Saimm 201503 mar by SAIMM

VOLUME 115

NO. 3

MARCH 2015

a member of the

The Southern African Institute of Mining and Metallurgy PAST PRESIDENTS OFFICE BEARERS AND COUNCIL FOR THE 2014/2015 SESSION Honorary President Mike Teke President, Chamber of Mines of South Africa Honorary Vice-Presidents Ngoako Ramatlhodi Minister of Mineral Resources, South Africa Rob Davies Minister of Trade and Industry, South Africa Naledi Pando Minister of Science and Technology, South Africa President J.L. Porter President Elect R.T. Jones Vice-Presidents C. Musingwini S. Ndlovu Immediate Past President M. Dworzanowski Honorary Treasurer C. Musingwini Ordinary Members on Council V.G. Duke M.F. Handley A.S. Macfarlane M. Motuku M. Mthenjane D.D. Munro G. Njowa

T. Pegram S. Rupprecht N. Searle A.G. Smith M.H. Solomon D. Tudor D.J. van Niekerk

Past Presidents Serving on Council N.A. Barcza R.D. Beck J.A. Cruise J.R. Dixon F.M.G. Egerton G.V.R. Landman R.P. Mohring

J.C. Ngoma S.J. Ramokgopa M.H. Rogers G.L. Smith J.N. van der Merwe W.H. van Niekerk

Branch Chairmen DRC

S. Maleba

Johannesburg

I. Ashmole

Namibia

N. Namate

Pretoria

N. Naude

Western Cape

C. Dorfling

Zambia

H. Zimba

Zimbabwe

E. Matinde

Zululand

C. Mienie

Corresponding Members of Council Australia: I.J. Corrans, R.J. Dippenaar, A. Croll, C. Workman-Davies Austria: H. Wagner Botswana: S.D. Williams United Kingdom: J.J.L. Cilliers, N.A. Barcza USA: J-M.M. Rendu, P.C. Pistorius

MARCH 2015

*Deceased * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

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

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

H. Britten (1955–1956) Wm. Bleloch (1956–1957) H. Simon (1957–1958) M. Barcza (1958–1959) R.J. Adamson (1959–1960) W.S. Findlay (1960–1961) D.G. Maxwell (1961–1962) J. de V. Lambrechts (1962–1963) J.F. Reid (1963–1964) D.M. Jamieson (1964–1965) H.E. Cross (1965–1966) D. Gordon Jones (1966–1967) P. Lambooy (1967–1968) R.C.J. Goode (1968–1969) J.K.E. Douglas (1969–1970) V.C. Robinson (1970–1971) D.D. Howat (1971–1972) J.P. Hugo (1972–1973) P.W.J. van Rensburg (1973–1974) R.P. Plewman (1974–1975) R.E. Robinson (1975–1976) M.D.G. Salamon (1976–1977) P.A. Von Wielligh (1977–1978) M.G. Atmore (1978–1979) D.A. Viljoen (1979–1980) P.R. Jochens (1980–1981) G.Y. Nisbet (1981–1982) A.N. Brown (1982–1983) R.P. King (1983–1984) J.D. Austin (1984–1985) H.E. James (1985–1986) H. Wagner (1986–1987) B.C. Alberts (1987–1988) C.E. Fivaz (1988–1989) O.K.H. Steffen (1989–1990) H.G. Mosenthal (1990–1991) R.D. Beck (1991–1992) J.P. Hoffman (1992–1993) H. Scott-Russell (1993–1994) J.A. Cruise (1994–1995) D.A.J. Ross-Watt (1995–1996) N.A. Barcza (1996–1997) R.P. Mohring (1997–1998) J.R. Dixon (1998–1999) M.H. Rogers (1999–2000) L.A. Cramer (2000–2001) A.A.B. Douglas (2001–2002) S.J. Ramokgopa (2002-2003) T.R. Stacey (2003–2004) F.M.G. Egerton (2004–2005) W.H. van Niekerk (2005–2006) R.P.H. Willis (2006–2007) R.G.B. Pickering (2007–2008) A.M. Garbers-Craig (2008–2009) J.C. Ngoma (2009–2010) G.V.R. Landman (2010–2011) J.N. van der Merwe (2011–2012) G.L. Smith (2012–2013) M. Dworzanowski (2013–2014)

Honorary Legal Advisers Van Hulsteyns Attorneys Auditors Messrs R.H. Kitching Secretaries The Southern African Institute of Mining and Metallurgy Fifth Floor, Chamber of Mines Building 5 Hollard Street, Johannesburg 2001 P.O. Box 61127, Marshalltown 2107 Telephone (011) 834-1273/7 Fax (011) 838-5923 or (011) 833-8156 E-mail: journal@saimm.co.za

The Journal of The Southern African Institute of Mining and Metallurgy

Editorial Board R.D. Beck J. Beukes P. den Hoed M. Dworzanowski M.F. Handley R.T. Jones W.C. Joughin J.A. Luckmann C. Musingwini R.E. Robinson T.R. Stacey R.J. Stewart

Editorial Consultant D. Tudor

Typeset and Published by The Southern African Institute of Mining and Metallurgy P.O. Box 61127 Marshalltown 2107 Telephone (011) 834-1273/7 Fax (011) 838-5923 E-mail: journal@saimm.co.za

Printed by

VOLUME 115

NO. 3

MARCH 2015

Contents Journal Comment by R.E. Robinson

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iv–v

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

vii

Inaugural African Corrosion Congress

Camera Press, Johannesburg

Barbara Spence Avenue Advertising Telephone (011) 463-7940 E-mail: barbara@avenue.co.za The Secretariat The Southern African Institute of Mining and Metallurgy ISSN 2225-6253 (print) ISSN 2411-9717 (online)

THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS. Copyright© 1978 by The Southern African Institute of Mining and Metallurgy. All rights reserved. Multiple copying of the contents of this publication or parts thereof without permission is in breach of copyright, but permission is hereby given for the copying of titles and abstracts of papers and names of authors. Permission to copy illustrations and short extracts from the text of individual contributions is usually given upon written application to the Institute, provided that the source (and where appropriate, the copyright) is acknowledged. Apart from any fair dealing for the purposes of review or criticism under The Copyright Act no. 98, 1978, Section 12, of the Republic of South Africa, a single copy of an article may be supplied by a library for the purposes of research or private study. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior permission of the publishers. Multiple copying of the contents of the publication without permission is always illegal. U.S. Copyright Law applicable to users In the U.S.A. The appearance of the statement of copyright at the bottom of the first page of an article appearing in this journal indicates that the copyright holder consents to the making of copies of the article for personal or internal use. This consent is given on condition that the copier pays the stated fee for each copy of a paper beyond that permitted by Section 107 or 108 of the U.S. Copyright Law. The fee is to be paid through the Copyright Clearance Center, Inc., Operations Center, P.O. Box 765, Schenectady, New York 12301, U.S.A. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale.

Comparison of linear polarization resistance corrosion monitoring probe readings and immersion test results for typical cooling water conditions by J.W. van der Merwe and A. Palazzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

173

General Papers Avoiding structural failures on mobile bulk materials handling equipment by M.J. Schmidt and B.W.J. van Rensburg. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

179

Utilization of the Brazilian test for estimating the uniaxial compressive strength and shear strength parameters by K. Karaman, F. Cihangir, B. Ercikdi, A. Kesimal, and S. Demirel . . . . . . . . . . . . . . . . . . . . .

185

Laser surface alloying of Al with Cu and Mo powders by S.L. Pityana, S.T. Camagu, and J. Dutta Majumdar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193

Chemical wear analysis of a tap-hole on a SiMn production furnace by J.D. Steenkamp, P.C. Pistorius, and M. Tangstad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines by S.R. Dindarloo, M. Osanloo, and S. Frimpong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

209

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation by N.V. Ramlall and B.K. Loveday. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

221

Enrichment of low-grade colemanite concentrate by Knelson Concentrator s ............................................. by T. Uslu, O. Celep, and M. Sava Ç

229

Multifractal interpolation method for spatial data with singularities by Q. Cheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

235

High-order additions to platinum-based alloys for high-temperature applications by B.O. Odera, M.J. Papo, R. Couperthwaite, G.O. Rading, D. Billing, and L.A. Cornish. . . . . . .

241

International Advisory Board VOLUME 115

NO. 3

MARCH 2015

R. Dimitrakopoulos, McGill University, Canada D. Dreisinger, University of British Columbia, Canada E. Esterhuizen, NIOSH Research Organization, USA H. Mitri, McGill University, Canada M.J. Nicol, Murdoch University, Australia H. Potgieter, Manchester Metropolitan University, United Kingdom E. Topal, Curtin University, Australia

The Journal of The Southern African Institute of Mining and Metallurgy

MARCH 2015

iii

Advertising Representative

Journal Comment Sustainability: Environmental, economic, and social

he title of this Comment is taken from the excellent recent paper by M. Mostert of SRK, in the Journal of the SAIMM (vol. 114, November 2014.). The paper was both relevant and significant to my previous contributions on strategy and tactics that I compiled for a SAIMM conference that regrettably never took place. Mostert’s paper advances the quantitative aspects of this topic. Sustainability is quantified in terms of the calculation of net present value (NPV) and other valuation criteria. The cost of electrical power and the possibility of co-generation of power and the relationship with the many global warming environmental aspects are meaningfully discussed. The inclusion of the remarkably large carbon credits in the NPV of the Beatrix Mine was for me fascinating, and like the whole paper it was very relevant to the mine cluster strategy concept that I prepared some 18 months ago. It deals with the main topics of sustainability, economics, environment, social integration, schools, and education in general. This is so because the mining cluster and all other clusters involving communities of employees have to survive and remain supported by stakeholders. They need profits from the sale of goods and services, both locally and from export, to make up a quantifiable value for the educational and community services of the cluster. My strategy and tactics paper was tabled at the Platinum Conference in November 2014, the proceedings of which are still to be published. It deals with the undertaking of some innovative steps to establish mining clusters with the objective of supporting marginal mines and ensuring where possible the sustainability of existing operations. This is to be achieved by creating a socially mixed community of mine personnel with educational and independent income earning capabilities that extend into the future. It is focused on employment creation where small-lot farming plays an important role. The paper is available for those readers who might wish to research the options. In this compilation I list the keywords (action steps) that are related to innovation and R&D.

Keywords for mining and metallurgical concepts Gold • Selective blast mining: economic evaluation for all cost-curve data, already available in a previous Journal publication

MARCH 2015

• Rectification of statistical sampling for mine call factors • Development of millisecond shock tube systems with delay detonators • Narrow slot cutting in hangingwall using hydraulic technology or diamond cutting wire • Underground roll crushing of reef material • Mine shaft pressure leaching to recover all toxic metals by CCIX to achieve zero toxic waste dumps • Underground hydraulic compaction of waste rock • Stope drilling automation with hydraulic supports.

Platinum • Demonstration plant for the KELL hydrometallurgical process for platinum and base metal recovery • Alternative chlorination for the KELL process leading to total metal recovery without roasting (three alternative options) • Recovery of chromite (WHIMS) to provide non-toxic dams for agricultural use.

Other minerals and metals • Improved hydrometallurgical processes for low-grade base metals, e.g. at Black Mountain, Gamsburg, Nkomati Nickel • Bipolar cell combined with the platinum fuel cell to produce reagents for downstream use in hydrometallurgy and the chemical industries • Coal fines treatment and utilization with zero waste (multi-options for the production of Fe, Al, S, SiO2, and uranium • Carbon capture using chemically generated CaCO3 (multi options) • Rare-earth metal recovery from fertilizer processing – lithium, potassium, and other strategic materials • Conversion of gypsum to building materials.

Cluster agriculture • Hydroponic fertigation (HPF): overseas expert consultations • HPF automation survey: cost reduction • Crops for biofuels (ethanol and aviation fuels): potential stakeholders such as SAA and Boeing • Food and industrial crops (Department of Agriculture a stakeholder) • Crops supporting automation in mines and industry • Automation of drip systems: seed rolls, computer protocols • Use of domestic effluent. R&D with DWAF and municipalities.

The Journal of The Southern African Institute of Mining and Metallurgy

Journal Comment

• Research on computerized teaching at primary school level • Establishment of language laboratories with international exchange visits in languages and cultures • High school curricula with innovative interaction with careers and experimentation • Technical college level cluster centres: mentors and professional institute supervision • Success criteria and economic sustainability values • Teachers’ new careers prospects, status and salary: analysis for sustainability valuation.

Social integration and activities • Sport and entertainment, stakeholders are sport sponsors and controlling bodies • Tourism attractions e.g. game, walking and cycling and ‘ox wagon’ trails, mineral collections and jewellery making, music and concerts, hospitality facilities, exchange scholars, visitors, and employment • Special training in animation presentations via concerts for music, culture, history, dances • Using virtual reality computer systems for innovative presentations.

Action steps and comments The concepts contained in the keyword list are by no means all my own work. Many were derived from my association with a number of projects and activities with a variety of organizations. Some have been derived from news in the media such as the Martin Creamer publications. The intention is to publish and make available the keywords to the SAIMM and its readership who may consider assembling a forum of people who might be interested in getting further details and who wish to undertake research or further studies on them. If considered of importance, then sponsorship of a portfolio project can be considered with those who wish to become stakeholders, as suggested by Mostert, where carbon credits for biofuels are economically attractive. Many of these concepts are being pursued and have been taken from press releases by active participants, in which case they may wish to include a mine cluster in their thinking. For example, with regard to biofuels for use in aviation there are a large number of major companies

The Journal of The Southern African Institute of Mining and Metallurgy

promoting urgent research as this topic is considered economically important. According to recent press releases, SAA is contributing to a portfolio of projects for using agricultural products, including tobacco, for aviation fuel. Mazda and Mitsubishi have announced bio-produced body parts for their current automobile manufacture. Japanese researchers have announced a process for the recovery of uranium from coal in the Springbok Flats deposits. Many government departments are actively financing work. Blade Nzimande, the Minister of Basic Education and Training, has announced in several public speeches the expenditure of many billions in setting up training facilities for teachers. Similar facilities for agriculture and the hospitality industry are just as important in generating new approaches to achieve rapid success. The road to success is to avoid focusing investment on single hunches, but to have a suite of well-considered options. This is where the SAIMM membership and its conferences and publications can make a significant contribution. It is believed that the mining industry, with prompting from the SAIMM, can catalyze such activities. Although mining and agriculture are not happy bedfellows, they have much in common. They are both well-proven industries and can employ millions of South Africans. It is believed that the first successful mining cluster will result in a snowball effect that will initiate many other clusters.

R.E. Robinson

MARCH 2015

Education and teachers (obvious stakeholders are the DOE, school investor entrepreneurs, universities

Mechanised Underground Excava on in Mining and Civil Engineering 23–24 April, 2015 - Conference Elangeni Maharani Hotel, Durban

25 April 2015 - Half Day Technical Visit Harbour Entrance Tunnel Site Visit and Harbour Boat Cruise THEME

PRESENTERS AND TOPICS INCLUDE:

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

TBM excavation under airports Dr Karin Bap• p• ler, Herrenknecht, Past Chairperson ITA Working Group

WHO SHOULD ATTEND

Use of the EPB TBM on Gautrain Alain Truyts, Gibb

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

HALF DAY TECHNICAL VISIT

Sea Outfalls, utility tunnelling Swen Weiner, Herrenknecht Utility tunnelling, the Durban Aqueous tunnel beneath the harbour entrance Frank Stevens, (ex Deputy Head, Water and Sanitation, eThekwini Municipality), President of IMESA Mechanised excavation – mining Danie Roos, Herrenknecht Vertical excavation utilising the V Mole System Allan Widlake, Murray & Roberts Cementation

Point Road Micro Tunnel Montso Lebitsa, Hatch Cutting in Stoping Rod Pickering, Sandvik Mining and Construction Cutting Technology – Past, Present and Future Trends Prof. Jim Porter, President SAIMM, (Jim Porter Mining Consulting) Mechanised excavation in the civils industry – Past, Present and Future Ron Tluczek, SANCOT Chairman (Executive – Geotechnical, Africa AECOM ZA) and Member of WG2 – Research and South African Representative to ITA on behalf of SAIMM Asset Management Monique Wainstein, Associate GIBB and Member of WG22 Mechanised Sprayed Concrete Chris Viljoen, Functional Head, Hydropower, Dams, Tunnels and Geotechnics SMEC

A site visit will be conducted where participants will have the opportunity to walk through the tunnel underlying the Durban Harbour entrance, visit the adjoining pump station and enjoy a 1 hour cruise around the Durban harbour

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

Second Announcement

t may be a function of my ‘maturing’ years but I am finding that the first two months of the calendar year are becoming much more of a challenge. One gets lulled in to a complete false sense that business and life are well under control, brought about by the shutdown of many companies over the holiday period. I tend to carry on working through this period, so my desk gets tidied, the ‘to do’ list gets shorter, etc. Even those chores around the home that tend to be conveniently ignored by all but the Home Manager get sorted out – usually preceded by hours of research at the local hardware store … Then suddenly and rudely the whole country catches a wake-up and there is a mad scramble as all and sundry are back in the work place, full of renewed purpose and Christmas pudding. And here I am, suddenly confronted with the realization that two months of the year have already passed. How it happened without me noticing, I don’t know. However, I am quite sure that other SAIMM Presidents before me have sat down to write an article for the President’s Corner and stared dolefully at their computer screen desperately trying to get their thoughts together before the editor’s deadline. At such times one turns to the contents of this month’s Journal for inspiration. At first glance it seems there is little in common between (for example) ‘Influence of surface preparation on the precision of electrochemical measurements’ and ‘A stochastic simulation framework for truck-shovel selection and sizing in open pit mines’. However, what has struck me about the papers in the March Journal is a common theme that reflects research at the microscopic and submicroscopic level of metallurgical, materials, and engineering science to resolve or improve understanding of issues in the macro world. Without meaning to be glib – the devil is in the detail, as the saying goes. The gap between laboratory research work and theoretical physics is getting smaller and the gap is managed through the use of mathematical algorithms. Algorithms are at the heart of our digital world. They are the universal translator between the theoretical physicist, the laboratory research that tests the variables and calculates physical constants, and the business world where decisions about corrosion protection standards or the optimal fleet selection for a new open pit have to be made by management. More on this management subject can be read in the relatively new publication ‘The Attacker’s Advantage: Turning Uncertainty into Breakthrough Opportunities’ by Ram Charan. In this book the author postulates that our next generation of leaders must ‘get ready for the most sweeping business change since the Industrial Revolution. To thrive, companies – and the execs who run them – must transform into math machines.’ His argument (and I believe we already experience this in our everyday lives) is that the rapid advances in the development of algorithms that describe our real world in digital terms (think of 3D simulation and optimization tools) and the related complex software, are disrupters of the status quo in today’s companies. Companies that are not up to the challenge of keeping abreast of these innovations are at risk of falling behind better equipped competitors. This has resulted in the ‘Age of Unicorns’, but more about that in my article next month … To close, these Journal papers bring to the fore yet again the reality that without strong math and physics skills our future engineers and managers will not be equipped adequately for business.

t’s iden s e r P er Corn

The Journal of The Southern African Institute of Mining and Metallurgy

MARCH 2015

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J.L. Porter President, SAIMM

PAPERS IN THIS EDITION These papers have been refereed and edited according to internationally accepted standards and are accredited for rating purposes by the South African Department of Higher Education and Training

Inaugural African Corrosion Congress Comparison of linear polarization resistance corrosion monitoring probe readings and immersion test results for typical cooling water conditions by J.W. van der Merwe and A. Palazzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

173

The accuracy of the linear polarization resistance (LPR) technique for measuring corrosion rate is evaluated by comparing probe readings with the results of mass loss tests using corrosion coupons in typical steel mill cooling water. The study demonstrated that LPR readings can differ significantly from mass loss results, and should be used with caution in an industrial environment.

179

A number of case studies are presented to demonstrate how insufficient controls or protection systems have contributed to structural failures on mobile bulk handling equipment. A revision of ISO 5049-1 (1994) is proposed to provide specific rules and guidelines pertaining to machine protection systems. Utilization of the Brazilian test for estimating the uniaxial compressive strength and shear strength parameters by K. Karaman, F. Cihangir, B. Ercikdi, A. Kesimal, and S. Demirel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

185

This study explores the applicability of the Brazilian test (BT), a simple, inexpensive, and less sophisticated method for both specimen preparation and testing, to estimate the uniaxial compressive strength (UCS) and shear strength parameters of rocks. A strong linear relationship was found between the BT and UCS values. Laser surface alloying of Al with Cu and Mo powders by S.L. Pityana, S.T. Camagu, and J. Dutta Majumdar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193

Laser surface alloying was used to develop copper and molybdenum aluminides by injecting premixed copper and molybdenum powder particles into a laser-generated melt pool on an aluminium substrate. The microstructure and phase constituents of the composite layer were studied by means of scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). Chemical wear analysis of a tap-hole on a SiMn production furnace by J.D. Steenkamp, P.C. Pistorius, and M. Tangstad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199

The refractory wear profile of the tap-hole area in an industrial silicomanganese furnace was analysed, and thermodynamic and mass-transfer calculations were conducted to quantify the potential for wear by chemical reaction between refractory and slag and refractory and metal. Chemical reaction offers only a partial explanation for the wear observed, and erosion is expected to contribute significantly to wear. A stochastic simulation framework for truck and shovel selection and sizing in open pit mines by S.R. Dindarloo, M. Osanloo, and S. Frimpong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This paper presents a comprehensive simulation framework for truck and shovel selection, including optimal number and capacities of haulage and loading units, their allocation, and operational strategies. As part of the study, a discrete-event system simulation was employed, and the simulations validated through real operations at a large open pit mine.

These papers will be available on the SAIMM website

http://www.saimm.co.za

209

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation by N.V. Ramlall and B.K. Loveday . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

221

Various batch flotation models for the recovery of minerals in a UG2 platinum ore were evaluated using statistical methods and an analysis of model- fit residuals. The results illustrate the importance of entrainment modelling to provide information on the recovery of gangue minerals that are not considered to be floatable. Enrichment of low-grade colemanite concentrate by Knelson Concentrator s ................................................................. by T. Uslu, O. Celep, and M. Sava Ă&#x2021;

229

The effects of particle size, fluidizing water velocity, and bowl speed on the enrichment of a low-grade colemanite concentrate using a Knelson centrifugal gravity concentrator. The B2O3 content of the concentrate was increased from 33.96% to an optimum of 40.2% at a recovery of 86.48%. The enrichment process also rejected arsenic and iron to some extent. Multifractal interpolation method for spatial data with singularities by Q. Cheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

235

This paper introduces the Multifractal Interpolation Method (MIM) that has been developed for handling singularities in data analysis and for data interpolation. It is demonstrated that incorporation of spatial association and singularity can improve the interpolation result, especially for observed values with significant singularities. High-order additions to platinum-based alloys for high-temperature applications by B.O. Odera, M.J. Papo, R. Couperthwaite, G.O. Rading, D. Billing, and L.A. Cornish . . . . . . . . . . . . . . . . . . . . . . . . . . Platinum-based alloys are being developed with microstructures similar to nickel-based superalloys, for potential high temperature applications in aggressive environments. This research focuses on the contribution of Vanadium and Nobium to the improvement in hardness of the as cast alloys when compared to the quaternary alloys.

These papers will be available on the SAIMM website

http://www.saimm.co.za

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http://dx.doi.org/10.17159/2411-9717/2015/v115n3a1 ISSN:2411-9717/2015/v115/n3/a1

Comparison of linear polarization resistance corrosion monitoring probe readings and immersion test results for typical cooling water conditions by J.W. van der Merwe*† and A. Palazzo*†‡

Owing to the corrosiveness of the untreated brackish cooling water typically used for steel mills (and other environments), it is important to treat the water and monitor corrosion in such systems. Generally, corrosion rates are monitored with corrosion probes inserted into a pipeline or vessel. This has been standard practice for many years, and is widely accepted in industry. Typically, two kinds of probes are used – electrical resistance and linear polarization resistance (LPR) probes. In this study, the effectiveness and accuracy of the LPR technique was evaluated by comparing the electrochemical measurements with the results of mass loss tests using corrosion coupons. The corrosivity of the environment, a synthetic brackish water, was varied by varying the calcium hardness and alkalinity, and to simulate actual plant conditions temperatures of 35°C and 45°C were used. In addition to the corrosion rate measurements, the iron concentration was measured, as well as the imbalance from the probe. The corrosion rates obtained by LPR were from 57% lower to 385% higher than those from the immersion tests. Most probe measurements were higher than the immersion results, and 50% of the probe results were 50% or more higher than the immersion results. The best correspondence between the two methods was obtained at low calcium levels, except for one measurement that was 93% higher than the coupon results. There was no clear correlation between parameters such as temperature and total alkalinity and the difference between the results. It would therefore appear that LPR measurements can differ significantly from immersion results, and LPR results should therefore be used with caution in industrial applications. Keywords linear polarization resistance, probe, corrosion rate, corrosion monitoring, cooling water.

Introduction The corrosion rates in the cooling water systems of steel mills can be significant, and should be carefully monitored and controlled by appropriate water treatment. Corrosion monitoring is a crucial tool in the water treatment programme. Industrial plants pursue zero effluent discharge (ZED) policies and reduce fresh water intake as well as limit the volume of water returned to the environment. The water quality in the plant therefore deteriorates, and such brackish cooling water can lead to increased corrosion and fouling of the carbon steel equipment. Since these brackish cooling waters are sufficiently conductive, corrosion rates can be monitored on a realtime basis with linear polarization resistance The Journal of The Southern African Institute of Mining and Metallurgy

* School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa. † DST/NRF Centre of Excellence for Strong Materials, University of the Witwatersrand, Johannesburg, South Africa. ‡ Buckman Africa (Pty) Ltd, Hammarsdale, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the, Africorr Inaugural African Corrosion Congress 2014, 27–30 July 2014, Farm Inn Country Hotel & Wildlife Sanctuary, Pretoria, South Africa. VOLUME 115

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(LPR) probes, which provide almost instantaneous results. The ease of making these measurements is very convenient and this corrosion monitoring technique is still widely used in a number of industries (Jaske et al., 2002). However, a number of studies have indicated that the LPR technique does not give very reliable results (Stern et al., 1957; Mansfeld, 1973; Walter, 1977; Jarragh et al., 2014; Wu et al., 2015), but since monitoring is generally not performed by corrosion experts the results are often incorrectly assumed to reflect the actual corrosion rates. In this study, the accuracy and variability of LPR corrosion probe measurements is investigated with the aim of making it possible to predict the actual corrosion rates within a certain margin of error, as well as to establish the (in)accuracy of LPR measurements. This has been the focus of a number of investigations over many years, but still remains a concern (Wu et al., 2015). Corrosion monitoring has been used for many years in a variety of industrial environments (Albaya, Cobo, and Bessone, 1973; Clément et al., 2012). Two trusted techniques of corrosion monitoring that are used extensively are electrical resistance (ER) and linear polarization resistance (LPR). Recently, other corrosion monitoring techniques have been developed but the principles have remained consistent. Corrosion can be monitored through the physical loss of metal from the probe or the vessel itself, or by

Comparison of linear polarization resistance corrosion monitoring probe readings an electrochemical measurement technique. This study will focus on the LPR technique, which stems from the work of Stern and Geary (1957) who found that the slope of currentpotential plot around the corrosion potential is essentially linear. The slope, which is called the linear polarization resistance (Rp), Rp defined mathematically as: [1]

Rp is related to corrosion current (Icorr) by Equation [2]: [2] The constant B is defined in Equation [3]: [3] where βa and βc are anodic and cathodic Tafel constants. Typical values for these constants have been presented by Rosen and Harris (1983). The current study originated from a more comprehensive investigation of the influence of carbonate and alkalinity on the corrosion rate of plain carbon steel. Two methods were employed to determine the progression of the corrosion rate over time, as opposed to the average corrosion rate over a certain exposure period. Firstly, the corrosion rate was determined by the exposure of corrosion coupons to the particular environment, and secondly, the rate was determined with a corrosion probe on a daily basis. The actual corrosion conditions were chosen to simulate the effect that certain critical brackish cooling water parameters would have on the corrosion of steel. Initially (although not reported here), the corrosion of steel exposed to actual brackish cooling water from a steel mill was investigated with regard to typical parameters, which were subsequently systematically studied by making up a synthetic solution to approximate the most suitable composition.

removed, cleaned with a water wash to finger-touch, followed by an ethanol wipe, and then oven-dried, weighed, and the corrosion rates calculated based on the weight loss. The method followed was in accordance with ASTM G31-72 and G1-90 methods (ASTM G31-72, 2004; ASTM G1-03, 2011. A commercial corrosion probe was used to measure the general corrosion rate. Only one type of commercial probe was used, and probes from different manufacturers were not compared. The test solutions were also analysed for total iron concentration and the results compared with the coupon method and probe readings. Each set of tests was performed in a batch of six tests over a period of three days. Two coupons were exposed to each solution, and four separate corrosion probe measurements were made over the exposure period. These measurements were averaged and compared to the average weight loss of the two coupons exposed to the same environment. New probe electrodes were used for each test, and these were of the same material as the steel used for the coupons. The corrosion testing set-up used a dedicated 5-litre beaker with an overhead paddle stirrer, temperature control, and two coupons plus the corrosion probe.

