuesrelated to strategy, social performance, environmentalsustainability and governance primarily in the coal, gold andplatinum sectors. The issues that he enjoys working andresearching on include calculating social return oninvestment, decision-making under conditions ofuncertainty, the role of business in society, corporatepurpose and managing environmental impacts. AnEconomist by background, he holds a Masters degree fromthe University of Oxford and is a fellow of the inauguralclass of the Young African Leadership Initiative. Rohiteshwas named one of Mail & Guardian’s 40 Climate changeLeaders and the South African Rising Star in theProfessional Services Category.

Introduction

The dominant ironmaking process in the worldtoday is the blast furnace process, and themost important raw material fed into the blastfurnace in terms of operational efficiency andhot metal quality is metallurgical coke. Insidethe blast furnace, coke performs threefunctions:➤ Thermal: as a fuel providing the energy

required for endothermic chemicalreactions and for melting of iron andslag

➤ Chemical: as a reductant by producingreducing gases for iron oxide reduction

➤ Mechanical: as a permeable mediumproviding passage for liquids and gasesin the furnace, particularly in the lowerpart of the furnace.

When coke passes through a blast furnace,it degrades and generates fines which affectbed permeability and the process efficiency.Coke quality is often characterized by the hotand cold strength, ash composition (Mayaleekeet al., 2009; Nagashanmugam and RejiMathai, 2012), and chemistry, which arelargely dictated by the coal properties(Grosspietsh et al., 2000). Unfortunately, coldstrength and hot strength are not clearlydefined, and the methods used for quantifyingthese parameters by experimentalmeasurement vary (Van Niekerk andDippenaar, 1991). A range of laboratory testsand procedures have been developed tocharacterize the physical and chemicalproperties of coke and gauge their potentialeffects in blast furnaces. The most commonlyused and well-known tests are the cokereactivity index (CRI) and coke strength afterreaction (CSR) developed by Nippon SteelCorporation in Japan in early 1970s to assessthe effect of CO2 reactions on coke. Generally,a high CSR is believed to prevent the coke frombreaking down, improve the permeability, andincrease the productivity of the ironmakingprocess as well as decrease the specific cokeconsumption (Grosspietsh et al., 2000).However, there is no international consensuson an ideal way to determine the quality ofcoke, as each industry relies on empiricalexperience for its interpretation. Theselaboratory tests are designed to test cokeproperties under a specific set of conditions,

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

Synopsis

Blast furnace performance depends strongly on the coke reactivity index(CRI) and coke strength after reaction (CSR) properties. An innovativeand cost-effective method, known as the Salem Box Test, has beendeveloped to prevent the mass production of inferior coke unsuitable forblast furnace use. This method consists of coal carbonization on a micro-scale and involves charging approximately 18 kg of coal blend in astainless steel box, carbonizing it together with coal cake in the plantcoke ovens, and testing the coke produced for CRI and CSR to determineits suitability for blast furnace use. Only coal blends that yield coke withCRI <25% and CSR >64% are permitted for mass production, and othercoal blends are either rejected or the blending ratios adjusted in anattempt to upgrade them. The experimental results reveal that, for agiven coal blend, the quality of coke produced by the Salem Box Test iscomparable with that produced by bulk production, indicating that thetest is acceptable as a screening tool for regular use.

The present paper describes the methodology and application ofSalem Box Test to predict the suitability of coke for blast furnace use atJSW Steel Limited, Salem Works (JSWSL), and illustrates its advantagesin adjusting the coal blending ratio to produce superior coke, indetecting coal contamination, and in preventing bulk production ofinferior coke.

Keywordscoke-making, blast furnace, metallurgical coke, Salem Box Test, cokereactivity index (CRI), coke strength after reaction (CSR).

* R & D Centre & Coke Ovens, JSW Steel Ltd, SalemWorks, Salem, India.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedJan. 2014; revised paper received Oct. 2014.



which might not be universally suitable. The reproducibilityof CRI and CSR values also varies considerably betweendifferent laboratories (Arendt et al., 2001).

Charging an inferior coke to the blast furnace posesproblems such as high dust generation, poor permeability,hanging, slips, high fuel consumption, reduction in thequantity of coal injection, low productivity etc. To avoid sucha scenario, a novel technique, known as the ‘Salem Box Test’,which employs micro-level carbonization of the coal blend ina stainless steel box, was developed at JSW Steel Limited’sSalem Works (JSWSL). The coke obtained by the box test isthen tested for CRI and CSR to determine its suitability forblast furnace operation. Only those coal blends that producecoke 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 unsuitablefor blast furnace operation and ensures the manufacture andconsistent supply of suitable coke. Coke with a maximum of25% CRI and a minimum value of 64% CSR was found to besuitable for the JSWSL blast furnaces. This paper describesthe use of the Salem Box Test to predict the suitability of cokefor ironmaking in a blast furnace.

Material and method

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

Procedure for Salem Box Test

The Salem Box Test (patent under application) is a cost-effective method for evaluating the suitability of different coalblends before mass production of coke. At JSWSL, thismethod is used for selection / optimization of coal blends thatwould produce coke with the required CRI and CSR in the realoperating environment.

Samples of various coals, which constitute the coal blend,are collected and crushed to below 3 mm in size. The coalsamples are mixed in the required proportions to prepare thecoal blend, the required quantity of water is added tomaintain approximately 10% moisture, and the sample isthen homogenized by manual mixing. The coal blend placedinside a stainless steel box (size 250 mm × 250 mm × 250mm, wall thickness 10 mm) in three to four increments andstamped with a metal stamper until approximately 18 kg ofcoal blend is compacted. The sample is now ready forcarbonization. As the box needs to remain uncovered, a lid isnot provided. The box is placed over the ‘ready-to-charge’stamped coal cake by removing a portion of the cake at thecenter of its width to accommodate the box. The coal cake is

then charged into the oven for carbonization, together withthe box of test blend (Nagashanmugam et al., 2012). Aftercarbonization, the coke cake is pushed onto the quenchingcar and is water-quenched. The box is then removed and thecoke obtained is air-cooled, crushed, and sieved to 19–21 mmsize. The sized coke is analysed for CRI and CSR (ASTM D-5341-99, 2004) using equipment from M/s. NaskarInstruments Ltd, Kolkata, India. The test results determinethe suitability of the coal blend for mass production in cokeovens. Only those coals / coal blends that yield coke (by theSalem Box Test) having CSR >64% and CRI <25% are beconsidered 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 andheated to 1100°C in an inert atmosphere of nitrogen. Purecarbon dioxide gas is then passed through the sample. Thereaction is sustained for 2 hours. The reacted coke is thencooled and weighed to determine the CRI (Nagashanmugamand Reji Mathai, 2012). The strength test is performed byplacing the sample in an I-shaped drum and rotating it at 20r/min for 30 minutes. The coke is then weighed to determinethe CSR (Nagashanmugam and Reji Mathai, 2012).


Before the introduction of the Salem Box Test, testing ofcoals/coal blends for bulk manufacture of coke was carriedout in large-scale oven trials. Although such a method hasobvious advantages, it also has several disadvantages. Forexample, oven trials must not interfere with the usual routineof the plant, but must await a convenient time. Sometimes,due to a considerable time lag between the arrival of the coalat the plant and its use at the ovens, serious deterioration ofthe coking properties occurs through weathering.Furthermore, the full oven test becomes quite expensive(assuming it results in an inferior coke), hence obtaining therequired information by simpler means leads to cost savings.Large-scale tests are needed only to confirm the resultobtained by the Salem Box Test.

Initially, the box tests were performed in cube-shapedmild steel box with a thickness of 7 mm and dimensions of300 mm × 300 mm × 300 mm. This was later optimized to250 mm × 250 mm × 250 mm and 10 mm thickness tofacilitate manual handling and increase the service life of theboxes. As boxes of both dimensions were found to giveconsistent results, trials with other dimensions were notconducted. Although the size of the box is not critical, it isbetter to have the box dimension at least 10 times the size ofcoke (19-21 mm) used for CRI and CSR analysis. Althoughboxes made of mild steel gave satisfactory results, the resultswere occasionally found to be inconsistent. Subsequently,this was found to be due to corrosion of the mild steel. It wassuspected that iron oxide, the product of corrosion, might

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

initiate the gasification reaction at low temperature and henceaffects the CRI and CSR indices of the coke. In order toconfirm this supposition, two box tests were carried out withthe same coal blend, one in presence of iron oxide and theother in its absence, and the resultant cokes were tested forCRI and CSR. The results of the test are presented in Table I.

Table I reveals that the CRI and CSR values of cokeobtained by carbonization in the absence of iron oxides weresuitable for blast furnace, as they meet the JSWSL specifi-cation of CRI <25% and CSR >64%. The coke obtained fromthe same coal blend in the presence of iron oxides was foundto have deteriorated in quality to a larger extent with respectto CRI and CSR, thus becoming unsuitable for blast furnaceuse. 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 haveyielded better coke (suitable for the blast furnace) in actualovens, had it not been affected by corrosion of the mild steelbox. The stainless steel boxes were found to yield highlyconsistent results, and therefore boxes made of othermaterials were not tested.

Figure 1 shows a schematic of the stainless steel boxused in the Salem Box Test, and Figure 2 illustrates the flow

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 highlysatisfactory for the prediction of the coke quality that wouldbe obtained by mass production in coke ovens. Table IIpresents few examples of box tests conducted with variouscoals / coal blends.

Those coal blends that yielded CRI values below 25% andCSR values above 64%, were passed for mass production,and other coal blends were either rejected or further tested byvarying the blending ratios.

In order to determine the applicability and suitability ofthe Salem Box Test, the coal blends that were found to yieldthe required CRI and CSR were carbonized in coke ovens formass production as per normal procedure and the resultantcoke was subjected to CRI and CSR analysis. The CRI and CSRvalues of coke obtained from box tests and those obtainedfrom coke ovens using same coal blends are compared inTable III.

It can be seen from Table III that the CRI and CSR valuesof coke obtained from box tests and bulk tests for the samecoal blends correlate well, and the variation is insignificant.This indicates that the coke obtained from the box test could



Table I

CRI and CSR values obtained in presence and absence of iron oxide

Coal blend / mix Test condition

Name of coal Fraction in blend (%) Iron oxide present Iron oxide absent

CRI (%) CSR (%) CRI (%) CSR (%)

Australian coal A 60Australian coal B 25Australian coal C 5 32.0 58.0 24.0 65.88Russian anthracite coal 10

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

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Table II

CRI and CSR of coke obtained by the Salem Box Test

Sl. no. Coal blend / coal mix CRI (%) CSR (%)

Source and category Fraction in blend (%)

1 Australian hard coking coal D 45Australian semi hard coking coal B 25 26.0 63.0

Australian hard coking coal E 30

2 Australian hard coking coal D 45Australian hard coking coal E 25

Australian semi-hard coking coal B 25 27.0 62.0Australian non-coking coal A 2.5

Russian non-coking coal 2.5

3 Australian hard coking coal B 45Australian hard coking coal E 20 25.0 65.33

Australian semi-hard coking coal B 30Australian hard coking coal C 5

4 Australian hard coking coal B 45Australian hard coking coal E 5Australian hard coking coal F 15

Australian semi-hard coking coal C 25 25.0 65.33Australian semi-hard coking coal A 5

Australian hard coking coal A 5

5 Australian hard coking coal C 50 26.5 63.94Australian semi-hard coking coal C 25

Australian hard coking coal E 10Russian non-coking coal 15

6 South African hard coking coal 27Australian hard coking coal B 20

Australian semi-hard coking coal B 20Australian semi-hard coking coal A 10 25.0 65.0

Indonesian hard coking coal 5Australian non-coking coal B 10

US non-coking coal 8

Figure 2–Steps involved in performing the Salem Box Test



Table III

Comparison of coke quality obtained by Salem Box and bulk tests

S. no. Coal blend / coal mix Test condition

Name of coal Fraction in blend (%) Box test Bulk test

CRI (%) CSR (%) CRI (%) CSR (%)

1 Canadian hard coking coal 35Australian hard coking coal E 30 20.5 71.0 21.0 70.8

Australian semi-hard coking coal C 35

2 Australian hard coking coal D 45Australian hard coking coal E 25

Australian semi-hard coking coal B 25 27.0 62.0 27.5 62.4Australian non-coking coal-A 2.5

Russian non-coking coal 2.5

be considered to represent coke that would be obtained bybulk production using the same coal blends. Thus, the SalemBox Test becomes an effective screening tool for selecting orrejecting a given coal blend for bulk coke manufacture.

Prior to the introduction of the Salem Box Test, therewere numerous instances where coke was rejected for use inthe blast furnace owing to poor quality. After the introductionof box tests, such instances have been avoided as the boxtest gives a clear indication as to the suitability of a particularcoal blend for producing blast-furnace quality coke.Furthermore, a continuous supply of suitable coke is alsoensured.

Salem Box tests are also helpful in making minoradjustments in the coal blend compostion in order to producebetter coke. There are instances where coal blends / coal mixwere changed based on the results of box tests. Table IVillustrates typical examples where the proportions of the coalblend were changed or modified suitably such that box testsyielded better CRI and CSR results.

Table IV illustrates three cases in which the designatedcoal blends produced coke with inferior CRI and CSR values.However, slight modification in the weight percentage ofindividual coals (especially Indonesian coal) led to animprovement in coke quality, making it suitable for blastfurnace use. It can be seen that, even by maintaining the totalweight fractions of hard coking, semi-hard, and non-cokingcoals constant, it becomes possible to obtain coke of therequired quality. Subsequent investigation revealed that highvolatile matter and alkali content (Na2O + K2O) in Indonesianhard coking coal in examples 1 and 2 and Australian semi-hard coking coal B in example 3 were responsible for the poorcoke quality.

The results in Table IV reveal that by suitably changingthe blending ratio of individual coals it is at times possible toobtain the required coke quality.

The Salem Box Test also serves to reject / screen out anindividual coal or coal blends that are unsuitable for bulkcoke manufacture. Table V illustrates few typical instanceswhere the bulk production of an inferior coke was averted byusing box tests.

Based on the composition and properties of these coals, itwas expected that both the coal blends should yield coke withthe required CRI and CSR properties, but the results obtainednegated the expectation. Detailed investigation and analysisrevealed that Australian hard coking coal E was contam-inated at the loading port (example 1) and Australian semi-hard coking coal C was contaminated with other non-cokingcoals at the unloading port (example 2). This contaminationwas found to be the root cause for the poor quality of coke.Thus, based on the results of box tests, the mass productionof inferior coke was averted.

Conclusions

➤ The quality of coke produced by the Salem Box Test iscomparable with that produced by bulk production,indicating that the test is acceptable as a screening toolfor regular use

➤ The Salem Box Test serves to reject an individual coalor coal blend as unsuitable for coke production andprevents bulk manufacture of inferior coke

➤ The Salem Box Test is also an effective method fordetecting contamination in coal, and can be used toindicate where coal blending ratios can be adjusted toyield a coke product with suitable CRI and CSRproperties.

➤ The Salem Box Test has been successfully used formore than 7 years at JSWSL to avoid the massproduction of unsuitable coke and ensure themanufacture, availability, and consistent supply of cokesuitable for blast furnace operation. This has resultedin better productivity and cost savings in blast furnaceoperation.

Acknowledgement

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

References

ARENDT, P., HUHN, F., and KUHL, H. 2001. CRI and CSR - a survey of interna-

tional 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

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

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

2000. Vol. 1. pp. 1–11.

MAYALEEKE, 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

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

782–787.

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

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

VAN NIEKERK, W.H. and DIPPENAAR, R.J. 1991. Blast-furnace coke: a coal-

blending model. Journal of the South African Institute of Minerals and

Metallurgy, vol. 91, no. 2. pp. 53–61. ◆

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Table V

Typical instances where the results of box tests were useful in detecting coal contamination

S. no. Coal blend / mix CRI (%) CSR (%) Remarks

Name of coal Fraction in blend (%)

1 Australian hard coking coal E 40Australian semi-hard coking coal B 30 31.0 60.86 Contamination of Australian hard

US hard coking coal 20 coking coal EAustralian hard coking coal A 10

2 Australian hard coking coal B 50Australian semi-hard coking coal C 30 30.0 60.0 Contamination of Australian semi-

Russian non-coking 20 hard coking coal C

Table IV

Details of changes made in blending ratio to obtain suitable coke

Sl. no. Coal blend / mix CRI % CSR % Coal blend /mix (%) CRI (%) CSR (%)

Name of coal Fraction in blend % Fraction in blend%

1 Australian hard coking coal B 17 20South African hard coking coal 25 27

Indonesian hard coking coal 10 5Australian semi-hard coking coal B 20 28.0 60.27 25 24.2 66.2Australian semi-hard coking coal A 10 5

US non-coking coal 12 10Australian non-coking coal B 6 8

2 Australian hard coking coal B 15 20South African hard coking coal 30 30

Indonesian hard coking coal 15 10Australian semi-hard coking coal B 5 28.0 61.8 5 23.5 65.0Canadian semi-hard coking coal 10 10

Australian semi-hard coking coal A 5 5US non-coking coal 8 10

Australian non-coking coal C 12 10

3 Australian hard coking coal 25 25South African hard coking coal 35 35

Australian semi-hard coking coal A 15 27.0 61.3 15 24.0 64.8Australian semi-hard coking coal B 5 0Australian semi-hard coking coal C 5 10

US non-coking coal 15 15

Introduction

In 2013, 1.600 Mt of steel were producedworldwide, approximately 77% of which wasproduced via ironmaking by the blast furnace(BF) and basic oxygen furnace (BOF) routes orby direct reduction (DR) and electric arcfurnace (EAF), while direct melting of scrap byEAF accounted for approximately 23% (WorldSteel, 2014). Iron ore pellets are a preferredfeedstock in ironmaking by both the BF andDR routes, and the demand for pellets ispredicted to increase markedly until at least2025 (Huerta et al., 2013). Of all the iron oremined in 2012, 23% was converted into pellets(Ericsson et al., 2013).