Results The mode of corrosion was slightly localized, not in the form of pitting corrosion or uniform corrosion across the entire surface, but rather in the form of a pattern where more severe corrosion occurred in certain regions (Figure 1). An optical micrograph of the corroded surface after cleaning is shown in Figure 2. The corrosion results obtained under different conditions are shown in Table II. Table III shows the concentrations of the test solutions at the beginning of each run. The percentage differences between the LPR corrosion rate (probe average) and the weight loss (coupon average) for each run are shown in Figure 3. Only 11 out of 30 runs resulted in a percentage difference of less than 40%, therefore

Experimental procedure This investigation formed part of a study to determine the relationship between the calcium hardness and alkalinity and the corrosion rate of mild steel in brackish cooling water at temperatures of 35°C and 45˚C. These temperatures were chosen in order to simulate the cooling water conditions on a plant. These tests are part of numerous other laboratory tests that were conducted for this programme using synthetic solutions that were prepared to simulate a typical steel-mill brackish cooling water. The calcium hardness and total alkalinity were varied by adding analytical grade calcium chloride and sodium hydrogen carbonate respectively. The balancing ions, e.g. chlorides and sodium, were adjusted by adding analytical reagent grade magnesium chloride, sodium chloride, sodium sulphate, and sodium fluoride. The pH was not adjusted, but the pH values were recorded. The calcium concentrations evaluated were 50, 62.6, 75, 87.5, and 100 mg/l Ca2+; and the total alkalinity levels 55, 82.5, 110, 165, and 220 mg/l CaCO3. Table I shows the target values of various parameters. C1010 (mild steel) corrosion coupons (12.7 × 76.2 × 1.59 mm with a 4.76 mm hole) were exposed to synthetic test solutions (4000 ml) for 36 hours. The coupons were then

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Table I

Key parameters and the target values Variable pH Magnesium (mg/l as Mg2+) Chloride (mg/l as Cl-) 2Sulphate (mg/l as SO4 ) Fluoride (mg/l as F-)

Target value 7.8 27.3 750 1125 10

Figure 1—A typical corrosion coupon after exposure The Journal of The Southern African Institute of Mining and Metallurgy

Comparison of linear polarization resistance corrosion monitoring probe readings 37% of the probe measurements were acceptably close to the corrosion coupon measurements. The standard deviation of this difference between the probe measurements and the coupon measurements was 110%. The coupon results showed a general consistency, and the standard deviation on the percentage difference between the two coupon results per run was 21%. The effects of the individual parameters on the corrosion rates as measured by both methods, and comparisons of these two methods for each parameter – pH, initial conductivity, total alkalinity, calcium, magnesium, and fluoride – are

shown in Figure 4–9. In most instances the initial parameter values did not change significantly throughout the test. The conductivity data (Figure 3) shows that at initial conductivities between 4000 and 4500 μS/cm, the LPR measurements are lower than the coupon measurements. The data for the total alkalinity (Figure 5) is widely scattered; there is a significant grouping of LPR corrosion rates that are significantly below the coupon corrosion rates. At the higher calcium concentrations (between 80 and 100 g/l), the corrosion rates measured with the LPR probe are significantly lower than the coupon corrosion rates (Figure 7). The initial magnesium concentration did not seem to have any effect on corrosion rate, as shown in Figure 8. For the fluoride concentrations (Figure 9), the spread of the corrosion data from the LPR probe is wider than for the coupon corrosion rates, and no correlation is evident. The influence of temperature on the corrosion rate measurements is shown in Figure 10. Unfortunately only five tests were performed at the lower temperature of 35°C, and a rigorous comparison is not possible. However, at the lower temperature it would appear that the LPR probe measurements were in general slightly lower than the coupon corrosion rates, except for one data point. At the higher temperature of 45°C, the spread of data was very similar. The correlation between the two corrosion rate measurement methods was compared and similar trends were found for calcium, fluoride, and chloride at the lower temperature.

Figure 2—Optical micrograph of the coupon in Figure 1, showing (a) etched zones and (b) darker, less-corroded zones

Table II

Corrosion rate results for the steel samples exposed to varying brackish water conditions

no.

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

Target concentrations/conditions

Coupon corrosion rates Corrosion probe corrosion rates

Ca mg/l Total alkalinity Temperature 1 mg/l as ºC mm/a as Ca2+ CaCO3 50 50 50 50 50 50 62.5 62.5 62.5 62.5 62.5 62.5 75 75 75 75 75 75 87.5 87.5 87.5 87.5 87.5 87.5 100 100 100 100 100 100

55 82.5 110 165 220 110 55 82.5 110 165 220 110 55 82.5 110 165 220 110 55 82.5 110 165 220 110 55 82.5 110 165 220 110

45 45 45 45 45 35 45 45 45 45 45 35 45 45 45 45 45 35 45 45 45 45 45 35 45 45 45 45 45 35

0.45 0.31 0.25 0.16 0.16 0.23 0.41 0.37 0.29 0.1 0.16 0.19 0.3 0.23 0.22 0.21 0.22 0.15 0.24 0.21 0.13 0.17 0.21 0.14 0.23 0.13 0.14 0.19 0.33 0.61

2 mm/a

Ave mm/a

Day 1 mm/a

Day 2 mm/a

Day 3 mm/a

0.48 0.38 0.32 0.25 0.24 0.3 0.5 0.39 0.3 0.28 0.32 0.34 0.37 0.26 0.19 0.14 0.25 0.25 0.34 0.23 0.16 0.22 0.29 0.16 0.26 0.13 0.14 0.22 0.37 0.69

0.46 0.35 0.28 0.21 0.2 0.26 0.46 0.38 0.29 0.19 0.24 0.26 0.34 0.25 0.2 0.18 0.24 0.2 0.29 0.22 0.15 0.2 0.25 0.15 0.24 0.13 0.14 0.21 0.35 0.65

0.92 0.39 0.35 0.22 0.25 0.33 0.92 0.72 0.76 0.48 0.54 0.43 0.75 0.2 0.72 0.64 0.79 0.96 0.32 0.39 0.25 0.24 0.31 0.56 0.25 0.12 0.21 0.13 0.19 0.11

0.9 0.34 0.36 0.21 0.22 0.36 0.85 0.66 0.73 0.51 0.5 0.42 0.79 0.2 0.74 0.71 0.76 0.94 0.25 0.35 0.27 0.21 0.25 0.47 0.22 0.14 0.21 0.11 0.2 0.34

0.85 0.32 0.28 0.21 0.22 0.25 0.86 0.53 0.75 0.53 0.5 0.45 0.76 0.23 0.79 0.76 0.75 0.99 0.29 0.37 0.26 0.19 0.27 0.48 0.16 0.12 0.19 0.1 0.22 0.39

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Comparison

Day 4 Probe av. Coupon av. Difference mm/a mm/a mm/a mm/a

0.89 0.3 0.27 0.23 0.22 0.26 0.84 0.57 0.52 0.49 0.49 0.44 0.76 0.3 0.81 0.84 0.84 0.97 0.36 0.38 0.2 0.19 0.24 0.42 -

0.89 0.34 0.32 0.22 0.23 0.3 0.87 0.62 0.69 0.5 0.51 0.44 0.77 0.23 0.77 0.74 0.79 0.97 0.31 0.37 0.25 0.21 0.27 0.48 0.21 0.13 0.2 0.11 0.2 0.28

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0.46 0.35 0.28 0.21 0.2 0.26 0.46 0.38 0.29 0.19 0.24 0.26 0.34 0.25 0.2 0.18 0.24 0.2 0.29 0.22 0.15 0.2 0.25 0.15 0.24 0.13 0.14 0.21 0.35 0.65

Difference %

0.43 -0.01 0.04 0.01 0.03 0.04 0.41 0.24 0.4 0.31 0.27 0.18 0.43 -0.02 0.57 0.56 0.55 0.77 0.02 0.15 0.1 0.01 0.02 0.33 -0.03 0 0.06 -0.1 -0.15 -0.37

93 -3 14 5 15 15 89 63 138 163 113 69 126 -8 285 311 229 385 7 68 67 5 8 220 -13 0 43 -48 43 -57

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Run

Comparison of linear polarization resistance corrosion monitoring probe readings Table III

Test solution concentrations at start-up Run

Temp (°C)

pH(i)

Calcium(i) (mg/l as Ca)

Magnesium(i) (mg/l as Mg)

Total alkalinity(i) (mg/l as CaCO3)

Chloride(i) (mg/l as Cl-

Sulphate(i) (mg/l as SO4)

Fluoride(i) (mg/l as Fa)

Cond(i) (μS/cm)

Oxygen(i) (mg/l as O2)

45 45 45 45 45 35 45 45 45 45 45 35 45 45 45 45 45 35 45 45 45 45 45 35 45 45 45 45 45 35

7.53 7.20 7.41 7.65 7.77 7.32 7.51 7.46 7.62 7.86 7.99 7.58 7.38 7.46 7.52 8.10 7.93 7.51 7.25 7.34 7.49 7.71 7.89 7.33 6.93 7.28 7.52 7.75 7.86 7.55

51.1 50.6 49.4 49.4 49.3 49.9 60.1 63.4 66.2 60.1 61.1 59.5 61.5 63.0 62.3 63.5 62.9 62.9 83.6 82.9 83.6 84.4 83.7

26.1 26.5 25.6 24.9 26.0 26.1 26.3 27.6 28.7 26.2 26.5 25.9 22.4 22.9 22.7 22.5 22.8 22.7 27.0 26.7 26.6 26.8 27.2

779 765 739 800 765 772 732 763 752 717 927 1188

94.1 93.6 92.5

26.8 26.2 26.2

89.1 90.1

25.1 24.7

27.6 35.6 45.3 51.7 61.7 38.8 20.1 26.4 24.3 45.4 57.9 32.6 20.3 27.6 31.9 47.3 58.0 30.8 19.0 24.9 30.5 42.6 55.3 26.6 23.5 29.5 37.5 53.7 67.5 38.6

1400 1400 1400 1400 1300 1400 1200 1300 1300 1300 1400 1400 1100 1100 1100 1100 1200 1100 1200 1300 1200 1200 1300 1300 1200 1300 1300 1300 1200 1200

9.3 9.1 9.6 9.0 9.2 9.3 9.5 9.5 8.3 8.3 8.5 8.3 9.0 9.0 8.4 8.3 9.9 9.6 9.3 8.6 9.0 8.0 9.7 9.7 8.9 9.9 9.4 9.8 9.1 9.9

4564 4216 4408 4384 4288 4180 4296 4352 3420 4176 4348 4208 3972 4024 3988 3964 4120 4004 4320 4376 4340 4348 4376 4216 4292 4224 4148 4176 4284 4232

5.3 6.6 5.9 5.8 5.9 6.2 5.3 6.2 5.8 5.9 5.8 6.1 6.9 5.7 6.7 6.5 6.6 6.3 6.6 5.9 6.3 6.5 6.9 6.1 6.5 6.8 6.5 5.9 6.6 6.1

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

741 758 750 760 725 2972 822 780 777 805 790 790 805 769 781

Figure 5—Correlation between initial conductivity and corrosion rate Figure 3—Difference between average corrosion rates (coupon and probe) for each run

Figure 4—Correlation between pH and corrosion rate

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Figure 6—Correlation between total initial alkalinity and corrosion rate The Journal of The Southern African Institute of Mining and Metallurgy

Comparison of linear polarization resistance corrosion monitoring probe readings

Figure 8—Correlation between initial magnesium concentration and corrosion rate

Figure 9—Correlation between initial fluoride concentration and corrosion rate measured by coupons as well as an LPR probe

It would appear that the only parameters that had any discernible effect on the difference between the two readings were calcium, alkalinity, and conductivity (over a limited region). For all of these parameters the probe corrosion rates over a certain region were less than the coupon corrosion rates.

Discussion Only 37% of the probe measurements gave acceptable results (less than 40% difference between the probe result and the coupon corrosion rate), and almost the same percentage of the measurements differed from the coupon corrosion results by between 100% and 385%. In an industrial environment, The Journal of The Southern African Institute of Mining and Metallurgy

Figure 10—Correlation between temperature and corrosion rate

erroneous measurement of high corrosion rates would lead to overdosing of the cooling water with corrosion inhibitor, increasing costs unnecessarily. Variations in the solution parameters did not have a significant effect on the measured corrosion rate, although higher alkalinity, calcium content, and conductivity seemed to slightly reduce the corrosion rates measured by the probe. The LPR probes have to be used in a conductive environment to ensure that they operate correctly according to the electrochemical basis of the measurement. However, with these probes the measurement time is very short and the results are available almost immediately; the results from an electrical resistance probe are only available after several days. The LRP probes have several other limitations (Walter, 1977; Jarragh et al., 2014; Scully, 2000; Glass and Kane, 2013). The type of corrosion probe that was used is not discussed in this study. Differences such as the scan rate used by the manufacturer would contribute to variation in the measurements (Zhang et al., 2009), but to eliminate further complexity this variable was excluded. The solution resistance is, naturally, important (Walter, 1977), but in this instance it would not have contributed to the error. The scan rate of the analysis plays an important role in ensuring accurate results, and due to the increase in capacitance with higher scan rates very low scan rates have to be used in order to obtain measurements that have a low error. Unfortunately this parameter could not be varied on the commercial instrumentation, but it has to be considered as introducing a consistent error, although it could have been the cause for the measurement errors. Electrode bridging is another factor that could cause an error, but this would occur at much longer exposure times and again does not apply to the current study. Turnbull and Robinson (2005) mention that the full charge transfer resistance is hardly ever measured, and therefore corrosion rates are easily overestimated. Jones (1996) mentions three other causes for errors: uncertain Tafel constants, nonlinearity of polarization curves (Mansfeld, 1973), and competing redox reactions. Of these, the uncertain Tafel constants and nonlinearity of the polarization curves would be the most likely factors that would have caused the measurement errors. Wu et al. (2015) also found that LPR corrosion rates were generally much greater than the coupon corrosion rates. They suggested that a reduction reaction that involves the corrosion product FeO.OH occurs under electrochemical conditions and thereby the anodic dissolution is enhanced. In VOLUME 115

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Figure 7—Correlation between initial calcium concentration and corrosion rate

Comparison of linear polarization resistance corrosion monitoring probe readings addition, the style of corrosion found on the corrosion coupons suggests a type of localized corrosion, in the sense that corrosion did not occur uniformly over the whole coupon, while on the corrosion probe elements, which were smaller than the coupons, the corrosion was more uniform and the effect not as noticeable.

Conclusions 1. The LPR probe results gave acceptable corrosion rate results for only 37% of all measurements made 2. The coupon corrosion rates were stable and relatively consistent, with a standard deviation of 21% 3. LPR results in the worst instance were almost four times the corrosion rate measured on the coupons. This could lead to overdosing with corrosion inhibitor, increasing the cost of water treatment unnecessarily.

Acknowledgements The support of the DST/NRF Centre of Excellence in Strong Materials (CoE-SM) towards this research is hereby acknowledged. The Department of Science and Technology and the National Research Foundation, South Africa are thanked for financial support.

References ALBAYA, H.C., COBO, O.A., and BESSONE, J.B. 1973. Some consideration in determining corrosion rates from linear polarization measurements. Corrosion Science, vol. 13, no. 4. pp. 287–293. ASTM G31-72. 2004. Standard Practice for Laboratory Immersion Corrosion Testing of Metals. ASTM International, West Conshohocken, PA. ASTM G1 - 03. 2011 Standard Practice for Preparing, Cleaning, and Evaluating Corrosion Test Specimens. ASTM International, West Conshohocken, PA. CLÉMENT, A., LAURENS, S., ARLIGUIE, G., and DEBY, F. 2012. Numerical study of the linear polarisation resistance technique applied to reinforced concrete

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for corrosion assessment. European Journal of Environmental and Civil Engineering, vol. 16, February. pp. 491–504. GLASS, J.P. and KANE, R. 2013. Oxygen and corrosion probes – performance and limitations in laboratory: an assessment for ethanol pipeline service. Corrosion 2013, Orlando, Florida,16–21 March 2013. NACE International, Houston, TX. JASKE, C.E., BEAVERS, J.A., and THOMPSON, N.G. 2002. Improving plant reliability through corrosion monitoring. Corrosion Prevention and Control, vol. 49, no. 1. pp. 3–12. JARRAGH, A., AL-SHAMARI, A.R., ISLAM, M., AL-SULAIMAN, S., LENKA, B., and PRAKASH, S. 2014. Evaluation of the effectiveness of online corrosion monitoring utilizing ER/LPR probes and coupon within hydrocarbon systems. Corrosion 2014, San Antonio, Texas, 9–14 March 2014. NACE International, Houston, TX. JONES, D.A. 1996. Principles and Prevention of Corrosion. 2nd edn. Prentice Hall, Upper Saddle River, NJ, USA. pp. 157–159. MANSFELD, F. 1973. Tafel slopes and corrosion rates from polarization resistance measurements. Corrosion, vol. 29, no. 10. pp. 397–402. ROSEN, M. and HARRIS, J.G. 1983. Tafel constants and changes in hydrogen coverage during corrosion of Fe18Cr. Journal of the Electrochemical Society, vol. 130, no. 12. pp. 2329–2334. SCULLY, J.R. 2000. Polarization resistance method for determination of instantaneous corrosion rates. Corrosion, vol. 56, no. 2. pp. 199–218. STERN, M. and GEARY, A.L. 1957. Electrochemical polarization: I. A theoretical analysis of the shape of polarization curves. Journal of the Electrochemical Society, vol. 104, no. 1. January. pp. 56–63. TURNBULL, I.A. and ROBINSON, M.J. 2005. Investigation into boiler corrosion on the historic vessel SL Dolly. Corrosion Engineering, Science and Technology, vol. 40, no. 2. pp. 143–148. WALTER, G.W. 1977. Problems arising in the determination of accurate corrosion rates from polarization resistance measurements. Corrosion Science, vol. 17, no. 12. pp. 983–993. WU, J.-W., BAI, D., BAKER, A.P., LI, Z.-H., and LIU, X.-B. 2015. Electrochemical techniques correlation study of on-line corrosion monitoring probes. Matererials and Corrosion, vol. 66, no. 2. pp. 143–151. ZHANG, X.L., JIANG, Z.H., YAO, Z.P., SONG, Y., and WU, Z.D. 2009. Effects of scan rate on the potentiodynamic polarization curve obtained to determine the Tafel slopes and corrosion current density. Corrosion Science, vol. 51, no. 3. pp. 581–587. N

The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n3a2 ISSN:2411-9717/2015/v115/n3/a2

Avoiding structural failures on mobile bulk materials handling equipment by M.J. Schmidt* and B.W.J. van Rensburgâ&#x20AC;

Bulk materials handling systems are extensively used in the mining and minerals industry, where a fairly high incidence of structural failure is experienced, notwithstanding design compliance with appropriate standards. A number of case studies are explored to demonstrate how insufficient controls or protection systems have contributed to structural failures on mobile bulk handling equipment. The importance of design integration across engineering disciplines is highlighted. The revision of ISO 5049-1 (1994) is proposed to provide specific rules and guidelines pertaining to machine protection systems. It is further recommended that the structural design engineer of the original equipment manufacturer (OEM) fulfils a more prominent role during the final acceptance and handover of mobile bulk handling equipment, with specific reference to protection systems. Keywords continuous bulk handling equipment, machine protection system, structural failure, ISO 5049-1 (1994).

Introduction Unfortunately. a fairly high incidence of structural damage or failure of bulk materials handling systems is experienced in the mining industry (Krige, 2012), notwithstanding design compliance with appropriate standards. Improved structural safety is in the interest of all employees and also facilitates steady company earnings. Catastrophic failures may cause injuries or fatalities and inevitably cause significant business interruptions since bulk materials mines are usually operated on a continuous basis with scheduled maintenance intervals. This paper specifically addresses railmounted mobile bulk materials handling (BMH) equipment such as stackers, reclaimers, and ship loaders, and focuses on design shortcomings pertaining to controls, protection systems, and integration across engineering disciplines. ISO 5049-1 (International Organization for Standardization, 1994) is internationally recognized and utilized throughout the industry (Krige, 2012) for the design of mobile BMH equipment. Compliance with this standard means that the designer has met the design obligation, notwithstanding that the limitations of the standard are widely The Journal of The Southern African Institute of Mining and Metallurgy

* Anglo American Coal. â&#x20AC; Department of Civil Engineering, University of Pretoria. ÂŠ The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Dec. 2013; revised paper received Sep. 2014.

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Synopsis

recognized (Krige, 2012; Morgan, 2012). Where equipment damage or failure occurs, potential disputes between the owner and supplier are not easily resolved when the latter can prove that the equipment design met the requirements stipulated in the standard or client specification. Although highly skilled and experienced design engineers are usually involved in the delivery of mobile BMH equipment, recent failures of machines designed in first-world countries by reputable original equipment manufacturers (OEMs) support claims in the literature that the skills shortage crisis in the engineering industry is yet to be resolved (Hays, 2012; Kaspura, 2011; Gardner, 2011). Failures cannot always be attributed to designrelated issues only. A wide range of factors may contribute to failures, including material quality, manufacturing, commissioning, abuse, etc. The fast-track nature of most mining projects nevertheless puts pressure on equipment suppliers to provide new designs with a minimum of engineering effort, and this may be exacerbated by the scarcity of design engineering resources. The drive towards more cost-effective designs may result in less conservative designs which leave little tolerance for unexpected loading conditions or possible future upgrades. Furthermore, the lack of a proper systems design approach restricts the extent of integration between protection systems limits and structural or mechanical strength. The risk of failure is often not understood when controls are wilfully over-ridden or have not yet been commissioned.

Avoiding structural failures on mobile bulk materials handling equipment The aim of this paper is to recommend actions to improve the overall safety of mobile BMH equipment by focusing on aspects specifically related to the design integration and commissioning of protection systems and controls. Three typical case studies have been selected from an assortment of mobile BMH machine failures in order to illustrate the significant impact that inadequate protection systems and lack of design integration across engineering disciplines had on these failures.

Design standards Standards related to the design of mobile MBH include: 1. ISO 5049-1 (1994) Mobile equipment for continuous handling of bulk materials – Part 1 Rules for the design of steel structures (International Organization for Standardization, 1994) 2. FEM SECTION II (1992) 2 Rules for the design of mobile equipment for continuous handling of bulk materials, Document 2.131 / 2.132 (De La Federation Europeenne de la Manutention, 1992) 3. AS 4324.1 (1995) Mobile equipment for continuous handling of bulk materials - General requirements for the design of steel structures (Standards Association of Australia, 1995) 4. DIN 22261 (2006) Excavators, spreaders and auxiliary equipment in opencast lignite mines (German Institute for Standardization, 2006). ISO 5049-1 (1994), FEM SECTION II (1992), and AS4324-1 (1995) focus on the design of the steel structures and some mechanical aspects associated with mobile BMH equipment. Although additional parts were initially planned for all of these standards, which would address mechanical, electrical, and other aspects, these were never published. With the exception of DIN 22261, which is not commonly utilized (Schmidt, 2014), the standards available to the mobile BMH equipment industry are therefore silent on rules and requirements for machine protection systems. By implication, it is therefore left to the equipment supplier to provide protection systems that are deemed adequate to ensure the safety of any equipment supplied. AS 4324-1 (1995) is currently under revision and it is envisaged that the revised standard will be published in May 2015 (George, 2014). Additional parts, which will address electrical and controls aspects, are planned for publication within the next two years.

Key findings from the investigation The lateral resistance of the machine was insufficient to withstand the forces generated within the structure when excessive digging was experienced. Proximity probes, detecting the stockpile height, are fitted to ensure that the digging depth of rake buckets is maintained within the prescribed limits. These devices did not function properly or had not yet been commissioned, so were switched off, resulting in excessive digging forces (Anon., 2007; Krige, 2012). Electric drive motors are equipped with protection relays to limit the electrical current that can be drawn during operation, i.e. the applied system torque can be limited. Industry practice suggests that the overload protection is set to a value of 5–10% above the peak system design load (Bateman, 2013). The protection study report, compiled subsequent to the failure, indicated that the motor protection relay setting on the reclamation drives was at a default value of 2 instead of 1.05 (Anon., 2007). Furthermore, the mechanical design for the scraper drive system dictated an installed motor power requirement of 154 kW, which implies that the next size up of 160 kW was specified. During procurement, 185 kW motors were supplied due to the unavailability of the 160 kW motors. This decision was made without consultation with the relevant design engineers.

Figure 1—Typical arrangement of a portal reclaimer

Case studies Case study 1 – collapse of a portal reclaimer Background Prior to failure, the machine had been in production use for several months, although commissioning of the collision protection system had not been completed. The general arrangement of a typical portal reclaimer is shown in Figure 1. At the time of the collapse, the designed reclamation rate was exceeded by approximately 30%. The stockpile proximity probes appeared to not be working, resulting in unexpectedly high digging forces which led to the failure of major structural connections as shown in Figure 2.

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Figure 2—Failure of the bogie on a portal reclaimer The Journal of The Southern African Institute of Mining and Metallurgy

Avoiding structural failures on mobile bulk materials handling equipment Upon investigation, it was also found that the fluid couplings installed between the drive motors and reducers were rated at service factors such that a reclamation drive torque could be delivered that was only marginally below the maximum electric motor torque. Torque transfer through fluid couplings can be limited according to the design requirement by reducing the percentage oil fill, which is normal practice. The commissioning data revealed that the fluid coupling was overfilled by approximately 15%. Small amounts of oil at high percentage fill levels will lead to a significant increase in torque transfer capacity (Anon., 2007). The machine could not withstand the motor starting torque as prescribed for the abnormal digging resistance criteria as outlined in ISO 5049-1 (1994). Depending on start-up torque control, the motor torque during start-up could exceed twice the operating torque on the motor, depending on the motor type selection, as shown in Figure 3. (Curves B and C represent a typical conveyor drive selection).

Multidisciplinary design integration – scraper drive system The lack of proper design integration between mechanical, structural, electrical, and control and instrumentation engineering disciplines was revealed during the investigation (Anon., 2007). It is essential that the structural design engineer understands the effect and magnitude of forces that could be exerted on machine structures under abnormal conditions. The mechanical, and likewise the electrical, design engineer must understand how the selection and commissioning of equipment such as fluid couplings and electric motors could have an adverse effect on structural design parameters. The importance of interaction between the control and instrumentation and the structural and mechanical designers to ensure that alarm levels and limits are correctly designed and commissioned cannot be overemphasized. Final acceptance and approval of the machine, and more specifically the validation of protection systems by the OEM’s structural design engineer or representative who understands the structural limitations of the equipment, are crucial. This collapse highlighted the importance of understanding the additional risks associated with the production use of a machine that has not been fully commissioned, and where protection systems may be inoperative and stockpile volumes have not yet been fully calibrated. The operation of machines that have not been fully commissioned must be prohibited, regardless of production pressures.

year before collapsing completely. An incident in which the boom conveyor belt was overloaded preceded the failure event. The failure of a critical tie-beam connection, which is highlighted in Figure 4, initiated the collapse of the boom and ultimately ruined the entire machine. The extent of the damage can be seen in Figure 5.

Key findings from the investigation Loading conditions were underestimated because an incorrect material bulk density was used in the design. The incorrect commissioning of the speed switch settings associated with the boom belt contributed to the structural overloading of critical tie-beam connections when slippage of the boom belt occurred. Based on the design requirements of ISO 5049-1 (1994), critical tie-beam connections were overloaded, although the ultimate carrying capacity exceeded the most severe design load combination. The design of these connections is therefore considered to be marginal. The tie-beam connections utilized bolts in double shear in such a way that fastener threads intercepted a shear plane. Furthermore, high-strength electro-galvanized fasteners, which are susceptible to hydrogen embrittlement (Erling, 2009), were used in this critical tie-beam connection. The topic of corrosion and embrittlement is discussed at length in the American Institute of Steel Construction (AISC) Guide to design criteria for bolted and riveted joints (Kulak et al.,

Case study 2 - Collapse of a slewing stacker Background The machine was successfully operated for approximately a

Figure 3—Characteristic start-up curves for different electrical motors (Baldor, 2004)

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Figure 4—General arrangement of slewing stacker. Critical connection highlighted

Avoiding structural failures on mobile bulk materials handling equipment The root cause of the stacker collapse can therefore be summarized as follows. Design deficiencies contributed to a marginal design of critical connections, which was further exacerbated by defective bolts, adversely affecting the carrying capacity. The absence, malfunctioning, and incorrect commissioning of machine protection systems allowed an overload condition to develop, which led to the catastrophic collapse of the stacker.

Case study 3 – structural damage to a drum reclaimer Background

Figure 5—Collapsed stacker

1987). From laboratory tests referenced, Kulak et al. note ‘… it became apparent that the higher the strength of the steel, the more sensitive the material becomes to both stress corrosion and hydrogen stress cracking. The study indicated a high susceptibility of galvanized A490 bolts to hydrogen stress cracking.’ It is ultimately concluded that ‘galvanized A490 bolts should not be used in structures. The tests did indicate that black A490 bolts can be used without problems from brittle failures in most environments.’ (A490 bolts are the direct equivalent of the Class 10.9 bolts used in South Africa). High hydrogen contents were confirmed by the metallurgical examination of the fasteners, while surface cracks were noted at the thread roots of some specimens. Through the application of fracture mechanics, it can be demonstrated that the load carrying capacity of the tie-beam connection fasteners may have been reduced by hydrogen effects to a value far below what would be required to sustain a boom load associated with the luffing operation of an overloaded stacker boom (Schmidt, 2014). Supervisory control and data acquisition (SCADA) recordings revealed that the boom loading significantly exceeded the intended design parameters prior to the collapse. The alarm set-point to alert a boom overload condition was specified at a level that was too high to prevent structural overload. The machine could therefore be exposed to severe loading conditions without any operator abuse. The probability that operator abuse contributed to the failure could not, however, be ruled out altogether. The protection systems on the machine were found to be inadequate to ensure that structural loading remained within the intended design parameters. At the time of the collapse, the machine had not been formally handed over to the operations team.

Although no failure occurred as such, significant damage was done to the support legs of a drum reclaimer when the control system of one of the long travel drives malfunctioned, resulting in a skewing action that imposed excessive loading which was not considered in the original design. A typical arrangement of the machine is shown in Figure 6.

Key findings from the investigation The overall machine control system was originally configured without interlocks between the independent long travel drive systems located on adjacent bogie wheel sets. When the control system for the drives at the one end malfunctioned, the drives on the opposite end continued with the long travelling sequence until the drives tripped on overload as a consequence of the skewing of the machine. Severe local damage and permanent deformation were caused to the boxed plate structural section of the fixed legs. The machine, as shown in Figure 7, had been in service for decades.

Discussion Skew control can be achieved by comparing signals from incremental encoders on both sides of the machine (McTurk, 1995). Skew should occur only if one side of the machine cannot travel for accidental reasons, e.g. an obstacle on the rails, and if this happens a signal must trigger the immediate shutdown of the machine. The control systems associated with the long travel of the machine were not fail-safe. Abnormal loads, not anticipated in the original structural design, were subsequently exerted on major structural members. The equipment was nevertheless operated successfully for many years prior to the skewing incident. Insufficient design integration existed between the OEM’s structural, mechanical, electrical, and control and instrumentation engineering disciplines during the detail design phase of the original project. The damage could have been avoided by the incorporation of additional protection instrumentation for negligible additional capital cost.

Figure 6—Typical arrangement of a drum reclaimer

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Avoiding structural failures on mobile bulk materials handling equipment following aspects characterize such a team structure:

Figure 7—Side elevation of drum reclaimer

The lack of interdisciplinary design integration, as discussed in the above case studies, is of concern. This is probably a highly controversial topic which design engineers would generally not want to embark upon. Of course, some BMH equipment OEMs will address this engineering challenge better than others. Unfortunately, the facts presented in the above case studies demonstrate that design engineers often design with an engineering discipline-specific approach, without the required understanding of design details from counterparts representing other engineering disciplines. This may have a direct influence on the overall performance of the equipment. The author acknowledges that discipline-specific specialists are nevertheless required for the successful design of mobile BMH equipment. The appeal is merely for better design integration, which is not based on perception but rather on a thorough understanding of interdependence between engineering disciplines. Although the competitive nature of the mobile BMH industry generally leads to a tendency amongst OEMs not to openly share design content with their client representatives, it would be advantageous to both parties, especially where the client appoints a third-party design auditor. While it is more common for larger corporate clients to have skilled engineering staff assigned to capital projects for the purposes of engineering oversight, smaller enterprises generally rely entirely on the OEMs for the successful delivery of functional mobile BMH equipment as specified in the supply contract. Liaison between the OEM’s design engineers and the client’s engineering discipline leads is invaluable for ensuring successful project delivery. Furthermore, larger corporate clients often have a number of operations where the same or similar mobile BMH equipment may be utilized in ways other than was envisaged under the supply contract. The input from operational personnel, who are responsible for the daily operation and general maintenance of existing equipment, must not be underestimated, but the ability of such individuals to influence new designs remains largely dependent on their skill and experience. A typical integrated design team organization structure that is conducive to a high level of design integration with a systems design approach is depicted in Figure 8. The The Journal of The Southern African Institute of Mining and Metallurgy

Although it is expected that most OEMs will embrace and advocate the integrated model, case studies unfortunately suggest that a low level of design integration is often encountered within the industry.

Conclusion The brief case studies as discussed have highlighted past incidents where incorrect commissioning or inadequate protection systems and controls contributed significantly to the collapse or severe damage of mobile BMH equipment. Deficient protection systems can often be linked back to the

Figure 8 – Ideal design team organisational structure for facilitating design integration VOLUME 115

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Integrated design approach

® Within the OEM’s design team organizational structure, there is a free flow of information directly related to design interfaces between engineering disciplines without interference in discipline-specific matters ® Design interfaces are approached as an integrated system with input from relevant role-players as a team effort across engineering disciplines ® The respective engineering disciplines have a sound understanding of how equipment selection and systems dictated by engineering counterparts influence their individual designs ® The client owner’s team participates in the design scope definition and design risk assessment with specific reference to machine protection and controls. Engineering input, oversight, liaison, and progressive review are provided by relevant representation from the client ® Specific design requirements are agreed between the OEM and client owner’s team within the agreed contractual arrangement ® There is a free flow of information between the discipline-specific engineers from the owner’s team and their OEM counterparts responsible for the design, without compromising the latter party’s intellectual property rights.

Avoiding structural failures on mobile bulk materials handling equipment lack of design integration across engineering disciplines. Although the end-user may be inclined to assume that a high level of interdisciplinary engineering integration is exercised, the studies demonstrate that this is not necessarily the case, which subsequently necessitates that the matter receives greater focus during current and future machine designs. While a design standard can never be a substitute for a pragmatic design approach, the only international design standard available for the design of mobile bulk handling equipment, ISO 5049-1 (1994), does not address rules pertaining to machine protection systems. The case studies discussed demonstrate the industry’s need for an updated standard to facilitate safe BMH designs in this regard. A design team organization structure is proposed to facilitate an integrated systems design approach.

Standards Association of Australia, New South Wales, Australia. BALDOR ELECTRIC CO. 2004. Understanding induction motor nameplate information, electrical construction and maintenance. http://ecmweb.com/motors/understanding-induction-motor-nameplateinformation [Accessed 10 Oct. 2013]. Bateman, P. 2013. Principal Electrical Engineer, Anglo American Thermal Coal. Personal communication. DIN 22261. 2006. Excavators, spreaders and auxiliary equipment in opencast lignite mines – Part 1 to 6. German Institute for Standardization, Berlin, Germany. ERLING, S. 2009. Methods of preventing hydrogen embrittlement in hot dipped galvanized high strength steel fasteners. Hot Dip Galvanizing Today, vol. 6, no. 2. pp. 21–21. FEGER, F. 2013. Surface Mining (IPCC) & Materials Handling, R&D/Engineering Manager, Sandvik Mining and Construction. Personal communication.

Recommendations A technical committee should be appointed to review the ISO 5049-1 (1994) standard to include rules and guidelines regarding machine protection systems. Consideration should be given to the revisions envisaged to the AS 4324-1 (1995) standard facilitated by the Australian Standards Committee ME43 in this regard. Although this paper focuses on machine protection systems, there is an opportunity to consider the use of alternative lightweight and compound construction materials, as well as new rope technology for tie systems, while revising the ISO 5049-1 (1994) standard. It is furthermore recommended that guidelines are provided for designers who wish to follow a limit-state design approach, since there are a number of reputable OEMs in the mobile BMH industry who do not follow allowable stress principles. It is recommended that the structural design engineer be closely involved with the verification of alarms and set-points associated with machine protection systems, in conjunction with other specialists responsible for the design and commissioning thereof, to make absolutely certain that these systems and controls comply with the design intent before final handover. A high level of interdisciplinary design integration must be pursued with specific reference to machine protection systems and controls. A risk-based design approach should be mandatory.

FEM SECTION II. 1992. Rules for the design of mobile equipment for continuous handling of bulk materials. De La Federation Europeenne de la Manutention, Brussels, Belgium. GEORGE, G. 2014. SIS Coordinator. Standards Australia. Personal communication. GARDNER, M. 2011. Industry complains of skills shortage. University World News, 27 March, no. 164. http://www.universityworldnews.com/ article.php?story=20110325204522328&query=Industry+complains+of+s kills+shortage [Accessed 8 August 2013]. HAYS. 2012. Hays Global Skills Index [produced in partnership with Oxford Economics, London]. http://www.oxfordeconomics.com/publication/ open/222621 [Accessed 10 Oct. 2013]. ISO 5049-1 1994. Mobile equipment for continuous handling of bulk materials – Part 1: Rules for the design of steel structures. International Organization for Standardization, Geneva, Switzerland. KASPURA, A. 2011. Skills shortage claims backed up by surveys. Engineers Australia, June 2010. pp. 74–75. KULAK, G.L., FISHER, J.W., and STRUIK, J.H.A. 1987. Guide to design criteria for bolted riveted joints. 2nd edn. American Institute of Steel Construction Inc., Chicago, Illinois, USA. KRIGE, G.J. 2012. Learning from structural failures of material handling equipment. Australasian Structural Engineering Conference 2012: The Past, Present and Future of Structural Engineering, Barton, Australia. Engineers Australia. pp. 130–137.

Acknowledgements Anglo American plc for permission to use the material published. The opinions expressed are those of the author and do not necessarily represent the policy of Anglo American plc Dr G.J. Krige for input into this study topic and my career Sandvik Mining and Construction for valuable input into the study OEMs who participated in the study survey.

KRIGE, G.J. 2013. WAH Engineering Consultants CC. Personal communication. MCTURK, J.R. 1995. Portal and bridge scraper reclaimers – a comparison. Beltcon Conference 1995. South African Institute of Materials Handling, Johannesburg, South Africa. MORGAN, R. 2012. Design of materials handling machines to AS4324.1. Australasian Structural Engineering Conference 2012: The Past, Present and Future of Structural Engineering, Barton, Australia. Engineers Australia. pp. 138–145. MORGAN, R. 2013. Revision to Australian Standard AS4324.1-1995 for materials handling of bulk products. 2013 Northern Regional Engineering

References

Conference Structural Engineering Conference, Townsville, Australia. ANONYMOUS. 2007. Reclaimer incident analysis reports and letter. Documentation compiled by a professional investigation team.

SCHMIDT M.J. 2014. Avoiding structural failures on mobile materials handling equipment. MSc dissertation, University of Pretoria, South Africa.

AS 4324–1. 1995. Mobile equipment for continuous handling of bulk

materials. Part 1: General requirements for the design of steel structures.

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http://dx.doi.org/10.17159/2411-9717/2015/v115n3a3 ISSN:2411-9717/2015/v115/n3/a3

Utilization of the Brazilian test for estimating the uniaxial compressive strength and shear strength parameters by K. Karaman*, F. Cihangir*, B. Ercikdi*, A. Kesimal*, and S. Demirel†

Uniaxial compressive strength (UCS) and shear strength parameters (cohesion and angle of internal friction, C and φ) of rocks are important parameters needed for various engineering projects such as tunnelling and slope stability. However, direct determination of these parameters is difficult and requires high-quality core samples for tests. Therefore, this study aimed to explore the applicability of the Brazilian test (BT) – a simple, less sophisticated and inexpensive method for both specimen preparation and testing – to estimate the UCS and shear strength parameters of rocks. Thirty-seven rock types were sampled and tested, 24 of which were volcanic, 8 were metamorphic, and 5 were sedimentary. Statistical equations were derived to estimate the UCS and shear strength parameters of rocks using the BT. The validity of the statistically derived equations was confirmed using predictive analytics software (PASW Statistics 18). A strong linear relation was found between BT and UCS values. BT and UCS values exhibited prominent linear correlations with the cohesion values of rocks. The Mohr envelope was also used to determine the cohesion and friction angle of rocks using BT and UCS values. It is deduced from the current study that the BT values can be used to estimate the UCS and cohesion. However, no relation was observed between the angle of internal friction values and the UCS and BT for all rock types. Therefore, different approaches are suggested for the estimation of the internal angle of friction for application in the preliminary design of projects. Keywords Brazilian test, shear strength parameters, triaxial compressive strength, uniaxial compressive strength, Mohr-Coulomb criterion

Introduction The mechanical and shear strength parameters (UCS, C, φ, etc.) of rocks are considered to be among the most significant properties in mining, civil, and engineering geology projects (Singh et al., 2011). The UCS is most commonly determined in accordance with the suggested methods of the International Society for Rock Mechanics (ISRM, 2007). UCS is also considered for a variety of issues encountered during blasting, excavation, and support systems in engineering applications (Hoek, 1977). Shear strength parameters (C and φ) are used to express the strength of rock materials and the resistance to deformation under shear stress. These parameters are affected by many factors such as lithological character, anisotropy, and environment of the rock materials (Yang et al., 2011). Shear The Journal of The Southern African Institute of Mining and Metallurgy

* Department of Mining Engineering, Karadeniz Technical University, Turkey. † NVS Construction Industry and Trade Limited Company. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Apr. 2014; revised paper received Jul. 2014.

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Synopsis

strength parameters of rock materials can be quantified by means of direct shear tests and triaxial compression tests as prescribed the American Society for Testing and Materials (ASTM, 2004) and ISRM (2007), respectively. The latter test is widely used and accepted in most mining practices. However, UCS and triaxial compression tests are expensive and time-consuming. In addition, the preparation of rock core samples for testing, placing the samples in a confining pressure cell (Hoek cell), and operating the confining pressure for triaxial testing requires considerable time and attention (Kahraman and Alber, 2008; Kilic and Teymen, 2008). Furthermore, weak, thinly bedded, or densely fractured rocks are not suitable for specimen preparation and the determination of UCS. Triaxial testing is also difficult for such rock types. Therefore, some alternative test methods such as point load index, Schmidt hammer, and ultrasonic pulse velocity tests are commonly used to estimate the UCS, C, and φ of rocks owing to their rapidity, simplicity, low cost, and ease of both specimen preparation and testing (Kahraman, 2001; Karaman and Kesimal, 2015). Although the aforementioned tests are known to be extensively used for estimation of rock strength (Kahraman, 2001; Kilic and Teymen, 2008; Bruno et al., 2012), there are few studies on the utilization of the Brazilian test (BT) for the estimation of UCS, C, and φ of intact rocks (Beyhan, 2008; Farah, 2011). The BT is one of the most popular and common tests to obtain the tensile strength of brittle materials such as concrete, rock, and rock–like

Utilization of the Brazilian test for estimating the uniaxial compressive strength materials (Li et al., 2013). It owes its popularity to the ease of specimen preparation, which does not require particular care and expensive techniques, compared to the direct tension test (Mellor and Hawkes, 1971; Hudson et al., 1972; Bieniawski and Hawkes, 1978; Coviello et al., 2005). The BT has also been suggested by many researchers for investigating the effect of anisotropy on the strength of coal (Evans, 1961), siltstone, sandstone, and mudstone (Hobbs, 1964), and gneiss and schist (Barla, 1974). Farah (2011) correlated the UCS of 145 weathered Ocala limestone samples with their point load strength and BT values. He stated that the BT is a useful method for prediction of UCS compared to the point load test. Kahraman et al., (2012) found a reasonable linear correlation between UCS and BT results. Many researchers have correlated BT results with indirect tests (P-wave velocity, block punch test, point load index, Schmidt hammer test etc.) to estimate indirect tensile strength of rocks (Kilic and Teymen, 2008; Mishra and Basu, 2012). Yang et al. (2011) conducted an experimental investigation on the mechanical behaviour of coarse marble, under different loading conditions, using the linear Mohr-Coulomb criterion to confirm the strength parameters (cohesion, C and internal friction angle, φ). However, as mentioned above, limited studies were performed to estimate C and φ using the BT and its confirmation with the Mohr-Coulomb criterion, which is widely accepted and used in the literature. The purpose of the present study is to correlate C, φ, and UCS of rock samples with BT values; to develop empirical equations for UCS, C and φ using the BT; and to confirm the predicted UCS and C with measured UCS and C values obtained from direct methods i.e. the Mohr-Coulomb criterion or triaxial compressive test.

Site description and geology The study area is located in eastern Black Sea Region (Figure 1), which has an abundance of sites suitable for small hydroelectric power plants. A total of 213 hydroelectric power plant (HEPP) projects incorporating tunnels have been planned or constructed in the region (Karaman et al., 2014). The study area is in the northeast part of the Eastern Pontides Tectonic Belt (Ketin, 1966). The geological formations along the tunnel route consist of volcanic, metamorphic, and sedimentary rocks. The lithology of the tunnel route consists mainly of basalt, metabasalt, limestone, dacite, and volcanic breccia. The lowest and the uppermost lithologies belong to the Jurassic (Hamurkesen Formation) and Quaternary (alluvium), respectively. The Hamurkesen Formation is composed mainly of basalt, metabasalt, and rarely seen maroon limestone with a thickness of 3 to 5 m. This formation comprises about 70% of the tunnel length (7132 m). The Hamurkesen Formation is overlain primarily by the Berdiga Formation, which is Upper Jurassic to Lower Cretaceous in age and consists mainly of grey to white medium to thickly bedded clayish or sandy limestone.

inspected for macroscopic defects to provide test specimens free from fractures, cracks, partings, or alteration zones. One of the important parameters affecting the strength of rocks is anisotropy. However, the volcanic rocks show no prismatic, pillow lava, and/ or flow structures. Additionally, the metamorphic rocks (metabasalts) contain no features such as schistosity or foliation that could lead to anisotropy. In order to obtain accurate results for best comparison, the experiments were carried out under the same (natural and unweathered) rock conditions. Laboratory core drill and sawing machines were used to prepare cylindrical specimens. The cut end faces of the cores were smoothened to maintain precision within 0.02 mm and made perpendicular to within 0.05 mm to the core axis using a comparator.

Uniaxial compressive strength The UCS tests were carried out on fresh rock samples with a length-to-diameter ratio of 2.5. The tests were performed using a servo-controlled testing machine with a load capacity of 300 t, using a stress rate of 0.75 MPa/s. Mean UCS values (Table I) were obtained by averaging the strength values of five core samples for each rock type.

Brazilian test A total of 370 core samples with a diameter of 54.7 mm and height of 27 mm were prepared using the sawing machine. In the test, a circular disk was placed between two platens and compression was applied to produce a nearly uniform tensile stress distribution normal to the loaded diametric plane, leading to the failure of the disk by splitting (Rocco et al., 1999). A loading rate of 200 N/s was applied until sample failure. A servo-controlled testing system connected to a 30 t capacity press was used for the BT tests in order to obtain accurate measurements.

Triaxial compressive strength After the preparation of the cores, the triaxial compression

Experimental procedure The rock samples used in the study were collected from various locations throughout the Çambası tunnel, 24 of which were volcanic, 8 were metamorphic, and 5 were sedimentary. UCS, triaxial compressive strength tests, and BTs were conducted on core samples (NX size, 54.7 mm) according to ISRM (2007) suggested methods. Each block sample was

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Figure 1—(a) Location map of the study area, (b) cross–section of the tunnel route The Journal of The Southern African Institute of Mining and Metallurgy

Utilization of the Brazilian test for estimating the uniaxial compressive strength Table I

Samples used and the average results of the tests Rock code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Rock type Basalt Metabasalt Metabasalt Metabasalt Metabasalt Basalt Basalt Basalt Metabasalt Metabasalt Metabasalt Metabasalt Basalt Basalt Dacite Basalt Dacite Dacite Dacite Dacite Limestone Limestone Basalt Limestone Limestone Basalt Volcanic breccia Limestone Basalt+dacite Basalt Dacite Dacite Basalt Dacite Dacite Dacite Dacite

UCS (MPa) BT (MPa) C (MPa) 197 158 66 146 133 95 115.3 152.4 96 111 71 81 75 34 61 87 61 72 65.5 56 117 92 100 91 75 77 41 120 94 125 68 66 107 132 87 110 90

34.4 26.3 9.36 22.4 20.64 14.7 22.5 23.7 18.6 16.8 15.95 14.88 12.15 4.4 5.7 10.48 5.33 5.84 9.83 6.61 17.16 11.1 16.11 12.67 10.78 10.92 5.45 17.99 13.54 19.33 8.55 7.16 18.78 20.6 16.46 17.1 14.6

36 32 15 22 28 21 24.5 30 24 27 17 18 14 8 14 19 14 18 14 11 23 18 20.7 19 15 16 11 24 20 25 14 12 21 25 19 18 19

φ (°) 50 46 41 56 44.5 43 44 47 37 39 43 43 49 43 43 43 40 37 44 47 47 45 45 44.5 50 45 39 47 44 47 49 49 47 47 43 54 44

tests for each rock sample were carried out using a servocontrolled testing machine with a servo-lateral pressure unit having a load capacity of 30 MPa. The confining fluid pressure around the cylindrical specimen was kept constant while the axial compressive load was raised until failure occurred. A Hoek cell unit with a diameter of 54 mm and height of 108 mm was used to apply the required confining fluid pressure. Six samples were used for triaxial compressive strength under three different lateral confining pressures; 5–15 MPa for each rock type. Shear envelopes of the rock samples were then drawn to obtain shear strength parameters (C and φ) by plotting the Mohr circles. A total of 222 core samples were subjected to triaxial compression testing.

Data analysis The BT (σt), UCS (σc), C, and φ values of the rocks showed normal distributions (Figure 2), and were subjected to parametric statistical tests. The data-sets were used for linear and nonlinear regression analysis. Exponential and logarithmic relationships were examined between the variables in order to derive the most reliable equations. Correlation analysis was performed to investigate the reliability of the predicted C values from regression analyses. One-way analysis of variance (ANOVA) was also performed to investigate the relationships and the mean differences between measured and predicted C values. Zero-intercept equations were not used for the estimation of C, since the best relationship between the cohesion and the UCS, and the BT, was linear.

Results and discussion

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Figure 2—Histograms and statistical evaluations of the data; (a) UCS, (b) BT, (c) C, and (d) φ

Utilization of the Brazilian test for estimating the uniaxial compressive strength Strength properties of intact rocks From the average results for the samples, which are summarized in Table I, the UCS values of the tested rocks were classified according to the strength classification of intact rock by Deere and Miller (1966) (Table II). Volcanic, metamorphic, and sedimentary rocks were individually evaluated according to the UCS classification. As shown in Table II, 56.8% of the rocks were classified as ‘moderately hard rock’ and 37.8% as ‘hard rock’. A ‘weak rock’ classification was obtained only for volcanic rocks at two points along the tunnel route.

Evaluation of strength ratio The literature contains some practical approaches for correlation between the UCS and tensile strength/BT (Farmer, 1983; Sheorey, 1997; Ramamurthy, 2001). Cai (2010) pointed out that when tensile strength data is not available, the general approach for predicting the tensile strength of rocks is to use correlation between UCS and tensile strength values (strength ratio, R). In addition, most rocks have a compressive strength value that is approximately 10 times greater than the tensile strength/BT (σc ≈ 10σt)(Kahraman et al., 2012; Farmer, 1983; Sheorey, 1997). Values given in the literature for strength ratios (R = σc / σt) show a large variation – from 2.7 to 39 with an average of 14.7 (Sheorey, 1997); from 10 to 50 for most rocks (Vutukuri et al. 1974); and from 4 to 25 for intact rocks and between 8 and 12 for more homogeneous and isotropic rocks (Ramamurthy, 2001). This variation in R depends on the type and origin of rocks (Brook, 1993; Cai, 2010). In the present study, R was determined as 6.26 for all rock type by means of the zerointercept equation, with a determination coefficient of 0.81. As regards the geological classification of the rock types, R values were 6.27, 5.97, and 7.02 for volcanic, metamorphic and sedimentary rocks respectively, within a determination coefficient of 81–84. The R values were distributed in a narrower range than those found in the literature. This could be attributed to the freshness of the rock samples collected along the tunnel route. R values obtained from the zerointercept equation were consistent with those of Brady and Brown (2004) (σc = 8σt) and Tahir et al. (2011) (σc = 7.53σt) for the prediction of UCS (Figure 3).

Relationship between BT and UCS The BT values of the rocks were subjected to regression

analysis with UCS data (Figure 4a). A strong positive linear relationship was obtained between the measured BT and UCS values, with a high determination coefficient of R2= 0.90 (Table III), which agrees well with Nazir et al. (2013). The correlation between the measured and estimated UCS for the studied rocks is significant (significance level=0.000) at the confidence interval of 95% (r = 0. 95) (Figure 4b). Considering these results, the BT can substitute for the UCS when problematic ground conditions (thinly bedded, blockin-matrix, pyroclastic rocks, and highly fractured rocks) are encountered.

Relationships between Brazilian test and shear strength parameters

Table II

Strength classification of intact rock (Deere and Miller (1966) Rock classification

UCS (MPa) Volcanic Metamorphic Sedimentary

Very weak rock

1–25

–

Weak rock

25–50

–

Moderately hard rock

50–100

Hard rock

100–200

> 200

–

Very hard rock

–

Figure 3—Comparison of R values with previous studies for the estimation of UCS

Figure 4—(a) Relationships between BT and UCS values and (b) measured UCS and estimated UCS from BT

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Utilization of the Brazilian test for estimating the uniaxial compressive strength Table III

Regression equations and their related statistics Estimated rock properties UCS – BT (Figure 4a) C – UCS (Figure 5a) C – BT (Figure 5b)

Regression equations

F test value

Sig. level

UCS = 24.301 + 4.874 × BT C3 = 3.427 + 0.17 × UCS

302.881 303.723

0.000 0.000

0.90 0.90

C4 = 7.255 + 0.85 × BT

190.371

0.000

0.85

Figure 5—Relationships between (a) C and UCS and (b) C and BT

The Journal of The Southern African Institute of Mining and Metallurgy

limestone are given in Figure 6. Moderate relationships (R2=0.40–0.41) were also found between φ–BT data pairs for basalt and limestone, and between φ–UCS (R2=0.47) for metabasalt. In addition to the regression analyses, φ values were evaluated according to variations of data for each rock type (Figures 7a, 7b, and Table IV). Minimum and maximum φ, and mean values with standard deviations, are given in Table IV. As can be seen from Figures 7a, 7b, and Table IV, suggested φ values (mean value–std. dev.) may be used with care for the initial stage of projects since they should result in a conservative design.

Comparison of measured and estimated C values In the current study, C values of rocks were obtained by triaxial tests (measured C (C1)). Another C value was found by means of the linear Mohr envelope of UCS and BT data together (C2). Additionally, C3 and C4 values were obtained by the regression analyses shown in Figure 5, the equations for which are given in Table III. The correlation plots of C2, C3, and C4 values against C1 values are depicted in Figures 8a–c. As can be seen, very high correlations were obtained between the C1–C2, C1–C3, and C1–C4 data pairs for the tested rocks within 95% confidence level (r > 0.90). The variations of these couples were also tested using ANOVA. The variances of C1, C2, C3, and C4 were homogeneous (Levene Statistic=0.879 and significance level=0.454). According to the ANOVA test results, no difference was obtained among the mean values of the groups (F=0.213 and significance level=0.884). The Dunnett two-sided T-test was used for comparison of multiple tests to investigate the relationships between C1, C2, C3, and C4, where C1 was considered as the control group. The mean values of C3 and C4 were very close to the C1 values with lowest variation (Table V and Figure 9). The minimum mean difference was obtained between C1 and C4 according to the multiple tests comparison by ANOVA. VOLUME 115

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Regression analysis was performed for the estimation of C values from UCS and the BT (Figures 5a, 5b and Table III). The strongest relationship was obtained between C and UCS values from laboratory tests with a determination coefficient value of R2 = 0.90 (Figure 5a). A very strong linear relationship between C and the BT was also found (R2 = 0.85) (Figure 5b). It can be inferred from the results that UCS and the BT can be used to obtain reliable C values. Zoback (2007) stated that all rocks have relatively high values of internal friction, whereas hard rocks (high compressive strength) have high C values and weak rocks low C values. Kahraman and Alber (2008) reported that C decreases while φ increases with an increase in specimen diameter size (height-to-diameter ratio of 2–2.5:1) for fault breccias in weak rocks. In the current study, C values were seen to increase with an increase in UCS, which is consistent with the literature. In the current study, the relationships obtained between φ–BT and φ–UCS data pairs were very weak, with coefficients of determination of R2=0.14 and R2=0.18, respectively. Beyhan (2008) correlated φ values with the BT and UCS for marl rocks from the Tunçbilek and Soma regions in Turkey. The determination coefficients were 0.09 and 0.06 for φ–BT for the rocks from the Tunçbilek and Soma regions, respectively. On the other hand, Beyhan (2008) found R2 values of 0.03 and 0.18 for rocks from the Tunçbilek and Soma regions respectively for φ–UCS data pairs. Considering the R2 values obtained from the present study (for all rock types) and the literature, these correlations are not reliable enough for the estimation of φ from BT and UCS values. Therefore, regression analyses were performed for each rock type (i.e. basalt, metabasalt, dacite, limestone) instead of all types of rocks (Table IV). As shown in Table IV, there is a very strong polynomial relationship between φ and UCS (R2=0.96) for limestone samples. Measured and estimated φ values of

Utilization of the Brazilian test for estimating the uniaxial compressive strength Table IV

Regression analyses based on rock type Parameters to be related

Findings of regression analyses Rock type

φ and UCS (N=37) φ and BT (N=37) φ and UCS (N=24) φ and BT (N=24) φ and UCS (N=11) φ and BT (N=11) φ and UCS (N=11) φ and BT (N=11) φ and UCS (N=8) φ and BT (N=8) φ and UCS (N=5) φ and BT (N=5)

All rock types All rock types Volcanic rock Volcanic rock Basalt Basalt Dacite Dacite Metabasalt Metabasalt Limestone Limestone

Practical estimates of φ φ° min.–max. (mean ± std. dev.)

Equations φ =0.05 × UCS + 40.23 φ =0.226 × BT + 41.74 φ =40.82e0.0011 UCS φ =37.12 × BT0.078 φ =0.036 × UCS + 41.93 φ =0.193 × BT + 42.44 φ =0.073 × UCS + 39.43 φ =36.06 × BT0.098 φ =0.0025 × UCS2 – 0.46 × UCS + 62 φ = 0.031 x BT2 - 0.54 × BT + 42.7 φ = 0.009 x UCS2 - 1.8 × UCS + 134.4 φ = 0.326 x BT2 - 9.4 × BT + 111.4

0.18 0.14 0.22 0.28 0.39 0.40 0.14 0.22 0.47 0.28 0.96 0.41

37–56 (45±4) 37–56 (45±4) 37–54 (45±4) 37–54 (45±4) 43–50 (46±2) 43–50 (46±2) 37–54 (45±5) 37–54 (45±5) 37–56 (44±6) 37–56 (44±6) 44.5–50 (47±2) 44.5–50 (47±2)

Suggested φ° values 41 41 41 41 44 44 40 40 38 38 45 45

Volcanic breccia: Insufficient data N: number of samples

rock materials can be reliably estimated from the BT, taking into account the correlation and ANOVA analyses.