Pelletizing is preferred because thechemical, physical, and metallurgical charac-teristics of pellets make them a more desirablefeed for the ironmaking processes (Mbele,2012). Moreover, because of their highstrength and suitability for storage, pellets canbe transported easily over long distances, withrepeated transhipments if necessary. Duringpelletizing, iron ore is crushed and milled to afine concentrate, mixed with additives and abinder, and balled into pellets prior to sinteringand induration (hardening) in the furnace. Inthe past, the size and form of the pellets variedmarkedly. Figure 1a shows iron ore pelletsproduced in Persberg, Sweden, during the1970s. Today, pellets are fabricated into amore uniform shape, with sizes typically 9–15 mm (Forsmo et al., 2008). Figure 1b

shows iron ore pellets produced in Kiruna,Sweden, during the 2000s.

Magnetite is a preferred feed in pellet-making because of the exothermal energyreleased during oxidation. The most commonmethod of pelletizing is the travelling-grateprocess (Huerta et al., 2013). This travellinggrate uses a stationary bed of pellets, whichare transported through the entire process,consisting of zones of drying, oxidation,sintering, and cooling (Potts, 1991). Thetravelling-grate process is often also used forpelletizing of haematite ores. The second mostcommon pelletizing processes is the grate-kiln-cooler (GKC) or just grate-kiln process, whichis often used for pelletizing magnetite ores.This process uses a shorter grate, with part ofthe oxidation (when using magnetite) andsintering taking place in the kiln, which is arotating furnace that achieves morehomogenous induration (Zhang et al., 2011).A third system used for pelletizing is the shaftfurnace, the most traditional of the facilities.However, very few plants use this systemtoday because of the limiting scale (Yamaguchiet al., 2010).

A pelletizing process consist of fourconsecutive steps (Yamaguchi et al., 2010):➤ Reception of raw material➤ Pre-treatment➤ Balling➤ Induration.

This paper deals with the fourth of thesesteps, the induration, as performed in thegrate-kiln process. A short description andhistory is given, the benefits and drawbacksare discussed, typical problems are raised, andan outline for the future of the process isgiven.

The grate-kiln induration machine — history,advantages, and drawbacks, and outline forthe futureby J. Stjernberg*†, O. Isaksson† and J.C. Ion‡

SynopsisIron ore pellets are a preferred feedstock for ironmaking. One methodused for pelletizing is the grate-kiln process, first established in 1960.During the past decade, the establishment of new grate-kiln plants hasincreased rapidly, especially in China, and new constructors of pelletplants have started to operate in the market. It is well known that thegrate-kiln method yields a superior and more consistent pellet qualitycompared with the straight-grate process. However, certain issues existwith the grate-kiln plant, which are discussed here, together withproposed practical solutions.

Keywordsgrate-kiln, pelletizing, iron ore.

* Division of Materials Science, Luleå University ofTechnology, Sweden.

† Loussavaara-Kiirunavaara Limited (LKAB),Sweden.

‡ Malmö University, Materials Science, Sweden.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. Paper receivedAug. 2014; revised paper received Oct. 2014.



The grate-kiln induration machine

The grate-kiln-cooler process

The main function of the grate (in the GKC process) is dryingand pre-heating of the green pellets (Feng et al., 2012). Thegrate 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), downdraftdrying (DDD), temperature pre-heat (TPH), and pre-heat(PH). The typical length of a full grate furnace in the GKC isapproximately 60 m, with a width of about 5 m.

A typical rotary kiln used in iron ore pellet productionusually has a length between 30–50 m, and a diameter of5–7.5 m (normally not more than 7.2 m, as difficulties withthe refractory lining may occur at larger diameters), and isfired by coal or natural gas. The highest temperatures in theprocess are achieved in the kiln, up to about 1400°C. Therefractory lining in the kiln normally comprises bricks basedon Al2O3 and SiO2. There are also kilns lined with castables.

A burner is located in the outlet of the kiln, where thepellets falls down in the cooler. In the case of pelletizing ofhaematite, burners are also located in the grate because ofthe lack of exothermal energy released by magnetiteoxidation. The burner fuel is normally coal or gas, with fueloil used as secondary fuel.

The annular cooler is functionally the same as thetraveling grate, except for its annular configuration. Thepellets fall from the kiln, down in the circular cooler carouselwhere they travel lying on the conveying elements (palletgrids). Ambient air is blown through the pallets and thetemperature of the pellets drops from approximately 1200°Cto 100°C during an orbit; the heated process gas istransported back to the grate for heat exchange. Cooledpellets discharge through the cooler’s discharge hopper to aproduct load-out system. The typical outer diameter of anannular cooler is between 15 m and 30 m. There are also afew grate-kiln plants with straight coolers.

Figure 2 shows the outline of a modern GKC plantaccording to Metso’s design (Metso, 2014).

Figure 3 shows the LKAB (Loussavaara KiirunavaaraLimited) kiln No. 4 in Kiruna, Sweden

History

Rotary kilns were originally developed in the late 19thcentury for Portland cement production, and the cementindustry is still the largest user (Boateng, 2008). To improveenergy efficiency in cement plants, a pre-heater in the form ofa Lepol grate was used for the first time in 1927 (invented byOtto Lellep, marketed by Polysius), and it is from this systemthat the grate-kiln for iron ore pelletizing originated (Trescotet al., 2000). Today, rotary kilns have been adopted forprocessing several different metal ores (besides iron ore), e.g.nickel (Tsuji and Tachino, 2012) and titanium (Folmo andRierson, 1992), as well as for direct reduction of iron ore(Tsweleng, 2013).

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a)

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

b)

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

Figure 3—Kiln at the LKAB plant No. 4 in Kiruna, Sweden (view lookingupstream)

Figure 2—Outline of a grate-kiln plant

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 orepellet plant of the grate-kiln type was established atHumboldt Mine, Michigan in 1960 (Sgouris and Oja, 2008).Allis-Chalmers (a predecessor company to Metso) have sincebuilt around 50 such plants. However, very few of the olderplants built before 1975 are still in use. Another constructorof grate-kiln plants is Kobe Steel, who built their first plant in1966 at Kobe Works, Nadahama, and have since thenconstructed more than ten plants, most of which are still inuse (Yamaguchi et al., 2010).

Since 2000, the grate-kiln process developed by theShougang Group has been rapidly adopted in China (Zhangand Yu, 2009). The establishment of new grate-kiln plants inChina has been very prominent in the last decade (Zhang etal., 2011), with the rise of new fabricators such as JiangsuHongda and Citic.

Figure 4 shows the growth in grate-kiln plants for ironore pelletizing since 1960, and their geographical distri-bution. An exponential increase can be seen since 2000,driven mainly by installations in China. Moreover, very fewplants were built anywhere in the world between 1985 and2000.

The grate-kiln process: benefits and drawbacks

The grate-kiln vs the travelling grate

The GKC process possesses both advantages and drawbackscompared with the travelling-grate process. A generalcomparison between the two (Sgouris and Oja, 2008; Zhanget al., 2011; Huerta et al., 2013) shows that:

➤ The grate-kiln yields a superior and more consistentpellet quality, and consumes less electrical energy.Since the speed of the grate, kiln, and cooler can becontrolled 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 expensivehigh-temperature-resistant steel alloys are used inconstruction. Significant drawbacks are the low

suitability for pelletizing of pure haematite feedstock,higher generation of fines in the process, and lowerenergy efficiency

➤ The travelling grate has a lower fuel consumption, asthere is less radiated heat loss and a better heatexchange between the solids and the air because of thedeeper bed of pellets. The maintenance and refractorycosts are lower, and the cold start-up time is shorter. Itis suitable for pelletizing of both magnetite andhaematite burden (and magnetite-haematite mixtures),and fewer fines are formed in the final product.Significant drawbacks are the higher electricityconsumption, and coal (or other solid fuels) cannot beused as primary fuel.

Typical problem issues with the grate-kiln machine

There are some typical problem issues and symptoms thatcan arise with the grate-kiln, summarized here (based on theliterature and drift-logs from LKAB).

Deposition of material on the refractory lining

Coal always contains inclusions of mineral matter that remainas fly-ash after combustion (Reid, 1984). Disintegratedpellets can, together with fly-ash from the coal burned to heatthe kiln, form accretions on the lining, sometimes as ring-forms in the kiln (Jiang et al., 2009; Xu et al., 2009). Thisphenomenon is also common in lime kilns (Potgieter andWirth, 1996) and cement kilns (Recio Dominguez et al.,2010). This material can also be deposited as stalagmitestructures in the kiln (Figure 5) or as accretions in thetransfer chute.

Air flows tend to be turbulent, especially in the transferchute (Burström et al., 2010), as this is the geometricalbottleneck of the induration machine. A thin layer of depositon the surface can act as protection for the lining, but whenthese deposits increase in thickness they contribute tomechanical strain. A fallout of such deposit causes a rapidincrease in the temperature of the lining at the new hot face,which may lead to thermal shock and spalling (Stjernberg etal., 2012). The Allis-Chalmers Corporation investigated fuel



Figure 4—Grate-kiln plants for iron ore pelletizing built since 1960, andtheir geographical distribution. Asia* excludes India, China, Japan, andthe Arabian Peninsula

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


combustion in grate-kiln plants in the 1970s (Cnare, 1977).It was observed that the key factor for deposition in the kilnwas the presence of dust, and that deposition of fly ash onthe lining could be minimized through correct selection ofcoal and optimization of overall process control. Tests withcombustion of natural gas showed that in addition to theabsence of fly ash deposits, another benefit was that thelining temperature could be maintained more than 50ºClower, because the radiant heat of natural gas combustion islower than that of coal or oil.

Kobe Steel noted in 1981 that when the burner fuel waschanged from heavy oil to coal (because of the sharp increasein the price of fuel), adhesion of deposits on the lining wasenhanced (Uenaka et al., 1983). Adhesion tests were carriedout on a water-cooled specimen inserted through a holesituated at the end of the pre-heat chamber, just before thestarting point of the process. Uenaka et al. investigated theamount of ash adhering to the detection bar, the density ofthe deposit, coal properties such as fine particle size, ashcontent, the proportion of pulverized ore in the pre-heatedpellets, and operating conditions such as kiln off-gastemperature. It was observed that even if the same type ofcoal was fired, the amount of deposit varied with operatingconditions. Similar tests with water-cooled probes werecarried out at LKAB in Kiruna, Sweden (Jonsson et al., 2013;Stjernberg et al., 2013). It was found that inertial impactionwas the dominant deposition mechanism, and that the flue-gas flow direction determines the texture and formation ofthe deposits. The deposits were mainly haematite particlesembedded in a bonding phase, comprising mainly calcium-aluminium-iron silicate. In addition to haematite and thebonding phase, the minerals anorthite, mullite, cristobalite,quartz, forsterite, and apatite were also observed in thedeposits after cooling to room temperature.

Lining problems in rotary kilns in Wuhan, China havebeen reported by Xu et al. (2009). These kilns also hadproblems with rapid accumulation of deposits on the liningthat were hard to remove. One of the main reasons for thiswas the use of burner coal with a high fly-ash content andlow ash melting temperature. Another important factor wasthe compressional strength of the iron ore pellets, which wasobserved 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 importantparameters in one of the plants, since these dictate where thefly 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 meltingtemperature of the slag phase in the deposits, in order todecrease the adhesiveness of particles on the lining. Anothermethod is to add silicon carbide (SiC) or some other carbon-bonded phase to the lining, in order to decrease thewettability of the slag.

Refractory failures

The refractory lining in the grate and in the cooler seldomcauses failures that lead to urgent shutdowns, as are causedby kiln failures. Upon heating, the lining in a rotary kilnexpands with temperature in proportion to the coefficient ofthermal expansion (CTE) of the refractory (Shubin, 2001a).

This aids in securing the bricks, but stresses from the linersin the ring brickwork arise in the metal casing (Shubin,2001b). Thermal shock induced by temperature gradients atstarts and stops in the operation, together with mechanicalstrain, may cause urgent stops in production because of brickspallation or fallouts of bricks. Moreover, an even largerthermal stress acts on the lining at starts and stops duringproduction, giving rise to several stress states in the form oflongitudinal and lateral bending, brickwork twisting,vibration, and torsion (Shubin, 2001c, 2001d). The coldcrushing strength of refractory bricks is often given in data-sheets However, it is more important that the material has alow Young’s modulus at high temperatures, such that thelining becomes more flexible during operation. Otherwise thebricks suffer thermomechanical strains far above the cold orhot crushing strength (Saxena, 2009).

Even during steady-state conditions, the lining is exposedto temperature oscillations as it rotates (Shubin, 2001a). Inrotary kilns used for iron ore pellet production the lining canbe assumed to be exposed to different temperatures duringeach revolution of the kiln. When the pellet bed covers thelining, it is exposed to less radiation from the flame, but isexposed to heat from the energy liberated from the oxidationof the pellets (when using magnetite). If the kiln rotates atapproximately 2 r/min (which is common for rotary kilns inthis application) it revolves 3000 times a day, with atemperature oscillation during each revolution. Thetemperature oscillation range varies from kiln to kiln,depending on operating conditions, but the temperaturevariation can be as high as 100ºC in the lining, down to adepth 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 withmicrocracks in ceramic materials.

A study carried out in a grate-kiln plant in Kiruna,Sweden (Stjernberg et al., 2012), showed that migration ofpotassium through the hot face of the lining caused theformation of feldspathoid minerals, leading to spallation.Moreover, haematite was found to migrate into the lining.This phenomenon was also found in rotary kilns for pelletproduction in China (Zhang et al., 2009). Although these arerelatively slow phenomena, they contribute to the overalldegradation of the lining. Zhu et al. (2003) reported liningproblems in a rotary kiln in Qian’an, China. Urgentproduction stops caused by fallouts of bricks occurred only afew months apart. Different types of brickwork and liningmaterial (e.g. chamotte, high alumina, and phosphate-bondedalumina) were tested in the kiln to avoid fallouts and rapiddeterioration of the lining. In 2000 a mullite brick of anincreased size was tested, which was still in service two yearslater.

Problems with fuel supply

Rapid changes in the fuel supply cause rapid thermal changesthat affect the pellet quality, but more critically, cause thermalshocks in the refractory lining. Problems with coal quality atdifferent production sites arise occasionally, and constitute athreat for uniform heat supply. This can arise from, forexample, wet coal. Problems with sensors or automation mayalso disturb the fuel supply. Grate-kiln plants using coal astheir primary fuel switch to a secondary fuel (oil or gas)

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when problems with the coal supply arise. Even if thistransfer appears perfunctory, the temperature profile in theplant changes markedly. An even worse scenario is when thesecondary fuel does not initiate. If the electricity supply to theproduction site is disrupted for any reason, it usually sometime before the backup power comes into operation, causingunwanted temperature variations. The alignment of theburner is also important to avoid overheating of the lining.Some of these incidents can lead to serious failures of thelining.

Wear of grate plates

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

refractoriness and resistance to wear and corrosion (Nilssonet al., 2013). Process parameters in the grate, such as thetime of drying and pre-heating, air quantity, blasttemperature, and blast velocity, affect the pellet temperatureand strength. However, they may also cause burnout of thegrate plate, shaft bending, chain breaking, and deviation ofgrate motion (Feng et al., 2012).

An investigation of a grate-link plate that had served in agrate-kiln plant for 8 months (Nilsson et al., 2013) showedthat alkaline vapours from warmer parts of the induratorcondense 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 staticand dynamic stresses caused by mechanical forces andtemperature gradients, of which only the stresses caused bymechanical forces can be influenced by the dimensions of thering. The initiation of a crack can be caused by either thestatic strength or the fatigue strength being exceeded.Hertzian pressures between the ring and the rollers reachtheir maximum beneath the surface, and consequently cracksare usually not visible until they reach an advanced stage.Riding ring cracks are in general not a consequence of poordimensioning, but of unfavourable running conditions and/ormaterial 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 thatnecessitate replacement and the associated actions are time-consuming. A replacement of the riding ring involves cuttingof the kiln on both sides of the ring, a heavy lift, and repairwelding. It is therefore important that the riding ring satisfiesthe requirements of high rigidity or stiffness, high surfacedurability, 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 atLKAB’s grate-kiln plant in Svappavaara, Sweden, in 2009.

Deformation of steel casing

The casing (shell) of the kiln deforms elastically owing to thepressure exerted by gravitational forces through the tyretowards the support rollers. This dynamic action can bedetrimental to the refractory lining, especially if the kiln isrotated in a cooled state. However, there are other factors thatcreate permanent deformation of the casing. At locations inthe kiln where bricks have fallen out, and the kiln has notbeen cooled down quickly enough, permanent deformationsmay occur that are difficult to rectify. It is therefore importantto monitor the casing with e.g. a kiln scanner, in order to



Figure 6b—Grate plate, worn-out

Figure 6a—Grate plate, as produced


identify heat abnormalities as soon as they occur(Zakharenko and Nikonenko, 2002). Permanent deformationcan also result when the temperature is increased too rapidlyand 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 tothe slow operating speeds of many of these parts, there arethermal, alignment, and cleanliness issues that need to beconsidered. Safe operation relies on a hydrodynamic oil filmto avoid metal-to-metal contact (Singhal, 2008). Use ofinadequate lubricants may decrease the service life of thesemechanical parts markedly. Hankes (2013) reviewed theselection and application of lubricants for rotary kiln girthgears and pinions. He stressed the importance of not onlyusing 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 identicalcement kilns: one that ran without problems; the other thatwas plagued with drive-related failures. On the problem kiln,the pinion had to be replaced three times and the gear

realigned three times over a five-year period. During this timethe identical gear and pinions on the comparison kilnremained as good as new. Analysis of the problem showedthat the uphill face of the thrust tyres comprised severalnoticeable discontinuities. When the tyre was cast there wereprobably voids in the cast, which were repair-welded andmachined. The welded portions of the tyre were much harderthan the surrounding areas, and high spots were developedat these locations. As the kiln revolved, these high spots onthe side of the tyre created a sharp impact load towards thedischarge end. This pressure caused pitting and wear of thepinion and gear. The problem was resolved by resurfacing thethrust tyre and eliminating the vertical load between thethrust rollers and tyres.

Cleveland Cliffs reported (Rosten, 1980) that theyimproved the lifetime of wear parts by using a nitriding hardsurfacing process on chrome-type spare parts, and carbur-ization of the surface of mild steels, used as fan wear linerapplications.

Process fans

The fans that are used to transport flue gas through thesystem sometimes fail due to power failures, or when theblades become eroded by particles carried by the flue gas, ifbearings are worn out, or due to problems with the frequencymodulation. Failures of the fans can lead to the pellets notbeing sufficiently dried before they enter the warmer parts ofthe furnace, where the sintering usually takes part. Erosionof the blades of the fan can be prevented by installing electro-static precipitators prior to the fan in the flue gas circuit.

Flue gas scrubbing

Flue gas scrubbing using e.g. electrostatic precipitators andNOx and SOx removers occasionally cause failures in pelletplants. There is the possibility to bypass such equipment;however, environmental regulations in many countries do notpermit this. Moreover, more stringent regulations motivatecompanies to evaluate alternatives to current combustiontechnologies (Fredriksson et al. 2011).

Alternative methods

Several improvements have been made at differentproduction sites, and other methods can be adopted fromrelated processes.

Cooling system

In 2001 the Minntac division of US Steel introduced a portedkiln in their plant (Trescot et al., 2004). It was a well-provendesign that had been in use for more than ten years at theTinfos direct reduction kiln for ilmenite in Norway. However,this was the first ported grate-kiln plant for iron orepelletizing. This system injects air under the bed of pellets inthe rotary kiln through slots in the joints between thespecially designed refractory bricks. This design results inmore rapid oxidation of the pellets. The company noticedseveral benefits. As the magnetite oxidizes more rapidly, alower kiln temperature can be used. With more energyliberated in the kiln, the heat load in the annular cooler isreduced, and therefore a higher tonnage can be produced. Animproved pellet quality was also observed.

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a)

b)

Figure 7—(a) Crane lifting a riding ring in Svappavarra, and (b) instal-lation in the gap of the casing

Monolithic lining in the kiln

In the Tinfos direct reduction kiln for ilmenite in Norway, thelining consists of a monolithic castable applied by shotcreting(Folmo and Rierson, 1992). The air slots present for coolingcomplicate the installation of bricks, and shotcreting is aquick method. This could be the method of choice for thelinings of rotary kilns for iron ore pelletizing in the future.Shotcreting is a fast installation method, and the lining doesnot have to be replaced as often as a brick lining. Thedrawback is that removal of the lining for maintenance ismore complex.

Development of burners

Many burners used in the grate-kiln plants today arebasically a lined steel pipe through which milled coal powderis blown. The combustion equipment used for the heat supplyin rotary kilns for cement production is often far morecomplex than the burners used in the grate-kiln. The use ofmulti-channel burners for different fuels and different airchannels allows adjustment of the flame shape duringoperation 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 overthe last 30 years have been built in China (around 40 plantsover the last 10 years). China will likely continue to buildgrate-kiln plants; however, one significant characteristic ofthe Chinese growth cycle is the relatively direct influence ofgovernmental 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 below3 Mt/a. Several plants with lower capacity are less dependenton the economic situation and access to raw material,compared with one large plant, when plants are used on anon-and-off basis. However, Metso has designed a plant with7 Mt/a capacity and Kobelco an 8 Mt/a plant, and manyexisting plants are continuously being upgraded to cater forthe increasing demand for iron ore pellets on the market.

Oxidation of magnetite in iron ore pellets occurs fastestbetween 1100°C and 1200°C. At higher temperatures theoxidation rate decreases as a result of increasing dissociationpressure and severe sintering in both the oxidized haematiteshell (which becomes denser) and the magnetite core(Forsmo, 2008). As fully oxidized pellets already in the grateare desired when using magnetite (Niiniskorpi, 2002), agrate of increased length and increased dwell time in a zonewhere the temperature is optimal for oxidation, this, incombination with oxygen injection to further improve theoxidation rate, may be an alternative in the future. This couldbe combined with a shorter kiln in a machine comparablewith a straight-grate plant completed with a short kiln, as therotary kiln is the more sensitive part in the grate-kilnconstruction.

Coal will continue to be the major fossil energy source inChina, 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 woodpellets) will in coming years be burned in pellet plants. Withthis innovation, new techniques may have to be developed,and plant outlines may have to change. A wood-based fuel

has a lower energy density (heating value of approx. 20MJ/kg compared with approx. 30 MJ/kg for coal), andtherefore the feeding rate of fuel has to increase by some50%. Moreover, wood-based fuels have in general higherfriction coefficients (compared with coal) duringtransportation through pipes, which may cause bridging andhold-ups in the fuel feeding system. Co-combustion ofblended coal (approx. 90%) and a wood-based fuel (approx.10%) is realistic already today. Possibly, it might bebeneficial to subject a wood-based fuel to pyrolysis prior tocombustion. The advantages of wood-based fuels comparedwith coal are lower CO2 emissions and a lower ash content(Nordgren, 2013). The use of waste material as an energysupply is required in cement production to make it econom-ically possible, and burners are developed for this (Vaccaro,2006). However, iron ore pellet makers can possibly be moreselective in their choice of fuels, but will in the future need touse more alternative fuels.

The iron ore price has decreased rapidly over the lastthree years (2011–2014). However, the demand for iron isunlikely to decrease in the medium term, and predictions arethat around 80 new pellet plants will need to be built in thecoming decade (Huerta et al., 2013). Many are likely to be ofthe grate-kiln type.

Acknowledgement

The authors are grateful to Johan Sandberg, Kent Tano, andHenrik Wiinikka for fruitful discussions.

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Introduction

This paper outlines the development of a newmethodology to define and measure mineplanning software utilization in the SouthAfrican gold mining sector. Although thecalculations can be done for any commodity, inthis paper calculations were done only forgold, as gold is not only used in a variety ofdifferent fields such as electronics,engineering, and health care, but also goldgenerated almost 13% of South Africa’smining income during 2013 (Statistics SouthAfrica, 2014).

An initial data-set showing the mineplanning software providers, theircorresponding software solutions, as well asthe software capabilities and information onthe number of licences was collected andcompiled in 2012 in an online database.Details of the database development andimplementation were published in the Journalof the Southern African Institute of Miningand Metallurgy in 2013 (Katakwa, et al.,2013). In 2014 the data-set was updated withadditional and new information. Using theupdated data-set, a methodology wasdeveloped to measure mine planning softwareutilization in the gold sector in order toultimately inform decision-making strategiesfor optimal utilization.

Utilization is a well-known concept withinthe mining industry because of its ties with thelevel of productivity. Higher utilization oftenleads to higher productivity, hence better profitmargins. From this point of view, utilization isan important factor regardless of the size ofany operation, including those in the goldsector. The root of the word of utilizationcomes from the word ‘utilize’ meaning ‘makepractical and effective use of’ (Oxford EnglishDictionaries, 2014). By using this definition,software utilization can be defined as theeffective use of mine planning software inSouth Africa; but in general, utilization isassociated with the overall equipmenteffectiveness, which is one of the keyperformance-based metrics. It is important tounderstand the fundamentals behind thesemetrics.

Overall equipment effectiveness

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

Estimating mine planning software utilizationfor decision-making strategies in the SouthAfrican gold mining sectorby B. Genc*, C. Musingwini*, and T. Celik†

SynopsisThis paper discusses a new methodology for defining and measuringmine planning software utilization in the South African gold miningsector within an evolving data-set framework. An initial data-setshowing the mine planning software providers, their correspondingsoftware solutions, as well as the software capabilities and informationon the number of licences was collected and compiled in 2012 in anonline database for software utilized in the South African miningindustry. Details of the database development and implementation werepublished in the Journal of the Southern African Institute of Mining andMetallurgy in 2013. In 2014 the data-set was updated with additionaland new information.

Using the 2012 and 2014 timestamps, a methodology for estimatingthe software utilization was developed. In this methodology, the threevariables of commodity, functionality, and time factor were used todefine and measure the software utilization in order to ultimately informdecision-making strategies for optimal software utilization. Using sixdifferent functionalities, namely Geological Data Management,Geological Modelling and Resource Estimation, Design and Layout,Scheduling, Financial Valuation, and Optimization, utilization in thegold sector was measured. This paper presents the methodologyemployed for measuring the mine planning software utilization. Themethodology is useful for stakeholders reviewing existing softwarecombinations or intending to purchase new software in the near futureand who want to estimate the comparative attractiveness of a certainsoftware 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 theSchool of Mining Engineering at the University of the Witwatersrand.

Keywordsgold sector, mine planning software, software utilization, database,South African mining industry.

* School of Mining Engineering, University ofWitwatersrand, Johannesburg, South Africa.

† School of Computer Science, University ofWitwatersrand, Johannesburg, South Africa.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedJul. 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 surfaceor underground operation, better equipment utilization oftenleads to higher level of productivity, hence profitability.

Although there are a number of ways to measureperformance against the various metrics, the most widelyused measure to determine performance against capability ofthe equipment is Overall Equipment Effectiveness (OEE). OEEmeasurement is also commonly used as a key performanceindicator (KPI) in Total Productive Maintenance (TPM) andLean Manufacturing programmes for measuring productionefficiency (Vorne Industries, 2008).

There are six factors, also known as the ‘Six Big Losses’,which are the main causes of production losses. TPM andOEE programmes aim to control these six factors. Nakajima(1998) listed these six factors affecting equipment utilizationas:

➤ 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 themeaning of equipment utilization and is calculated bymultiplying equipment availability by performance rate andquality rate. Figure 1 shows time factors effecting equipmentutilization. In Figure 1, operation time is associated with thetotal available time for a given period, as this can be anythingfrom a single shift to a whole month. As shown in Figure 1,loading time can be calculated by deducting downtime fromthe operation time.

Availability can be calculated by dividing loading time byoperation time. As the loading time calculation is alreadygiven, the availability formula is then (Shirose, 2013):

Furthermore, speed loss time is the lost time caused byoperating below the planned speed, and can be calculated byusing the actual time to make the production quantity minusthe design time to make the same quantity, as formulatedbelow (Shirose, 2013):

Speed loss time = Parts produced × (Design cycle time −Actual cycle time)

Cycle time is the time taken to produce one part. Designcycle time is used to calculate the equipment’s designedproduction rate, and actual cycle time used to calculate the

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

Quality loss time is the time lost making nonconformingmaterial. Valuable operating time is the time the equipmentspends making conforming material. Quality rate is the ratioof conforming parts produced to total parts produced, asshown below (Shirose, 2013):

Quality loss time = Nonconforming parts × Actual cycletime

Although OEE is a very powerful tool to measureefficiency, hence utilization; it is fundamentally designed forequipment utilization, which can be defined as hardwareutilization. The aim of this study is to define strategicsoftware utilization in the South African mining industry,which can be defined as software utilization. Although OEEgives some ideas regarding utilization, it is not designed toestablish a framework that can bring a new approach towardsstrategic mine planning software utilization.

El-Ramly and Stroulia (2004) stated that there are anumber of techniques available to understand how oftensoftware is being used, as well as to what extent it is beingused. Many software systems collect, or can be set up tocollect, data about how users employ them, i.e., system-userinteraction data. Such data can be of great value for programunderstanding and re-engineering purposes. Sequential datamining methods can be applied to identify patterns of useractivities from system-user interaction traces (El-Ramly andStroulia (2004).

Despite the fact that user data may be available in someinstances, using the data mining methods based on userbehaviour to measure mine planning software utilization isinappropriate when considering the size of the South Africangold sector and user privacy. By selecting the number oftargeted mining sites, limited research output could bepossible, but most probably would not be sufficient to satisfythe entire gold mining industry in South Africa.

To achieve a successful research initiative which coversthe whole South African gold mining sector, a methodologywas developed in such a way that optimal utilization of thevarious mine planning software packages that are used ingold mining sector could be measured. The next sectiondefines this measurement framework.

Strategic software utilization

By using an analogy to the one given earlier, strategicsoftware utilization can also be defined by associating many-to-many, one-to-many, and many-to-one relationshipsbetween entity types. In this association, the relationshipsbetween software vendors, commodity, functionality, andtime factor were used to develop the following terminology:

{Ci, Fl} → Sk={i, l}

▲


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

{Ci, Fl} → Sk = {i,l}

where ‘Ci ’ denotes commodity (i) and ‘Fl’ denotesfunctionality (l).

Furthermore, Sk is the software that performs tasks oncommodity (i) and functionality (l). In the market, there isusually more than one software solution specifically designedfor commodity (i) and functionality (l). In order to identifyand evaluate each particular software solution, a new index(m) is used so that Sk

(m) is defined to represent a uniquesoftware solution, whereas k={i,l} is an index which is aspecific combination of {i,l}, and

m=1, 2, 3…Mwhere M is the total number of software solutions. Forexample, assume there are three software companies, X, Y,and Z. Each of these three companies might have a numberof software solutions, i.e. software company X has threetypes of software, namely X1, X2, and X3; company Y hasonly one type, namely Y1; and company Z has two types ofsoftware, namely Z1 and Z2. Table I displays how to find M.

From Table I, the total number of available softwaresolutions, M, is 6.

Using a similar approach, the utilization of the softwarecan also be defined. Although there is no rigid definition ofsoftware utilization, it can be defined as a numeric value thatfalls in to the range between 0 and 1 inclusive, i.e.

where ui,l(m) 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 softwareutilisation can be accomplished. Furthermore, the utilizationformula can be extended by considering the time factor (t) asfollows:

where fi,l(m,t) is a quantity factor that relates to the software

that performs a specific task on commodity (i) andfunctionality (l) using software (m) at a specific time (t), andwi,l

(m,t) is the weighing factor, which will handle the missingdata-related issues and/or other factors such as marketcapitalization of the companies. For instance fi,l

(m,t) can bedefined as the total number of sites. For example, if themarket capitalizations of the software companies X and Y areUS$1 million and US$100 million respectively, but bothcompanies have a software solution with the samefunctionality, then the weighing factor for the small companywill be higher than that for the larger company. Furthermore,the price of the mine planning software as well as supportavailability plays an important role when considering theweighing factor.

Software utilization is already defined in a generic way.However, the software utilization can also be defined in aspecific way, i.e. the relative utilization (r). Relativeutilization can be considered as a weighted softwareutilization and can be formulated as:

wheren=1

Mui,l

(n,t)is total utilisation of all software which is used

for normalization.Calculating relative utilization leads to weighted market

impact of the software utilization. However, calculatingrelative utilization, three variables were used to generate theresults, namely:

➤ Commodity (i)➤ Functionality (l)➤ Time factor (t).For example, the following results were calculated for

only one commodity (i), namely gold, using six differentfunctionalities (l) (Katakwa et al., 2013):

1. Geological Data Management2. Geological Modelling and Resource Estimation3. Design and Layout4. Scheduling5. Financial Valuation6. Optimization.The six functionalities listed by Katakwa et al. originated

from the Open Group’s Business Reference Model, whichcategorizes not only the functionalities of mine planningsoftware, but also mine value chain stages and miningmethods (The Open Group, 2010). The Open Group’sBusiness Reference Model illustrates how the varioussoftware solutions interact with each other, although thisclassification can be debateable. For example, Mine 2-4Dsoftware, which is used in mine scheduling, is often used inconjunction with Enhanced Production Scheduler (EPS) as itcannot produce a schedule without the use of EPS. Figure 2shows the names of available mine planning softwaresolutions and their functionalities along the mining valuechain.

The time (t) factor has two timestamp indicators showingdifferent data collection dates, namely:

➤ September 2012, t=1➤ April 2014, t=2By using all three variables, the weighted software

utilization, hence the market impact of each participatingmine planning software solution, was calculated. The data-set was extracted from the updated database and theprogramming language GNU Octave was used for the dataanalysis and the calculation of the software utilization perfunctionality for the selected commodity (gold) using twodifferent timestamps as mentioned previously.

It is important to note that if fi,l(m,t) is 0, the subject

software either does not support the specific functionality ordoes not support the specific commodity. Furthermore, whencalculating ui,l

(m,t) and wi,l(m,t), 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 companyName of software company Company X Company Y Company ZName of software solution X1 X2 X3 Y1 Z1 Z2

m 1 2 3 4 5 6

utilization did not have any impact on the calculation of therelative software utilization. Unique identifiers of eachparticular software solution are not named in this researchwork, and the identifiers (ids) have been numberedrandomly.

Results for the gold sector

In this section, strategic mine planning software utilizationfor commodity (i) gold is calculated. Six functionalities (l)with two timestamps (t) were used for the calculations andthe results for each functionality with two timestamps arepresented as tables and figures, respectively. Accordingly, atotal number of {6(l) × 2(t) = 12} tables were created for eachcommodity. According to the functionality list providedearlier, the first functionality, ‘Geological Data Management’was used with two different timestamps to produce the firstsets of two tables. After generating the tables, pie charts werecreated for each table for easy interpretation of the results.Consequently, using the functionality list, the remainingtables and figures were created in a similar manner.

The following software providers participated in thisstudy: Geovia, MineRP Solutions, Sable,RungePincockMinarco, Maptek, Cyest Technology, and CAEMining. Note that the data on CAE Mining was only madeavailable in the April 2014 data-set. The results presentedhere do not cater for either the mining methods or the type ofmine (surface or an underground operation).

Geological data management software results forgold

Table II shows the market impact of the individual softwaresolutions for gold using the functionality Geological DataManagement as at September 2012 while Table III shows thesame results using the second timestamp, i.e. April 2014.Figure 3 illustrates both tables graphically. Note that fi,l

(m,t),wi,l

(m,t), ui,l(m,t), ri,l

(m,t) and column headings in Tables II to XIIwere defined previously.

When comparing the diagrams in Figure 3, there is asignificant difference between the two pie charts; CAE Mining’sGeological Data Management Solution software with a 31%market impact in the April 2014 chart is clearly visible. PegsLite and MRM have each a 23% market impact in this field.

Geological modelling and resource estimationsoftware results for gold

Table IV shows the market impact of the individual softwarefor gold using the Geological Modelling and ResourceEstimation functionality as at September, 2012, while Table Vshows the same results using the second timestamp, April2014. Figure 4 illustrates both tables graphically.