Conclusion

Figure 6—Measured and estimated φ values for limestone

Therefore, one can infer from these findings that cohesion of Table V

Correlation analyses and their related values Estimated rock properties

Minimum

Maximum

Mean

Std. error

8.00

36.00

19.63

1.014

6.10

41.20

18.65

1.241

9.21

36.92

19.63

0.961

10.99

36.48

19.63

0.932

Determination of the UCS and shear strength parameters of rocks (C and φ) requires high-quality core samples. It is sometimes troublesome to determine these parameters using direct test methods on core samples obtained from problematic ground conditions such as thinly bedded, block–in–matrix, and highly fractured or pyroclastic rocks. The aim of this study was to overcome these problems and to propose useful equations for the estimation of UCS and shear strength parameters based on the BT method. The regression, correlation, and one-way variance analyses of the data showed that the estimation of UCS and C by the BT is robust and reliable. Determination coefficients (R2) of 0.90 and 0.85 were obtained from the regression analyses between UCS-BT and C-BT, respectively. High correlation coefficients (r>0.90) were also achieved between measured and estimated data (UCS and C). Low determination coefficients of 0.14 and 0.18 for BT-φ and UCS-φ data pairs suggested that the BT and UCS were not reliable for the prediction of φ when all rock types were evaluated together. However, a relatively higher determination coefficient for a particular set of data e.g. data collected from the same rock

Figure 7—Variations of friction angle in (a) basalt and (b) limestone samples

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Figure 8—Relationships between (a) C1 and C2, (b) C1 and C3, and (c) C1 and C4

Figure 9—Comparison of the mean values of C obtained by different methods

type (basalt, metabasalt, dacite, limestone) was obtained for the estimation of φ. ANOVA indicated that there is no difference in C values acquired by different test methods (F=0.213 and significance level=0.884). These findings suggest that the BT, as a low-cost, less time-consuming, and practical method, can be reliably used to determine the UCS and C of rocks in problematic ground conditions.

method for triaxial compressive strength of undrained rock core specimens without pore pressure measurements. D2664. BARLA, G. 1974. Rock anisotropy: theory and laboratory testing, Rock Mechanics. Muller, L. (ed.). Springer-Verlag, New York. pp. 132–169. BEYHAN, S. 2008. The determination of G.L.I and E.L.I marl rock material properties depending on triaxial compressive strength. PhD thesis, Osman

Acknowledgements The authors would like to express their sincere thanks and appreciation to Energy–SA Company for providing help during the study, and to Karadeniz Technical University (KTU) for funding this work through research project no. 9706.

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applied to Exploration, Production and Wellbore Stability. Cambridge Press. p. 449.

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http://dx.doi.org/10.17159/2411-9717/2015/v115n3a4 ISSN:2411-9717/2015/v115/n3/a4

Laser surface alloying of Al with Cu and Mo powders by S.L. Pityana*†, S.T. Camagu‡, and J. Dutta Majumdar§

Laser surface alloying was used to develop copper and molybdenum aluminides by injecting premixed copper and molybdenum powder particles into a laser-generated melt pool on an aluminium substrate. Different laser processing parameters were used to produce the composite thin layers on the substrate material. The microstructure and phase constituents of the composite layer were studied by means of scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and Xray diffraction (XRD) techniques. Experimental results show that the matrix structure of the metal matrix composite layer consists of θ-CuAl2 and MoAl5. Surface hardness was increased by a factor of 3. Keywords laser surface alloying, metal matrix composites, intermetallic phases.

Introduction Aluminium is a very important material which is used in the automotive and aerospace industries. The choice of aluminium is based on its excellent physical properties such as low density and high specific strength. However, its application is limited by its low surface hardness and surface wear resistance, resulting in premature failure as a result of surface damage. Remedial solutions for improving surface durability in terms of wear and corrosion resistance include surface coating. Surface coating is considered to be an economical and effective process for extending the service lifetime of industrial components. In surface coating, the working surface of the substrate is coated with hard-wearing resistant particles such as carbides, borides, nitrides, oxides, and multi-component systems to form metal matrix composites (MMCs) (Wang, Lin, and Tsai, ,2003). The very distinctive feature of MMCs is that individual particles in the composite retain their properties, as well as complement each other to impart properties that cannot be found in any one of them alone (Rohatgi, Asthana, and Das, 1986; Kohara, 1990). The main goal of fabrication of Al-MMC The Journal of The Southern African Institute of Mining and Metallurgy

* CSIR, National Laser Centre, Pretoria, South Africa. † Department of Chemical and Metallurgical Engineering, Tshwane University of Technology, Pretoria, South Africa. ‡ CSIR, Light Metals and Metals Processing, Pretoria, South Africa. § Metallurgical and Materials Engineering Department, Kharagpur, India. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received May 2013; revised paper received Oct. 2014.

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Synopsis

surfaces has been to obtain improved mechanical and chemical properties while retaining the attractive properties of aluminium such as low specific weight, high specific strength, and excellent formability. The deposited coatings must be well bonded to the substrate, with no pores, micro-cracks, or splat boundaries. Laser surface alloying techniques have received much attention as an alternative to more conventional techniques for fabricating MMCs (Dubourg, et al., 2002; Dutta Majumdar and Manna, 2011; Pityana, 2009; Aravind et al., 2004; Chong, Man, and Yue, 2001; Popoola, Pityana, and Popoola, 2011). During the laser surface alloying process the powder mixture, together with a thin surface layer of the substrate, is melted using a laser beam that is scanned across the surface. This leads to rapid solidification and formation of a coating. The rapid cooling and solidification of the laser-generated melt pool can result in the formation of various non-equilibrium phases and refined microstructures. If the powder that is injected into the melt pool contains particles which do not melt, an MMC coating is produced on the substrate surface. Due to the combination of non-equilibrium microstructures and the dispersion of hard phases, the surface properties tend to be superior to those of the untreated aluminium base material.

Laser surface alloying of Al with Cu and Mo powders The current study aims at the development of an intermetallic (copper aluminide and molybdenum aluminide) dispersed composite surface on an aluminium AA1200 substrate by laser surface alloying with Cu-Mo powder mixtures. The powder composition was adjusted in order to form coatings consisting of Al-Cu and Al-Mo intermetallic phases in the laser-modified layer. Cu is added to Al alloys to increase tensile strength, hardness, and fatigue resistance, whereas Mo is added to increase corrosion resistance. The microstructure, phases, and the hardness of the coatings were investigated in detail.

used for microstructure investigations. EDS was used for elemental analysis. The phases formed in the layer were identified by X-ray diffraction (XRD) using a Pan Analytical X’Pert Pro powder diffractometer with a X’Celerator detector. The radiation source used was Cu Kα (1.5402 Å). The phases were indexed using X’Pert High-score Plus software. The hardness profiles of the alloyed samples were obtained using a Matsuzawa hardness tester with a load of 100 g. Hardness profiles were constructed for each alloying process depicting the hardness from the alloyed surface down to a depth of approximately 1.6 mm.

Experimental procedure

Results and discussion

Materials and coating process

Microstructures of the alloyed surface

The substrate material used in the study was aluminium (AA 1200) with chemical composition 0.59% Fe, 0.12% Cu, 0.13% Si, and the balance Al. Specimens with dimensions of 100×100×6 mm were laser-cut from a large sheet. The surfaces were sandblasted, rinsed, and cleaned with acetone prior to laser surface processing. Sandblasting removes the oxide layer and improves the absorption of the laser energy at the specimen surface. The Cu and Mo powder compositions and the laser processing parameters that were varied during the study are shown in Table I. For ease of reference, the samples are numbered as shown in Table I. The energy density is defined as E = P/(VD), where P is the laser power, V the scanning speed, and D the beam diameter. Laser surface alloying was carried out using a Rofin Sinar DY044, CW Nd:YAG laser . An off-axis powder feeding nozzle with 2.5 mm diameter and a Precitec YW50 laser cladding head were mounted on a KUKA articulated arm robot. A 600 μm optical fibre was used to guide the laser beam to the cladding head. The powder nozzle and the laser beam were mounted 12 mm above the substrate and were arranged such that the powder stream coincided with the laser beam at the interaction zone. The mixed powders were fed into the melt pool by means of an argon gas carrier which also acted as a shield against oxidation of the melt pool. A GTV powder feeder was used to feed the powder at a rate of 2 g/min. The average particle size of the Cu and Mo powders was between 50 and 100 μm . After laser melt injection, cross-sections of the samples were prepared for metallurgical examination. The mechanically polished surfaces were etched with Kellers’s reagent (5 ml HNO3, 1.5 ml HCl, 1.0 ml HF, and 95 ml distilled H2O). The Leo 1525 scanning electron microscope, equipped with energy dispersive spectroscopy (EDS), was

Laser surface modification was carried out by melting the Al substrate and feeding with 50%Cu-50%Mo (wt%). Figures 1 A and B show the scanning electron micrographs of the precursor Cu and Mo used in the study. The Mo particles (Figure 1A) were spherical in shape and the size distribution ranged from 5 to 50 μm. The Cu particles (Figure 1B) ranged between 5-100 μm in size and consisted of spherical and

Figure 1—SEM micrographs of the (A) Mo and (B) Cu particles

Table I

Powder mixture composition and laser processing parameter values 50%Cu-50%Mo (wt%) 641 642

194

25%Cu-75%Mo (wt%)

75%Cu-25%Mo (wt%)

Laser scan speed, V (m/min)

Beam diameter, D (mm)

Laser power (P) (kW)

Energy density (E) (J/mm2)

645 646

649 650

2.0 1.5

4.0 4.0

30 40

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Laser surface alloying of Al with Cu and Mo powders

Microstructure analysis 50% Cu-50% Mo powder mixture Figures 3A and 3B show the scanning electron micrographs of the laser surface-alloyed aluminium surface lased with (A) an energy density of 30 J/mm2 and interaction time of 0.12 seconds and (B) energy density of 40 J/mm2 and interaction time of 0.16 seconds. The microstructure consists of a coarse continuous network of secondary phase precipitates appearing as a light (white) areas at the dendrite boundaries enveloping the aluminium matrix phase, which appears as a grey region. Light grey, block-like structures can be observed together with un-dissolved Cu and Mo particles. The fine white eutectic network contains CuAl2 and the grey regions contain α-Al. The plate-like structures are MoAl5 intermetallics. Figure 3B shows the presence of a refined CuAl2 eutectic network. The degree of fineness is greater in Figure 3B than in Figure 3A. In addition, an increased amount of MoAl5 plate-like structures is observed in the processed layer. This can be explained in terms the higher laser energy density and longer dwell time, which increase the lifetime and the temperature of the melt pool. This allows more particles to enter the melt pool. Some particles entering the melt pool are completely dissolved, while others are retained. The melted particles react with liquid Al to form

Figure 2—Scanning electron micrograph of the cross-section of laser surface alloyed Al with Cu + Mo at a laser power of 4.0 kW and scan speed of 2.0 m/min The Journal of The Southern African Institute of Mining and Metallurgy

intermetallic phases of Al-Cu and Al-Mo. The Cu-Mo system is mutually immiscible in both liquid and solid states and does not form any compounds. The un-melted Cu and Mo particles form composite coatings with the Al substrate.

25%Cu-75% Mo powder mixture Figures 4A and 4B show the scanning electron micrographs of the top surface of laser surface alloyed aluminium treated with (A) an energy density of 30 J/mm2, and (B) an energy density of 40 J/mm2. The variation in the microstructure of the alloyed layer can be explained on the basis of the laser absorption coefficients of the Cu and Mo particles. The Mo powder absorbs more laser power, resulting in a higher melt pool temperature which leads to the melting of the Cu powder. However, the high volume fraction of the Mo particles in the powder mixture and their high melting temperature (2620ºC) ensures the retention of a significant number of Mo particles in the alloyed layer. Due to the low melting temperature of Cu (1083ºC), most Cu particles

Figure 3—SEM micrographs showing macrostructures of the Al-Cu-Mo laser alloyed layer lased with (A) energy density of 30 J/mm2 (B) energy density of 40 J/mm2 VOLUME 115

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elliptical shapes. Figure 2 shows the scanning electron micrograph of the cross-section of the alloyed region perpendicular to the scanning direction. The laser process parameters used to obtain this layer were a laser power of 4.0 kW and a scan speed of 2.0 m/min. The larger unmelted particles are uniformly distributed throughout the molten zone to form a composite layer. The distribution and mixing of the particles in the solidified melt pool could be related to convective flow in the liquid melt pool. EDS analysis confirmed the presence of un-melted Cu and Mo particles. It can be observed that the alloyed layer has considerable porosity. This is due to the ‘de-bonding’ of the particles from the matrix.

Laser surface alloying of Al with Cu and Mo powders density of 40 J/mm2, the Cu particles were completely melted and reacted with Al to form very fine cellular and columnar dendritic structures (Figure 5B). The partially melted Mo particles are randomly distributed in the alloyed zone. Owing to the low mutual solubility of Mo and Cu, no Mo-Cu intermetallic phases were observed in the alloyed layer.

XRD analysis Figure 6 shows the XRD profiles of the alloyed zone developed on the surface of Al+50%Cu-50% Mo. The dominant XRD reflection peaks are due to the Al substrate at 2θ = 38.47º (111), 44.72º (200), 65.1º (220), 78.23º, (311), and 82.44º (222). The in situ synthesized θ-CuAl2 intermetallic has XRD peaks at 2θ = 42.7º (112) and 73.64º. The Al5Mo intermetallics reflection peaks are found at 40.47º and 58.59º. No Cu-Mo phases were detected in the laser processed layer, due to limited solubility of the Cu and Mo. The XRD peak positions do not show any significant difference in the structural compositions between the layers formed using 30 and 40 J/mm2 laser energy densities.

Figure 4—SEM micrograph of the microstructures of the aluminium +75%Cu-25%Mo samples lased with (A) energy density of 30 J/mm2 and (B) energy density of 40 J/mm2

dissolve to form the θ-Al2Cu intermetallics. In both cases, the matrix consists of a eutectic mixture of alternate layers of Al2Cu and α-Al inside the dendritic regions. Figure 4A shows the Al-Mo intermetallic phases existing in different morphologies. The Al-Mo dendrites have flower-like and long needle-like appearances. Figure 4B shows the Al-Mo dendrites as needle-like or plate-like morphologies. This expected variation is due to the incident energy and longer dwell time of the laser beam on the substrate, which lead to high heating and cooling rates. Qiu, Almeida, and Vilar (1998) reported that the MoAl5 intermetallic can be observed in different allotropic forms, depending on the heating and cooling rates.

75% Cu-25% Mo powder mixtures Figures 5A and 5B show the scanning electron micrographs of the top surface of laser surface alloyed aluminium lased with (A) an energy density of 30 J/mm2, and (B) energy density of 40 J/mm2. The microstructure (Figure 5A) consists of a continuous θ-Al2Cu eutectic network engulfing the α-Al phase. Unmelted Mo particles are evident in the microstructure. The Mo-Al intermetallic phases can be distinguished by their block-like appearance. At an energy

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Figure 5—SEM micrographs of aluminium+75%Cu-25% Mo lased with (A) energy density of 30 J/mm2 and (B) energy density of 40 J/mm2 The Journal of The Southern African Institute of Mining and Metallurgy

Laser surface alloying of Al with Cu and Mo powders

Figure 6—XRD analysis of Al+50%Cu-50% Mo laser alloyed at energy densities 30 J/mm2 (sample 641) and 40 J/mm2 (sample 642)

Figure 7—XRD analysis of Al+25%Cu-75% Mo laser alloyed at energy densities 30 J/mm2 (sample 645) and 40 J/mm2 (sample 646)

Figure 7 shows the XRD profiles of the alloyed zone developed on the surfaces alloyed with 25%Cu+75%Mo at energy densities of 30 and 40 J/mm2. It is evident that the coatings contain the Al, MoAl5, and Al2Cu intermetallic phases. SEM micrographs of the layers showed unmelted particles of Cu and Mo distributed homogeneously throughout the laser deposited layer. Figure 8 shows the XRD profiles of the alloyed zone developed on the surfaces of samples 649 and 650 (alloyed with 75%Cu-25%Mo). Sample 649 shows additional peaks for the θ-Al2Cu intermetallic detected at 2θ = 20.62º (110), 29.63º (200), and 47.25º (310). The results also show the possible formation of Cu9Al4 at reflection angles of 26.63º and 49.56º. The MoAl5 intermetallics are detected in the alloyed surface. In the case of sample 650, the lower angle (20-35º) diffraction peaks corresponding to Al2Cu are not detected; however, the peak at 47.25º was detected. From these results it is evident that dominant phases synthesized in the alloyed layer are the Al2Cu and MoAl5 intermetallics.

Hardness

micro-hardness tester at an applied load of 100 g. Hardness measurements were made across the depth of the laser treated surface. Figure 9 shows the Vickers micro-hardness profiles of the samples. The alloyed zone extends up to 1000 μm into the substrate. The hardness of the alloyed zone is much higher (approx. 100-250 HV) than that of the Al substrate (approx. 25 HV). In all the alloyed samples, the hardness is always higher for samples treated at the higher energy density, regardless of the powder composition. The highest hardness was obtained for samples alloyed with 50%Cu+50%Mo at 40 J/mm2 (approx. 280 HV), 25%Cu+75% Mo at 40 J/mm2 (approx. 250 HV), and 75%Cu+25% Mo at 40 J/mm2 (approx. 200HV). The increase in hardness is attributed to the presence of the CuAl2 and MoAl5 intermetallic phases in the alloyed layer. The microstructures of these samples showed a much-refined θ-CuAl2 eutectic and a higher volume fraction of MoAl5 intermetallic phases; these factors are related to the increased hardness values obtained.

Conclusions

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Hardness measurements were carried out using a Vickers

Laser surface alloying of Al with Cu and Mo powders

Figure 9—Hardness profiles of the alloyed layers (sample numbers indicated in Table I)

Figure 8—XRD analysis of Al+25%Cu-75%Mo laser alloyed at energy densities 30 J/mm2 (sample 649) and 40 J/mm2 (sample 650

1. An Al-Cu-Mo composite was successfully synthesized by the laser melt injection method. The composite layer consisted of the CuMo reinforcements and Al-Cu and AlMo intermetallic phases 2. XRD analysis of the layers showed that the main phases are θ-CuAl2 and MoAl5. The Cu9Al4 phase was obtained with an increased Cu content in the mixture at higher laser scan speed 3. The Cu-Mo compounds were not formed in the alloyed layers, as shown by the XRD analysis 4. The microstructures of the processed zones are refined, cellular, and columnar according to the laser processing parameters and cooling rates 5. The composite coatings have much higher hardness than the Al substrate.

Acknowledgements The authors would like to thank the CSIR National Laser Centre for financial support, and Mr Lucas Mokwena for helping with the experiments 5.5

ROHATGI, P.K., ASTHANA, R., and DAS, S. 1986. Solidification, structure and properties of cast metal-ceramic particle composites, International Materials Reviews ,vol. 31. pp. 115–139. KOHARA, S. 1990. Fabrication of SiCp-Al composite materials. Materials and Manufacturing Processes, vol. 5, no. 1. pp. 51–62. DUBOURG, L., PELLETIER, H., VAISSIERE, D., HLAWKA, F., and CORNET, A. 2002. Mechanical characterisation of laser surface alloyed aluminium-copper systems. Wear, vol. 253. pp. 1077–1085. DUTTA MAJUMDAR, J. and MANNA, I. 2011. Laser material processing. International Materials Reviews, vol. 56, no. 5–6. pp. 341–388. Pityana, S. 2009. Hardfacing of aluminium by means of metal matrix composites produced by laser surface alloying. 5th International WLTConference on Lasers in Manufacturing, Munich, Germany, 15-18 June 2009. pp. 439–444. ARAVIND, M., YU, P., YAU, M.Y., and NG, D.H.L. 2004. Formation of Al2Cu and AlCu intermetallics in Al(Cu) alloy matrix composites by reaction sintering. Materials Science and Engineering A, vol. 380. pp. 384–393. CHONG, P.H , MAN, H.C., and YUE, T.M. 2001. Microstructure and wear properties of laser surface cladded Mo-WC MMC on AA6061 aluminium alloy. Surface and Coatings Technology, vol. 145. pp. 51–59.

References

POPOOLA, A.P.I. PITYANA, S. L., and POPOOLA, O.M. 2011. Laser deposition of (Cu+Mo) alloying reinforcements on AA1200 substrate for corrosion improvement. International Journal of Electrochemical Science, vol. 6. pp. 5038–5051.

WANG, S.W., LIN, Y.C., AND TSAI, Y.Y. 2003. The effects of various ceramic-metal on wear performance of clad layer. Journal of Materials Processing Technology, vol. 140. pp. 682–687.

QIU, Y.Y., ALMEIDA, A., and VILAR, R. 1998. Structure characterisation of a laserprocessed Al-Mo alloy. Journal of Materials Science, vol. 33. pp. 2639–2651. N

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http://dx.doi.org/10.17159/2411-9717/2015/v115n3a5 ISSN:2411-9717/2015/v115/n3/a5

Chemical wear analysis of a tap-hole on a SiMn production furnace by J.D. Steenkamp*§, P.C. Pistorius*†, and M. Tangstad‡

al., 2014). The paper presented here expands on the previous work by including masstransfer calculations to estimate the possible extent of wear by chemical reaction.

Synopsis In April 2013 a 48 MVA submerged arc furnace producing silicomanganese was excavated in South Africa. Since the high shell temperatures recorded in the tap-hole area resulted in the furnace being switched out for relining, the tap-hole area was excavated systematically. A refractory wear profile of the tap-hole area with affected hearth and sidewall refractory was obtained in elevation. The carbon ramming paste in front of, above, and below the tap-hole was worn, as was the SiC with which the tap-hole was built. A clay mushroom formed but was detached from the refractories. Thermodynamic and mass-transfer calculations were conducted to quantify the potential for wear by chemical reaction between refractory and slag and refractory and metal in the tap-hole area. It was found that chemical reaction between refractory and slag or metal could offer only a partial explanation for the wear observed; erosion is expected to contribute significantly to wear.

Background

Introduction Silicomanganese (SiMn) is an alloy used in steelmaking to provide both Si and Mn additions at low carbon contents. In South Africa, SiMn is produced in three-phase alternating current (AC) submerged arc furnaces (SAFs) by carbothermic reduction of manganese ores in the form of lump, sinter, and briquettes (Steenkamp and Basson, 2013). In a SAF the electrode tips are submerged in a porous charge mix, with electrical energy being liberated by microarcing to a slag-rich coke bed floating on top of a molten metal bath (Olsen and Tangstad, 2004; Matyas et al., 1993). Typical power ratings for SAFs producing SiMn are 15–40 MVA; such furnaces produce 80–220 t of metal per day (Olsen and Tangstad, 2004). The furnaces are circular, with an external diameter of 11.6 m and height of 6.2 m being typical of a 40 MVA furnace (Brun, 1982). The initial report on the tap-hole wear profile and thermodynamic calculations conducted to understand the potential for chemical reaction as wear mechanism in the tap-hole area was presented at the SAIMM Furnace Tapping Conference (Steenkamp et The Journal of The Southern African Institute of Mining and Metallurgy

* § † ‡ ©

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Keywords excavation, dig-out, post-mortem, submerged arc furnace, silicomanganese, refractory, tap-hole, thermodynamics, FACTSage, mass transfer

The SAF under investigation was of circular design with an open roof and outer diameter of 12 m. The furnace containment system consisted of a refractory lining installed in a steel shell. The refractory design from the hearth to the top of the sidewall is indicated in Figure 1. The compositions of the refractory materials of interest are presented in Table I. In the hearth, fireclay was cast onto the steel shell to level the floor. The fireclay is an aluminosilicate aggregate with alumina cement binder. Five layers of super-duty fireclay bricks were installed as back lining, with highgrade carbon ramming as working lining. In the lower sidewall, a single layer of super-duty fireclay brick was installed as back lining with high-grade carbon ramming as working lining. As safety lining, a low-grade carbon ramming was emplaced between the steel shell and the back lining. In the upper sidewall, the lining design was similar to the lower sidewall but with the super-duty fireclay brick layer forming both the working lining and the back lining, i.e. no high-grade carbon ramming was installed. The two single-level tap-holes were built with SiC bricks supported by super-duty fireclay bricks. The original lining was installed in April 2003. In September 2007 the refractory was partially demolished and rebuilt, including both tap-holes. Tap-hole A was partially repaired in March 2012 (the front two rows of

Chemical wear analysis of a tap-hole on a SiMn production furnace SiC carbide bricks were replaced), but no repairs were done on tap-hole B. Finally, the complete lining was demolished and rebuilt in April 2013. From the time of the partial reline in September 2007, 7520 taps were made through tap-hole A and 1880 through tap-hole B. During the final excavation, tap-hole B was studied in detail. Owing to the single-level tap-hole design, slag and metal were tapped simultaneously at three-hour intervals. Tapping duration varied between 30 and 45 minutes. The tap-hole was typically opened with a drill and closed with a mudgun pushing clay into the tap-hole – see Table I for the clay composition. In cases where problems were experienced with keeping the tap-hole open, oxygen lancing was applied. High steel shell temperatures (above 300°C and below 480°C), which were measured at the tap-hole area using

thermal imaging techniques (Figure 2), were the major factor leading to the switch-out of the furnace for a total reline. A typical tap consisted of 22 t of alloy and 17.6 t of slag. The tapping temperatures, as measured at the tap-hole, varied between 1420°C and 1520°C (however, see the note later regarding the likely difference between the measured tapping temperatures and the actual temperature inside the furnace). Slag and metal were sampled at each tap. The slag sample was taken with a metal rod in the launder and the metal sample in the metal ladle with a ‘lollipop–sample’ dipstick. Slag and metal compositions were determined by powdered X-ray fluorescence (XRF) analysis. The carbon content of metal samples was determined by LECO. For the purpose of thermodynamic and kinetic calculations, the chemical compositions of slag and metal were normalized per tap for the six-component slag system (MnO, SiO2, MgO, CaO, FeO, and Al2O3, typical total 97.1%) and four-component metal system (Mn, C, Si, and Fe, typical total 99.8%). The average and standard deviation of the normalized results calculated for a 4-month period (November 2012 to February 2013) are reported in Table II and Table III. To correct the slag composition for entrained metal, it was assumed that all FeO in the slag was associated with entrained metal droplets with average composition shown in Table III. The assumption was validated through SEM-EDS analysis of a slag sample obtained from Transalloys – see Appendix A. Mass balance calculations were conducted to correct the slag composition for FeO, SiO2, and MnO, as reported in Table II.

Figure 1—SiMn lining design (drawing to scale)

Table I

Composition (mass percentages) and thermal conductivity of refractory materials (as obtained from supplier data sheets, except where indicated differently) Material Fireclay brick Carbon ramming –

Raw materials

Thermal conductivity

–

1.2 W/mK at 1000°C

Carbon

11 W/mK at 1000°C

high-grade SiC brick Tap-hole clay

Composition (weight %) SiO2

Al2O3

Fe2O3

TiO2

CaO

MgO

Alks

53.6

42.0

1.5

1.6

0.15

0.3

0.85

Al2O3

Anthracite

Clay

Graphite

Resin

Tar

1–5

50–70

1–5

15–25

6–12

2–7

Silicon carbide

15-20 W/mK at 1200°C

SiC

Si3N4

Fe2O3

Al2O3

(nitride-bonded)

(Fickel, 2004)

23.4

0.3

Silica and alumina

1.3 W/mK at 1000°C

Al2O3

SiO2

TiO2

Fe2O3

0.5

0.8

(resin-bonded)

CaO 0.2

Figure 2—Tap-hole area of SiMn furnace with (a) thermal image and (b) low-quality photograph of section of steel shell on which thermal image was based. The maximum temperature of 480°C (indicated) was measured at the tap-hole itself

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Chemical wear analysis of a tap-hole on a SiMn production furnace Table II

Average and standard deviation of as-received slag analyses and slag composition corrected for metal inclusions (mass percentages)

Average (as-received) Average (normalized) Standard deviation Corrected

MnO

SiO2 MgO

CaO FeO Al2O3 Total

13.3 13.7 1.8 11.9

46.0 47.4 0.8 48.3

25.3 26.1 1.0 27.1

6.0 6.2 0.4 6.4

0.5 0.5 0.3 0.0

5.9 6.1 0.7 6.3

97.0 100.0 -

furnace was dug out from the side by excavating a circular sector of 120° between electrode 3 and electrode 1. The centreline of electrode 1 was positioned in the middle of the centrelines of tap-hole A and tap-hole B, which were positioned 60° apart. For the macro-scale investigation, photographs were taken of the refractory in situ, using a Canon EOS 30D camera installed on a tripod and triggered remotely. The camera settings for aperture and shutter speed were adjusted manually based on the lightmeter readings on the camera. Lighting was provided by free-standing floodlights, and no flash was used. The refractory thickness was measured with a tape measure and/or laser measurement device. The original design drawing was marked up with the measured wear profile (Figure 3).

Table III

Average (as-received) Average (normalized) Standard deviation

Total

Mn:Fe

66.2 66.3 0.5

1.8 1.8 0.2

17.0 17.1 0.8

14.8 14.8 0.6

99.8 100.0 -

4.6

Since the formation of SiC as a product of the reaction between slag or metal and carbon-based refractory material at tapping temperatures was demonstrated in laboratoryscale studies (Steenkamp et al., 2013; Mølnås, 2012) and the reported tapping temperatures of 1420 to 1520°C were significantly lower than the tapping temperatures reported for SiMn elsewhere (Olsen, Tangstad, and Lindstad, 2007), it was expected that SiC tap-blocks would not show any significant chemical wear when placed in contact with SiMn metal or slag (however, see the note later regarding the likely difference between the measured tapping temperatures and the actual temperature inside the furnace). Another aspect contributing to the expected reduced wear of the tap-hole was the use of reconstructive tap-hole clay (Table I). The purpose of reconstructive tap-hole clay is not only to plug the tap-hole at the end of the tap, but also to reconstruct the sidewall by forming a ‘mushroom’ of clay protecting the sidewall from slag and metal wear (Dash, 2009; Ko, Ho, and Kuo, 2008; Inada et al., 2009; Nelson and Hundermark, 2014). Evidence of a mushroom in the furnace was therefore expected at the time of the digout based on observations reported for ironmaking blast furnaces (Steenkamp et al., 2013; Mølnås, 2011; Olsen, Tangstad, and Lindstad, 2007). However, Nelson and Hundermark (2014) (based on a paper by Tsuchiya et al. (1998)) suggested that a tap-hole clay mushroom is not typically formed in ferroalloy production furnaces if the coke bed does not extend to the tap-hole. Oxygen lancing was frequently applied in opening the tap-hole or to keep it open, potentially contributing to increased wear of the tap-hole. The condition of the tap-hole area was therefore of keen interest to the team involved in the investigation.