▲


Table II

Geological Data Management functionalitysoftware utilization for gold as of September 2012

m Software_id fi,l(m,t) wi,l

(m,t) ui,l(m,t) ri,l

(m,t)

1 9 4 1 4 0.04352 10 12 1 12 0.13043 13 2 1 2 0.02174 38 1 1 1 0.01095 68 1 1 1 0.01096 72 0 1 0 07 83 9 1 9 0.09788 93 0 1 0 09 95 0 1 0 010 97 32 1 32 0.347811 98 31 1 31 0.33712 100 0 1 0 013 113 0 1 0 0

Table III

Geological Data Management functionalitysoftware utilization for gold as of April 2014



(m,t)

1 9 4 1 4 0.02262 10 12 1 12 0.06783 13 2 1 2 0.01134 38 1 1 1 0.00565 68 1 1 1 0.00566 72 0 1 0 07 83 9 1 9 0.05088 93 2 1 2 0.01139 95 81 1 81 0.457610 97 32 1 32 0.180811 98 31 1 31 0.175112 100 2 1 2 0.011313 113 0 1 0 0

Figure 2—Available mine planning software solutions and their function-alities along the mining value chain



Figure 3 – Geological Data Management functionality software utiliszation for gold

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

Design and layout software results for gold

Table VI shows the market impact of the individual softwaresolutions for gold using the Design and Layout functionalityas at September 2012, while Table VII shows the same resultsusing the second timestamp, April 2014. Figure 5 illustratesboth tables graphically.

When comparing the diagrams in Figure 5, similar to theprevious results, there is significant difference between thetwo pie charts; Studio 3 - Engineering is the leading softwarewith a 26% market impact in the April 2014 chart. MRM andCADSMine have each a 20% market impact in this field.

Scheduling software results for gold

Table VIII shows the market impact of the individual softwaresolutions for gold using the Scheduling functionality as atSeptember 2012, while Table IX shows the same resultsusing the second timestamp, April 2014. Figure 6 is agraphical representation of Table VIII, while Figure 7 showsthe graphical representation of Table IX.

There is not much difference between Figure 6 and Figure 7; MRM and CADSMine software still have the biggestmarket impact, both with 20%, in the Scheduling softwarefield. Enhanced Production Scheduler (CAE) has 15% marketimpact in this field.

Financial valuation software results for gold

Table X shows the market impact of the individual softwaresolutions for gold using the Financial Valuation software

Table IV

Geological Modelling and Resource Estimationfunctionality software utilization for gold as ofSeptember 2012



(m,t)

1 9 4 1 4 0.04942 10 12 1 12 0.14813 13 2 1 2 0.02474 48 0 1 0 05 68 1 1 1 0.01236 72 0 1 0 07 84 0 1 0 08 93 0 1 0 09 94 0 1 0 010 98 31 1 31 0.382711 99 31 1 31 0.3827

Table V

Geological Modelling and Resource Estimationfunctionality software utilization for gold as of April2014



(m,t)

1 9 4 1 4 0.01842 10 12 1 12 0.05533 13 2 1 2 0.00924 48 0 1 0 05 68 1 1 1 0.00466 72 0 1 0 07 84 134 1 134 0.61758 93 2 1 2 0.00929 94 0 1 0 010 98 31 1 31 0.142911 99 31 1 31 0.1429

Figure 4—Geological Modelling and Resource Estimation 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, witha 51% market impact in the gold sector when it comes to theFinancial Valuation software. Carbon Economics is in secondplace with a 21% market impact in this field.

Optimization software results for gold

Table XII shows the market impact of the individual softwaresolutions for the commodity gold using the Optimizationfunctionality as at September, 2012, while Table XIII showsthe 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 anoteworthy difference between the two pie charts; Studio 3 –Geology has emerged as a new leader with a 62% marketimpact in April 2014, followed by MRM with a 14% marketimpact in the Optimization software field.ConclusionIn this paper, a methodology for the evaluation of mineplanning software for measuring utilization in the South

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 calculationscan be done for any commodity in a similar manner, in thispaper, calculations were done only for gold; six function-alities namely Geological Data Management, GeologicalModelling and Resource Estimation, Design and Layout,Scheduling, Financial Valuation, and Optimization wereapplied using two different timestamps (September 2012 andApril 2014). It is important to note that data on CAE Miningwas only made available in the April 2014 data-set. Whencomparing the results, the CAE Mining market impact isclearly visible in the gold sector, especially in the fields ofGeological Data Management, Geological Modelling andResource Estimation, Design and Layout, and Optimization.

By using this newly developed framework, utilization ofthe various mine planning software solutions was measured.This methodology provides an opportunity for software usersto review existing software combinations, or for thoseintending to purchase new software, a tool for estimating thecomparative attractiveness of certain software packages. Forexample, mining companies can position themselves better by


▲


Table VI

Design and Layout functionality software utilizationfor gold as of September 2012



(m,t)

1 2 8 1 8 0.08422 5 3 1 3 0.03163 9 4 1 4 0.04214 10 12 1 12 0.12635 13 2 1 2 0.02116 31 0 1 0 07 32 0 1 0 08 46 0 1 0 09 48 0 1 0 010 49 0 1 0 011 68 1 1 1 0.010512 70 2 1 2 0.021113 85 0 1 0 014 86 0 1 0 015 88 0 1 0 016 89 0 1 0 017 90 0 1 0 018 96 0 1 0 019 98 31 1 31 0.326320 99 31 1 31 0.326321 101 1 1 1 0.010522 102 0 1 0 0

Table VII

Design and Layout functionality softwareutilization for gold as of April 2014



(m,t)

1 2 8 1 8 0.05132 5 3 1 3 0.01923 9 4 1 4 0.02564 10 12 1 12 0.07695 13 2 1 2 0.01286 31 0 1 0 07 32 0 1 0 08 46 0 1 0 09 48 0 1 0 010 49 0 1 0 011 68 1 1 1 0.006412 70 2 1 2 0.012813 85 40 1 40 0.256414 86 0 1 0 015 88 0 1 0 016 89 1 1 1 0.006417 90 0 1 0 018 96 20 1 20 0.128219 98 31 1 31 0.198720 99 31 1 31 0.198721 101 1 1 1 0.006422 102 0 1 0 0

Figure 5—Design and Layout functionality software utilization for gold



Table VII

Scheduling functionality software utilization forgold as of September 2012



(m,t)

1 2 8 1 8 0.07482 4 9 1 9 0.08413 7 0 1 0 04 12 6 1 6 0.05615 14 2 1 2 0.01876 20 1 1 1 0.00937 21 0 1 0 08 33 1 1 1 0.00939 69 1 1 1 0.009310 71 4 1 4 0.037411 74 0 1 0 012 75 0 1 0 013 76 9 1 9 0.084114 80 1 1 1 0.009315 81 2 1 2 0.018716 86 0 1 0 017 87 0 1 0 018 88 0 1 0 019 89 0 1 0 020 91 0 1 0 021 96 0 1 0 022 98 31 1 31 0.289723 99 31 1 31 0.289724 101 1 1 1 0.009325 102 0 1 0 026 108 0 1 0 027 109 0 1 0 028 111 0 1 0 029 112 0 1 0 030 113 0 1 0 031 114 0 1 0 0

Table IX

Scheduling functionality software utilization forgold as of April 2014



(m,t)

1 2 8 1 8 0.05232 4 9 1 9 0.05883 7 0 1 0 04 12 6 1 6 0.03925 14 2 1 2 0.01316 20 1 1 1 0.00657 21 0 1 0 08 33 1 1 1 0.00659 69 1 1 1 0.006510 71 4 1 4 0.026111 74 0 1 0 012 75 0 1 0 013 76 9 1 9 0.058814 80 1 1 1 0.006515 81 2 1 2 0.013116 86 0 1 0 017 87 23 1 23 0.150318 88 0 1 0 019 89 1 1 1 0.006520 91 2 1 2 0.013121 96 20 1 20 0.130722 98 31 1 31 0.202623 99 31 1 31 0.202624 101 1 1 1 0.006525 102 0 1 0 026 108 0 1 0 027 109 0 1 0 028 111 0 1 0 029 112 0 1 0 030 113 0 1 0 031 114 0 1 0 0

acquiring optimal combinations of mine planning software;consulting companies can advise their clients more effectivelyto make the right choices of software solutions; tertiaryeducation institutions offering mining-related qualificationscan strategically choose which software to expose theirstudents to; and software providers can strategically positionthemselves within the mine planning software market.

Figure 6—Scheduling functionality software utilization for gold as ofSeptember 2012

Figure 7—Scheduling functionality software utilization for gold as ofApril 2014

Table X

Financial Valuation functionality softwareutilization for gold as of September 2012



(m,t)

1 7 0 1 0 02 15 4 1 4 0.07553 73 0 1 0 04 77 13 1 13 0.24535 78 1 1 1 0.01896 79 0 1 0 07 80 1 1 1 0.01898 91 0 1 0 09 92 0 1 0 010 98 31 1 31 0.584911 103 3 1 3 0.056612 104 0 1 0 013 105 0 1 0 014 106 0 1 0 015 109 0 1 0 016 110 0 1 0 0


▲


Table XI

Financial Valuation functionality softwareutilization for gold as of April 2014



(m,t)

1 7 0 1 0 02 15 4 1 4 0.06673 73 0 1 0 04 77 13 1 13 0.21675 78 1 1 1 0.01676 79 0 1 0 07 80 1 1 1 0.01678 91 2 1 2 0.03339 92 5 1 5 0.083310 98 31 1 31 0.516711 103 3 1 3 0.0512 104 0 1 0 013 105 0 1 0 014 106 0 1 0 015 109 0 1 0 016 110 0 1 0 0

Table XII

Optimization functionality software utilization forgold as of September 2012



(m,t)

1 1 0 1 0 02 15 4 1 4 0.07843 21 0 1 0 04 73 0 1 0 05 74 0 1 0 06 77 13 1 13 0.25497 79 0 1 0 08 82 0 1 0 09 84 0 1 0 010 87 0 1 0 011 88 0 1 0 012 91 0 1 0 013 92 0 1 0 014 98 31 1 31 0.607815 102 0 1 0 016 103 3 1 3 0.058817 105 0 1 0 018 106 0 1 0 019 107 0 1 0 020 110 0 1 0 0Table XIII

Optimization functionality software utilization forgold as of April 2014



(m,t)

1 1 3 1 3 0.01382 15 4 1 4 0.01833 21 0 1 0 04 73 0 1 0 05 74 0 1 0 06 77 13 1 13 0.05967 79 0 1 0 08 82 0 1 0 09 84 134 1 134 0.614710 87 23 1 23 0.105511 88 0 1 0 012 91 2 1 2 0.009213 92 5 1 5 0.022914 98 31 1 31 0.142215 102 0 1 0 016 103 3 1 3 0.013817 105 0 1 0 018 106 0 1 0 019 107 0 1 0 020 110 0 1 0 0

Figure 9 —Optimization functionality software utilization for gold

Figure 8 —Financial Valuation functionality software utilization for gold

References

EL-RAMLY, M. and STROULIA, E. (2004. Mining software usage data.International Workshop on Mining Software Repositories MSR 2004W17S. Workshop, 26th International Conference on SoftwareEngineering, 25 May 2004, Edinburgh, UK. Vol. 2004, no. 917. pp. 64–68.

KATAKWA, T.P., MUSINGWINI, C., and GENC, B. 2013. Online database of mineplanning and peripheral software used in the South African miningindustry. Journal of the Southern African Institute of Mining andMetallurgy, vol. 113, no. 6. pp. 497–504.

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

OXFORD DICTIONARIES. 2014. Utilize. http:// www.oxforddictionaries.com/

definition/english/utilize?q=utilize [Accessed 15 June 2014].

SHIROSE, K. 2014. Equipment Utilization Metrics.

http://www.ombuenterprises.com/LibraryPDFs/Equipment_Utilization_Me

trics.pdf [Accessed 15 June 2014].

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

2014].

VORNE INDUSTRIES. 2008. The Fast Guide to OEE. http://www.vorne.com/pdf/

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

Introduction

Geothermal research provides insights into awide variety of geological problems, includingtectonic studies (Jones, 1988, 1992, 1998),maturation of oil and natural gas (Royden etal., 1980), exploration for geothermal energy(e.g. Martinelli et al., 1995), and evenclimatology (e.g. Jones et al., 1999). In SouthAfrica, the most important application is inmine engineering. South Africa leads the worldin deep mining, and knowledge of virgin rocktemperatures, geothermal gradients, andthermal properties of rocks is essential forplanning refrigeration and ventilationrequirements of deep mines (Jones, 1988;Jones and Rawlins, 2002; Rawlins et al., 2002;Jones, 2003a, 2003b).

One of the most important challenges ofmining is to control the environmentalconditions, particularly temperature, of deepunderground workings. The primary cause ofelevated temperatures is heat derived fromexposed rock surfaces, whose temperature isdetermined by the natural increase of virginrock temperature (VRT) with depth (Rawlins etal., 2002; Jones 2003a). Knowledge of VRT istherefore fundamental for calculating refrig-

eration requirements. Knowledge of rockthermal properties is also fundamental forrefrigeration studies because (1) theydetermine the rate at which heat flows fromthe rock into underground excavations, and(2) they control the thermal gradient in strataoverlying and underlying these excavationsand hence their VRT (Jones, 2003a, 2003b).

Because of its long history of gold mining,which has progressively approached greaterdepths, the Witwatersrand Basin has been thesubject of geothermal research for more than75 years. Measurements of rock temperatureand thermal conductivity dating back to 1938(Weiss 1938; Bullard, 1939; Krige, 1939)provided the foundation for later studies andeventually an intensified collaborative researchprogramme (from about 1980–2000) betweenthe University of the Witwatersrand (Wits) andthe Chamber of Mines Research Organisation(COMRO), and later, CSIR Mining Technology.The net product was a database of VRT,thermal gradient, and rock properties thatprobably surpasses any in the world (Jones,1988, 2003a, 2003b).

In the latter period, platinum miningdepths in the Bushveld Complex (to the northof the Witwatersrand Basin) increased signifi-cantly. Earlier measurements at two localitiesshowed that the geothermal gradient in theBushveld Complex is substantially higher thanthat in the Witwatersrand Basin (Carte andvan Rooyen, 1969), and it was obvious thatthere was a need for a detailed geothermalinvestigation of the Complex. Subsequentresearch collaboration between Wits, COMRO,and other key stakeholders from industryensued. New measurements of the geothermalgradient were made in many of the existingand potential platinum mining areas, andthese measurements confirmed that the

Thermophysical properties of rocks from theBushveld Complexby M.Q.W. Jones*

SynopsisThis paper presents a compilation of physical properties of rocks from theBushveld Complex. The database consists of more than 900 measurementseach of thermal conductivity and density. The data are well distributedfrom localities around the Complex and most rock types are wellrepresented. Thermal conductivity and density are shown to vary widely inthe ranges 1.8–4.2 W m-1 K-1 and 2600–4200 kg m-3 respectively. Althoughonly 190 heat capacity measurements are available, this parameter is quiteuniform for most rock types present, 800–900 J kg-1 K-1, except forchromitite, which has a lower average, 750 J kg-1 K-1. Rocks encountered indeep platinum mines are particularly well characterized and this hasimportant implications for prediction of mine refrigeration requirements.The heat flux into a semi-infinite region with properties typical of theBushveld Complex as a function of time is substantially lower than anequivalent model for the Witwatersrand Basin.

Keywordsthermophysical rock properties, platinum mines, mine cooling.

* School of Geosciences, University of theWitwatersrand, Johannesburg.

© The Southern African Institute of Mining andMetallurgy, 2015. ISSN 2225-6253. Paper receivedFeb. 2013; revised paper received Oct. 2014.



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 ofthermal conductivity, density, and heat capacity on Bushveldrocks gradually grew and the database currently rivals thatfrom the Witwatersrand Basin in size and coverage of rocktypes. It is the purpose of this paper to present a summary ofthe Bushveld database, characterize the thermal properties ofvarious rock units constituting the Bushveld Complex, anddiscuss the implications for deep mining.

Geological background

The Bushveld Complex is an enormous igneous provinceoccupying an area of approximately 65 000 km2 to the north,east, and west of Pretoria (Figure 1). The Complex hosts theworld’s largest reserves of platinum, chromium, andvanadium, and consequently there is a vast literature. Thisbrief 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 typesdiscussed below.

The Bushveld Complex consists of coarse-grained igneousrocks with a wide range of composition from ultramafic tofelsic. It is temporally, and possibly genetically, related tovolcanic rocks of the Rooiberg Group (Cawthorn et al., 2007)(Figure 1). However, this paper deals solely with the plutonicrocks that were intruded into the Transvaal Supergroup andolder rocks 2060 Ma ago. The Complex is formally classifiedinto three distinct units: the ultramafic to mafic RustenburgLayered Suite and the felsic Rashoop Granophyre andLebowa Granite Suites (Figure 2).

The average thickness of the Rustenburg Layered Suite isapproximately 6 km, and it crops out in four main ’limbs’.The geology of the eastern limb (northwest to southwest ofBurgersfort) and the western limb (Thabazimbi to Pretoria) isreasonably 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 kilometresand, although the succession is seldom complete, geophysicalevidence suggests that at least the eastern and western limbsare connected at depth (Cawthorn et al., 1998).

The Rustenburg Layered Suite consists of five main‘zones’, which is the most convenient classification fordiscussing similar rocks from similar levels (Figure 2).Although specific rock types are listed below and in Table I, itshould be noted that there is often a gradational change fromone rock type to another. For example, there can be an almostcontinuous variation from anorthosite to pyroxenite and, aswill be seen, this results in a gradation of thermal properties.

▲


Figure 1—Suboutcrop map of the Bushveld Complex showing the distribution of major geological units after artificially removing overlying TransvaalSupergroup 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 forrock property analysis

Table I

Glossary of major rock types in the BushveldComplex

Rock type Major minerals

Felsic rocks Quartz, feldsparGranite Quartz, plagioclase/alkali feldspar (mica)Granophyre Quartz, alkali feldspar

Mafic rocks Feldspar, pyroxene, olivineAnorthosite Plagioclase feldsparNorite Plagioclase feldspar, orthopyroxeneGabbro Plagioclase feldspar, clinopyroxeneGabbronorite Undifferentiated gabbro/noriteDiorite Plagioclase feldspar, pyroxene, olivine

Ultramafic rocks Pyroxene, olivinePyroxenite Orthopyroxene and/or clinopyroxenePeridotite Olivine, pyroxeneHarzburgite Olivine, orthopyroxeneWehrlite Olivine, clinopyroxeneDunite Olivine

Ore-bearing unitsMagnetitite layers MagnetiteMerensky Reef PyroxeneChromitite layers Chromite (pyroxene)

The lowermost ‘Marginal Zone’ consists predominantly ofuniform, relatively fine-grained norite with minor amounts ofpyroxenite. The overlying ‘Lower Zone’ is ultramafic incomposition and dominated by pyroxenite, but includes thicklayers of harzburgite with some dunite.

From a mining point of view, the ‘Critical Zone’ is themost important because it is the host of chromium-rich andplatiniferous ores. The lower part of the Critical Zone isessentially pyroxenite with some olivine-bearing rocks,whereas the upper section is represented by cyclic layersinvolving pyroxenite, norite, and anorthosite. Numerouschromitite layers may be well developed in the Critical Zone,and these are divided into the Lower Group (LG1-7), MiddleGroup (MG1-4), and Upper Group (UG1-3). The LG6 andMG1 layers have been extensively mined for chromium. Themain sources of platinum and associated platinum groupelements (PGEs) are the UG2 chromitite layer andpyroxenites within the Merensky Reef (Figure 2).

The ‘Main Zone’ is a thick uniform sequence of noriteand gabbronorite with a few layers of anorthosite andpyroxenite. It is overlain by the ‘Upper Zone’ in whichmagnetite 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 whichexceed 2 m in thickness. Vanadium is associated with all themagnetitite layers and can reach concentrations of up to 2%.The Main Magnetite Layer in the lower part of the UpperZone (Figure 2) is mined for vanadium.

The last stage of Bushveld magmatism involved theemplacement of large volumes of granophyric and graniticrocks of the Rashoop Granophyre Suite and the LebowaGranite 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 theproportions of major minerals (quartz and feldspar) as wellas the content of minor mafic phases (mainly hornblende,biotite, and pyroxene). This results in subtle variations ofthermal properties.

Measurement of thermophysical properties

The methods used to measure the thermophysical propertiesare essentially the same as those reported in this journal byJones 10 years ago (Jones, 2003b). The reader is referred tothat paper for details, and only the most salient points will berepeated here. The measured parameters are thermal conduc-tivity (K, units W m-1 K-1), density (ρ, kg m-3), and heatcapacity (C, J kg-1 K-1). A fourth parameter, thermaldiffusivity (κ, m2 s-1) is calculated from the relation κ=K/Cρ.All measurements reported here were made in the heat flowlaboratory at Wits.

Thermal conductivity

Thermal conductivity measurements were made using adivided bar apparatus that is specifically designed for preciseanalysis of competent rocks such as those encountered in theBushveld Complex. This is a steady-state method in whichthe conductivity of a rock sample is measured relative to thatof a material of known conductivity (the ‘substandard’).Precisely machined rock discs (30 or 38 mm in diameter andapproximately 20 mm in length) and discs of the substandardare placed in the divided bar. Heat is supplied and abstractedat either end of the divided bar using temperature-controlledwater circulators. When a condition of linear heat flow alongthe stack of discs is established, the conductivity of thesample disc relative to that of the substandard is determinedby measuring the temperature gradient across each disc andapplying Fourier’s Law of heat conduction, q=KdT/dx, whereq is heat flux (W m-2) and dT/dx is thermal gradient (K m-1).The contact resistance between discs constituting the dividedbar is minimized by applying an axial pressure of 5 MPa, andradial heat loss is minimized by carefully insulating thestack. All samples were saturated with water prior tomeasurement and average sample temperatures were close to25°C in all experiments. The thermal conductivity of thesubstandard was calibrated against gem-quality quartz coredperpendicular to the c-crystallographic axis, for which aninternational standard conductivity was used. The overallerror in determining conductivity, including reproducibilityand calibration errors, using this method is estimated to beless than 0.1 W m-1 K-1.

Density

Most densities were determined from accurately measuredmasses (using a precision balance) and volumes (using amicrometer and digital vernier) of samples prepared forthermal conductivity measurement. Measurements, first dryand then saturated with water, on 30 samples across thespectrum of rocks analysed showed no significant difference(<5 kg m-3 on average); this is to be expected because the



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

porosity of the rocks is essentially zero. Repeatmeasurements on the same 30 specimens also yieldeddifferences averaging at approximately 5 kg m-3, and themaximum uncertainty in measuring density is estimated at10 kg m-3. The densities of damaged conductivity samplesand other irregular specimens were determined from theirmeasured masses while suspended in air and water andapplying Archimedes’ Principle. Measurements were made on10 control samples using both methods and the averagedifference was found to be less than the above overalluncertainty.

Heat capacity

Heat capacities of samples, crushed and sieved to sand-sizedgrains, were determined by calorimetry using the method ofmixtures. The temperature of measurement in all experimentswas as close as possible to ambient temperature, but in mostcases a small correction for Newtonian cooling wasnecessary. The uncertainty in heat capacity measurement,based on repeat measurements on 10 samples, is estimated atapproximately 25 J kg-1 K-1.

Database overview

During the past 30 years, routine thermal conductivitymeasurements have been made for both mine engineeringapplications and for tectonic studies of the BushveldComplex. Initially, density measurements reported here weremade primarily for mine refrigeration studies, but these weresupplemented during the past year with measurements madeon specimens originally prepared for conductivity analysis.The total number of measured values for each parameterexceeds 900.

The distribution of boreholes and mines from whichsamples were derived is indicated in Figure 1. Averagethermal conductivities and densities of different rock types indifferent stratigraphic units of the Bushveld Complex arelisted in Table II. It is clear that the database is representativeof the Complex as a whole and that all important rock typesconstituting the Complex are well characterized. Conductivityand density variations are discussed in the next section.Additional detailed density measurements are available fromboreholes and mines in the Complex (Cawthorn and Spies,2003; Ashwal et al., 2005; Davis et al., 2007).

Heat capacity measurements were made specifically formine refrigeration investigations. The data are summarizedin Table III. Although only 190 values are available, mostrock types encountered in the deep platinum mines arereasonably well represented. Fortunately, heat capacity is arelatively uniform rock parameter, and the data in Table IIIsuffice for most purposes. Heat capacity data are discussed inthe last section of this paper.

Thermal conductivity and density

Variations of thermal conductivity and density of rockswithin different stratigraphic units of the Bushveld Complexare illustrated in the form of histograms and plots of conduc-tivity versus density (Figures 3–11). Histograms for differentrock types, identified from hand specimens, are all drawn tothe same horizontal and vertical scales so that the results canbe compared directly. Conductivity and density of rocks are

essentially controlled by their constituent minerals (Table I),which have distinct thermal properties (Table IV). As notedpreviously, the distinction between different rock types is notalways clear-cut, and there is continuous gradation from onerock type to another. This is particularly relevant in theBushveld Complex, where rock density variations may revealthe relative proportions of the major rock-forming minerals(Cawthorn and Spies, 2003; Davis et al., 2007). It was notpossible to generate conductivity-density plots with the samehorizontal (density) scale, so data for the Critical Zone andMain Zone (Figure 6a) are reproduced in all such diagrams(Figures 4, 7, 9, and 11) to facilitate comparison.


▲


Table II

Thermal conductivity and density of rocks from theBushveld Complex

Rock unit and type K±s.d., N ρ±s.d., NW m-1 K-1 kg m-3

GRANITIC ROCKSGranite (undifferentiated) 3.43±0.41 103 2660±60 100

MAFIC ROCKSUPPER ZONE

Diorite 2.17±0.07 8 3100±250 8Anorthosite 2.09±0.08 7 2790±40 7Gabbronorite 2.40±0.20 73 2980±120 70Gabbronorite 2.34±0.24 48 3200±200 48(magnetite-bearing)Pyroxenite 3.39±0.37 23 3170±70 23Pyroxenite 3.35±0.15 6 3310±30 6(magnetite-bearing)

MAIN ZONE and CRITICAL ZONE

Anorthosite 1.92±0.12 144 2780±40 144Norite 2.28±0.15 360 2910±50 359Pyroxenite 3.45±0.35 89 3190±70 89Chromitite 2.45±0.18 16 3970±120 16

LOWER ZONEPyroxenite, harzburgite, 4.13±0.21 39 3280±40 39dunite

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

Table III

Heat capacity and thermal diffusivity

Rock unit and type C±s.d., N κ±s.d., NJ kg-1 K-1 10-6 m2 s-1

MAFIC ROCKSMAIN ZONE and CRITICAL ZONE

Anorthosite 820±30 36 0.83±0.04 36Norite 840±40 46 0.95±0.09 46Pyroxenite 870±60 82 1.27±0.16 82Chromitite 750±50 16 0.84±0.11 16

LOWER ZONEPyroxenite, harzburgite, 870±20 9 1.28±0.14 9dunite

C, heat capacity; κ, thermal diffusivity; s.d., standard deviation; N,number of observations.

Lower Zone

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

Critical Zone and Main Zone

Thermal characterization of the Critical Zone and Main Zone(particularly its lower part) is crucial for investigations ofrefrigeration requirements of platinum mines and potentialdeep-level chromium mines. These zones are the best studiedand are discussed together here for convenience. The mostimportant rocks vary in composition from anorthosite (almostpure plagioclase feldspar) through norite and gabbro topyroxenite (almost pure pyroxene) (Table I). Table III, thehistograms (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 isnot quite complete, as reflected in the paucity of data atapproximately 3000 kg m-3. Nor is the relationship betweenthe parameters purely linear. Average conductivities forsuccessive 50 kg m-3 density intervals suggest that arelationship is better defined by two lines, one dominated byplagioclase and the other by pyroxene (Figure 6b). This maybe understood by the fact that density is controlled simply bythe percentages of plagioclase and pyroxene, whereasconductivity is controlled by more or less conductive pathsassociated with connectivity of these minerals, as well astheir relative abundance. Measuring density is quick andeasy, and the correlation between conductivity and density ispotentially useful for estimating conductivity wheremeasurements of this parameter are not available.

Results for chromitite (Table II, Figure 7) are shiftedsubstantially toward much higher density. All data are fromthe UG2 Reef. The data adequately characterize thisimportant platinum-bearing horizon and serve as bestestimates for Lower and Middle Group layers exploited for



Table IV

Thermal conductivity and density of importantminerals found in rocks listed in Table I (Clark,1966; Deer, Howie, and Zussman, 1966; Horai,1971; Cermak and Rybach, 1982)

Mineral K, W m-1 K-1 ρ, kg m-3

Quartz 7.69 2650Feldspar

Alkali feldspar 2.31-2.49 2560-2630Plagioclase feldspar 1.53-2.14 2620-2760

PyroxeneOrthopyroxene 4.16-4.47 3210-3960Clinopyroxene 3.82-4.94 3220-3560

Olivine 3.45-5.16 3220-4390Chromite 2.52 4800Magnetite 5.10 5150

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

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

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

chromium. Few samples are known to be derived from theMerensky Reef, but this layer consists essentially ofpyroxenite, and the data from the Critical and Main Zones inTable II are appropriate for mine engineering purposes.

Upper Zone

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

samples of anorthosite and pyroxenite, and all data areplotted in the same histograms, the only distinction beingbetween rocks that are apparently depleted in magnetite(Figure 8a and Figure 8c) and those that are obviouslymagnetite-bearing (Figure 8b and Figure 8d). Approximatelyhalf of the results fall close to the trend defined byanorthosites-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 andare displaced to higher densities as indicated by the least-squares line fitted to successive 50 kg m-3 density intervalsfor this subset of the data Figure (9b). Although magnetitehas a high conductivity (Figure 9b) (Table IV), its relativelylow abundance in most rocks means that the connectivity


▲


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

Figure 9—(a) Thermal conductivity plotted against density for all rocksfrom the Upper Zone (red dots). ( b) Average thermal conductivity andstandard deviation for 50 kg m-3 density intervals derived frommagnetite-bearing gabbronorites and two magnetitites from the UpperZone (green dots); the line is the least-squares fit to the averaged data.Blue dots indicate data for anorthosite, norite, and pyroxenite from theCritical and Main Zones (Figure 6)

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

Figure 7—Thermal conductivity plotted against density for UG2chromitite from the Critical Zone (red dots). Blue dots indicate data foranorthosite, norite, and pyroxenite from the Critical and Main Zones(Figure 6)



between magnetite grains is incomplete and apparently notefficient enough to substantially enhance thermal conduc-tivity. Density, on the other hand, is purely a function of theamount of magnetite present. Vanadium mining on the MainMagnetite Layer is shallow at present, but the data arepotentially useful for engineering purposes if deeper levelsare exploited in the future.

Bushveld granites and granophyres

Granitic rocks of the Rashoop Granophyre Suite and LebowaGranite Suite have not been differentiated. Table II andFigures 10 and 11 indicate that these rocks are thermallyquite distinct from the mafic and ultramafic rocks of theRustenburg Layered Suite. The thermal parameters are typicalof granites, except for a few magnetite-bearing specimensthat have higher densities (Figure 11a). Although there is nostrong relationship between conductivity and density inFigure 11a, a plot of average conductivity versus averagedensity for different sample localities suggests a negativecorrelation (Figure 11b).

Heat capacity and thermal diffusivity

Heat capacity measurements were made on 190 samples andresults are summarized in Table III. Most of the samples werederived from the Critical Zone and lower part of the MainZone because data on rocks from these levels is mostimportant for applications in mine refrigeration. Althoughheat capacity increases with increasing pyroxene content(Table I and Table III) this effect is small, and an overallaverage value for the Critical and Main Zones (850 ±50 J kg-1

K-1) will suffice (in the absence of measured values) forcalculating thermal diffusivity for mine refrigerationpurposes. Chromitite has a lower average heat capacity (750 Jkg-1 K-1, Table III), which should be used for calculationsinvolving mining of the UG2 chromitite layer. The value forMain Zone pyroxenite is recommended for similar analysesinvolving 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 inunderground workings is a complicated process because itdepends on several factors including mine geometry, VRT,rock properties, and boundary conditions at newly exposedrock surfaces. This is beyond the scope of this paper, but theeffect of thermal properties alone reported here can beillustrated by calculating the heat flux at the surface of asemi-infinite region initially at a temperature of Ti andinstantaneously exposed to a constant surface temperature ofT0 at time t=0:

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

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

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

Figure 12—Heat flux as a function of time into the surface of a coolinghalf-space with initial temperature Ti=50°C and surface temperatureT0=25°C. Blue, calculated using thermal properties typical of theBushveld 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 ofrocks 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=6.4W m-1 K-1, κ=2.9×10-6 m2 s-1)


situation in gold mines of the Witwatersrand Basin (red).Estimation of the controlling effect of rock properties on heatflux into underground workings obviously requires specificand in-depth calculations (C.A. Rawlins, personal communi-cation, 2014).

Conclusions

The extensive thermophysical rock property database fromthe Bushveld Complex permits reliable estimation of theaverage thermal conductivity, density, heat capacity, andthermal diffusivity of most rock types in various stratigraphicunits constituting the Complex. The results provide importantinputs for calculations of the heat load on deep platinummines and would be potentially important in chromium andvanadium mines if such mining proceeds to deeper levels. Apositive correlation between conductivity and density of rocksin the Critical Zone and Main Zone may be useful as aconductivity estimator where conductivity data are notavailable. The high magnetite content of many rocks in theUpper Zone results in a correlation that differs from theCritical and Main Zones and the rest of the Upper Zone; thedensity is substantially higher but the conductivity is largelyunaffected. Illustrative calculations indicate that the generallylower thermal conductivity and thermal diffusivity of rocks inBushveld platinum mines results in a lower heat flux intounderground workings compared with gold mines in theWitwatersrand Basin.

Acknowledgements

The research leading to this publication was made possible byfinancial and logistical support from Bluhm BurtonEngineering (Pty) Ltd, the Chamber of Mines ResearchOrganisation (later CSIR Mining Technology), General MiningUnion Corporation Ltd, the Geological Survey of South Africa(now Council for Geoscience), Gold Fields of South Africa Ltd,Johannesburg Consolidated Investment Company Ltd, andImpala Platinum Ltd. Individual geologists who assisted infield surveys and sample collection are too numerous to list,but their efforts have not been forgotten. John Sorour’sassistance in measuring thermal conductivity is greatlyappreciated. The author gratefully acknowledges reviews byGrant Cawthorn, Alex Rawlins, and Russel Ramsden.

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BULLARD, E.C. 1939. Heat flow in South Africa. Proceedings of the Royal Societyof London, Series A, vol. 173. pp. 474–502.

CAIRNCROSS, B. and DIXON, R. 1995. Minerals of South Africa. Geological Societyof South Africa. Johannesburg.

CARTE, A.E. and VAN ROOYEN, A.I.M. 1969. Further measurements of heat flowin South Africa. Special Publication of the Geological Society of SouthAfrica, vol 2. Johannesburg. pp. 445–448.

CAWTHORN, R.G., COOPER, G.R.J., and WEBB, S.J. 1998. Connectivity between thewestern and eastern limbs of the Bushveld Complex. South AfricanJournal 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.,

Anhaeusser, C.R., and Thomas, R.J. (eds). Geological Society of SouthAfrica/Council for Geoscience, Johannesburg/Pretoria. pp. 261–281.

CAWTHORN, R.G. and SPIES, L. 2003. Plagioclase content of cyclic units in theBushveld Complex, South Africa. Contributions to Mineralogy andPetrology, vol. 145. pp. 47–60.

CERMAK, V. and RYBACH, L. 1982. Thermal conductivity and specific heat ofminerals and rocks. Landholt-Bornstein Numerical Data and FunctionalRelationships in Science and Technology. New Series, Group V, vol. 16.Angenheister, G. (ed.). Springer-Verlag. Berlin. pp. 305–343.