Method The top 2 m of the burden below sill level was dug out from the top of the furnace by manual labour. Thereafter the The Journal of The Southern African Institute of Mining and Metallurgy

Results In Figure 3 the dimensions of the worn lining are superimposed onto the refractory design drawing presented in Figure 1. The dimensions of the red-line drawing were obtained on a vertical plane passing through the centre of the tap-hole. As can be seen in Figure 3 and the series of photographs in Figure 4 to Figure 9, wear of the tap-hole area was extensive. As indicated in Figure 3, more than 50% of the SiC brick was worn away, with most of the wear occurring at the hot face of the sidewall. Wear of the SiC brick was more extensive above the tap-hole than below. Furthermore, the carbon ramming above the SiC brick was worn all the way to the top of the carbon. In plan view (not indicated), the wear pattern was in the form of a channel 500 mm wide, with fairly straight sidewalls rather than funnel-shaped as is typical of the wear pattern in open bath furnaces. The SiC brick and the carbon ramming paste in the hearth in front of the tap-hole were worn away both below and above the taphole. Figure 4 shows a side view of the tap-hole region (compare Figure 3) with the high-grade carbon ramming and SiC tap-hole extension (c) removed on the left-hand side, exposing the super-duty fireclay brick installed as back lining

Figure 3—Refractory design drawing with red line indicating the refractory hot face as determined during excavation, indicating (a) the worn tap-hole filled with slag-and-coked-bed top layer and (mainly metal) bottom layer (refer Figure 6), and (b) the worn 500 mm wide channel partially filled with slag with coke bed (refer Figure 4). Drawing to scale VOLUME 115

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As-received metal composition (mass percentages)

Chemical wear analysis of a tap-hole on a SiMn production furnace

Figure 4—Side view of tap-hole region with (a) super-duty fireclay bricks, (b) high-grade carbon ramming, (c) SiC bricks, (d) slag with burden and/or coke bed material, (e) mixed material containing metal, and (f) clay mushroom. The tap-hole (Figure 8) is to the left of (e) and lower (d), with (A) indicating the worn tap-hole filled with a slag and coke bed top layer and (mainly metal bottom layer and (B) the worn channel 500 wide and partially filled with slag with coke bed

(a). On the right-hand side, the high-grade carbon ramming (b) was left intact. The 500 mm wide slot worn into the highgrade carbon ramming and SiC all the way vertically from the tap-hole to the top of the ramming crucible is clearly visible (d). The vertical slot was partially filled with a slag/burden layer (d) that extended into the upper part of the worn taphole. The lower part of the tap-hole was filled with mixed material containing metal (e). A clay mushroom, which formed when clay forced through the tap-hole spread radially when coming in contact with burden, was observed (f). Instead of the clay rebuilding the tap-hole by forming a new interface between the refractory and slag/metal, the slag and metal channelled around the clay mushroom before exiting the furnace through the worn tap-hole. Note that the mushroom was not in contact with the tap-hole, as would be expected of a reconstructive tap-hole clay (Dash, 2009; Ko, Ho, and Kuo, 2008; Inada et al., 2009; Nelson and Hundermark, 2014). The lack of attachment of the mushroom to the taphole broadly agrees with the suggestion by Nelson and Hundermark (2014) that ferroalloy furnaces generally do not develop a mushroom attached to the taphole. Figure 5 and Figure 6 show the tap-hole area in close-up, making it easier to distinguish the different zones, comprising super-duty fireclay brick back lining (a), highgrade carbon ramming crucible (b), SiC bricks used to build the tap-hole (c), slag with burden and/or coke bed material present either in the worn channel (vertical section or upper (d)) or in the worn tap-hole (horizontal section or lower (d)), mixed material containing metal (e), and tap-hole clay mushroom (f). Figure 7 and Figure 8 show the interior of the tap-hole, illustrating the extent to which the SiC brick in the tap-hole had worn away. Figure 9 shows the tap-hole in perspective, with operating personnel standing on the steel shell/fireclay castable of the furnace hearth. This photograph was taken at the same stage of furnace excavation as was the photograph in Figure 8.

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Figure 5—Closer view of tap-hole B showing (a) super-duty fireclay bricks, (b) high-grade carbon ramming, (c) SiC bricks, (d) slag with burden and/or coke bed material, (e) mixed material containing metal, and (f) clay mushroom

Figure 6—Closer view of zones in front of tap-hole B, showing (b) highgrade carbon ramming, (c) SiC bricks, (d) slag with burden and/or coke bed material, (e) mixed material containing metal, and (f) clay mushroom

Figure 7—Detail of tap-hole B with (c) SiC bricks, (d) slag with burden and/or coke bed material, (e) mixed material containing metal, and (f) clay mushroom infiltrated with slag

Discussion The three main observations made during the excavation were as follows. 1. Contrary to expectations based on laboratory test work, the SiC brick in the tap-hole itself wore The Journal of The Southern African Institute of Mining and Metallurgy

Chemical wear analysis of a tap-hole on a SiMn production furnace 3. A clay mushroom did form, but rather than being attached to the SiC brick reconstructing the tap-hole, it was detached from the SiC brick and metal and slag channelled around it.

Potential refractory wear mechanisms

Figure 8—Interior of tap-hole B with (c) SiC bricks, (d) slag with burden and/or coke bed material, and (e) mixed material containing metal

The refractory wear mechanisms reported for SAFs producing manganese ferroalloys are corrosion, densification, spalling, and erosion. Corrosion is caused by slag or metal components dissolving refractory components that they are not saturated with, or chemical reactions between refractory and slag, metal, or gas consuming the refractory materials (Hancock, 2006). Examples are alkali attack of carbon tamping paste (Brun, 1982), slag attack of carbon paste and tar dolomite brick (Brun, 1982), oxidation by water leakages (Tomala and Basista, 2007), and metal attack of carbon refractory (Tomala and Basista, 2007). Densification is caused by slag or metal penetrating pores and/or reacting with refractory (Hancock, 2006). Examples are alkali attack of alumina brick with subsequent volume increase (Brun, 1982) and metal penetrating open pores (Tomala and Basista, 2007). Spalling is caused by thermal stress across a single refractory body (Hancock, 2006), for example when hot face refractory material fractures and breaks away due to densification and/or thermal stress (Coetzee, Duncanson, and Sylven, 2010). Erosion is caused by slag, metal, and solid material abrading the refractory (Hancock, 2006). Of these mechanisms, corrosion, erosion, and spalling (which would be affected by densification) are all potentially applicable to the tap-hole area.

Potential for chemical reaction between slag or metal and refractory

extensively. From laboratory-scale experiments it was expected that the C-based refractory material would wear, but not the SiC-based refractory material, as it was found that SiC formed as a product of the reaction between SiMn slag and C-based refractory material at tapping temperatures (Steenkamp et al., 2013; Mølnås, 2011) 2. Above the tap-hole, not only did the SiC brick extension wear but so did the high-grade carbon ramming. Wear took place in a channel 500 mm wide and extended to the top of the carbon ramming. Again, wear of the SiC was not expected, but should the C-based refractory material have come into contact with slag at tapping temperatures, reaction between slag and carbon would have been possible (Steenkamp et al., 2013). The fact that the channel extended to the top of the C-based refractory material was unexpected. The channel may have been worn by gas formed during lancing or evolved from the clay as reported for Ni matte smelters (Thomson, 2014) and PGM smelters (Nelson and Hundermark, 2014). The Journal of The Southern African Institute of Mining and Metallurgy

In order to investigate the potential for chemical reaction (corrosion) between slag or metal and refractory as a mechanism contributing to the wear observed in the tap-hole area, thermodynamic calculations were conducted in FACTSage 6.4 (Bale et al., 2002). The Equilib model was used, and depending on the type of calculation the FToxid and/or FSstel and FACTPS databases were selected. Default gas, liquids, and solids were selected as pure species with duplicates suppressed, with the order of preference being the FToxid, FSstel, and then the FACTPS databases. As solution species, only liquid slag (SLAGA) and liquid metal (LIQU) were selected where applicable. In all calculations the temperature range was 1500–1700°C at 25°C intervals and the pressure 1 atmosphere, although the ambient pressure at the plant is typically 0.85 atmosphere (Anon., 2014). The temperature range was selected based on the following criteria: 1. The process temperature required (by reaction thermodynamics) for the production of SiMn with 17.0% Si in equilibrium with slag with an activity of 0.2 (typical of SiMn production) is calculated as 1600°C (Olsen, Tangstad, and Lindstad, 2007) 2. The actual temperature experienced by the hot face refractories would therefore have been 1600°C or more. Actual tap temperatures measured at the plant ranged between 1420°C and 1520°C. A difference between tapping temperature and process temperature of 50–100°C, caused by VOLUME 115

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Figure 9—Tap-hole B in perspective. The operating personnel, standing on the steel shell/fireclay castable of the furnace hearth (g), are sampling the metal layer at the interface between the high-grade carbon ramming (i) and burden (j). Also note the five layers of superduty fireclay bricks (h) in the hearth

Chemical wear analysis of a tap-hole on a SiMn production furnace heat losses during tapping, is typical of plant operations (Olsen, Tangstad, and Lindstad, 2007). Flow modelling predicted that significant cooling of metal occurs as the metal flows to the furnace bottom and the taphole (Steenkamp et al., 2014). To obtain an initial understanding of the system under investigation, the equilibrium phase distributions of both slag and the metal were calculated for the as-received and the corrected slag compositions in Table II and normalized metal composition in Table III. Initial conditions were not specified. For the slag calculations, 10 g of argon was added to enable the calculation to converge. The predicted equilibrium phase compositions, for both as-received slag not corrected for metal entrainment and slag corrected for metal entrainment, are presented in Figure 10. In both instances the slag would be fully liquid at the temperatures under investigation (1500–1700°C) with the calculated melting points at 1260°C and 1274°C respectively. As the composition of liquid slag phase was not changed by the presence of a second phase, the slag composition in Table II could therefore be used ‘as is’ in thermodynamic calculations to study the potential of chemical reactions between slag and refractory. The predicted equilibrium phase composition of the asreceived metal and chemical composition of the metal phase as a function of temperature are presented in Figure 11. At temperatures below 1625°C the metal is saturated in SiC, as seen by the precipitation of SiC as a separate phase and the lower Si and C contents of the liquid metal phase. As the temperature increases, the solubility of the SiC in the metal increases to the point (above 1625°C) where the metal becomes unsaturated in SiC. This is in agreement with the C solubility diagram for Si-Mn-Fe alloys presented in Figure 12 and constructed from thermodynamic calculations conducted in FACTSage 6.4 (Bale et al., 2002). Once the temperature has increased to such an extent that the metal is unsaturated in SiC, the metal will dissolve any SiC it comes into contact with, except for limitations posed by reaction kinetics (Einan, 2012). The possibility of reaction was assessed by calculating the equilibrium phase distribution of the reaction products for the reaction of equal masses of slag and refractory as a function of temperature. Equal masses of slag and refractory were assumed for convenience; in reality, the refractories are exposed to large volumes of process materials (slag, metal) that are continuously being replenished by fluid flow past the

hot face and due to new process material being continuously produced. This means that the effective ratio of process material to refractory material is usually very large, which may affect refractory consumption. Estimates of the actual quantity of slag participating in the reactions (based on mass transfer) are presented later. As the slag was fully liquid in the temperature range under investigation, the composition in Table II could be utilized in calculations. For the metal, the normalized composition in Table III was utilized. The fact that the metal was already saturated in SiC (and unsaturated in C) was taken into account when interpreting the results of the thermodynamic calculations. The refractory was assumed to consist of 100% C or 100% SiC.

Figure 10—Equilibrium phase composition of industrial slag based on Table IV. Solid lines: slag corrected for metal entrainment; dotted lines: uncorrected

Figure 12—Calculated carbon solubility in Mn-Si-Fe alloys with fixed Mn:Fe ratio of 4.5 at 1500–1700°C. The black cross indicates the composition of the alloy given in Table III

Figure 11—Predicted (a) phase composition of as-received metal and (b) chemical composition of liquid metal phase, as a function of temperature

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Chemical wear analysis of a tap-hole on a SiMn production furnace

Figure 13—Predicted phase composition of 100 g slag reacted with 100 g of (a) SiC and (b) C-based refractory material, as a function of temperature. Solid lines represent the borders for phase composition calculated from the as-received slag analysis, and dotted lines calculated from the corrected slag analysis

The predicted equilibrium phase compositions when slag reacts with SiC or C refractories are presented in Figure 13, and chemical compositions of the slag, metal, and gas phases in Figure 14. The difference in phase composition of slag not corrected for metal and slag corrected for metal was insignificant. The remainder of the discussion will focus on slag corrected for metal only. This means that the elements actively participating in slag/refractory interaction were considered as being Mn, Si, O, and C (Fe excluded). The reaction products for SiC-based refractory reacting with slag consist of metal and gas phases (Figure 13a). Metal formation is significant throughout the temperature range under investigation (1500°C to 1700°C). Metal formation increases with increasing temperature from 1575°C, which is the temperature at which gas formation also becomes significant. The formation of SiMn metal (Figure 14b) is associated with a decrease in the MnO content of the slag (Figure 14a), a decrease in SiC (Figure 13a), and formation of a CO-rich gas phase (Figure 14c). Wear of the SiC-based refractory was therefore due to the formation of a metal phase – through both the reduction of MnO and the subsequent dissolution of SiC into the metal phase formed – and formation of a CO-rich gas phase. The reaction products for C-based refractory reacting with slag are metal, gas, and SiC phases (Figure 13b). Metal formation commences at 1525°C and SiC formation at 1575°C, with gas formation being significant from 1550°C. The formation of SiMn (Figure 14b), metal, and SiC is associated with decreases in both MnO and SiO2 contents of the slag (Figure 14a), a decrease in C (Figure 13b), and formation of a CO-rich gas phase (Figure 14c). Wear of the Cbased refractory was therefore due to the formation of metal phase (through the reduction of MnO and SiO2 and subsequent dissolution of C into the metal phase formed), formation of a SiC-phase through reduction of SiO2, and formation of a CO-rich gas phase.

This is expected, as slag reactions involve both chemical reaction and dissolution as discussed above, whereas metal reaction involves only dissolution. Dissolution of SiC into metal (Figure 15b) occurs only once the metal becomes unsaturated in SiC (Figure 11a and Figure 12), whereas the metal is already unsaturated in C (Figure 12), with carbon potentially dissolving in metal throughout the temperature range (Figure 15a).

Refractory consumption

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Figure 14—Predicted chemical composition of (a) slag, (b) metal, and (c) gas phases that form when reacting 100 g slag with 100 g of SiC-based (open symbols) or C-based (filled symbols) refractory material as a function of temperature VOLUME 115

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The predicted refractory consumption is plotted as kilograms of refractory consumed (Wref ) per ton of slag or metal in Figure 15. The highest wear predicted was for carbon-based refractory reacting with slag at temperatures exceeding 1675°C, followed by SiC-based refractory reacting with slag.

Chemical wear analysis of a tap-hole on a SiMn production furnace

Figure 15—Refractory consumption when reacting 100 g metal or 100 g slag with 100 g of (a) C-based or (b) SiC-based refractory material, as a function of temperature

Table IV

Actual mass of C and SiC refractory worn as calculated from the wear profile in Figure 3 High-carbon ramming Assume vertical cuboid

SiC H

2.4 m

0.5 m

Assume vertical cuboid

0.5 m

0.9 m

0.7 m

Volume

1.08 m3

Volume

0.2 m3

Density

1.8 t/m3

Density

2.6 t/m3

Mass

1.9 t

Mass

0.4 t

When C-based refractory reacts with slag, if the SiC reaction product were to form an in situ refractory layer at the slag/refractory interface, the potential for refractory wear by chemical reaction would be reduced (Lee and Moore, 1998). However, experimental work has shown that SiC detaches from the refractory rather than forming an in situ protective layer (Steenkamp et al., 2013; Mølnås, 2011). To put the results in Figure 15 into perspective, the actual refractory consumption was calculated over the lifetime of tap-hole B. The results are reported in Table IV. The mass of refractory worn was calculated from the wear profile in Figure 3. The total amount of slag tapped through the taphole was calculated from production figures (see Table V). From Table IV and Table V the actual refractory consumption relative to the total amount of slag tapped through the tap-hole was calculated. The result obtained – 0.07 kg refractory per ton of slag – was far lower (by a factor of a thousand) than the predicted equilibrium consumption (around 70 kg refractory per ton of slag, depending on temperature) (see Figure 15). A likely explanation for the significant difference is that not all slag that was tapped through the tap-hole participated in chemical wear. According to the principles of fluid dynamics, velocity and diffusion boundary layers develop near the wall inside a circular pipe (tap-hole) due to the effects of viscosity. The boundary layers influence the heat and mass transfer in the pipe. In the case of slag/refractory interaction in the tap-hole, although equilibrium calculations indicate that SiO2 and MnO would tend to react with the refractory, only the SiO2 and MnO that diffuse through the

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boundary layer would be available to react with the refractory at the slag/refractory interface. The amount of slag participating in the reaction was estimated from the mass transfer coefficient for laminar flow inside a circular pipe, taking account of entrance effects (Asano, 2006) (see Table VI). The transition from laminar to turbulent flow takes place at a Reynolds number (Re) of around 2300, therefore the flow in the tap-hole was taken to be laminar for slag tapping (see Re in Table VI). In this calculation, it was assumed that no freeze layer of slag would form on the refractory. This appears to be justified, based on the low slag melting point and the absence of forced cooling of the tap-block. The rate at which slag is transported across the boundary layer to react with the interior surface of the tap-hole is given by the product of the mass transfer coefficient (kC), the inner

Table V

Total amount of slag tapped through the tap-hole (estimated from production figures) Symbol R A S n Wc{à@

Description Slag/alloy ratio Size of a tap: Alloy Size of a tap: Slag Number of taps Total amount of slag

Equation

R×A n×S

Value 0.8 22 17.6 1880 33088

Unit

ton ton ton

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Chemical wear analysis of a tap-hole on a SiMn production furnace Table VI

Calculation of mass transfer coefficient for silica for laminar flow inside a circular pipe Symbol

Description

Equation

Value

Unit

References

t m

Duration of tap Size of tap

30 17.6

min tons

Table VII

Slag mass flow rate

35.2

t/h

Slag density

2774

kg/m3

Steenkamp et al., 2013)

Volumetric slag flow rate

0.003525

m3/s

M × 60 t -

m × 1000 ρ × 3600

Inner diameter of the pipe

0.1

Figure 17

Average velocity over the cross-section of the pipe

v· × DT2 π 4

0.448791

m/s

(Asano, 2006)

Slag viscosity

0.74

kg/ms

Steenkamp et al., 2013)

Reynolds number

ρDTUm μ

168

(Asano, 2006)

Effective binary diffusivity of silica in slag

10-11

m²/s

(Liang, 1994)

Schmidt number

D μ/ρ

2.67 x 107

(Asano, 2006)

Length of the pipe

Figure 17

Graetz number for mass transfer

π × DT × Re × Sc 4 L

3.52 x 108

(Asano, 2006)

Sherwood number

1.65× Gz1/3

1166

(Asano, 2006)

kå

Mass transfer coefficient of silica

Sh × D DT

1.2 x 10-7

m/s

(Asano, 2006)

area of the tap-hole, and the slag density; the proportion of slag reacting is then given by Equation [1]. This calculation indicates that the mass of slag reacting is approximately 0.01 kg per ton of slag tapped. Based on the estimate of 70 kg of refractory consumed per ton of slag tapped, the predicted refractory wear due to chemical reaction is 0.0007 kg per ton of slag tapped, two orders of magnitude smaller than the observed wear. The conclusion is thus that mass transfer effects in the tap-hole significantly limit the extent of reaction. It should be noted that wear by metal was not considered in this calculation; the mass transfer coefficient for metal (with lower viscosity and higher diffusivity) would be much higher than for slag. [1]

Conclusions One of the reasons for the excavation and reline of the 48 MV SiMn furnace was the high shell temperatures (300–480°C) in the vicinity of the tap-hole. Prior to the excavation, SiC brick used as tap-hole refractory (which should not react with slag) and reconstructive tap-hole clay used to form a mushroom attached to the refractory were expected to protect the tap-hole from wear. The furnace excavation revealed two areas of high refractory wear – the tap-hole area and the furnace hearth. It was found that the SiC brick in the tap-hole itself wore extensively. Above the tap-hole, not only did the SiC brick extension wear but also the high-grade carbon ramming, with wear taking place unexpectedly in a channel 500 mm wide and extending vertically upwards to the top of the The Journal of The Southern African Institute of Mining and Metallurgy

carbon ramming. A clay mushroom did form, but rather than being attached to the SiC brick reconstructing the tap-hole it was detached from the SiC brick, with metal and slag channelling around it. Thermodynamic calculations predicted wear of both Cand SiC-based refractory through chemical reaction with slag and dissolution in metal. The SiC-based refractory wore when metal and CO-rich gas phases formed through the reduction of MnO in the slag and by the subsequent dissolution of SiC to form a SiMn alloy saturated in C and SiC. The C-based refractory wore when metal, SiC, and CO-rich gas phases formed through the reduction of MnO and SiO2 in the slag and by the subsequent dissolution of SiC and C to form a SiMn alloy saturated in C and SiC. The metal tapped from the furnace was typically saturated in SiC but not in C, therefore C-based refractory would dissolve in the metal and the SiCbased refractory dissolve at temperatures where the metal becomes unsaturated in SiC. The potential for chemical wear was therefore highest for C-based refractory material. Furthermore, mass transfer calculations indicated that not all the slag tapped from the furnace was available for participation in chemical reactions responsible for wear. Comparison with estimated wear rates indicates that slag mass transfer was too slow to account for the observed wear. Although chemical reaction between slag and refractory is a potential mechanism responsible for refractory wear in the tap-hole, it appears not to be the only wear mechanism. Further work to investigate flow conditions in the taphole region, and their possible effects on wear, would be useful, as would investigations into the effect of lancing and tap-hole clay studies. VOLUME 115

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Chemical wear analysis of a tap-hole on a SiMn production furnace Acknowledgements The Management of Transalloys (Pty) Ltd for including me on the team excavating the furnace. Johan Gous (Transalloys) for discussions and information on operational aspects of the furnace. Johan Zietsman (University of Pretoria) for discussions on FACTSage modelling software and constructive questioning of my interpretation of the results. The Management of the Centre for Pyrometallurgy, University of Pretoria, for utilization of the FACTSage software. The PhD research project sponsor (who has chosen to remain anonymous) for providing guidance on the research topic in general and much-needed industry funding of the PhD project, of which the work presented here was part of. The National Research Foundation of South Africa (Grant TP2011070800005) for leveraging the funding provided by the project sponsor.

References ANONYMOUS. 2014. Real time wind and weather report eMalahleni (9 January 2014 to 15 January 2014). www.windfinder.com [Accessed 16 January 2014]. ASANO, K. 2006. Heat and mass transfer in a laminar flow inside a circular pipe. Mass Transfer: From Fundamentals to Modern Industrial Applications. Wiley-VCH, Weinheim. pp. 89–100. BALE, C., CHARTRAND, P., DEGTEROV, S., and ERIKSSON, G. 2002. FactSage thermochemical software and databases. Calphad, vol. 26. pp. 189–228. BRUN, H. 1982. Development of refractory linings for electric reduction furnaces producing Mn alloys at Elkem A/S-PEA Plant, Porsgrunn, Norway. Journal of the Institute of Refractories Engineers, Spring. pp. 12–23. COETZEE, C., DUNCANSON, P.L., and SYLVEN, P. 2010. Campaign extensions for ferroalloy furnaces with improved tap hole repair system. Infacon XII: Sustainable Future, Helsinki, Finland, 6-9 June 2010. pp. 857–866. DASH, S.R. 2009. Development of improved tap hole clay for blast furnace tap hole. National Institute of Technology, Rourkela, India EINAN, J. 2012. Formation of silicon carbide and graphite in the silicomanganese process. Norwegian University of Science and Technology, Trondheim, Norway. HANCOCK, J.D. 2006. Practical Refractories. Cannon & Hancock, Vereeniging, South Africa.

OLSEN, S.E., TANGSTAD, M., and LINDSTAD, T. 2007. Production of Manganese Ferroalloys. Tapir Academic Press, Trondheim, Norway.. STEENKAMP, J.D. and BASSON, J. 2013. The manganese ferroalloys industry in southern Africa. Journal of the Southern African Institute of Mining and Metallurgy, vol. 113. pp. 667–676. STEENKAMP, J.D., GOUS, J.P., PISTORIUS, P.C., TANGSTAD, M., and ZIETSMAN, J.H. 2014. Wear analysis of a taphole from a SiMn production furnace. Furnace Tapping Conference 2014, Muldersdrift, Gauteng, South Africa, 27–28 May 2014. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 51–64. STEENKAMP, J.D., TANGSTAD, M., PISTORIUS, P.C., MØLNÅS, H., and MULLER, J. 2013. Corrosion of taphole carbon refractory by CaO-MnO-SiO2-Al2O3-MgO slag from a SiMn production furnace. INFACON XIII, Almaty, Kazakhstan, 9-12 June 2013. pp. 669–676. THOMSON, L. 2014. Monitoring, repair and safety practices for electric furnace matte tapping. Furnace Tapping Conference 2014, Muldersdrift, Gauteng, South Africa, 27–28 May 2014. The Southern African Institute of Mining and Metallurgy, pp. 87–96. TOMALA, J. and BASISTA, S. 2007. Micropore carbon furnace lining. Infacon XI: Innovation in Ferroalloy Industry, New Delhi, India, 18-21 February 2007. pp. 722–727. TSUCHIYA, N., FUKUTAKE, T., YAMAUCHI, Y., and MATSUMOTO, T. 1998. In-furnace conditions as prerequisites for proper use and design of mud to control blast furnace taphole length. ISIJ International, vol. 38, no. 2. pp. 116–125.

Appendix A. SEM-EDS analysis of industrial slag sample To verify the assumption that no FeO was present in the slag, an industrial slag sample (supplied by Transalloys) was crushed and milled. A polished section was prepared and sputter-coated with gold. The number of phases present in the slag samples was determined by FEGSEM (ZEISS LEO 1525 FEGSEM based at NMISA on the CSIR campus in Pretoria). The compositions of the phases were determined by EDS (Oxford INCA Energy System) at 15 kV (point analyses). Five different point analyses were conducted per phase. Four different phases were identified – see Figure 16. Three were slag phases (a-c) and one metal phase (d). None of the three slag phases contained Fe. The only phase that contained Fe was the metal phase (d). N

INADA, T., KASAI, A., NAKANO, K., KOMATSU, S., and OGAWA, A. 2009. Dissection investigation of blast furnace hearth—Kokura No. 2 blast furnace (2nd campaign). ISIJ International, vol. 49, no. 4. pp. 470–478. KO, Y., HO, C., AND KUO, H. 2008. The thermal behavior analysis in tap-hole area. China Steel Technical Report no. 21. pp. 13–20, LEE, W.E. and MOORE, R.E. 1998. Evolution of in situ refractories in the 20th century. Journal of the American Ceramic Society, vol. 81, no. 6. pp. 1385–1410. MATYAS, A.G., FRANCKI, R.C., DONALDSON, K.M., and WASMUND, B. 1993. Application of new technology in the design of high-power electric smelting furnaces. CIM Bulletin, vol. 86, no. 972. pp. 92–99. MØLNÅS, H. 2011. Compatibility study of carbon-based refractory materials utilized in silicomanganese production furnaces. Norwegian University of Science and Technology. NELSON, L.R. and HUNDERMARK, R. 2014. ‘The tap-hole’ – key to furnace performance. Furnace Tapping Conference 2014, Muldersdrift, Gauteng, South Africa, 27–28 May 2014. pp. 1–32. OLSEN, S.E. and TANGSTAD, M. 2004. Silicomanganese production – process understanding. INFACON X: Transformation through Technology, Cape Town, South Africa, 1–4 February 2004. pp. 231–238.

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Figure 16—SEM BSE image of industrial slag (scale bar 2 µm) with three different slag phases (a, b and c) and metal phase (d) indicated The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n3a6 ISSN:2411-9717/2015/v115/n3/a6

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines by S.R. Dindarloo*, M. Osanloo†, and S. Frimpong*

Material handling in open pit mining accounts for about 50% of production costs. The selection and deployment of efficient, safe, and economic loading and haulage systems is thus critical to the production process. The problems of truck and shovel selection and sizing include determination of the optimal number and capacities of haulage and loading units, as well as their allocation and operational strategies. Critical survey and analysis of the literature has shown that deterministic, stochastic, and experimental approaches to these problems result in considerably different outputs. This paper presents a comprehensive simulation framework for the problem of truck and shovel selection and sizing based on the random processes underlying the network-continuous-discrete event nature of the mining operation. The framework builds on previous research in this field and attempts to address limitations of available methodologies in the form of a comprehensive algorithm. To test the validity of the framework a large open pit mine was evaluated. The stochastic processes governing the uncertainties underlying the material loading and haulage input variables were defined and built into the stochastic model. Discrete event simulation was used to simulate the stochastic model. The proposed model resulted in several modifications to the case study. Keywords truck and shovel operation, stochastic simulation framework, equipment selection and sizing.

Introduction There are three different mechanisms of discrete, continuous, and hybrid material transportation in open mines, including (but not limited to) (i) truck and shovel system (discrete), (ii) slurry piping (continuous), and (iii) in-pit crusher and conveyor belts (hybrid). Although each mechanism has its own advantages, the truck and shovel system is the dominant method of material loading and handling in open pit mines, owing to its high production rate, excellent flexibility, relatively low operating and capital costs, and good maintainability. General objectives of optimal equipment selection include (i) meeting the long- and short-term requirements of production rates, (ii) human and equipment safety, (iii) environmental protection, and (iv) economic operations (Figure 1). The two most important decision factors regarding selection of a truck and shovel system are the equipment geometry and size. The Journal of The Southern African Institute of Mining and Metallurgy

* Department of Mining and Nuclear Engineering, Missouri University of Science & Technology, Rolla, MO. † Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jul. 2014; revised paper received Feb. 2015.