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DAVIS, M. D., WILSON, A. H., and VAN DER MERWE, A. J. 2007. The use of densityas a stratigraphic and correlative tool for the Bushveld Complex, SouthAfrica. Proceedings of Exploration 07: Fifth Decennial InternationalConference on Mineral Exploration. Milkereit, B. (ed.). Decennial MineralExploration Conferences, Toronto. pp. 1193–1197.

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Introduction

Coking coal is scarce in China, accounting forless than 10% of total coal reserves (Tao et al.,2009). Most of these valuable resources aredifficult to wash, with high ash content in theproduct and low recoveries. Reducedavailability of good quality coking coal hasresulted in Chinese steel plants using low-ashimported coal as a sweetener in coal blends(Liu et al., 2009). Ash has a highly adverseeffect on blast furnace productivity and cokeconsumption (Dey and Bhattacharyya, 2007).Thus it is necessary to develop an efficientpreparation scheme to produce coking coalwith a low ash content.

Froth flotation, which is the most commonseparation technique used for cleaning finecoals, has been widely applied since the 1920s(Hacifazlioglu and Toroglu, 2007;Hacifazlioglu and Sutcu, 2007). Columnflotation has been developed into an efficienttechnology in the past few decades. In manystudies, it has been claimed that columnflotation can give a higher recovery with lowerash content (Jena et al., 2008; Jena, Biswal,and Rudramuniyappa, 2008; Tao Luttrell, andYoon, 2000). Furthermore, recent work hasproved that coal floatability can be improvedconsiderably by grinding (Sokolovic,Stanojlovic, and Markovic, 2012; Xia et al.,

2012; Feng and Aldrich, 2000). The floata-bility of low-rank coal is enhanced throughgrinding in the presence of bituminous coalpitch (Atesok and Celik, 2000). Grinding hasalso been adopted to improve the floatability ofoxidized coal (Xia, Yang, and Zhu, 2012).Clean coals have been successfully producedfrom Mecsek bituminous coal by flotationfollowing ultrafine liberation (Bokanyi andCsoke, 2003). Thus by combining the twoprocesses of grinding pretreatment and columnflotation, the separation efficiency may begreatly improved.

In this investigation, we compar the ashcontent and combustible matter recovery byconventional flotation with that by columnflotation for different grinding times. Acyclonic-static microbubble flotation column(Cao et al., 2012; Li et al, 2010), a novelcolumn developed by China University ofMining and Technology, was used for theflotation tests. Size analysis, density analysis,and contact angle measurements were used toinvestigate the effects of grindingpretreatment. The efficiency of combinedgrinding pretreatment and column flotation isalso discussed.

Experimental procedure

Materials

A coking coal sample of -0.5 mm size fractionwas collected from Kailuan Mine, Tangshan,China. An SPB200 vibrating Taylor screen wasused for size analysis. Size fractions of +0.5, -

A new preparation scheme for a difficult-to-float coking coal by column flotation followinggrindingby Yinfei Liaoa*, Yijun Caoa*, Zhongbo Hub†, and XiuxiangTaoc‡

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

Keywordscoal flotation, column flotation, grinding, difficult-to-float, liberation.

* National Engineering Research Center of CoalPreparation and Purification, China University ofMining 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 andMetallurgy, 2015. ISSN 2225-6253. Paper receivedJun. 2014; revised paper received Nov 2014.



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 analysedfor ash. Ash content (%) = Wt. of baked ash/Wt. of unbakedcoal. A GL-21 M high-speed centrifuge was used for densityanalysis, with a centrifuge speed of 3000 r/min. The organicsolutions 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 pretreatmentA cylindrical laboratory ball mill with a diameter of 160 mmand a length of 200 mm was used for wet grinding in all theexperiments. The ball mill was run at a constant speed of 120r/min and a media filling of 20%. A 500 g coal sample wasadded to the ball mill at a pulp concentration of 40%. Thegrinding times were 10, 20, 30, and 40 minutes. Sizeanalysis, density analysis, and contact angle measurementswere carried out on the grinding products. Contact angleswere determined by the sessile drop method using a digitalgoniometer (Drop Shape Analysis System, DSA100, KrüssGmbH, Hamburg, Germany). The measurements wererepeated three times for every sample.

Flotation testsThe flotation tests were carried out in both a conventionalflotation cell and a laboratory flotation column. Diesel oil wasused as collector and 2-octanol as frother. Sodiumhexametaphosphate was used as silica depressant as well asdispersant.

About 100 g of coal was taken for conventional flotationexperiments using a 1.5 L XFD flotation cell with an impellerspeed of 1590 r/min and an air flow rate of 2 L/min. Theslurry was prepared with 6.25% solid concentration andconditioned with sodium hexametaphosphate (1.5 kg/t) for 2minutes. It was then treated with the required amount ofdiesel oil (310 g/t) for an additional 2 minutes. 2-octanol(120 g/t) was then added and the slurry further conditionedfor 1 minute. The flotation was carried out by introducing airand the froth was collected for 3 minutes. The flotationproducts 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 a100 mm diameter by 2000 mm tall laboratory flotationcolumn. A schematic diagram of the experimental set-up isshown in Figure 1. The slurry was treated with sodiumhexametaphosphate (1.5 kg/t) at 6.25% solid concentrationin the conditioner for 2 minutes. It was then agitated withdiesel oil (310 g/t) for an additional 2 minutes, after which 2-octanol (120 g/t) was then added and the slurry furtherconditioned for 1 minute. The slurry was fed with a peristalticslurry pump at a specified rate to the column. The requiredair rate was monitored by flow meter. The slurry flow rates offeed and tailings were checked, and when both remainedmore or less constant, the concentrate and tailings werecollected simultaneously at a certain time interval. Theoperating parameters for column flotation are presented inTable I. The samples were analysed using a similar procedureto that followed for the conventional flotation products. Eachexperiment was replicated to ensure the reproducible of datawithin the acceptable experimental error.


Characterization of coal samples

The particle size characterization data is given in Table II. Itcan be seen that the ash content increases with decreasingcoal particle size. The lowest ash content was found in the+0.5 mm fraction. The majority of the material falls in thesize range -0.074 +0.045 mm, with 34.27% yield. The finesand ash contents of the fine-grained samples are both high.The -0.074 mm fraction accounted for 39.78% of yield at anash content of 26.27%, which is significantly higher than theother fractions. The fine particles with high ash content floatinto clean coals easily through mechanical entrainment, thusgenerating a high-ash concentrate. The selective recovery offine fractions has thus become the key to preparation of thesetypes of coal.

The density analysis results for this fine coal are shownin Table III. It can be observed that the major yield of the coalis in the density range of -1.5 g/cm3. At a theoreticalseparation density of 1.4 g/cm3 and 1.5 g/cm3, the content of

▲


Figure 1—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 II

Size analysis results

Size fraction Weight Ash content Combustible (mm) (%) (%) recovery (%)

+0.5 3.21 15.31 2.34 -0.5+0.25 15.73 16.66 12.48 -0.25+0.125 24.75 17.05 20.09 -0.125+0.074 16.53 19.47 15.33 -0.074+0.045 34.27 26.06 42.53 -0.045 5.51 27.58 7.24 Total 100.00 21.00 100.00

Table I

Operating parameters of column flotation

Operating parameters Index

Collector Diesel oil (310 g/t)Frother 2-octanol (120 g/t)Dispersant Sodium hexametaphosphate (1.5 kg/t)Solid concentration 6.25%Feed rate 3 l/minAir rate 5-6 l/minFroth depth 400 mmCirculating pressure 0.20 Mpa

δ±0.1 is 68.36% and 46.49%, respectively. This demonstratesthat the washability of such fine coal is poor. The ash contentin the density range of 1.6 g/cm3 to 1.8 g/cm3 is relativelylow. It can be indicated that there are numerous non-liberated intergrowth particles formed by gangue mineralsand coals. It was supposed by the above density analysisresult that a microscopic investigation will be done for furtherverification. It is therefore difficult to obtain low-ash cleancoals and high-ash tailings by direct conventional flotation.

Grinding properties

Figure 2 shows that the yield of the +0.074 mm size fractiondecreases 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 changesquickly during the first 10 minutes, and changes slowlywhen the grinding time is more than 20 minutes. Thisindicates that the grinding efficiency is high during the first10 minutes and then declines.

Figure 3 presents the relationship between cumulativeyield and ash content, according to the density analysis ofgrinding products, for different grinding times. It can be seenthat the yield increases with ash content. Moreover, the yieldincreases with the grinding time for a given ash content. Thisindicates that intergrowth particles are liberated in thegrinding process and some coals are enriched to a certainextent.

Contact angle has been extensively used to characterizethe hydrophobicity and floatability of coal samples. Figure 4shows that the contact angle initially increases with grindingtime, then decreases slowly, decreasing more rapidly whenthe grinding time is more than 30 minutes. This phenomenonindicates that the hydrophobicity of this fine coal can beimproved by appropriate grinding, but will deteriorate withovergrinding. Some coals are liberated and enriched throughgrinding. The coal floatability is improved in the attritionprocess. However, if the grinding time is too long, the coalwill be overground and the coal surface will be covered byhigh-ash slime.

Flotation results

Figure 5 shows that combustible matter recovery by bothcolumn flotation and conventional flotation increases initiallywith grinding time and then decreases when the grindingtime is more than 10 minutes. However, the combustiblematter recovery by column flotation is consistently higherthan that by conventional flotation at all grinding times. In

both cases, the concentrate ash content at first decreases withgrinding time and then increases when the grinding time ismore than 10 minutes. The ash content of the columnflotation product is lower than that of conventional flotationat all grinding times. These results indicate that the schemeof column flotation following grinding is beneficial forobtaining products with low ash content.



Table III

Density analysis results

Density Yield (%) Ash (%) Float Sink Content of δ±0.1

(g/cm3) Yield (%) Ash (%) Yield (%) Ash (%) Density (g/cm3) Yield (%)

-1.3 8.56 4.32 8.56 4.32 100 20.87 1.3 40.371.3-1.4 31.81 11.36 40.37 9.87 91.44 22.42 1.4 68.361.4-1.5 36.55 19.62 76.92 14.5 59.63 28.32 1.5 46.491.5-1.6 9.94 24.35 86.86 15.63 23.08 42.1 1.6 13.551.6-1.8 7.21 41.94 94.07 17.64 13.14 55.53 1.7 7.21+1.8 5.93 72.05 100 20.87 5.93 72.05 1.8 9.54Total 100 20.87 — — — —

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

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

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


It is worth mentioning that both column flotation andconventional flotation achieve the best performance at agrinding time of 10 minutes. The contact angle at firstincreases with the grinding time, and then decreases asshown in Figure. 4. Appropriate grinding can be used toimprove flotation performance, but overgrinding willexacerbate mechanical entrainment, leading to a deteriorationin flotation performance. Therefore, these results demonstratethat 10 minutes is the optimum grinding time.

Conventional flotation reduces the ash content from21.36% to 12.42%, with 69.15% combustible recovery. Usingcolumn flotation, the ash content of the clean coals is reducedto 11.15%, with 74.47% combustible recovery. It is obviousthat column flotation is superior to conventional flotation,producing cleaner coals in terms of lower ash content andhigher combustible matter recovery. Column flotation hasadvantages in recovering valuable fines at a better grade dueto the minimization or prevention of hydraulic entrainment ofundesirable fines (Li et al., 2012; Demir et al., 2008; Finch,1995). The fine fractions increase after grindingpretreatment. Column flotation can selectively separate thesefines to obtain clean coals of lower ash content. Moreover,the cyclonic-static microbubble flotation column featuresmultiple mineralization steps, including countercurrentmineralization, cyclone mineralization, and pipe flowmineralization, which provide sufficient retention time toensure fines recovery (Zhang et al., 2013).

Conclusions

Investigations carried out on coking coal collected fromKailuan Mine indicate that it is difficult to obtain low-ashclean coals and high-ash tailings through direct conventionalflotation. Improved hydrophobicity and floatability can beachieved by appropriate grinding. The effect of grinding oncoal floatability is attributed to the liberation of intergrowthsand improvement of the coal surface properties.

It is concluded that 10 minutes is the optimum grindingtime, and overgrinding results in a deterioration in flotationperformance. With a grinding time of 10 minutes, conven-tional flotation has the potential to yield a product withapproximately 12.42% ash content and 69.15% combustiblerecovery, while the product ash content can be furtherreduced to 11.15% with 74.47% combustible recovery in caseof column flotation. Flotation tests results show that columnflotation is more efficient than conventional flotation for suchcoking coal fines. The scheme of column flotation following

grinding is beneficial for obtaining products with low ashcontent.

Acknowledgments

This research was supported by the National Key BasicResearch Program of China (Grant no. 2012CB214905) andthe Fundamental Research Funds for the Central Universities(Grant no. 2014XT05). The authors also acknowledge theassistance of Project Funded by the Priority AcademicProgram Development of Jiangsu Higher EducationInstitutions.

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Figure 5—Effect of grinding time on flotation performance

Prologue

Daniel Krige’s influence on soil science, and onsoil survey in particular, has been profound.Yet, I suspect that Daniel did not realize it. Some25 years ago I found myself close to his holidayhome in the Cape Province and visited himthere. We chatted over coffee. I told him how wewere adapting and developing geostatistics formapping soil. He was encouraging, but he wasnot surprised: of course the technology wasapplicable in our domain. What he did not knowwas the base from which we were starting. Hecould not know how Aristotelian logic, with itsemphasis on classification, had constrainedboth soil scientists’ thought and their mappingpractices for more than half a century; it wasnot his field. So before I describe some of ourachievements, I provide a little history.

I began my professional career in Africa.The year was 1957. The British Colonial Office,with the fiasco of the East African GroundnutScheme fresh in its collective mind, recruited meto evaluate the suitability of land foragricultural development in what was thenNorthern Rhodesia, now Zambia. It became myjob to survey and map soils for that purpose.My training in Britain, based on the identifi-cation of distinct classes of soil in a way similarto that of much geological survey at the time,ill-equipped me for what I should find. Therewere no obvious boundaries between one kindof soil and another on the deeply weatheredrocks of the Zambian plateaux. The soil seemedto vary gradually over the landscape, though inrepetitive patterns on a grand scale insequences for which Milne (1936) had earliercoined the term ‘catena’. Furthermore, the fairlydense miombo woodland in the north of countrymeant that one could rarely see for more than afew tens of metres, and air-photo interpretationhad so far been of little help. Surveys had to bedone almost entirely by sampling, and mappingby interpolation from the point observations.How was I to interpolate? There must be somemethod better than by hand and eye. I was nonearer to answering my question when in 1960I was invited by Philip Beckett to pursue thematter at Oxford University alongside the RoyalEngineers. The aim was to predict soilconditions at unvisited places.

Many influential pedologists at the timewere convinced that if one knew to which classof soil a site belonged then one would be able topredict the soil’s properties there. Beckett and Iwere far from convinced.

A few engineers had begun to realize thatthe problem was essentially statistical and weretoying with a combination of classical soil mapsand prediction statistics based on stratifiedrandom sampling in which the classes of the

Technological developments for spatialprediction of soil properties, and DanieKrige’s influence on itby R. Webster*

SynopsisDaniel Krige’s influence on soil science, and on soil survey in particular, hasbeen profound. From the 1920s onwards soil surveyors made their maps byclassifying the soils and drawing boundaries between the classes theyrecognized. By the 1960s many influential pedologists were convinced that ifone knew to which class of soil a site belonged then one would be able topredict the soil’s properties there. At the same time, engineers began to realizethat prediction from such maps was essentially a statistical matter and toapply classical sampling theory. Such methods, though sound, provedinefficient because they failed to take account of the spatial dependencewithin the classes.

Matters changed dramatically in the 1970s when soil scientists learned ofthe work of Daniel Krige and Georges Matheron’s theory of regionalizedvariables. Statistical pedologists (pedometricians) first linked R.A. Fisher’sanalysis of variance to regionalized variables via spatial hierarchical designsto estimate spatial components of variance. They then applied the mainstreamgeostatistical methods of spatial analysis and kriging to map plant nutrients,trace elements, pollutants, salt, and agricultural pests in soil, which has led toadvances in modern precision agriculture. They were among the first Earthscientists to use nonlinear statistical estimation for modelling variograms andto make the programmed algorithms publicly available. More recently,pedometricians have turned to likelihood methods, specifically residualmaximum likelihood (REML), to combine fixed effects, such as trend andexternal variables, with spatially correlated variables in linear mixed modelsfor spatial prediction. They have also explored nonstationary variances withwavelets 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 fromresearch projects.

Keywordssoil, variogram, spectral analysis, gilgai, drift, mixed models, REML, non-stationary, variance.

* Rothamsted Research, Great Britain.© The Southern African Institute of Mining and

Metallurgy, 2015. ISSN 2225-6253. Paper receivedJun. 2013; revised paper received Jun. 2014.



Technological developments for spatial prediction of soil properties

maps were the strata. Morse and Thornburn (1961) publishedstatistics obtained by sampling agricultural soil maps inIllinois, and a year later Kantey and Williams (1962) reportedresults of sampling engineering soil maps in South Africa. Weplanned a thorough study along similar lines. We classifiedand mapped a large part of the Oxfordshire landscape, sampledit to a stratified random design, and measured properties of thesoil. Then by analysis of variance we assessed our classifi-cation for its effectiveness in (a) diminishing the varianceswithin the classes and (b) predicting values with acceptableprecision. We also assessed maps made and sampled byseveral of our collaborators. We had mixed success. Our map ofthe Oxford region enabled us to predict the mechanicalproperties of the soil reasonably well. It predicted relativelypoorly the soil’s pH and organic matter content, and it wasuseless for predicting the plant nutrients in the soil. Table Ifrom Webster and Beckett (1970) summarizes our results.