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Synopsis

Geometry (equipment width, weight, turning radius, swing angle, etc.) is controlled mostly by mine design and layout, as well as the operational constraints. After selection of a favorable geometry based on the constraints, the next step is to select the equipment manufacturer. Once the geometry and manufacturer are selected, the next step is to decide on the models (bucket size) and required numbers of each unit. Because of the undeniable effect of a proper truck-shovel system selection and sizing plan on open pit mines economics, many researchers have tried to study this issue using different techniques such as linear programming (Edwards, Malekzadeh, and Yisa, 2001), analytical hierarchy process (Ayağ, 2007), nonlinear programming (Søgaard and Sørensen, 2004), genetic algorithms (Aghajani, Osanloo, and Akbarpour, 2007; Marzouk and Moselhi, 2003), mixed integer programming (Camarena, Gracia, and Cabrera Sixto, 2004), machine repair modelling (Krause and Musingwini, 2007), queuing theory (Komljenovic, Paraszczak, and Fytas, 2004), and conventional spreadsheet calculations based on experience, engineering judgment, and manufacturers’ catalogues (Burt et al., 2005). Due to the large number of parameters that affect the system performance and the stochastic nature of the input variables, developing a deterministic mathematical optimization solution for the problem is extremely difficult, if not impossible (Haldar and Mahaderan, 2000).

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines

Figure 1—Essentials of equipment selection

A truck and shovel operation is a set of discrete-event activities, i.e. loading, hauling, dumping, and returning, which all occur in a stochastic manner (Figure 2). Discreteevent system simulation (DES) is a modelling method for such time-discrete and probabilistic phenomena (Schriber, 1992). Other abovementioned techniques have different limitations in addressing this problem comprehensively and accurately (Schriber, 1991). These limitations include deterministic pre-assumptions and/or not considering the real-world system specifications through derivation and application of relevant time-frequency distributions for the different operations involved. Most of these techniques therefore do not lead to robust models (Burt et al., 2005). However, DES has been employed by different researchers in mining engineering through available software and languages such as GPSS, SIMAN-ARENA, and SLAM (Baffi and Ataeepur, 1996; Runciman, Vagenas, and Newson, 1996; Awuah-Offei, Temeng, and Al-Hassan, 2003; Ross et al., 2010; Sturgul, and Thurgood, 1993). Most of the studies to date have endeavoured to evaluate some what-if scenarios in order to understand the possible effects of changing different input variables on the overall economics of current operating mines. For instance, (Stout et al., 2013) used Arena to simulate a truck and shovel operation. Very good background reviews of the application of this technique in the mining industry can be found in Sturgul, (1995, 1999) and Hollocks, (2006). Previous studies (Burt et al., 2005) have shown that different approaches, including deterministic, stochastic, and experimental methodologies, result in considerable differences in outputs. These techniques lead to different solutions regardless of the quality of the technique/software itself or the knowledge of the modelling team. Hence, the first step is to develop a comprehensive simulation framework for the problem of truck and shovel selection, sizing, and dispatching in open pit mines that obtains nearly the same optimal results for the same input variables, regardless of the technique employed (Burt, and Caccetta, 2014). In this study, a simulation technique was selected to solve the problem due to: i) possibility of incorporating uncertainties in different governing activities of the system, ii) extensive background of application of the technique in previous research and real world practices, iii) dynamic nature of the technique, which makes it applicable during the entire life of mine, and iv) relatively wide range of available software and languages.

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This paper introduces a methodology and sensitivity analysis procedure for mine loading and haulage system selection and sizing. In addition, the capability of the DES technique in bulk material handling simulation is demonstrated through the application of the GPSS/H simulation language. The proposed framework was validated and tested in a large open pit mine. All steps in the proposed framework were followed attentively to ensure its effectiveness. However, due to space limitation, only the most important main components are discussed here, i.e. problem definition, data acquisition, statistical analysis, simulation language (technique) selection, model construction, model verification and validation, and sensitivity analysis. More information about the simulation history and GPSS/H background is given in Schriber (1992), Hollocks (2006), Nance (1995), Pidd and Carvalho (2006), and Robinson, (2005). The paper is organized as follows: ® Proposed simulation frameworks for both new and existing systems ® Introduction of the case study ® Model building, verification, and validation ® Flow chart for performing sensitivity analysis ® Results, with a discussion of the optimal results ® Concluding remarks. Several important steps of simulation of the case study are presented in more details in the Appendix.

Simulation framework The lack of a comprehensive simulation framework in this field has resulted in considerably different solutions to the problem of truck and shovel system selection and sizing. Major source of these confusing differences include, but are not limited to: ® Different simulation approaches ® Different data requirements (quantity, quality, and statistical methodology) ® Insufficient technical communication during all phases of the project ® Insufficient determination of the objectives, resources, and constraints. This study proposes a truck and shovel simulation framework for minimizing the errors due to erroneous or inaccurate assumptions and procedures, and provides a stepby-step simulation guideline. The algorithm attempts to render a framework for truck and shovel operation simulation. In the construction of the simulation framework, different blocks (Figure 2) were obtained from most of the

Figure 2—Schematic of truck and shovel operation The Journal of The Southern African Institute of Mining and Metallurgy

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines available journal articles. The most important findings of the previous studies were selected and incorporated to achieve an efficient simulation strategy. The first step in developing this framework, with the goals of completeness, comprehensiveness, and robustness, was to identify the major components of a general simulation modelling practice, regardless of the area of application. A general simulation framework is illustrated in Figure 3. This primary platform was set to serve as the structure of the framework and consequently was customized through the introduction of open pit mining specifications. These specified characteristics were derived from published articles in the field of mining operations simulation and modelling and were incorporated in the base structure. The base framework was composed of the following components: ® Problem definition, objectives, resources, and limitations: ® Data acquisition and statistical processing ® Model construction ® Model modification, verification, and validation ® Sensitivity analysis and decision-making strategies. It should be noted that there are several pitfalls in a general simulation practice (Maria, 1997) as follows: unclear objective, invalid model, simulation model too complex or too simple, erroneous assumptions, undocumented assumptions, and using the wrong input probability distribution. The above pitfalls were incorporated in the proposed simulation framework for the truck and shovel selection and sizing problem. The secondary mine-specific characteristics that contribute to mining operational performance include:

(1) Incorporation of the mining-environmental induced constraints (2) Different traffic-dispatching scenarios (3) Different loading methods (4) Selection of hybrid or uniform loading/haulage fleets. The framework is divided in two categories, for new and existing systems (Figures 4 and 5 respectively). Since in a new mine there is no operational data available, the simulation procedure needs extra considerations. These considerations are illustrated in the flow chart of Figure 4. In addition, a sensitivity analysis algorithm is presented later (Figure 12) that follows the simulation framework to evaluate different scenarios in mine truck and shovel system selection and sizing. The main advantage of this simulation framework in comparison with other research is its comprehensive addressing of the problem of truck and shovel selection. All other available practices try to find solutions to specific parts of the problem, mainly in the form of what-if analysis. For instance, what would be the effect of adding one extra truck to the haulage fleet? Moreover, the framework is capable of addressing both a new and an existing open pit mine operation. This framework can add to the strength of simulation techniques in solving the problem compared to other abovementioned methods, which address the problem only partially. Although this framework was validated in a large surface mine in this study, for other projects some modifications should be incorporated accordingly. For instance, production planning strategies in a mine with restricted processing plant requirements or ore grade limits dictate more frequent

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Figure 3—A general simulation flow chart (Banks, 2010)

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines

Figure 4—The proposed simulation framework for new mines

Figure 5b—The proposed simulation framework for existing mines; Phase II: Execution

Figure 5a—The proposed simulation framework for existing mines; Phase I: Preparation and information

relocations of working faces, compared with a mine with more stable and predictable ore grade fluctuations. These differences introduce frequent changes in haulage distances and, hence, to the simulation approach at hand. Another example is the difference between a small surface mine with more short-term concentrated production plans and a large mine with more strategic and long-term plans. These types of specifications require more or less consideration of some blocks of the framework than others, accordingly (Figure 5).

Case study Golegohar iron ore mine is located in southern Iran, 50 km from Sirjan, in the southwest of Kerman Province (latitude 29°7′N and longitude 55°19′E, Figure 6). This iron complex includes six known ore reserves and is one of the largest producers and exporters of iron concentrate in the country. It has a measured and indicated reserve of over 1 100 Mt of ore (Golegohar Iron Ore Complex, 2006). In Golegohar, over 10 Mt of iron concentrate is produced annually, through

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Figure 5c—The proposed simulation framework for existing mines; Phase III: Evaluation and decision

crushing, dry and wet grinding, and low-intensity magnetic separation (Figure 7). To test the simulation framework, the operation of the current haulage system at Golegohar was investigated and necessary data collected. After statistical analysis of the raw data and deriving probabilistic distributions for each data-set The Journal of The Southern African Institute of Mining and Metallurgy

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines

Figure 6—Location of Golegohar iron ore mine

production rate, and the main goal to accomplish the job with a minimum amount of equipment. Different scenarios were tested to find the highest equipment utilization and minimum idle and waiting time in queues. A dispatching system was introduced with the main objective of minimizing shovel idle time and the number of trucks in queues. The most important data acquired in the observation phase included mine production plans and layouts, current fleet geometry and parameters, and time data of the real system. Details of the applied methodology are illustrated in Figure 5.

Observation of the current system Direct observation of the loading and haulage operations over 150 days, during different shifts and hours, resulted in recognition of the following problems in the current system:

(Carlsberg, 2011), a DES model was coded in GPSS/H (student version). In the next step, the proposed model was verified and then validated by comparing it with the real mine data by means of statistical tests (e.g. chi square) (Zimmermann, 2008). A series of sensitivity analyses were performed for the purpose of establishing the optimum number of cable shovels and dump trucks required to meet the production targets with the maximum possible system productivity. The simulation model was run with different combinations of the truck and shovel system numbers in a matrix pattern in order to identify the most appropriate system. The major constraint was set to achieve the annual The Journal of The Southern African Institute of Mining and Metallurgy

® Truck loading method was single-sided ® On many occasions, only two out of the four cable shovels were operating. The other two were not used, either because of frequent mechanical failures or lack of proper working faces ® Shovels idle times were too long (mostly over 6 minutes). On some occasions, three or more trucks were arriving together (trucks queue) ® Almost always, there was a long queue before the primary crusher. The average queue length was four trucks (in random observations) and the average waiting time for each truck was 11.5 minutes ® There was no haulage fleet dispatching system in the mine ® The main bottleneck in the haulage fleet flow was at the primary crusher. VOLUME 115

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Figure 7—Working area showing main ramps, roads and dumps (source: Google Earth, 2014)

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines Model construction After obtaining and analysing all necessary data, the next step was to construct a simulation model by programming in GPSS/H (student version). GPSS/H (General Purpose Simulation System) is both a computer language and a computer program. It was designed for studying systems represented by a series of discrete events. GPSS/H is a lowlevel, nonprocedural language. GPSS/H was selected as the simulation language as part of this study, for the following reasons: (i) It is multivendor, so it is continually being upgraded (ii) It is widely available (iii) It is written in machine language and, therefore, is inherently very fast (iv) It can solve a wide variety of problems rapidly and accurately (v) It has proved to be extremely versatile for modelling mining operations. These include both surface and underground operations, as well as material flow through a smelter, mill, and refinery. It is easily coupled with PROOF for making animations (Sturgul, 2000). A block diagram of the case study is illustrated in Figure 8. It should be noted that, due to the application of probability distribution and random numbers in a queue system simulation (Figure 8), and for error reduction purposes, the final model, which consisted of 115 GPSS blocks, was executed with different random data-sets.

Model animation and validation Some issues can be investigated easily in an animation that would be very hard to catch in the simulation model – like collisions or subtle logic glitches. The ability to see a model in action makes animation a great verification tool for the model

builder (Ståhl et al., 2011; Wolverine Software, 2013; University of Nevada, 2013). An animation model of the case study was executed by importing the GPSS/H model outputs to Proof Animation software. Observation of the animation model for different durations demonstrated that the proposed model logic had been achieved. Model validation, as the most important phase of a DES exercise, was performed through comparison of the model outputs with the real system’s data by designing statistical tests, e.g. chi-square and Kolmogorov-Smirnov at 5% significance level (Ross, 2006; Zimmermann, 2008). For this purpose, a new set of actual data, separate from the data used in the model, was collected. Three samples of the comparison results are illustrated in Figures 9–11, which were validated through chi-square method at the 5% significance level.

Further processing A flow chart of the sensitivity analysis is presented in Figure 12. To evaluate the effectiveness of employing an appropriate traffic-dispatching strategy, the current model was modified to take this issue into account. The main goal of the dispatching algorithm was set to assign the incoming trucks first to the idlest shovels. However, many other whatif type questions may be answered by this model with minimum cost, safety issues, and disturbance to the current operation routines e.g. the effect of operators’ skills, changes in road grades, the possibility of increasing the primary crusher’s capacity, purchasing new trucks to replace some older ones with low mechanical availabilities (Burt et al., 2011), changing the current truck sizes (Bozorgebrahimi, Hall, and Morin, 2005), employing a hybrid haulage fleet, different dispatching strategies (Alarie and Gamache, 2002), feasibility of changing the current system to a conveyor belt system as the depth of the mine increases (Mcnearny and Nie, 2000).

Figure 8—Block diagram of the case study operation flow

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Figure 9—Production comparison

Figure 10—Shovel utilization

Figure 11—Number of idle trucks (queue length)

Discussion of the simulation results

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To demonstrate the capabilities of simulation in what-if analysis, a sample of the many analyses conducted in this study is discussed below: Figure 13 illustrates the effect of the haulage fleet size on the number of production cycles per shift when only two shovels are operating. Increasing the number of dump trucks results in an increase of production rate per shift up to an optimum point. After that, because of the haulage fleet oversize and limited number of shovels (only two in this case), much time will be wasted in different truck queues at shovels. This results in no further increase in production. After this point the extra dump trucks in the system will be

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines shift. Obviously, operating four loaders is not justified. At this point, three cable shovels will be the best choice for the mine’s production target. The effect of the size of both the haulage and the loading fleet on effective shovel working hours is illustrated in Figure 15. Figure 16 shows the effect of application of a traffic dispatching system on the mine’s operation. Regardless of the number of trucks, a proper dispatching strategy increases the productivity of the system.

Summary of the optimal results Figure 13—Effect of number of trucks on production (per simulated shift) for two cable shovels

Table I shows that the modifications suggested by the simulation result in a 10% increase in production. Furthermore, using one less cable shovel contributes to increasing the mine profitability by introducing lower operational costs (assuming that the operational cost of one large shovel outweighs the associated costs of operating two additional trucks).

Conclusions

Figure14—Effect of both the number of trucks and cable shovels on ore production (per simulated shift)

Figure 15—Effect of both the number of trucks and cable shovels on shovels working time % (per simulated shift)

put into queues, thus making no contribution to production (Sturgul, 1995). Thus, the maximum production rate with two shovels is achieved with 22 dump trucks. This rate is 9.5 M t/a, which does not meet the minimum production requirements of the mine. At least three shovels are needed. The effect of the number of shovels on production rate is demonstrated in Figure 14. The simulation outputs show that increasing the number of shovels will result in more production with an equal number of trucks. For this range of truck numbers, operation with three and four shovels results in almost the same production rates. This is due to the fact that at least one of the shovels would be idle for most of a

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The truck and shovel system is the dominant method of material loading and haulage in open pit mining. Proper selection and sizing of the equipment has considerable effects on a mine’s productivity and economics. A critical survey and analysis of the literature showed that deterministic, stochastic, and experimental methodologies for optimizing truck and shovel systems result in considerably different outputs. Thus, designing a comprehensive modelling framework is of high importance in system selection and sizing for mining operations. In addressing this issue, a stochastic simulation framework for truck and shovel system selection and sizing, for both new and existing open pit mines, was proposed. As part of the study, a proper simulation technique (discrete-event system simulation) and language (GPSS/H) were employed. Simulations were validated through real operations at a large open pit mine. The proposed framework is a useful guideline and should be applied accordingly based on the specific characteristics of the particular loading and haulage operation. Consideration of all the effective parameters and their interactions with the system, which are elaborated in the proposed framework, should be the top priority of a mine simulation team. Application of the proposed methodology resulted in considerable improvements in loading and haulage operations at

Figure 16—Regular vs. Dispatching (3-month plan) The Journal of The Southern African Institute of Mining and Metallurgy

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines Table I

Comparison of results – current system vs. simulation recommended

No. of trucks No. of shovels Traffic dispatching strategy Loading method Ore production (kt/a) Average target rate (t/a) Variance from the mine target (%)

Current system

Recommendations

18a 4 electric cable shovels (7.6 m3) No Single 9.130c (std. dev. = 143) 10 100 ± 200 -9.6 %

20b 3 electric cable shovels (7.6 m3) Yes Double 10.140d/ 72 (std. dev. = 143) 10 100 ± 200 + 0.4 %

aHaulage

fleet of 18 mechanically available dump trucks with nominal capacity of 105 t (actual 83-87 t) fleet of 20 dump trucks with nominal capacity of 105 t (actual 83-87 t) cMean production rate in the past five years. dMean value of the normal frequency distribution obtained from 2000 iterations. bHaulage

a large open pit mine, with production rate increasing by about 10%.

linear programming machinery selection model for multifarm systems. Biosystems Engineering, vol. 87, no. 2. pp. 145–154. CARLBERG, C. 2011. Statistical analysis; Microsoft Excel 2010. Indianapolis,IN.

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system of Gol-e-Gohar iron ore mine of Iran by genetic algorithm. Australasian Institute of Mining and Metallurgy Publication Series. Melbourne. pp. 21–215.

EDWARDS, D.J., MALEKZADEH, H., and YISA, S.B. 2001. A linear programming decision tool for selecting the optimum excavator. Structural Survey, vol. 19, no. 2. pp. 113–120.

ALARIE, S. and GAMACHE, M. 2002. Overview of solution strategies used in truck dispatching systems for open pit mines. International Journal of Surface

GOLEGOHAR IRON ORE COMPLEX. 2006. Golegohar production reports.

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HALDAR, A. and MAHADERAN, S. 2000. Probability, Reliability and Statistical

AWUAH-OFFEI, K., TEMENG, V.A., and AL-HASSAN, S. 2003. Predicting equipment requirements using SIMAN, a case study. Mining Technology, vol. 112. pp. A180–A184. AYAĞ, Z.Z. 2007. A hybrid approach to machine-tool selection through AHP and simulation. International Journal of Production Research, vol. 45, no. 9. pp. 2029–2050. BAFFI, E.Y. and ATAEEPUR, M. 1996. Simulation of a truck- shovel system using

Methods in Engineering Design. John Wiley & Sons, New York. pp. 304. HOLLOCKS, B.W. 2006. Forty years of discrete-event simulation: a personal reflection. Journal of the Operational Research Society, vol. 57, no. 12. pp. 1383–1399. KOMLJENOVIC, D., PARASZCZAK, J., and FYTAS, K. 2004. Optimization of shoveltruck systems using the queuing theory. CIM Bulletin, vol. 97. p. 76. KRAUSE, A. and MUSINGWINI, C. 2007. Modelling open pit shovel-truck systems

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1997. Andraótthir, A., Healy, K.J., Withers, D.H., and Nelson, B.L. (eds.).

Domains. Sokolowski, J.A. and Banks, C.M. (eds.). Wiley, Hoboken, NJ.

Association for Computing Machinery, New York. pp. 7–13.

pp. 1–24. BOZORGEBRAHIMI, A., HALL, R.A., and MORIN, M.A. 2005. Equipment size effects on open pit mining performance. International Journal of Surface Mining, Reclamation and Environment, vol. 19, no. 1. pp. 41–56. BURT, C., CACCETTA, L., HILL, S., and WELGAMA, P. 2005. Models for mining equipment selection. MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, University of Melbourne, 12–15 December 2005. Zerger, A. and Argent, R.M. (eds.). pp. 1730–1736. BURT, C., CACCETTA, L., WELGAMA, P., and FOUCHÉ, L. 2011. Equipment selection with heterogeneous fleets for multiple-period schedules. Journal of the Operational Research Society, vol. 62, no. 8. pp. 1498–1509. BURT, C.N. and CACCETTA, L. 2014. Equipment selection for surface mining: a review. Interfaces, vol. 44, no. 2. pp. 143–162.

MARZOUK, M. and MOSELHI, O. 2003. Constraint-based genetic algorithm for earthmoving fleet selection. Canadian Journal of Civil Engineering, vol. 30, no. 4. pp. 673–683. MCNEARNY, R.L. and NIE, Z. 2000. Simulation of a conveyor belt network at an underground coal mine. Mineral Resources Engineering, vol. 9, no. 3. pp. 343-–355. NANCE, R.E. 1995. Simulation programming languages: an abridged history. Proceedings of the 1995 Winter Simulation Conference, Arlington, VA. pp. 1307–1313. PIDD, M. and CARVALHO, A. 2006. Simulation software: not the same yesterday, today or forever. Journal of Simulation, vol. 1, no. 1. pp. 7–20. ROBINSON, S. 2005. Discrete-event simulation: from the pioneers to the present, what next? Journal of the Operational Research Society, vol. 56, no. 6. pp. 619–629.

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CAMARENA, E.A., GRACIA, C., and CABRERA SIXTO, J.M. 2004. A mixed integer

A stochastic simulation framework for truck and shovel selection and sizing in open pit mines ROSS, I., CASTEN, T., MARSH, D., and PEPPIN, C. 2010. The role of simulation in ground handling optimization at the Grasberg block cave mine. Hoist and

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Haul 2010 - Proceedings of the International Conference on Hoisting and Haulage, Las Vegas, 12–17 September 2010. Brokenshire, P. and Andersen, S. (eds.). Society for Mining, Metallurgy & Exploration, Littleton, CO. pp. 257–265. ROSS, S.M. 2006. Statistical Modeling and Decision Science: Simulation. 4th edn. Academic Press, Burlington, MA. RUNCIMAN, N., VAGENAS, N., and NEWSON, G. 1996. Simulation modeling of underground hard - rock mining operations using WITNESS. Proceedings of the 26th International Symposium on the Application of Computers and Operations Research in the Mineral Industries (APCOM), Pennsylvania, USA. pp. 148–151. SCHRIBER, T.J. 1991. An Introduction to Simulation using GPSS/H. John Wiley & Sons, New York. SCHRIBER, T.J. 1992. Perspectives on simulation using GPSS. Proceedings of the 24th Conference on Winter Simulation, Arlington, VA, 13–16 December 1992 . ACM Press. pp. 338–342. SØGAARD, H.T. and SØRENSEN, C.G. 2004. A model for optimal selection of machinery sizes within the farm machinery system. Biosystems Engineering, vol. 89, no. 1. pp. 13–28. STÅHL, I., HENRIKSEN. J., BORN, R., and HERPER, H. 2011. GPSS 50 years old, but still young. Proceedings of the Winter Simulation Conference, Phoenix, AZ, 11–14 December 2011. Jain, S. Creasey, R.R., Himmelspach, J., White, K.P., and Fu, M. (eds). Institute of Electrical and Electronics Engineers, New York. pp. 3947–3957. STOUT, C.E., CONRAD, P.W., TODD, C.S., ROSENTHAL, S., KNUDSENINT, HP. Simulation of a large multiple pit mining operation using GPSS/H. Journal of Mining and Mineral Engineering, 2013, vol. 4, no. 4. pp. 278–295. STURGUL, J.R. 1992. Using exact statistical distributions for truck shovel simulation studies. International Journal of Surface Mining and Reclamation, vol. 6, no. 3. pp. 137–139. STURGUL, J.R. 1995. Simulation and animation come of age in mining. Engineering and Mining Journal, vol. 6, no. 2. pp. 38–42. STURGUL, J.R. 1999. Discrete mine systems simulation in the United States. International Journal of Surface Mining Reclamation and Environment, vol. 13. pp. 37–41. STURGUL, J.R. 2000. Mine Design Examples Using Simulation. Society of Mining, Metallurgy and Exploration, Littleton, CO.. STURGUL, J.R. and THURGOOD, S.R. 1993. Simulation model for materials handling system for surface coal mine. Bulk Solids Handling, vol. 13, no. 4. pp. 817–820. UNIVERSITY OF NEVADA. 2013. Mine Systems Optimization and Simulation Laboratory. Mining and Metallurgical Engineering Department, University of Nevada, Reno. http://www.unr.edu/mining/research/mine-systemsoptimization-and-simulation

Appendix A The basic steps of the proposed simulation program are summarized here to familiarize readers with the underlying process. Readers might follow the procedure to simulate their own mining operations. However, detailed application of the proposed simulation framework is recommended for a more comprehensive practice.

Step 1 All loading units should be monitored carefully during different operating conditions. Necessary loading cycle times are measured during this phase. Table A.1 shows a sample of data collected in this case study.

Step 2 All haulage cycle times for all dump trucks should be collected. Loading and dumping stations, as well as, the number of idle trucks in queues, are required data in this phase. (Table A.2).

Data statistical analysis Step 3 The raw data obtained in the previous two phases should be analysed to derive statistical information. Probability distribution functions (PDFs) are required data for Monte Carlo stochastic sampling (Burt et al., 2011) and discrete-event simulation by GPSS/H (Bozorgebrahimi, Hall, and Morin, 2005) (Table A.3).

Step 4 All the PDFs in Table A.4 are needed along with Step 3, as the minimum requirements of the simulation program.

Step 5 Finally, a simulation program should be coded. A block diagram of the program is illustrated in Figure 8. For a very good source of GPSS/H programming see Sturgul (1995).

Step 6 Based on the simulation purposes, relevant sensitivity analysis might be executed (see Figure 12). Some important specifications of the introduced model of the case study are summarized in Table A.5. N

WOLVERINE SOFTWARE. 2013. http://www.wolverinesoftware.com.

Table A.1

Sample shovel data form (all times in minutes) No.

Shovel posing

1.73

0.36

3.2

1.64

0.32

1.6

1.82

0.56

6.8

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Shovel idle time

Truck waiting time in queue

Trucks in queue

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A stochastic simulation framework for truck and shovel selection and sizing in open pit mines Table A.2

Sample truck data form (all times in minutes) No.

Haulage

Dump station

Dumping

Maneuver

Return

Waiting in queue

Loader

1.5

6.3

Crusher

2.9

0.5

----

Shovel 1

1.3

6.5

Crusher

1.8

0.4

5.6

0.5

Shovel 1

1.9

7.5

Waste Dump

1.2

0.45

7.1

----

Shovel 2

2.2

7.2

Waste Dump

1.6

0.55

6.6

----

Shovel 2

1.4

6.8

Stockpile

1.4

0.56

5.3

1.3

Shovel 3

1.8

7.1

Stockpile

1.5

0.62

5.2

1.8

Shovel 4

Table A.3

Normal distributions of loaded haulage times from all shovels to all destinations (minutes) Loader

Crusher Standard deviation

Waste dump Average

Standard deviation

Stockpile Average

Standard deviation

Average

Shovel 1

0.9

5.5

1.3

7.6

1.1

6.2

Shovel 2

0.95

6.1

1.2

8.1

1.4

6.8

Shovel 3

1.1

6.5

1.5

8.5

1.55

7.2

Shovel 4

1.02

6.4

1.4

7.9

1.42

7.05

Table A.4

Frequency distribution of other required times (minutes) No.

Operation

Distribution

Parameters

Quantity (minutes)

Exponential

Mean

1.54

Truck spot time

Uniform

Min - Max

0.52-0.13

Maneuver and return time

Uniform

Min - Max

0.42-0.11

Spot time to dump station

Uniform

Min - Max

0.42-0.10

Dumping time

Exponential

Mean

1.7

Maneuver and return from dumping station

Uniform

Min - Max

0.54-0.13

Table A.5

Model specifications Item OOutput validation

Description 1- Chi Square or Klomogorov-Smirnov test at 5% significance level 2- Direct observation of real results

No. of iterations

2000 iterations completed, after this, the changes in the mean and variance of the results were negligible.

Domain of validity

The proposed models are valid under current operational conditions. Any major future changes shall be incorporated accordingly.

Applicability

Before introducing the recommended changes, a detailed economical evaluation is needed.

Model maintenance

The model should become up-to-dated with operation progress, e.g. opening of new working benches, increase in haulage distances,

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and etc.

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

Mintek, Randburg

Mining, Environment and Society Conference

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

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

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

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

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

Conference Announcement

http://dx.doi.org/10.17159/2411-9717/2015/v115n3a7 ISSN:2411-9717/2015/v115/n3/a7

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation by N.V. Ramlall* and B.K. Loveday†

A study was carried out to evaluate various batch flotation models for the recovery of minerals in a UG2 platinum ore. The major minerals in a UG2 ore can be grouped as platinum group minerals, chromite, and siliceous gangue. This study also examined the entrainment of minerals during flotation. The models were ranked using statistical methods and an analysis of model-fit residuals. Entrainment parameters obtained from model fitting were evaluated for logic using a simple mineral-to-water ratio versus time plot. The foremost conclusion from the study was the importance of entrainment modelling. The measurement of water recovery increased the size of the data-set, and the inclusion of a simple entrainment model was statistically significant. The overall fit to the data was improved, and the entrainment model provided logical information on the recovery of gangue minerals that were not considered to be floatable. Keywords Batch flotation modelling, entrainment modelling, PGM flotation.

Introduction The batch flotation test is used extensively in mineral processing for assessing the effect of flotation reagents on mineral floatability and depression, and for the characterization of an ore with respect to mineral recovery and upgrading. It has also been used, in combination with plant data, for examining potential improvements in plant performance through simulation. In most cases, periodic hand-scraping is the preferred method of removing froth, allowing separation to occur in the froth between scrapes. More recently, the batch flotation test has been used to assess the variability in mineral recovery for a range of samples taken across an orebody. Samples of UG2 ore, for expansion of current mining activities or new projects, are usually obtained using diamond drill core sampling. The sample mass obtained from this type of sampling is generally 2–3 kg. Therefore, the batch flotation test must be developed for a limited sample mass, and provide quantitative information on the changes in flotation response. The fitting of a batch flotation model to mineral recovery data is necessary for obtaining a better understanding of the floatability of the main mineral types. A suitable The Journal of The Southern African Institute of Mining and Metallurgy

Methods Sample A UG2 ore from the eastern limb of the Bushveld Complex was used in this study. The sample had a Pt, Pd, plus Au (2PGE+Au) feed grade of 2.64 g/t. The chromite (FeO·Cr2O3) feed grade was 28.09%. The valuable minerals are the platinum group minerals (PGMs) and associated base metal sulphides. The gangue minerals are chromite and siliceous gangue. Siliceous gangue, which is referred to as ‘gangue’ in the text that follows, includes talc, which was depressed.