First steps

Despite our partial success, even in the most favourablesituations there was substantial residual variation for whichwe could not account. Some we might treat as white noise, butmuch was evidently structural. We had not solved the problemof the catena or any other form of gradual change or trend. Ifwe simply drew boundaries in those situations then theresiduals would be spatially correlated. At about the same time,trend-surface analysis was becoming fashionable in geographyand petroleum exploration, but it was unsatisfactory because(a) fluctuation in one part of a region affected the fit of thesurface elsewhere and (b) the residuals were correlated so thatcalculated prediction variances were biased.

I was joined from Mexico by H.E. Cuanalo in 1968. Hepointed out that time-series analysts have similar problems,and they treat actuality as realizations of stochastic processesto describe quantitatively fluctuations in time. Could we not dothe same for soil? So we switched our thinking from theclassical mode and took a leap of imagination; we should treatthe soil as if it were random – against all the tenets of the day!

The Sandford transect I

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

and Cuanalo, 1975). Correlograms computed from the datashowed strong spatial correlation extending to 200–250 m.This distance corresponded approximately to the average widthof the outcrops and to the evident changes in soil. If we filteredout the variation due to the presence of the distinct underlyingJurassic strata we discovered that there was still spatialcorrelation in the residuals, though with a range of only about80 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 experimentalsemivariances, are computed by the usual method of momentsat 10 m intervals, and the spherical functions are fitted byweighted least squares with the ‘fitnonlinear’ command inGenStat (Payne, 2013) with weights proportional to thenumbers of paired comparisons – see also below. Table IIsummarizes the statistics and Table III lists the fittingparameters.

From today’s viewpoint the situation seems obvious. Wehad two sources of variation, one from class to class, which wemight treat as a fixed effect; and the other within classes,which we should treat as random. We needed a mixed model todescribe it. I return to the matter below.

Nested sampling and analysis

I spent the year 1973 working with B.E. Butler, doyen ofAustralian pedology in the CSIRO. Our first task was to

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Table I

Components of variance and intra-classcorrelations for a soil classification in Oxfordshire(from Webster and Beckett, 1970)

Variance componentsSoil property Mean Between Within ri

classes classes

Strength (cone index) 138 1248 510 0.71Clay content, % 37.2 112.3 90.2 0.61Plastic limit, % 38.8 125.6 111.4 0.53pH 7.1 0.161 0.326 0.40Organic matter, % 9.8 3.96 9.48 0.28Available P, % 0.031 0.000113 0.00114 0.09Available K, % 0.013 6.0×10-6 93.9×10-6 0.06

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

and exponential with nugget:

The values of the parameters c0, c, r, and a are listed in Table III

discover the spatial scale(s) on which soil properties varied onthe Southern Tablelands of Australia. We sampled to abalanced spatially nested design and estimated the componentsof variance from a hierarchical analysis of variance (ANOVA),quite unaware that the technique had been proposed 36 yearsearlier by Youden and Mehlich (1937) and published in theirhouse journal, or that it had been used by geologists Olsen andPotter (1954) and Krumbein and Slack (1956) in theintervening years, and then forgotten. We summed thecomponents to form rough variograms and discovered thatdifferent soil properties varied on disparate scales in thatcomplex landscape (Webster and Butler, 1976). Figure 2shows such variograms of two of the variables. Notice that thespacings in the design increase in logarithmic progression. Atabout the same time Miesch (1975) had the idea of applyingthe same techniques for geochemistry and ore evaluation.

Along the bottom of the graph are the degrees of freedomwith which the components are estimated. You will see that asone moves from right to left on the graph, i.e. as the scalebecomes increasingly fine, the number of degrees of freedomincreases twofold with each step after the first. If one wantsmore steps on the graph for a more refined picture, thenmaintaining balance by doubling the sampling soon becomesunaffordable. The increased precision at the shorter lagdistances is also unnecessary. Margaret Oliver recognized theproblem and sacrificed balance and analytical elegance forgreater efficiency for studying the soil in the Wyre Forest ofEngland. She and I designed a five-stage hierarchy but withoutdoubling all branches of the hierarchy at the lowest stage, andwe programmed Gower’s (1962) algorithm to estimate thecomponents of variance (Oliver and Webster, 1987). Shortlyafterwards Boag and I devised an extreme form of unbalancedhierarchy with equal degrees of freedom at all stages, apartfrom the first, in a study of the distribution of cereal cystnematodes in soil, and again we estimated the components ofvariance by Gower’s method (Webster and Boag, 1992). Wehave since replaced Gower’s method, which though unbiased isnot unique, by the more efficient residual maximum likelihoodmethod (REML) of Patterson and Thompson (1971). A fullaccount of the procedures and guide to computer code can befound in Webster et al. (2006).

That paper, however, is not the last word; since then Lark(2011) has sought to optimize hierarchical spatial sampling. Ifhe assumed that the variances contributed at spacingsincremented in a logarithmic progression were equal, thenneither the fully balanced design nor the one that distributedthe degrees of freedom equally was best; the optimal designwas intermediate between the two. Webster and Lark (2013)summarize the search mechanism using simulated annealingto find the optimum and the results.

The last three publications cited above should ensure thatthis efficient, economical way of obtaining a first roughestimate of the variogram in unknown territory will not beforgotten. It should be a part of any geostatistician’s toolkit.

Gilgai and spectral analysis

A second topic in my Australian research was to investigatethe repetitive spatial patterns of gilgais. Gilgais are typicallyshallow wet depressions a few metres across in otherwise flatplains, and their patterns seemed to be regular. The questionwas: is there some regularity? and if so what are its character-istics?

As in north Oxfordshire, I sampled the soil at regularintervals on a transect on the Bland Plain of New South Wales(Webster, 1977). The transect was almost 1.5 km long andwas sampled at 4 m intervals. Table IV summarizes thestatistics. The correlograms of several properties appearedwavy, and I transformed them to their corresponding powerspectra. I illustrate the outcome with results for just onevariable, the electrical conductivity in the subsoil (30–40 cm)converted to logarithms to stabilize the variance and with thevariogram instead of the correlogram (Figure 3). The functionfitted to the experimental variogram comprises fourcomponents, namely nugget, spherical, linear, and periodic.Figure 4 shows the spectrum computed with a Parzen lagwindow of width 60 sampling intervals. Notice the strong peakat approximately 0.12 cycles corresponding to the wavelengthof 34 m in the model fitted to the variogram.



Table II

Statistical summary of data on the clay content(percentage by mass) in the subsoil (±65 cm) ofnorth Oxfordshire

Raw data

Mean 39.1Median 36.0Variance 936.81Class means OLS REML

estimates estimatesSharp’s Hill Beds (clay) 61.3 75.4Great Oolite (limestone) 33.4 42.3Lower Estuarine Beds (silt) 12.9 15.1Chipping Norton Limestone (limestone) 9.5 10.8Chipping Norton Limestone (sand) 15.1 25.2Upper Lias (clay) 69.9 50.3Pleistocene and Recent (silt and clay) 55.8 51.0Middle Lias (ironstone) 41.4 43.4Middle and Lower Lias (clay) 68.6 67.5

Figure 2—Accumulated variances as proportions of the total variancesforsoluble potassium in the soil and the water held at –10 kPaestimated by hi-erarchical analysis of variance from nested spatialsampling at Ginninderra, Australia. The numbers immediately above theabscissa are the degrees of freedom at the spacings (data fromWebster and Butler, 1976)


The patterns are two-dimensional, of course, and onewould like to extend the above analysis in two dimensions.Despite the remark above, sampling the soil at sufficient placesfor such analysis was prohibitively expensive. An alternativewas to analyse aerial photographic images of the Bland Plain,on which the gilgais appear typically as roughly circular darkpatches on a paler background. Milne et al. (2010) took thisapproach.

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. Acomplete stranger breezed into my office without a by-your-leave and asked me bluntly, ‘What’s this kriging?’. I had neverheard the term before, and rather than plead completeignorance I played for time. Who was this intruder? and whydid he ask? He was Daniel Sampey, a mining geologist. I lethim talk, which he did for about 20 minutes. He told me of acertain Professor Krige and Georges Matheron, of the theory ofregionalized 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 thatas I was about to return to Britain I should visit LeedsUniversity, where mining engineers knew a thing or two. Inthose 20 minutes I realized that my problem of spatialprediction of soil conditions at unvisited places had beensolved, at least in principle, and in general terms I understoodhow. On my return to Britain I contacted Anthony Royle atLeeds. He amplified what Daniel Sampey had told me, and hegenerously gave me a copy of his lecture notes on the subjectand some references to the literature, including Matheron’s(1965) seminal thesis.

Back in Oxford I was joined by Trevor Burgess, a youngmathematician. Together we turned Matheron’s equations intoalgorithms and the algorithms into computer code. Our firstscientific papers appeared in 1980 (Burgess and Webster,1980a, 1980b; Webster and Burgess, 1980). They were thefirst to describe for soil scientists the variogram as we know ittoday and the first to display maps of soil properties made bykriging. It is from there that geostatistics in soil scienceburgeoned 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

Soil is almost the ideal medium for practical geostatistics. Itforms a continuous mantle over large parts of the Earth’s landsurface. Access is easy over much of that, so that sampling atthe working scale of the individual field, farm, or estate can becheap. 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 measurenowadays: pedometricians need not be short of data for thesevariables, and from large databases they can estimate spatialcovariance functions or variograms accurately. The statistical

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

Table IV

Summary statistics of electrical conductivity in thesoil at 30–40 cm on the Bland Plain of New SouthWales

Electrical conductivity

mS cm-1 log10(mS cm-1)

Minimum 0.06 –1:214Maximum 5.10 0.707Mean 0.958 –0:2298Median 0.54 –0:2668Variance 0.95948 0.19205Skewness 1.64 0.10

Table III

Estimated parameters of variogram of the claycontent in the subsoil (≈65 cm) of northOxfordshire

Model typeSpherical Exponential

c0 c r/m c0 c a/m

Raw data 120.6 580.3 207.0OLS residuals 108.4 296.4 79.2REML residuals 77.5 505.3 67.3

Figure 4—Power spectrum log10 (electrical conductivity) in the subsoil(30 – 40 cm) of the soil on the Bland Plain of New South Wales derivedfrom the correlogram and smoothed with a Parzen lag window of width60 sampling intervals. Note that frequency is the reciprocal of samplinginterval

distributions of these properties are in most instances ‘well-behaved’ in that they are either close to normal (Gaussian) orto lognormal, so that in the latter situation a simple transfor-mation to logarithms makes analysis straightforward andefficient. Furthermore, although the laws of physics must beobeyed as soil is formed, the numerous processes that operateand have operated in combination over many millennia to formthe present-day soil have produced a complexity that isindistinguishable from random (Webster, 2000). So, we canoften treat soil as the outcome of random processes withoutharming our professional reputations.

It is a small step from there to assume that soil variablesare intrinsically stationary. In the conventional notation

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 xand x + h, and vector h is the separation, or lag, between themin the two dimensions of soil survey. Ordinary kriging follows.From the 1980s onwards it has become the workhorse ofgeostatistics in surveys, not only of soil itself but also in therelated fields of agronomy, pest infestation, and pollution;there are hundreds of examples of its application described inthe literature. It is proving to be valuable in modern precisionagriculture in particular – see Oliver (2010) and the topicstherein.

Disjunctive kriging required a somewhat larger advance intechnique. Matheron (1976) formulated it for selection inmining. Pedologists saw in it the means of estimating andmapping the probabilities of nutrient deficiencies over landgrazed by cattle and sheep (Webster and Oliver, 1989; Websterand Rivoirard, 1991) and of contamination by potentially toxicmetals (von Steiger et al., 1996). In both situations thresholdsare specified to trigger action. In the first, the thresholds areminimum concentrations of available trace elements such ascopper and cobalt. If the soil contains less than thesethresholds then stock farmers are advised to supplement theiranimals’ feed or to add salts of the elements to their fertilizers.However, kriged estimates of concentrations are subject toerror, and farmers do not want to risk deficiencies by takingthose estimates at face value. They want in addition estimatesof the probabilities, given the data, that patches of ground aredeficient. In the second situation the thresholds are maxima, inexcess 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 theprobabilities of excess before spending taxpayers’ money onunnecessary remediation or risking poisoning people orgrazing animals by doing nothing.

Nonlinear modelling of variograms

Throughout the 1980s one of the most serious stumblingblocks in practical geostatistics was the lack of software in thepublic domain for fitting models to sample variograms. Manypractitioners fitted models by eye, and they defended theirpractice with vigour. In some instances, estimatedsemivariances fell neatly on smooth curves that matched oneor other of the standard valid variogram functions, and inthose circumstances the practice was reasonable. But in manyother situations, choosing and fitting functions to experimental

variograms were, and remain, problematic. Some experimentalvariograms are erratic, usually because they are derived fromrather sparse data. In some the numbers of paired comparisonsvary greatly so that it is hard to know how to weight the pointson graphs. The variation can be strongly anisotropic, so thatagain one cannot see what models might fit. And there can becombinations of these.

All of the popular functions, apart from the unboundedlinear model, contain nonlinear distance parameters, and thesecannot be estimated by ordinary least-squares regression.Some, such as the exponential and power functions, can be re-parameterized so that they are linear. Others, such as thespherical and related functions, cannot; they must be estimatedby numerical approximation, and doing that requires expertisein numerical analysis. Rothamsted had that expertise; Ross(1987) had written his program, MLP, for nonlinearestimation, and we soil scientists used it to advantage forfitting models to experimental variograms (McBratney andWebster, 1986). The algorithms were incorporated in GenStat,Rothamsted’s general statistical program, now in its 16threlease (Payne, 2013), to provide the facilities for estimatingand modelling spatial covariances completely under the controlof the practitioner and with transparent monitoring of theprocesses. These facilities include the choice of steps, bins andmaximum lags; the robust estimates of Cressie and Hawkins(1980), Dowd (1984), and Genton (1998) in addition to theusual method of moments; and variable weighting according tothe expected values, as suggested by Cressie (1985). Theyinclude also the linear model of coregionalization for two ormore random variables.

The facilities are readily called into play from menus.Alternatively they can be built into programs in the GenStatlanguage, so that one can proceed from raw data, via theirscreening, distributions, and transformation, variography,kriging and cross-validation, to final output as griddedpredictions, back-transformed if necessary, and their variancesready for mapping. GenStat (http://www.vsni.co.uk/software/genstat) is immensely powerful and is available inthe public domain for a modest price.

Statistical modelling is no longer novel, and it has largelyreplaced fitting models by eye. Unfortunately, the pendulumhas swung too far towards automation. Too often, modelling isnow a blinkered push-button exercise in a geographicinformation system applied with little understanding or controland no facilities for monitoring.

Where are we now? Mixed models incorporating trendand other knowledge

Although the assumption of intrinsic stationarity has seemed,and continues to seem, easily satisfied there are two situationsin which it is not satisfactory. The first is where there isgeographic trend – ‘drift’ in the geostatistical jargon – and forwhich Matheron (1969) devised universal kriging. Universalkriging itself requires no more than an augmentation of theordinary kriging system. Obtaining a valid estimate of thevariogram of the random component of variation is moredifficult, because what is required is the variogram of theresiduals from the drift, and one cannot know what theresiduals are until one has correctly identified the drift.Webster and Burgess (1980) recognized the situation, and to




map the soil’s electrical resistivity over an archaeological sitethat had been sampled on a dense grid they programmed thealgorithms set out by Olea (1975) for the purpose.

Trend is a kind of knowledge in addition to the sampledata. There are other kinds of additional knowledge about soil.The variable of interest, the target variable, might be related toone or more other variables that the pedologist knows or canmeasure cheaply at the prediction points. Or, as at Sandfordmentioned above, there might already be a classification of theregion that could partition the variance. How should one takethis knowledge into account?

For some years pedologists answered with a pragmaticapproach; they called it ‘regression kriging’. They regressed byordinary least squares (OLS) the target variables on thepredictors, which could be the geographic coordinates for trend,the ancillary variables they measured, or the classes of theregions being mapped. They estimated variograms of the OLSresiduals, kriged the residuals, and then added back theestimates from the regression equations at the prediction sites– see, for example, Odeh et al. (1995). The predictions areunbiased, but the prediction variances are underestimated,partly because the variograms are biased (Cressie, 1993) andpartly because there is no valid way of combining the errorsfrom the kriging and the OLS regressions. The problem was toestimate simultaneously the regression or deterministiccomponent with minimum variance and the random residualsfrom the regression without bias, and then to sum thepredictions of the deterministic and random variation atunsampled sites with known minimized variance. The solution,pointed out by Stein (1999), was to use maximum likelihoodmethods and to obtain what he called the empirical best linearunbiased predictor (E-BLUP). The basic maximum likelihoodtechnique can be biased; residual maximum likelihood (REML)is not, and soil scientists, especially my colleague Murray Larkand co-workers, are now at the forefront among geostatis-ticians in its application.

The basic model underlying the E-BLUP is the linear mixedmodel:

Z(x) = wβ + ε(x); [3]

in which the vector w, with K+1 columns, contains the K+1elements 1; x1; … ; xK of the regression function and βcontains the coefficients. The quantity ε(x) is the randomresidual from the regression. It is assumed to be second-orderstationary with mean zero and covariance function C(h),which, because of that assumption, has the equivalentvariogram ϒ(h) = C(0) – C(h). Its parameters are typically anugget variance, c0, a structural variance, c, and a distanceparameter, a, which we may denote in short by θ ≡ (c0, c, a}.

One finds values for the parameters in θ numerically bymaximizing the log-likelihood of the residuals given the data:L [θ|z (Xd)]. Having found them, one then estimates thecoefficients in β by generalized least squares, and with bothsets of values known one can proceed to the kriging forprediction with its variances. Webster and Oliver (2007, pp.200–202) provide the details. In 2006 Lark et al. (2006) calledthe technique ‘state of the art’, and though their solution bysimulated annealing was still in the research phase, theyshowed the way forward. Now, with facilities in the publicdomain 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 asbest practice.