* SGS South Africa. † School of Chemical Engineering, University of KwaZulu Natal. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Dec. 2013; revised paper received Sep. 2014.

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Synopsis

model is required for providing a reasonable description of the mineral recovery, and the model parameters should be meaningful. The model parameters, together with appropriate scale-up parameters, are used to evaluate various flotation circuit configurations. Fichera and Chudacek (1992) reviewed different batch flotation models, but these were applied to the desirable floatable minerals (i.e. the valuables) –their study did not examine entrainment and the modelling of gangue minerals, which are usually ‘less floatable’ than the valuable minerals. These minerals can be recovered due to association (interlocking) with floatable minerals, flotation, and entrainment. The objective of this study was to evaluate the application of batch flotation models (including entrainment) for the recovery of the valuable minerals and the gangue minerals in a UG2 ore. It was decided that using data on mineral recovery by size fraction was not practical, in view of the cost and quantity of sample required for platinum group element (PGE) assaying. Only models with a limited number of parameters could be considered.

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation The PGEs consisted of a distribution of elements (Pt, Pd, Ru, Os, Rh, and Ir), which were present in various mineral types (PGMs). Table I shows the distribution of 17 PGMs in the sample. The floatabilities of these minerals can vary, but a detailed mineralogical examination was not possible (or affordable) on the small test samples, particularly the flotation tail, which had a low PGE grade. The recovery of 2PGE+Au was determined by a conventional fire assay method, which consisted of a fusion technique, using NiS as the collector, followed by acid digestion and determination by inductively coupled plasma mass spectrometry (ICP-OES). The recovery of Cr2O3 was determined using a digestion technique on the sample, followed by ICP-OES measurement.

Experimental The sample was crushed in stages to -1.7 mm using a laboratory jaw and cone crusher. Intermediate screening was used to limit the generation of fines. The crushed material was homogenized and split into 1 kg lots for replicate grinding and flotation tests. A laboratory rod mill, with stainless steel media, was used for wet grinding of the 1 kg lots to a target grind size of 80% passing 75 μm. The solids concentration during grinding was 50% (in tap water); additional water was added to transfer the pulp into a 2.5 l flotation cell. A Denver D-12 flotation mechanism was used for the flotation test, at a feed solids concentration of about 35%. Tap water was added to maintain the pulp level. Table II shows the chemical reagents, dosages, and conditioning times used. The pulp was conditioned first with collector SIBX; this was followed by a guar-based talc depressant, KU5; and then a frother, Dowfroth 200. After conditioning, the flotation test was conducted using an air flow of 6.3 l /min. The froth built up naturally, allowing mineral separation to occur in the froth. The froth was removed every 15 seconds by hand scraping. The bottom edges of the scrapers were kept at a constant level of 0.5 cm above the froth/pulp interface. This is typical of most batch flotation test procedures that use hand scraping of the froth.

Table I

Distribution of PGMs in the test sample PGM

Table II

Flotation reagents Reagent SIBX KU5 Dowfroth 200

Dosage (g/t)

Conditioning time (min)

150 30 20

2 3 1

Figure 1—Replicate 2PGE+Au recovery from batch rougher flotation tests

Concentration by volume (%)

PtS PtPdS PdSb Ru(Os,Ir)S PtRhCuS PdHg PdSn PtRhAsS PtFe PtAsSb PtAsS PdS PtPdSb PtBiTe PdPb PtAs PdAsSb ¨

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A total of five concentrates were collected, ending at the cumulative flotation times of 1, 3, 7, 20, and 30 minutes. The concentrates and flotation tail were weighed before filtration and again after drying. This made it possible to calculate the cumulative recovery of water in addition to the recovery of the minerals. The flotation tail and concentrates were assayed for 2PGE+Au and Cr2O3. The chromite content was calculated from the Cr2O3 assay, using a typical ratio of Cr2O3/FeO·Cr2O3 of 0.679. The gangue content was determined by difference. A total of ten replicate batch flotation rougher tests were done. Figures 1–4 show the cumulative recovery of 2PGE+Au, gangue, chromite, and water respectively. The 95% confidence interval was determined at each time point, and this envelope is shown as dotted lines. Most of the experimental data occurs within the 95% confidence interval, which demonstrates that total variance due to sub-sampling of the ore and experimental error was acceptable. Hence, the average of each data point provides an unbiased data-set that is suitable for evaluating flotation models.

27.3 23.8 15.6 12.0 4.1 4.0 3.9 2.9 1.5 1.4 0.9 0.7 0.6 0.5 0.4 0.2 0.1 100.0

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Figure 2—Replicate gangue recovery from batch rougher flotation tests VOLUME 115

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A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation the complete range of particle size, to link entrainment to water recovery. The recovery by flotation can be modelled using any of the models listed in Table III. [1]

Figure 3—Replicate chromite recovery from batch rougher flotation tests

Various models have been used to calculate the entrainment term. They are all based on the quantity of water recovered into the flotation concentrate samples. A simple empirical model, which has been used extensively, assumes that cumulative recovery of a mineral by entrainment is directly proportional to cumulative recovery of water. However, this model neglects the fact that the composition of the pulp changes during the test, particularly the amount of floatable mineral. Runge (2010) has described a more accurate method to estimate entrainment, and this method was used to compare models. It takes into account the changing amounts of minerals in the cell as time progresses. In this paper, the authors used an average amount of a mineral remaining in the flotation cell for each time interval, this being the amount potentially available for entrainment during that time interval. Hence, knowing the average mineral-to-water ratio, the entrainment efficiency can be calculated, based on the water in the concentrate. This efficiency is called the classification factor for entrainment (Cf ), which varies between zero and one. An average classification factor (for all time intervals) was calculated when fitting models that combine flotation and entrainment. The additional parameter (Cf ) per mineral was required, but it should be noted that the number of data points was increased from 5 to 10.

Figure 4—Replicate water recovery from batch rougher flotation tests

Table III

Batch flotation models reviewed Batch flotation model

Equation

Classic

R = Rmax (1 – e–kt)

Klimpel

1 R = Rmax 1 – Kmax t (1– e–Kmax t )

Second-order Klimpel

R = Rmax

Second-order

Batch flotation models Table III presents the batch flotation models that were selected for this study. The basic assumption of all these models is that minerals are recovered by the mechanism of bubble-particle attachment. The second-order flotation models have not been used extensively, but were included for completeness. Entrainment is the unselective recovery of mineral particles associated with the water in the froth. Equation [1] has been applied when entrainment is included in modelling (Runge et al., 1998; George et al., 2004). The overall mineral recovery (R) is the sum of the recovery by flotation (Rfloat) and the recovery by entrainment (Rent). Water and suspended solids enter the froth in the wake of the air bubbles. Coarse particles are usually not present just below the froth due to sedimentation, and hence recovery by entrainment decreases as particle size increases. A simplified model is required for The Journal of The Southern African Institute of Mining and Metallurgy

[ ] 1 [1 – K t ln(1 + K t)] max

max

2 Rmax kt

1 + Rmaxkt R = Rfast (1– e–kfast t ) + Rslow (1– e–kslow t )

Kelsall

Rfast + Rslow = 100% Modified Kelsall

–kfast t )

R = Rfast (1– e

–kslow t )

+ Rslow (1– e

Rmaxt = Rfast + Rslow Nomenclature: Rmax Maximum recovery of floatable mineral

Rfast

Maximum recovery of fast-floatable mineral

Rslow

Maximum recovery of slow-floatable mineral

k kmax

Rate of mineral flotation (min-1) Maximum rate of mineral flotation (min-1)

kfast

Rate of flotation for fast-floating minerals (min-1)

kslow

Rate of flotation for slow-floating minerals

t R

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It should be noted that it may not be practical to do replicate tests, and a single flotation test would often be used for each ore sample. This would provide a total of five data points per mineral and five water recovery points. The number of parameters used in a model should be less than the number of data points to have statistical significance. Therefore, for modelling mineral recovery without entrainment, the number of model parameters should not exceed four.

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation Methods used for evaluating models Statistical analysis was used to examine the goodness-of-fit. Two statistics were used: namely, the coefficient of determination (R2) and the model selection criterion (MSC). R2 is defined by Equation [2], and was used to measure the quality of a model fit. This statistic varies between zero and unity. A value of unity indicates a perfect model fit, and a value of zero indicates that the model does not offer a statistically meaningful interpretation of the data.

recovery occurs by both flotation and entrainment, therefore the apparent value of Cf will be too high initially, but it should approach the true value as the floatable material is depleted. The values obtained for the last time interval were compared to the ‘best’ regressed values. [4]

Results and discussion [2] where

SSres =

dp ¨i=1 (Rexp,i −

dp Rmodel,i)2 and SStotal =¨i=1 (Rexp,i −

Rexp)2 Rexp,i is the experimental recovery of a mineral at time i Rmodel,i is the mineral recovery obtained from the model at time i is the average of the experimental mineral Rexp recovery is the number of experimental data points.

There are a number of statistical tests that can be used for determining the best model from a list of candidate models. Some of the common statistics are the Akaike information criteria (AIC), Bayesian information criteria (BIC), Mallow’s Cp, Sp criterion, and MSC (Kadane and Lazar, 2004; and Tu and Xu, 2012). These statistics are a function of SSres, and different penalties are assigned for the number of model parameters used. The MSC statistic (Equation [3]) was selected, since it penalizes models that have a large number of parameters relative to the number of data points available. This is important when there is a limited amount of data available. A large value for MSC is desirable. [3] where is the number of model parameters.

The above two statistics provide a numerical measure of the goodness-of-fit for a model, but this is not sufficient for evaluation of a model. The residuals must be examined to determine if the model describes the batch recovery of a mineral adequately. The residual is defined as the different between Rexp,i and Rmodel,i. A model that provides a good description of mineral recovery will have small and randomly distributed residuals. This indicates an impartial fit to all data points. Conversely, a model is not suitable if it has large residuals or the residuals follow a trend. This indicates a bias in the fit. The test procedure outline by Warren (1985) for the determination of minerals recovered by flotation and entrainment requires several tests to be carried out under varying froth conditions. This procedure is difficult to implement when there is limited sample mass. Runge (2010) presented a simple method for estimating the value of Cf from batch flotation test data. The value of Cf defined in Equation [4] is determined for each time interval and plotted against cumulative time. It should be noted that mineral

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Modelling of PGM recovery data Table IV presents the statistical results and parameters obtained from model fitting to PGM recovery data. The results are arranged in descending order with the best model, according to R2, at the top and the worst at the bottom. It is apparent that the best model for the PGM recovery data is one that considers entrainment. According to the goodness-of-fit statistics (R2 and MSC), the modified Kelsall model with entrainment is the ‘best’ model. This model indicates that the floatable PGMs can be modelled as a fast- and slow-floating fraction together with entrainment. The Kelsall model, which assumes that all minerals are floatable, reveals an interesting feature of model fitting, with and without entrainment. The entrainment model has a classification parameter of zero, which indicates that no PGMs are recovered by entrainment. Strictly speaking this is not true. Some PGMs are encapsulated in gangue minerals (unfloatable) and have been found in tailings samples, indicating that there should be an unfloatable fraction. It appears that the Kelsall model (without entrainment) provides a good fit to batch data, but logic says that it may not be adequate when extrapolated to steady-state plant data. Figure 5 shows the residual plot for the models fitted to PGM recovery data. The residuals are smaller for models that took entrainment into account – note the change in scale for models with entrainment. The Classic, Klimpel, second-order Klimpel, and second-order models all show systematic structure in their residuals. This was observed for modelling with and without entrainment. The systematic structure is less noticeable when entrainment modelling is considered and the residuals are small. The residuals start positive and oscillate in a distinct pattern, particularly with the models that do not fit well. The modified Kelsall model and the Kelsall model both have small and randomly distributed residuals with and without entrainment modelling. These models give an impartial fit. The goodness-of-fit statistics and the model residual plots indicate that the modified Kelsall model with entrainment is the ‘best’ model for modelling PGM recovery data. Figure 6 shows the application of Runge’s method for estimation of Cf for the PGMs. The values are greater than unity, indicating that flotation was dominating throughout. This interpretation is confirmed by the fact that the grade of the final concentrate sample was 2.28 g/t (similar to the feed grade) and the tailings grade was 0.4 g/t. Clearly, the test would have to be continued for a much longer time to provide a realistic estimate of Cf. Hence, there is no evidence to reject the model regression value of Cf of 0.95 (refer to Table IV) for the modified Kelsall model. The Journal of The Southern African Institute of Mining and Metallurgy

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation Table IV

Summary of model fit statistics and model parameters for PGM recovery data Model

MSC

Model fit parameters

Modified Kelsall with entrainment

1.0000

19.8266

Rmax = 75.34, Rfast = 58.55, Rslow = 16.79 kfast = 2.23 min-1, kslow = 0.16 min-1 Cf, PGM = 0.95

Modified Kelsall

0.9991

5.4183

Rmax = 88.26, Rfast = 61.81, Rslow = 26.45 kfast = 2.26 min-1, kslow = 0.13 min-1

2nd-order Klimpel with entrainment

0.9974

5.3686

Rmax = 75.39 Kmax = 7.47 min-1 Cf, PGM = 1.0

2nd-order model with entrainment

0.9908

4.0854

Rmax = 73.94 Kmax = 0.04 min-1 Cf, PGM = 1.0

Kelsall with entrainment

0.9839

3.3270

Rmax = 100, Rfast = 69.01, Rslow = 30.99 kfast = 1.77 min-1, kslow = 0.04 min-1 Cf, PGM = 0.00

Kelsall

0.9839

2.9270

Rmax = 100, Rfast = 69.01, Rslow = 30.99 kfast = 1.77 min-1, kslow = 0.04 min-1

Klimpel with entrainment

0.9815

3.3904

Rmax = 73.18 Kmax = 3.59 min-1 Cf, PGM = 1.0

2nd-order Klimpel

0.9709

2.7387

Rmax = 89.26 Kmax = 4.83 min-1

2nd-order model

0.9424

2.0542

Rmax = 86.87 k = 0.02 min-1

Classic with entrainment

0.9129

1.8409

Rmax = 70.57 kmax= 1.41 min-1 Cf, PGM = 1.0

Klimpel

0.9032

1.5348

Rmax = 85.38 Kmax = 2.73 min-1

Classic

0.7255

0.4929

Rmax = 81.62 k = 1.15 min-1

Figure 6â&#x20AC;&#x201D;Estimation of Cf,PGM from batch flotation data

Modelling of gangue recovery data

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Figure 5â&#x20AC;&#x201D;Model residuals for PGM data modelled with floatability model only (a) and entrainment and floatability model (b)

Table V shows the goodness-of-fit statistics for the different models fitted to the gangue recovery data. In general, all models provide a good fit to the experimental data as indicated by the large R2 values. The models with

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation Table V

Summary of model fit statistics and model parameters for gangue recovery data Model

MSC

Model fit parameters

Rmax = 15.24, Rfast = 7.47, Rslow = 7.77 kfast = 0.97 min-1, kslow = 0.08 min-1 Cf, Gangue = 0.19

Modified Kelsall with entrainment

1.0000

14.8111

2nd-order Klimpel with entrainment

0.9993

6.6844

Rmax = 13.23 Kmax = 1.40 min-1 Cf, Gangue = 0.25

2nd-order model with entrainment

0.9991

6.4297

Rmax = 11.39 k = 0.06 min-1 Cf, Gangue = 0.27

Klimpel with entrainment

0.9989

6.1749

Rmax = 9.97 Kmax = 1.40 min-1 Cf, Gangue = 0.30

Modified Kelsall

0.9987

5.0641

Rmax = 28.03, Rfast = 9.09, Rslow = 18.94 kfast = 0.88 min-1, kslow = 0.05 min-1

Classic with entrainment

0.9980

5.6199

Rmax = 8.26 k = 0.76 min-1 Cf, Gangue = 0.33

Kelsall with entrainment

0.9980

5.4185

Rmax = 100.00, Rfast = 8.27, Rslow = 91.73 kfast = 0.76 min-1, kslow = 3.26 x 10-5 min-1 Cf, Gangue = 0.33

Kelsall

0.9955

4.2053

Rmax = 100.00, Rfast = 11.78, Rslow = 88.22 kfast = 0.62 min-1, kslow = 4.90 x 10-3 min-1

2nd-order Klimpel

0.9814

3.1846

Rmax = 27.88 Kmax = 0.48 min-1

2nd-order model

0.9730

2.8114

Rmax = 25.67 k = 9.05 x 10-3 min-1

Klimpel

0.9551

2.3041

Rmax = 24.16 k = 0.41 min-1

Classic

0.9320

1.8889

Rmax = 22.01 Kmax = 0.20 min-1

entrainment have a marginally better fit statistic in comparison to the models that do not have entrainment modelled. This is also evident from the residual plots for the models shown in Figure 7. The Classic, Klimpel, second-order Klimpel, and second-order models show structures in their residuals â&#x20AC;&#x201C; this indicates bias. The modified Kelsall model with entrainment gives the best fit to the experimental data, and is the best model from the candidate models evaluated, according to the MSC statistic. Furthermore, this model provides an impartial fit. Some of the gangue is present as floatable minerals, such as talc, chlorite, and other altered silicates. The rate of flotation of these minerals was depressed by the addition of KU5, but the model fit suggests the presence of fast- and slow-floating species, together with entrainment. Figure 8 shows the estimation of Cf for gangue. According to the plot, Cf approaches a value of 0.22 towards the end of the flotation test. The modified Kelsall model has a Cf value of 0.19 from model fitting (refer to Table V). The lower value for the classification parameter (Cf, GANGUE = 0.19) is more typical of values reported by Runge (2010) for non-sulphide gangue.

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Modelling of chromite recovery data Table VI shows the goodness-of-fit statistics and model parameters for models fitted to the chromite data. All the models gave a good fit to the experimental data, but the models with entrainment modelling gave a better fit than those without entrainment modelling. The MSC statistic indicates that the modified Kelsall model with entrainment is the best model. Figure 9 shows the residual plots for the models. The models that did not consider entrainment have larger residuals with a systematic structure compared to models that considered entrainment. This was expected, as chromite is hydrophilic and recovered primarily by entrainment. According to the goodness-of-fit statistics and the residual plot, the modified Kelsall model is the best model. However, this model has an estimated floatable chromite fraction of almost 13%. Chromite is a non-floatable mineral, and there is insufficient floatable material available to float this amount of chromite in the form of composite particles. The combination of the Classic flotation model and entrainment provides a much more realistic value for the proportion of floatable material (Rmax = 0.42%). The Journal of The Southern African Institute of Mining and Metallurgy

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation Figure 10 shows the estimation of Cf using the Runge method. The value was still declining at the end of the test, yielding a final Cf value of 0.13. This compares well with the value obtained by regression, using the Classic model (Cf = 0.14).

Figure 7â&#x20AC;&#x201D;Model residuals for gangue data modelled with floatability model only (a) and entrainment and floatability model (b)

Figure 8â&#x20AC;&#x201D;Estimation of Cf, GANGUE from batch flotation data

Table VI

Summary of model fit statistics and model parameters for chromite recovery data R2

MSC

Model fit parameters

Modified Kelsall with entrainment

1.0000

14.9888

Rmax = 12.98, Rfast = 1.20, Rslow = 11.78 kfast = 0.71 min-1, kslow = 0.02 min-1 Cf, Chromite = 0.04

Modified Kelsall

0.9997

6.1198

Rmax = 20.57, Rfast = 1.63, Rslow = 18.94 kfast = 0.61 min-1, kslow = 0.01 min-1

Kelsall with entrainment

0.9996

7.1085

Rmax = 100.00, Rfast = 1.35, Rslow = 98.65 kfast = 0.57 min-1, kslow = 1.28 x 10-3 min-1 Cf, Chromite = 0.05

Kelsall

0.9994

5.7988

Rmax = 100.00, Rfast = 1.97, Rslow = 98.03 kfast = 0.48 min-1, kslow = 2.10 x 10-3 min-1

Classic with entrainment

0.9979

5.5495

Rmax = 0.42 k = 0.91 min-1 Cf, Chromite = 0.14

Klimpel with entrainment

0.9978

5.5109

Rmax = 0.55 Kmax = 1.59 min-1 Cf, Chromite = 0.14

2nd-order Klimpel with entrainment

0.9977

5.4878

Rmax = 0.74 Kmax = 1.50 min-1 Cf, Chromite = 0.14

2nd-order model with entrainment

0.9977

5.4855

Rmax = 0.62 k = 1.35 min-1 Cf, Chromite = 0.14

2nd-order Klimpel

0.9876

3.1927

Rmax = 14.02 Kmax = 0.10 min-1

2nd-order model

0.9859

3.0584

Rmax = 12.04 k = 4.78 x 10-3 min-1

Klimpel

0.9819

3.2107

Rmax = 10.28 Kmax = 0.12 min-1

Classic

0.9792

3.0709

Rmax = 8.62 k = 0.07 min-1

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Model

A comparison of models for the recovery of minerals in a UG2 platinum ore by batch flotation

Figure 10—Estimation of Cf, CHROMITE from batch flotation data

Figure 9—Model residuals for chromite data modelled with floatability model only (a) and entrainment and floatability model (b)

Figure 11 shows another way of analysing entrainment, namely a plot of chromite recovery versus water recovery. The linear relationship demonstrates that recovery is primarily by entrainment. However, it should be noted that the line does not pass through the origin, and therefore a small amount (0.39%) of ‘floatable chromite’ is present. The model parameters (Rmax and Cf), in Table VI for the Classic model are comparable to the estimates obtained from Figure 10 and Figure 11.

Figure 11—Estimation of ‘floatable’ chromite fraction and degree of entrainment

Conclusions ® PGM recovery data is best modelled using the modified Kelsall model with entrainment. This model delineates the floatable recovery as the sum of a fast- and a slowfloating component. Entrainment modelling provides a better fit, and it differentiates between recovery of slow-floating PGMs and entrainment of PGMs contained in fine particles ® Gangue is best modelled using the modified Kelsall model with entrainment. Other models such as the second-order Klimpel model with entrainment, the second-order model with entrainment, and the Klimpel model with entrainment, offer good fits to experimental data, but the model-fitted parameters are not logical ® Chromite is recovered predominantly by entrainment, but model fitting suggests that a small fraction is recovered by flotation. This may be due to composite particles in which chromite is associated with floatable minerals. A single rate constant was adequate for characterizing the rapid recovery of a relatively small amount of chromite (0.39%) into the first concentrate. This could be due to composite particles, or entrapment in the initial mineral-rich froth ® The importance of entrainment, in combination with flotation, has been demonstrated. Consideration should be given to extending the time of the batch flotation test to provide better information on the recovery of non-floatable minerals, (including minerals trapped within non-floating minerals).

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Acknowledgements The first author would like to acknowledge the financial assistance provided by the Department of Minerals and Energy in South Africa, and the resources provided by Mintek for this study.

References FICHERA, M.A. and CHUDACEK, M.W. 1992. Batch cell flotation models–a review. Minerals Engineering, vol. 5. pp. 41–55. GEORGE, P., NGUYEN, A.V., and JAMESON, G.J. 2004. Assessment of true flotation and entrainment in the flotation of submicron particles by fine bubbles. Minerals Engineering, vol. 17. pp. 847–853. KADANE, J.B. and LAZAR, N.A. 2004. Methods and criteria for model selection. Journal of the American statistical Association, vol. 99. pp. 279–290. RUNGE, K.C., ALEXANDER, D.J., FRANZIDIS, J.P., MORRISON, R.D., and MANLAPIG, E.V. 1998. JKSimFloat – a tool for flotation modelling. Proceedings of the AusIMM Annual Conference, Mt Isa, 19–23 April 1998. Australasian Institute of Mining and Metallurgy, Melbourne. pp. 361–370. RUNGE, K. 2010. Laboratory flotation testing – n Essential Tool for Ore Characterisation. Flotation Plant Optimisation – A Metallurgical Guide to Identifying and Solving Problems in Flotation Plants. Greet, C.J. (ed.). AUSIMM Spectrum Series.16. Australasian Institute of Mining and Metallurgy, Melbourne. pp. 155–173. TU, S. and XU, L. 2012. A theoretical investigation of several model selection criteria for dimensionality reduction. Pattern Recognition Letters, vol. 33. pp. 1117–1126. WARREN, L.J. 1985. Determination of the contributions of the true flotation and entrainment in batch flotation tests. International Journal of Mineral Processing, vol. 14. pp. 33–44. N The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n3a8 ISSN:2411-9717/2015/v115/n3/a8

Enrichment of low-grade colemanite concentrate by Knelson Concentrator s† SavaÇ by T. Uslu*, O. Celep*, and M.

This study investigates the enrichment of a low-grade colemanite concentrate (-3 mm) using a Knelson centrifugal gravity concentrator. Due to its low boron content, the concentrate is unsaleable and has to be stored under appropriate conditions to avoid potential environmental problems. The low-grade colemanite concentrate was comminuted to size fractions of -1 mm, -0.5 mm, and -0.15 mm before treatment in the Knelson Concentrator. The effects of particle size, fluidizing water velocity, and bowl speed on the enrichment process were examined. The B2O3 content of the concentrate was increased from 33.96% to a maximum of 45.52%. B2O3 recovery increased with increasing bowl speed and particle size, and decreased with increasing fluidizing water velocity. The enrichment process also rejected arsenic and iron to some extent, with a maximum reduction of arsenic from 1360 g/t to 765 g/t and iron from 0.88% to 0.33%. Keywords boron, colemanite, gravity concentration, centrifugal concentration, Knelson Concentrator

Introduction Turkey has the world’s largest boron deposits, with 72% of the global resources (Uslu, 2007). The commercially most important boron minerals are borax (Na2B4O7.10H2O), colemanite (Ca2B6O11.5H2O), and ulexite (NaCaB5O9.8H2O) (Christogerou et al., 2009). Colemanite is the most abundant boron mineral in the Turkish deposits (Koca, Savas Ç, and Koca, 2003; Yıldız, 2004). The major gangue minerals associated with colemanite ores are clays, carbonate minerals, and, to a less extent, arsenic minerals (Koca and Savas Ç, 2004). Colemanite ores are concentrated by attrition scrubbing followed by screening and classification to remove clay minerals (Koca and Savas, Ç 2004; Uslu and Arol, 2004; Acarkan et al., 2005; Gül, Kaytaz, and Önal, 2006). Colemanite deposits in Turkey are exploited by two sub-units of Eti Mine Works (Emet Boron Works, Bigadiç Boron Works). In the two concentrators of Emet Boron Works, approximately 1.5 Mt/a = of ore containing 25–28% B2O3 is processed to produce 700 kt of concentrate containing up to 36–42% B2O3. However, 70 kt/a of concentrate is stockpiled since it cannot be marketed or used in the The Journal of The Southern African Institute of Mining and Metallurgy

* Division of Mineral & Coal Processing, Department of Mining Engineering, Karadeniz Technical University, Trabzon, Turkey. † Emet Baron Works, Kütahya, Turkey. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Apr. 2014; revised paper received Jul. 2014.

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Synopsis

production of boric acid due to its low grade. Approximately 600 kt of low-grade concentrate have already been accumulated in the stockpiles (EBW, 2014). Stockpiling of this concentrate brings about potential problems, including the occupation of large areas of land and the environmental pollution due to its exposure to atmospheric effects. Figure 1 shows a stockpile of low-grade colemanite concentrate in the area of Espey Mine. Treatment of this low-grade concentrate by a suitable method is important for resource efficiency and elimination of the problems associated with stockpiling. The Knelson Concentrator is essentially a hindered settling device, related to the hydrosizer, with centrifugal force substituting for the force of gravity. It consists of a rotating ribbed cone (bowl) with fluidized concentrate retention zones between the ribs. Feed slurry enters through a central feed tube at the bottom of the cone and is thrown outwards by centrifugal force. Heavy (or large) particles are trapped in the retention zone between the ribs, while the light particles (or fine particles) are carried upward into the tailings stream by the slurry stream. Injection of water through small holes located in the retention zones promotes the formation of a fluidized and permeable concentrate bed consisting of heavier particles (Uslu, Sahinoglu, and Yavuz, 2012). Despite its wide range of applications, the utilization of the Knelson Concentrator for enrichment of boron minerals has not been previously reported. In the case of colemanite enrichment by the Knelson Concentrator, clay and other light or low specific gravity particles that are generally dispersed finely in the slurry would be removed from the bowl as overflow, while colemanite particles would remain in the bowl

Enrichment of low-grade colemanite concentrate by Knelson Concentrate

Figure 1—Stockpiles of low-grade colemanite concentrate (-3 mm) Figure 2—Schematic illustration of colemanite enrichment in a Knelson Concentrator (Modified by authors from Kawatra and Eisele, 2001)

Ç 2012) (Figure 2). Preliminary tests (Uslu, Celep, and Savas, demonstrated that the Knelson Concentrator can be used for enrichment of the low-grade colemanite concentrate. In this study, effects of various factors including particle size, bowl speed, and fluidizing water velocity on the enrichment process are investigated.

Materials and method Materials A sample of low-grade colemanite concentrate (-3 mm) was obtained from the Espey colemanite concentrator of Emet Boron Works. The Espey concentrator and sampling point are illustrated in Figure 3. The chemical analysis and particle size analysis of the sample are given in Table I and Figure 4, respectively. As seen from Figure 4, 80% of the low-grade concentrate is <1.5 mm. The B2O3 grade is higher in coarse particle fractions due to greater amount of fine clay particles in the fine fractions.