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

Drift at Yattendon

Oliver and Carroll (2004) sampled a 23 ha field on the Chalkdownland of southern England, and they measured, amongother variables, the percentage of sand in the topsoil (0–15 cm)at 230 places. Their map in pixel form showed a strongregional trend. In addition they measured the apparentelectrical conductivity, ECa, of the soil. In this field the ECa andthe sand content are linearly related, and so one can treat theECa as an external drift variable when predicting theproportion of sand. Webster and Oliver (2007) compared fourtreatments 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 sphericalmodels fitted to them. The variogram of the raw data, Figure5(a), increases to an apparent sill at a lag distance of approxi-mately 200 m, and then increases beyond with ever-increasinggradient. The latter increase is characteristic of long-rangetrend. If one fits a quadratic trend surface by OLS regressionand then models the variogram of the residuals one obtainsFigure 5(b). Evidently the residuals are much less variablethan the raw data, but they are also autocorrelated, and so theOLS regression gives a faulty representation of the truth.Figure 5(c) shows the REML variogram of the residuals withthe quadratic trend fitted as a deterministic (fixed) effect. Itssill at 176.4%2 is substantially larger than that of the OLSresiduals in Figure 5(b). By taking into account the additionalknowledge of the relation between sand content and ECa asexternal drift in the REML analysis one obtains the variogramshown in Figure 5(d), now with a sill of only 151.6%2.

Webster and Oliver (2007) went on to map the sandcontent by punctual kriging from the data at the nodes of a finegrid of 5 m × 5 m using these variograms. The maps of thepredicted values were similar, as might be expected becausekriging is so robust. The kriging variances differed substan-tially. Those for ordinary kriging from the raw data were quitethe largest; they had a mean of 63.2%2. The mean variance forthe OLS residuals was 52.0%2, which we know to be anunderestimate. That for the universal kriging, i.e. incorporatingthe spatial coordinates only, item (c) above, was 53.5%2, andthe mean for kriging with the external ECa in addition, item(d), was 48.2(%)2. The example shows that the more pertinentinformation we have the more accurate are our predictions.

The Sandford transect II

The analysis above of the Sandford transect also used what weknow about the environment, in that instance, the stratigraphy

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of the rocks beneath the soil as represented by the geologicalmap. We may criticize it for the same reasons as we nowcriticize the early regression kriging based on the simple OLSresiduals: the sampling was at regular intervals, not random,and so the within-class variances would be biased and perhapsthe mean values also. Murray Lark, whom I thank, has kindlyre-analysed the data based on the model of Equation [3], butnow with β containing the mean value of the stratigraphic classto which position x belongs and w taking the value 1 if xbelongs to that class. So again we use REML to find both theclass means and the variogram of the residuals.

Table II lists the mean values as estimated both byordinary least squares and by REML. The differences amongthe latter are highly significant statistically; the Wald statistic is33.12, which, with 8 degrees of freedom, gives a probability of< 0:0001 for the null hypothesis. The best-fitting variogram ofthe residuals estimated by REML turns out to be exponential

with parameter values listed in Table III. I have plotted it on thesame graph as the variograms of the raw data and OLSresiduals 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 tothe range (207.0 m) of the variogram of the raw data andmuch larger than that of the OLS residuals (79.2 m).

Nonstationary variances

A more intractable stumbling block in the spatial prediction ofsoil properties is that of nonstationary variances. The fieldpedologist knows well that some parts of a landscape are morevariable than others: for example, the flood plain of a braidedriver does not vary in the same way or on the same spatialscale as the river’s higher catchment. At Sandford the soilseemed not to vary equally on all sedimentary outcrops, andLark and Webster (1999) introduced wavelets into soil scienceto investigate the matter.

A wavelet is a function that varies within a narrow windowand is constant at zero outside. The window is moved step bystep, i.e. translated, over the field of data, and at each positionit is convolved with the data in the window to obtain itscoefficient. The window can be also be dilated and againtranslated so that a new set of coefficients are obtained at adifferent scale. By plotting the results against spatial positionone can see where and on what spatial scales the variancechanges (e.g. Lark and Webster, 2001; Milne et al., 2011).Milne et al. (2010) also analysed aerial photographs of thegilgai patterns on the Bland Plain. Their results accorded wellwith, and augmented, those from the earlier spectral analysisabove.

What is not yet clear is how we should take into accountnonstationary variance in prediction, unless we have abundantdata and can segment fields of data into zones of stationaryvariance. Some progress has been made by Paciorek andSchervish (2006) using a new class of nonstationarycovariance functions, and by Haskard et al. (2010), whocombined the linear mixed model with spectral tempering. Thisshould be a matter of further research in the years to come.

Epilogue

We soil scientists are greatly indebted to Daniel Krige. Perhapswithout his realizing, it was on his pioneering technology thatwe built and advanced in our own field; the technology and theideas behind it released us from the constraining mind-set ofearlier years and opened up a whole new field of endeavour –pedometrics. We should thank also Daniel Sampey; I shall notforget him. Who knows how much longer we should havegroped at snail’s pace towards the solution of our problem ofspatial prediction had he not burst into my office in Australiathat day 40 years ago? Finally, we should recognize the recentachievements of Murray Lark. His penetrating study of drift inits various forms, its estimation as part of the linear mixedmodel, and his clear and convincing writing (e.g. Lark 2012)have set soil scientists on a new and sound course in regionalprediction and mapping – see, for example, the papers byPhilippot et al. (2009) and Villanneau et al. (2011).Furthermore, with packages for the analysis now in the publicdomain we have no excuse for inferior practice.



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

Table V

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

Analysis c0 c c0 + c r/m

Raw data 26.1 208.7 234.8 252.4OLS residuals from quadratic trend 10.4 104.3 114.7 101.5REML with quadratic trend 16.6 1 59.8 176.4 175.8REML with quadratic trend and ECa 21.7 129.9 151.6 208.7

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


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▲


For further information contact:Head of Conferencing

Raymond van der Berg, SAIMMP O Box 61127, Marshalltown 2107

Tel: +27 (0) 11 834-1273/7E-mail: [email protected]

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

Copper CobaltAfrica

Copper CobaltAfrica

In association withThe 8th Southern African Base Metals Conference

6–8 July 2015Zambezi Sun Hotel, Victoria Falls

Livingstone, Zambia

ConferenceAnnouncement

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, andnetworking for all those interested in the processing of copper and cobalt in an African context, inone of the worldʼs most spectacular settings.

The African Copper Belt has experienced a huge resurgence of activity in recent years followingmany years of political and economic instability. Today, a significant proportion of capital spending,project development, operational expansions, and metal value production in the Southern Africanmining industry are occurring in this region. The geology and mineralogy of the ores aresignificantly different from those in other major copper-producing regions of the world, often havingvery high grades as well as the presence of cobalt. Both mining and metallurgy present someunique challenges, not only in the technical arena, but also with respect to logistics and supplychain, human capital, community engagement, and legislative issues. This conference provides aplatform for discussion of these topics, spanning the value chain from exploration, projects, throughmining and processing.

For international participants, this conference offers an idealopportunity to gain in-depth knowledge of and exposure to theSouthern African base metals industry, and to better understandthe various facets of mining and processing in this part of theworld that both excite and frustrate the industry.

A limited number of places are available for post-conferencetours to Zambiaʼs most important commercial operations,including Kansanshi, the largest mine in Zambia, with 340 kt/ycopper production and its soon-to-be-completed 300 kt/ysmelter, and Chambishi Metals.

Jointly hosted by the mining and metallurgy technicalcommittees of the Southern African Institute of Mining andMetallurgy (SAIMM), this conference aims to:

• Promote dialogue between the mining and metallurgicaldisciplines on common challenges facing the industry,

• Encourage participation and build capacity amongst youngand 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.

SPONSORS:

Premium

The Journal of The Southern African Institute of Mining and Metallurgy FEBRUARY 2015 ▲vii

201511 – 12 March 2015 — Mining Business OptimisationConference 2015Mintek, Randburg, JohannesburgContact: Camielah JardineTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail:[email protected], Website: http://www.saimm.co.za8–10 April 2015 — 5th Sulphur and Sulphuric Acid 2015ConferenceSouthern Sun Elangeni Maharani KwaZulu-Natal, South AfricaContact: Camielah JardineTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za23–25 April 2015 — SANCOT Conference 2015Mechanised Underground ExcavationElangeni Maharani, DurbanContact: Yolanda RamokgadiTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za12–13 May 2015 — Mining, Environment and SocietyConference: Beyond sustainability—Building resilienceMintek, Randburg, South AfricaContact: Yolanda RamokgadiTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za10-11 June 2015 — Risks in Mining 2015 ConferenceJohannesburgContact: Camielah JardineTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail:[email protected], Website: http://www.saimm.co.za14–17 June 2015 — European Metallurgical ConferenceDusseldorf, Germany, Website: http://www.emc.gdmb.de14–17 June 2015 — Lead Zinc Symposium 2015Dusseldorf, Germany, Website: http://www.pb-zn.gdmb.de16–20 June 2015 — International Trade Fair for MetallurgicalTechnology 2015Dusseldorf, Germany, Website: http://www.metec-tradefair.com24–25 June 2015 — Mine to Market Conference 2015Emperors PalaceContact: Yolanda RamokgadiTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za6–8 July 2015 — Copper Cobalt Africa IncorporatingThe 8th Southern African Base Metals ConferenceZambezi Sun Hotel, Victoria Falls, Livingstone, Zambia Contact: Raymond van der BergTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156E-mail: [email protected], Website: http://www.saimm.co.za13–14 July 2015 — School production of Clean SteelEmperors Palace, JohannesburgContact: Yolanda RamokgadiTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za15–17 July 2015 — Virtual Reality and spatial informationapplications in the mining industry Conference 2015University of PretoriaContact: Camielah Jardine

Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail:[email protected], Website: http://www.saimm.co.za11–14 August 2015 — The Tenth InternationalHeavy Minerals Conference ‘Expanding the horizon’Sun City, South AfricaContact: Camielah Jardine, Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.z19–20 August 2015 — The Danie Krige GeostatisticalConferenceGeostatistical geovalue —rewards and returns for spatialmodellingJohannesburgContact: Yolanda RamokgadiTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za15–17 September 2015 — Formability, microstructure andtexture in metal alloys ConferenceContact: Yolanda RamokgadiTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za28 September-2 October 2015 — WorldGold Conference 2015Misty Hills Country Hotel and Conference Centre,Cradle of HumankindGauteng, South AfricaContact: Camielah Jardine, Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.z12–14 October 2015 — Slope Stability 2015:International Symposium on slope stability in open pit miningand civil engineeringIn association with theSurface Blasting School15–16 October 2015Cape Town Convention Centre, Cape TownContact: Raymond van der BergTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za21–22 October 2015 — Young Professionals 2015 ConferenceMaking your own way in the minerals industryMintek, Randburg, JohannesburgContact: Camielah JardineTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail:[email protected], Website: http://www.saimm.co.za28–30 October 2015 — AMI: Nuclear Materials DevelopmentNetwork ConferenceNelson Mandela Metropolitan University, North CampusConference Centre, Port ElizabethContact: Raymond van der BergTel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: [email protected], Website: http://www.saimm.co.za8–13 November 2015 — MPES 2015: Twenty ThirdInternational Symposium on Mine Planning & EquipmentSelectionSandton Convention Centre, Johannesburg, South AfricaContact: Raj Singhal, E-mail: [email protected] or E-mail: [email protected], Website: http://www.saimm.co.za

INTERNATIONAL ACTIVITIES

▲

viii FEBRUARY 2015 The Journal of The Southern African Institute of Mining and Metallurgy

Company AffiliatesThe following organizations have been admitted to the Institute as Company Affiliates

AECOM SA (Pty) Ltd

AEL Mining Services Limited

Air Liquide (PTY) Ltd

AMEC Mining and Metals

AMIRA International Africa (Pty) Ltd

ANDRITZ Delkor(Pty) Ltd

Anglo Platinum Management Services (Pty) Ltd

Anglo Operations Ltd

Anglogold Ashanti Ltd

Atlas Copco Holdings South Africa (Pty) Limited

Aurecon South Africa (Pty) Ltd

Aveng Moolmans (Pty) Ltd

Axis House (Pty) Ltd

Bafokeng Rasimone Platinum Mine

Barloworld Equipment -Mining

BASF Holdings SA (Pty) Ltd

Bateman Minerals and Metals (Pty) Ltd

BCL Limited

Becker Mining (Pty) Ltd

BedRock Mining Support (Pty) Ltd

Bell Equipment Company (Pty) Ltd

BHP Billiton Energy Coal SA Ltd

Blue Cube Systems (Pty) Ltd

Bluhm Burton Engineering (Pty) Ltd

Blyvooruitzicht Gold Mining Company Ltd

BSC Resources

CAE Mining (Pty) Limited

Caledonia Mining Corporation

CDM Group

CGG Services SA

Chamber of Mines

Concor Mining

Concor Technicrete

Council for Geoscience Library

CSIR-Natural Resources and theEnvironment

Department of Water Affairs and Forestry

Deutsche Securities (Pty) Ltd

Digby Wells and Associates

Downer EDI Mining

DRA Mineral Projects (Pty) Ltd

Duraset

Elbroc Mining Products (Pty) Ltd

Engineering and Project Company Ltd

eThekwini Municipality

Evraz Highveld Steel and Vanadium Corp Ltd

Exxaro Coal (Pty) Ltd

Exxaro Resources Limited

Fasken Martineau

FLSmidth Minerals (Pty) Ltd

Fluor Daniel SA (Pty) Ltd

Franki Africa (Pty) Ltd Johannesburg

Fraser Alexander Group

Glencore

Goba (Pty) Ltd

Hall Core Drilling (Pty) Ltd

Hatch (Pty) Ltd

Herrenknecht AG

HPE Hydro Power Equipment (Pty) Ltd

Impala Platinum Limited

IMS Engineering (Pty) Ltd

JENNMAR South Africa

Joy Global Inc. (Africa)

Leco Africa (Pty) Limited

Longyear South Africa (Pty) Ltd

Lonmin Plc

Ludowici Africa

Lull Storm Trading (PTY)Ltd T/A WekabaEngineering

Magnetech (Pty) Ltd

Magotteaux(PTY) LTD

MBE Minerals SA Pty Ltd

MCC Contracts (Pty) Ltd

MDM Technical Africa (Pty) Ltd

Metalock Industrial Services Africa (Pty)Ltd

Metorex Limited

Metso Minerals (South Africa) (Pty) Ltd

Minerals Operations Executive (Pty) Ltd

MineRP Holding (Pty) Ltd

Mintek

MIP Process Technologies

Modular Mining Systems Africa (Pty) Ltd

Runge Pincock Minarco Limited

MSA Group (Pty) Ltd

Multotec (Pty) Ltd

Murray and Roberts Cementation

Nalco Africa (Pty) Ltd

Namakwa Sands (Pty) Ltd

New Concept Mining (Pty) Limited

Northam Platinum Ltd - Zondereinde

Osborn Engineered Products SA (Pty) Ltd

Outotec (RSA) (Proprietary) Limited

PANalytical (Pty) Ltd

Paterson and Cooke Consulting Engineers (Pty) Ltd

Polysius A Division Of ThyssenkruppIndustrial Solutions (Pty) Ltd

Precious Metals Refiners

Rand Refinery Limited

Redpath Mining (South Africa) (Pty) Ltd

Rosond (Pty) Ltd

Royal Bafokeng Platinum

Roymec Tecvhnologies (Pty) Ltd

RSV Misym Engineering Services (Pty) Ltd

Rustenburg Platinum Mines Limited

SAIEG

Salene Mining (Pty) Ltd

Sandvik Mining and Construction Delmas (Pty) Ltd

Sandvik Mining and Construction RSA(Pty) Ltd

SANIRE

Sasol Mining(Pty) Ltd

Scanmin Africa (Pty) Ltd

Sebilo Resources (Pty) Ltd

SENET

Senmin International (Pty) Ltd

Shaft Sinkers (Pty) Limited

Sibanye Gold (Pty) Ltd

Smec SA

SMS Siemag South Africa (Pty) Ltd

SNC Lavalin (Pty) Ltd

Sound Mining Solutions (Pty) Ltd

SRK Consulting SA (Pty) Ltd

Time Mining and Processing (Pty) Ltd

Tomra Sorting Solutions Mining (Pty) Ltd

TWP Projects (Pty) Ltd

Ukwazi Mining Solutions (Pty) Ltd

Umgeni Water

VBKOM Consulting Engineers

Webber Wentzel

Weir Minerals Africa

2015◆ CONFERENCE

Mining Business Optimisation Conference 201511–12 March 2015, Mintek, Randburg, Johannesburg

◆5th Sulphur and Sulphuric Acid 2015 Conference8–10 April 2015, Southern Sun Elangeni Maharani KwaZulu-Natal

◆SANCOT Conference 2015 Mechanised Underground Excavation23–25 April 2015, Elangeni Maharani, Durban

◆Mining, Environment and Society Conference12–13 May 2015, Mintek, Randburg, Johannesburg

◆Risks in Mining 2015 Conference10–11 June 2015, Johannesburg

◆Mine to Market Conference 201524–25 June 2015, Emperors Palace, Johannesburg

◆Copper Cobalt Africa Incorporating The 8th Southern African Base Metals Conference6–8 July 2015, Zambezi Sun Hotel, Victoria Falls, Livingstone,Zambia

◆Production of Clean Steel13–14 July 2015, Emperors Palace, Johannesburg

◆Virtual Reality and spatial information applications in the mining industry Conference 201515–17 July 2015, University of Pretoria, Pretoria

◆The Tenth International Heavy Minerals Conference11–14 August 2015, Sun City, South Africa

◆The Danie Krige Geostatistical Conference 201519–20 August 2015, Johannesburg

◆Formability, microstructure and texture in metal alloysConference 201515–17 September 2015

◆World Gold Conference 201528 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 engineering12–14– October 2015In association with theSurface Blasting School 15–16 October 2015, Cape TownConvention Centre, Cape Town

Forthcoming SAIMM events...

For further information contact:Conferencing, SAIMM

P O Box 61127, Marshalltown 2107Tel: (011) 834-1273/7

Fax: (011) 833-8156 or (011) 838-5923E-mail: [email protected]

F

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

EXHIBITS/SPONSORSHIP

Companies wishing to sponsor

and/or exhibit at any of these

events should contact the

conference co-ordinator

as soon as possible

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