Method The sample was ground to three different size fractions (-1 mm, -0.5 mm, and -0.15 mm) in a rod mill. Each fraction was subjected to the enrichment process in a laboratory batch-type Knelson Concentrator (KC-MD3) (Figure 5). The effects of bowl speed [500 r/min (11.2 G-force), 1000 r/min, (45 G-force), 1500 r/min, (100 G-force), and 2000 r/min (179 G-force)] and fluidizing water velocity (1 L/min, 3 L/min, 5 L/min, and 7 L/min) were investigated. Feed pulp at approximately 10% solids by weight was prepared in a volume of 500 mL in a 1000 mL beaker. The beaker contents were agitated for 15 minutes using an IKA RW-20 type overhead stirrer equipped with a 45° pitched blade turbine (four blade, 50 mm in diameter). The dispersed slurry was fed to the Knelson Concentrator at a rate of 25 g/min. Overflow (tailings) was collected in a bucket while underflow (colemanite concentrate) remained in the bowl. The bowl contents (concentrate) were washed into beakers. After dewatering by using a vacuum filter, the products were dried, weighed, and analysed for boron oxide (B2O3), iron (Fe), and arsenic (As). Analyses were conducted in the laboratory of Emet Boron Works. The B2O3 recovery and Fe and As removal were calculated by using the following equations:

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Figure 3—Espey Colemanite Concentrator and sampling point

Table I

Chemical analysis of the -3 mm low-grade colemanite concentrate (%) B2O3

SiO2

33.96 15.38

Fe2O3

Al2O3

CaO MgO

SrO

SO4

Fe As (g/t)

1.26

4.02

18.42 4.15

1.44

0.06 0.88 1360

Figure 4—Particle size distribution of the low-grade colemanite concentrate (-3 mm) The Journal of The Southern African Institute of Mining and Metallurgy

Enrichment of low-grade colemanite concentrate by Knelson Concentrate The adverse effect of increasing bowl speed and decreasing water velocity on iron removal (Figure 7) can be explained in the same manner. The increased centrifugal force at high bowl speeds caused clay particles to be retained between the ribs, despite their fine sizes, i.e., fine and light particles were also affected by the centrifugal force. Higher fluidizing flow assists in removing clay but also adversely affects colemanite recovery. Since iron is associated with the clay minerals, iron removal is linked with the rejection of clay minerals in the tailings. Arsenic removal generally increased with decreasing bowl speed and increasing water velocity (Figure 8), following a similar trend as clay/iron. Although a lower particle size affected the B2O3 recovery adversely, it had a positive effect on iron and arsenic removal (Figures 6â&#x20AC;&#x201C;8). Size reduction generated considerable amounts of colemanite fines, together with liberated clay particles. In the enrichment process, fine colemanite particles, as well as the clays, were lost in the overflow as tailings. The enhancement of iron removal by size reduction is attributed to the improved liberation of iron-bearing clay minerals.

Figure 5â&#x20AC;&#x201D;Photograph and schematic of Knelson Concentrator (Celep et al., 2008)

where C is amount of concentrate (g), c the grade of concentrate (%), T the amount of tailings (g), and t the grade of the tailings (%).

Results and discussion

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In three of the total of 36 tests, no concentrate was produced and all of the feed reported to the tailings due to the interactive combination of fine particle size, low bowl speed, and high fluidizing water velocities. The remaining 33 tests were used for the evaluation of the results. The B2O3 recovery increased generally with increasing the bowl speed or decreasing fluidizing water velocity (Figure 6). The increase in B2O3 recovery results from increasing centrifugal forces at high bowl speeds. Decreasing the velocity of fluidizing water resulted in lower B2O3 grades at the same bowl speeds due to particles being rejected by fluidizing water. At the lowest particle size (-0.15 mm), fluidizing water flow had little effect on the concentrate grade, and grades were in general higher than those at coarser particle sizes. This is probably due to more complete liberation of colemanite at a finer grind, resulting in a purer concentrate, and more evenly-sized concentrate at finer grind with lesser susceptibility to changes in the fluidizing water flow rate.

Enrichment of low-grade colemanite concentrate by Knelson Concentrate

Figure 7—Effect of bowl speed and fluidizing water velocity on iron removal and grade

The minimum B2O3 content for -3 mm concentrate in the boron market is 36%. On the other hand, only concentrates with a B2O3 content of >40% are used to produce boric acid in Emet Boron Works. In terms of resource efficiency, a B2O3 recovery exceeding 70% is considered to be acceptable in the plant. Test results and conditions that provided acceptable B2O3 recoveries (v70%) and B2O3 grades (v36%) are summarized in Table II. Although concentrates containing up to 45.52% B2O3 were produced after grinding to -0.5 mm, at the expense of high losses from the -3 mm concentrate, a B2O3 content of 40.2% could be produced at a recovery of 86.48%. While up to 91.41% of the arsenic and up to 97.85% of the iron could be removed, arsenic removals of 1.36–22.66% and iron removals of 13.80–62.97% were achieved in tests that yielded acceptable B2O3 recoveries and grades. Iron and arsenic removals at optimum grade-recovery combination of B2O3 were 57.95% and 15.39%, respectively. The minimum arsenic and iron grades of the concentrates were 765 g/t and 0.33%, respectively. High levels of arsenic removal were accompanied by a decrease in B2O3 recovery. This can be attributed to the finely

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Figure 8—Effect of bowl speed and fluidizing water velocity on arsenic removal and grade

disseminated nature of the arsenic, mainly as realgar in the colemanite concentrate. It is therefore extremely difficult to liberate arsenic minerals by size reduction, and the behaviour of arsenic was similar to that of boron in the concentration process. Size reduction to -0.15 mm allowed considerable arsenic removal at the expense of a sharp decrease in B2O3 recovery due to high boron losses as fines. When the results of this study were compared to those of Ç 2012), in the preliminary study (Uslu, Celep, and Savas, which low-grade concentrate (-3 mm) was processed directly in the Knelson Concentrator, without prior size reduction, it was found that size reduction of low-grade concentrate (-3 mm) did not result in a concentrate with higher B2O3 grade, due to the high of B2O3 losses to the tailings. Only one study has been reported previously that resulted in enrichment of -3 mm low-grade colemenite concentrate of Emet Boron Works. The B2O3 grade was increased up to 49%, with recoveries over 80%, by grinding to -0.25 mm followed by ultrasonic pre-treatment and flotation (Ozkan and Gungoren, 2012). The Journal of The Southern African Institute of Mining and Metallurgy

Enrichment of low-grade colemanite concentrate by Knelson Concentrate Table II

Test conditions providing acceptable B2O3 recovery-grade combinations

-1

-0.5

-0.15

Bowl speed, r/min

Water flow, L/min.

Concentrate amount, g

Tailings amount, g

B2O3 grade of tailings,%

B2O3 grade of concentrate %

B2O3 recovery, %

500

46.86

7.03

9.00

36.34

96.34

1500

47.92

3.20

3.48

36.06

99.36

500

38.00

13.70

12.74

39.86

89.67

1000

45.20

6.77

6.16

36.38

97.53

1500

50.05

6.16

1.04

36.27

99.65

2000

46.97

4.52

3.28

36.12

99.13

1000

39.63

7.61

6.09

36.4

96.89

1000

41.46

12.53

9.44

39.26

93.23

1500

44.80

7.60

4.39

36.21

97.98

500

40.40

11.18

16.14

36.49

89.09

500

32.21

20.39

17.75

41.88

78.85

1000

42.88

10.52

11.00

37.54

93.29

1000

37.75

12.48

11.22

38.15

91.14

1500

44.79

8.89

7.35

36.33

96.14

2000

46.17

7.56

5.38

35.17

97.62

1000

33.71

16.89

12.54

40.2

86.48

1500

41.43

10.56

7.42

37.67

95.22

2000

44.55

8.6

6.16

36.79

96.87

1500

40.03

22.19

24.14

37.04

73.46

Conclusions The Knelson Concentrator was applied for the beneficiation of a low-grade colemanite concentrate. The B2O3 grade was increased from 33.96% to a maximum of 45.52%. However, optimum concentration was carried out by increasing the B2O3 grade to 40.2% at a recovery of 86.48%. A B2O3 grade of 41.88% at 78.85% recovery is another remarkable result. With the optimum concentration process, the iron content was reduced from 0.88% to 0.68%, and arsenic content from 1360 g/t to 1240 g/t. Reduction of low-grade colemanite (-3 mm) to finer sizes (-0.15 mm) did not enhance the enrichment process. The separation performance depended on the bowl speed and fluidizing water velocity, with a close interaction between these two parameters. The results show that the Knelson Concentrator is a promising candidate for producing marketable concentrates from low-grade colemanite concentrate. The current study is the first to use a Knelson Concentrator for processing boron minerals. A large-scale Knelson Concentrator, such as Knelson KC-CVD, should be used in further studies due to its suitability for industrial mineral applications.

References ACARKAN, N., BULUT, G., KANGAL, O., and ÖNAL, G. 2005. A new process for upgrading boron content and recovery of borax concentrate. Minerals Engineering, vol. 18. pp. 739–741. · CELEP, O., ALP, I., DEVECI, H., VıCıL, M., and YıLMAZ, T. 2008. Gold recovery from · Mastra (Gümüshane) ore using Knelson Centrifugal Saparator. Istanbul Earth Sciences Review, vol. 19, no. 2. pp. 175−182. (In Turkish). CHRISTOGEROU, A., KAVAS, T., PONTIKES, Y., KOYAS, S., TABAK, Y., and ANGELOPOULOS, G.N. 2009. Use of boron wastes in the production of heavy clay ceramics. Ceramics International, vol. 35. pp. 447–452.

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EBW (Eti Mine Works). 2014. Daily Work Report of Emet Boron Works, February 2014. GÜL, A., KAYTAZ, Y., and ÖNAL, G. 2006. Beneficiation of colemanite tailings by attrition and flotation. Minerals Engineering, vol. 19. pp. 368–369. KAWATRA, S.K. and EISELE, T.C. 2001. Coal desulphurization, high-efficiency preparation methods. Taylor and Francis, New York. Ç, M. 2004. Contact angle measurements at the colemanite KOCA, S. and SAVAS and realgar surfaces. Applied Surface Science, vol. 225. pp. 347–355. Ç, M., and KOCA, H. 2003. Flotation of colemanite from realgar. KOCA, S., SAVAS Minerals Engineering, vol. 16. pp. 479–482. OZKAN,S. and GUNGOREN, C. 2012. Enhancement of colemanite flotation by ultrasonic pre-treatment. Physicochemical Problems of Mineral Processing, vol. 48, no. 2. pp. 455−462. USLU, T. and AROL, A.I. 2004. Use of boron waste as an additive in red bricks. Waste Management, vol. 24. pp. 217–220. USLU, T. 2007. Use of boron as energy source. Proceedings of the Sixth Energy Symposium of Turkey. Chamber of Electrical Engineers. pp. 433–450. Ç, M. 2012. A preliminary research for upgrading USLU, T., CELEP, O.E., and SAVAS of low grade colemanite concentrate by scrubbing and Knelson Concentrator. Proceedings of the 13th International Mineral Processing Symposium, Bodrum, Turkey, 10–12 October 2012. pp. 653–658 USLU, T., SAHINOGLU, E., and YAVUZ, M. 2012. Desuluphurization and deashing of oxidized fine coal by Knelson concentrator. Fuel Processing Technology, vol. 101. pp. 94–100. YıLDıZ, O. 2004. The effect of heat treatment on colemanite processing: a ceramics application. Powder Technology, vol. 142. pp. 7–12.

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Particle size, mm

Copper Cobalt Africa

In association with The 8th Southern African Base Metals Conference

6–8 July 2015 Zambezi Sun Hotel, Victoria Falls Livingstone, Zambia

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

Conference Announcement

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

http://dx.doi.org/10.17159/2411-9717/2015/v115n3a9 ISSN:2411-9717/2015/v115/n3/a9

Multifractal interpolation method for spatial data with singularities by Q. Cheng*

This paper introduces the multifractal interpolation method (MIM) developed for handling singularities in data analysis and for data interpolation. The MIM is a new moving average model for spatial mapping and interpolation. The model decomposes the raw data into two components: singular and nonsingular components. The former can be characterized by a localized singularity index that quantifies the scaling invariance property of measures from a multifractal point of view. The latter is a smooth component that can be estimated using ordinary kriging or other moving average models. The local singularity index characterizes the concave/convex properties of the neighbourhood values. The paper utilizes a binomial multiplicative cascade model to demonstrate the generation of one- and two-dimensional data with multi-scale singularities which can be modelled by asymmetrical multifractal distribution. It then introduces a generalized moving average mathematical model for analysing and interpolating data with singularities. Finally, it is demonstrated by a one-dimensional case study of de Wijs’ data from a profile in a zinc mine, that incorporation of spatial association and singularity can improve the interpolation result, especially for observed values with significant singularities. Keywords data analysis, spatial mapping, moving average models, multrifractal interpolation.

Introduction Since the term ‘kriging’ was coined by Georges Matheron in the early 1960s on the basis of Krige’s master’s thesis dealing with interpolation of point samples, geostatistics has been rapidly developed as a branch of science and relevant techniques have been commonly applied in many fields of science for mapping, estimation, simulation, and prediction (Journel and Huijbregts, 1978; Goovaerts, 1997). The International Association for Mathematical Geosciences (IAMG) is proud of the invention and further development of the subject by our members. Kriging and other geostatistical techniques have been widely applied outside of geosciences, where users unaware of its origins and mathematical evolution refer to it simply as a type of spatial analysis. The semivariogram, a function of distance between locations, can measure the spatial autocorrelation between values at locations separated by a distance. Models empirically fitted to The Journal of The Southern African Institute of Mining and Metallurgy

* Department of Earth and Space Science and Engineering, Department of Geography, York University, Toronto. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. VOLUME 115

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Synopsis

semivariograms are used for assigning weights to linear equations whose solutions provide weighted averages for kriging data with stationarity (Goovaerts, 1997; Deutsch and Journel, 2008). Traditionally, kriging is for interpolating data in the neighbourhood and estimating values at locations where no data is available. Interpolation algorithms have been developed for a variety of simple, indicator, and higher-order kriging as well as kriging with transformed and compositional data. Algorithms for interpolation of data with anisotropic spatial association (e.g. Chiles and Delfiner, 1999), mixed categorical and/or continuous data (Journel and Huijbregts, 1978; Goovaerts, 1997), and compositional data (Pawlowsky-Glahn and Olea, 2004), have been created. Case studies comparing these methods are available in the literature (e.g. Park and Jang, 2014). Application of kriging depends heavily on stationarity of the mean and second-order moments involving the variogram and standard deviation of a regionalized random variable. Simple kriging (SK) may be applied if the mean of the data has a known but constant trend, whereas ordinary kriging (OK) may be applied if the trend is constant but unknown. If the trend is unknown but follows some polynomial model, other types of kriging accounting for trends can be used (Hansen et al., 2010). In most cases stationarity of second order moments is also assumed. However, the real data, especially exploratory data involved in characterizing mineralization and hazardous events, often does not meet stationarity requirements because of singularities.

Multifractal interpolation method for spatial data with singularities A new approach is the multiple point geostatistics which is a new field of spatial and temporal analysis (Mariethoz and Caers, 2014). Multiple point geostatistics uses training images to quantify and to include structural information into stochastic simulation (Guardiano and Srivastava, 1993; Strebelle, 2002). The multifractal interpolation method (MIM) based on multifractal theory (Cheng, 1999a, 2000) has been developed for the analysis and interpolation of data with singularities. Multifractal theory integrates both spatial association and local singularities and can enhance and retain the local structure properties (Cheng, 2006a,b). This paper introduces a generalized binomial multiplicative cascade process to demonstrate the generation of one- and two-dimensional data with multi-scale singularities which can be modelled by asymmetrical multifractal distribution. It will then introduce a generalized moving average mathematical model for analyzing and interpolating between data with singularities. Finally the method will be demonstrated by a onedimensional example.

Multiplicative cascade processes and multifractal distributions Singular physical, chemical, and biological processes can result in anomalous energy release, mass accumulation, or matter concentration, all of which are generally confined to narrow intervals in space or time (Cheng, 2007a). The end products of these nonlinear processes can be modelled as fractals or multifractals. Singularity is a property of nonlinear natural processes, examples of which include, but are not limited to, cloud formation (Schertzer and Lovejoy 1987), rainfall (Veneziano 2002), hurricanes (Sornette, 2004), flooding (Malamud et al., 1996; Cheng 2008; Cheng et al., 2009), landslides (Malamud et al., 2004), forest fires (Malamud et al.,1996), earthquakes (Turcotte 1997), mineral deposits and related geochemical anomalies (Agterberg, 1995; Cheng et al., 1994; Xie and Bao, 2004; Cheng and Agterberg, 2009), solar wind turbulence (Macek, 2007), DNA series (Rosas et al., 2002), heat flow at the mid oceanic ridges (to be published by the author elsewhere) and landscape textures (Plotnick et al., 1993). Multifractal modeling involves quantification of multi-scale singularities and various types of properties associated with spatial distribution of the singularities (Halsey et al., 1986; Feder, 1988; Mandelbrot, 1989; Evertsz and Mandelbrot, 1992). This section introduces an asymmetrical cascade process that generates results with singularities characterized by asymmetrical multifractal models. There are several formalisms for describing multifractal distributions, one of which is the multifractal model based on the partition function (Halsey et al., 1986). This model involves three functions: the mass exponent function or Renyi exponent τ(q), the coarse Hölder exponent α(q), and the fractal spectrum function f(α) (Halsey et al., 1986). In order to demonstrate the singularities and their distributions in one- or two-dimendional data, I introduce the theories and concepts of multiplicative cascade processes (MCPs), which play a fundamental role in quantifying turbulent intermittency and other nonlinear processes (Schertzer and Lovejoy 1985, Schertzer et al. 1997). MCPs have been extensively discussed in the literature (e.g. Gupta

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and Waymire 1993, Over and Gupta 1996, Menabde and Sivapalan 2000; Serinaldi 2010). The model of de Wijs is a simple two-dimensional multiplicative cascade model (de Wijs 1951, Agterberg 2001, 2007a) described in terms of multiplicative canonical cascade processes (Lovejoy and Schertzer, 2007). Other modifications exist, e.g. a cascade model with functional redistribution rate (Agterberg 2007b); a two-dimensional cascade model with anisotropic partition (Cheng 2005); a generalized two-parameter binomial multiplicative model as proposed by Koscielny-Bunde et al. (2006) and applied for describing multifractal spectra of runoff time series; a three-parameter binomial multifractal model proposed by Macek (2007) and applied to characterize solar wind turbulence data based on a generalized two-scale weighted Cantor set for characterizing asymmetrical multifractal distribution; a two-dimensional cascade model with variable and conditional dependence partition (Cheng, 2012); and a five-parameter binomial multiplicative cascade model has been recently proposed by the author (Cheng, 2014) for describing fundamental multifractal indices characterizing asymmetrical multifractal distribution of real-world data. The five-parameter generalized multiplicative cascade processes involve the partitioning of with a length L (e.g. in unit of meter) into h equal segments of which m1 receive d1 (> 0) and m2 receive d2 (> 0) proportion of mass in the previous segment, respectively, where m1 + m2 ≤ h. For a closed system with preservation of unit measure (e.g. with total mass M), d1 + d2 = 1. Otherwise, d1 + d2 < 1 or d1 + d2 > 1, corresponding to a loss or gain of mass during the cascade process, respectively. At the nth partition, the segment length will be εn = L(1/h)n; the segments are subject to k times segments with measure d1/m1 and n - k times segments with measure d2/m2, and thus the measures of these segments are κ = M(d1/m1)k(d2/m2)n-k. Therefore, the numbers of segments with k will be Nk = m1k m2n-k(kn). Letting n →∞, we can find series of n and k with k = ξn, 0 ≤ ξ ≤ 1, where ξ is independent of n or k. We then obtain the following relationships [1] where α is the coarse Hölder exponent which quantifies the singularity of the distribution of [2] and the subset of segments with singularity α is an intertwined set which is a fractal with fractal dimension f(α). The number of segments in each of the intertwined fractals can be expressed as [3] where f(α) is the fractal dimension spectral function, which can be expressed as [4]

The Journal of The Southern African Institute of Mining and Metallurgy

Multifractal interpolation method for spatial data with singularities

[5] where the maximum and minimum values of singularity α are [6] assuming d1/m1 > d2/m2; otherwise, the two extremes will be reversed. Accordingly, the corresponding fractal dimensions with special singularities are shown to be [7] The multifractality and symmetry of the multifractal distribution can be characterized by the asymmetry and multifractality indices [8]

The asymmetry index corresponds to the ratio of the values m2 and m1; whereas the multifractality is proportional to the ratio of average measures, (d1/m1)/(d2/m2).

Singularities and nonstationarity The singularity in the multifractal model introduced in the previous section characterizes how the statistical behaviour varies as the measuring scale changes. For example, at some locations the mean value calculated from the neighbourhood values might be independent of the size of the vicinity within which the values are averaged. In other cases, the mean value might proportionally depend on the size of the vicinity. The former case represents nonsingular location but the latter is for singular location. Singularity property has been commonly observed in geochemical and geophysical quantities (Cheng et al., 1994). Generally speaking, as the size of segment εn → 0 (n →∞), then the measure defined for each segment (Equation [1]) tends to zero and the number of segments tends to infinity. In order to explain the singularity of geochemical and geophysical quantities according to the notation of the multifractal model shown in Equations [1]–[7], the ‘fractal density’ of measure with singularity (α) is defined by the author as the ratio of mass (εn) over scale α εn which can be calculated as follows: [9] the new fractal density ρα has a unit of M over the unit of α L , for example, g/m0.3. [10] where α = 1- α represents an index quantifying the local singularity of the measure at locations with singularity α. The ordinary density ρ can be decomposed into two components: fractal density ρα and ε- α, the former is independent of scale ε whereas the latter dependent on the The Journal of The Southern African Institute of Mining and Metallurgy

scale. The former component is a non-singular component and the latter is singular component if the singularity index α ≠ 0. The inconvenience property of the measure with following singularity properties implies nonstationarity of the measure or the density (Cheng, 1999a): (1) α = 1 or α = 0, if ρ(ε) = constant, independent of scale size ε (2) α < 1, α > 0, if ρ(ε) is a decreasing function of ε, which normally implies the ‘convex’ property of (ε) at the location with α (3) α > 1, α < 0, if ρ(ε) is an increasing function of ε, which normally implies the ‘concave’ property of (ε) at the location with α. Therefore, the α-values as the fractal dimension of the fractal density (Δα – value as the co-dimension) can be used to characterize the nonlinear structural property of the measure (ε). This approach has already been used for texture analysis of remote sensing Landsat TM images (Cheng, 1997b, 1999c), in multifractal interpolation of geochemical concentration values for mineral exploration (Cheng, 1999a, 2000a, 2000b), in well log curve reconstruction (Li and Cheng, 2001), flood event modelling (Cheng et al., 2009), in hyperspectral image analysis (Neta et al., 2010), faults and geochemistry (Wang et al., 2013), and in geochemical element concentration mapping (Chen et al., 2007; Zuo et al., 2009). In order to introduce how singularities can be included in data interpolation, we here introduce the following scaleinvariant property of the measure, (ε) and density, ρ(ε). Due to the properties of power-law type functions we can associate the densities at two different scales (εn and εm) as follows [11] The MIM to be introduced in the next section is developed according to the scale invariance property (Equation [11]).

MIM incorporating spatial association and singularity Statistical properties derived at one scale may be used to estimate properties at another scale on the basis of the scaling property (Cheng, 1999a, 2000). The main purpose of data interpolation, including kriging, is to estimate values at unknown locations based on the neighbourhood values and their spatial association with the value being estimated. Spatial association represents a type of statistical dependency of values at separate locations. If the value at a location (x) is considered as the realization of a regionalized random variable Z(x), the spatial association or variability can be measured by means of the variogram [12] where the semivariogram γ(x, h) is a function of vector distance h separating locations x and x + h, measuring the symmetrical variability between Z(x) and Z(x + h). Under an assumption of second-order stationary, the semivariogram (Equation [12]) becomes a function of h that is independent of location x. This strong assumption on the nature of the regionalized random variable is generally required in kriging. Equation [12] has been commonly used for structural VOLUME 115

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It can be seen from Equations [1] to [4] that both the measure κ and Nk follow power-law relationships at scale εn. Since the value of ξ falls within the range [0, 1], the value of the singularity index takes any value between αmin and αmax following the linear relationship between α(ξ) and ξ:

Multifractal interpolation method for spatial data with singularities analysis and interpolation in geostatistics (Journel and Huijbregts, 1978). It has also been applied for texture analysis in image processing (Atkinson and Lewis, 2000; Herzfeld and Higginson, 1996). To incorporate both spatial association and singularity in supporting the interpolation model based on Equation [11], the following average density within a small vicinity ⏐ (x0, ε) around location x0 with linear size ε was defined by Cheng (2006) [13] Assume that Equation [11] holds true within a range of window sizes, ε ≤ ε ≤ εmax. Then the average density ρ(ε, x0) within the window ⏐ (x0, ε), where it may not contain samples with observed values, can be associated with the average density within the larger window ⏐ (x0, εmax) where it contains samples with observed values and can be estimated by kriging as follows: [14] Equation [14] is a general weighted average model that can be used to estimate the value at the centre of ρ(ε, x0) from the neighbourhood values within ⏐ (x0, εmax) (Cheng, 1999a, b, 2006). Since the above discussions are valid for all dimensions and here we will use E to present the dimension of problem, E=1, 2, 3 stand for 1D, 2D and 3D problems. It has the following properties : (1) If it does not show singularity, α = E or α = 0, then Equation [14] reduces to the ordinary moving average function that has been used commonly in kriging and other data interpolation methods (2) If all locations show the same singularity strength with α = constant or α = constant, then Equation [14] becomes the same as the ordinary moving average function used in kriging and other methods (3) If the singularity varies from location to location, α ≠ constant or α ≠ constant, then Equation [14] is equivalent to the ordinary moving average function multiplied by a scale ratio factor, (ε / εmax)-Δα, with three possible situations given ε < εmax:

obvious advantages: it not only improves the accuracy of the interpolated results but also retains the local structure of the interpolation map. The latter property is essential for geochemical and geophysical data processing and for pattern recognition. This will be demonstrated using the assay values from the Pulacayo sphalerite-quartz vein in Bolivia studied by De Wijs (1951).

Analysis of de Wijs’s Bolivia sphalerite data De Wijs (1951) studied assay values from the Pulacayo sphalerite-bearing quartz vein in Bolivia. Along a drift 118 channel samples had been collected at 2.00 m intervals (Figure 1A). These values have been analysed by multifractal modeling and spatial analysis (Cheng and Agterberg, 1996; Cheng, 1997b) and they can be approximated by fiveparameter binomial multiplicative cascade models (Cheng, 1999c, 2014). The fractal dimension spectra of the distribution of de Wijs's zinc values are estimated by the gliding window method with order of moment ranging from -20 to 20 and cell size ε ranging from 2 m to 30 m (Cheng, 1999c) (Figure 2) and fitted by the five-parameter binominal multiplicative cascade model (Cheng, 2014) (seen in Figure 2). The estimated values of f(α) are as follows

Therefore,

[15] These properties indicate that if the data used for interpolation satisfies a multifractal distribution, then Equation [14] must be used as an extended form of the ordinary weighted averaging model. In this case, the scale ratio factor modifies the ordinary average in such a way that if there is positive singularity with α > 0, then the new result is to be increased by a factor (Equation [15]), whereas if α < 0, then the new result is reduced by a factor (Equation [15]). This modification is reasonable because α > 0 and α < 0 correspond to convex and concave properties of the surface ρ(ε, x0) around the location x0, respectively. The new model (Equation [14]) not only describes the spatial association reflected in the calculation of the weight λ, but also incorporates the singularity characterized by the singularity index α. The new model therefore has two

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Figure 1—Multifractal interpolation of de Wijs’s zinc values (de Wijs, 1951). (A) Observed values %Zn; (B) singularity α-values estimated by multifractal interpolation method; (C) correlation coefficients associated with the estimation of the α-values; and (D) interpolated results for zinc values. Blue dots represent the observed values; red and yellow lines present the results obtained by moving averaging method and the multifractal interpolation method, respectively The Journal of The Southern African Institute of Mining and Metallurgy

Multifractal interpolation method for spatial data with singularities anisotropic cascade processes (Cheng, 2004), more general structural property and generalized self-similarity characterized by the singularity can be incorporated in the data interpolation. The multifractal interpolation based on prior knowledge and training images should be further explored

Acknowledgements

The results indicate that the distribution follows a multifractal model that is nearly symmetrical (Δαmax - min > 0). In order to show the distribution of singularity and the data interpolation results, Figure 1B shows the resulting distribution of singularity values calculated for the data and the correlation coefficients associated with the linear model fitted after double log-transformation of measure and scale. It shows that the estimated values of α are within a range from 0.6 to 1.4 with correlation coefficients greater than 0.975 (Figure 1C). Figure 2D illustrates the interpolated and reconstructed results obtained by means of MIM and moving averaging. The yellow line represents the results obtained using MIM with window size 20 m (10 point values) and the thicker red line represents the results obtained using the averaging technique with window size 6 m (two to three point values). The blue dots represent the observed data. Comparing the results obtained using MIM and moving averaging shows that MIM provides better results not only with smaller fitting errors for the observed data, but also that localized multifractality of the data is preserved.

Discussion and conclusions It has been demonstrated that the multifractal distribution generated by binomial multiplicative cascade processes has multiple singularities that can be quantified by singularity index and fractal dimension spectrum. According to MIM, the singularity of multifractally distributed data can be used in data interpolation for mapping purposes with the localized structural properties (multifractality) preserved. The model used in MIM can be considered as an extended form of the ordinary moving average or weighted average used in various data interpolation methods, including inverse distance weighting and kriging. For most quantities in exploration geochemistry showing singularities, in order to retain the localized structural property, the multifractal interpolation method can be used to extend the ordinary moving average techniques, including ordinary kriging. Since the singularity can be estimated using various methods, for example, integration of multiple patterns by weights of evidence method (Cheng, 2012) and other The Journal of The Southern African Institute of Mining and Metallurgy

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Figure 2 – Results showing fractal dimension functions (dots) calculated for de Wijs's zinc values by means of gliding box method (Cheng, 1999) and binomial multiplicative cascade model with m1=1, m2=1.1, h = 2.1, d1 = 0.38 and d2 = 0.63. αmax-min = 0.75, τ = 0.033 and R = 1.1

The author wishes to thank Professor Richard Minnitt for accepting this paper for the Danie Krige Commemorative Volume. Thanks are due to Dr Frits Agterberg and Dr Zhijun Chen for their critical review of the paper and constructive comments. This research has been jointly supported by a research project on ‘Quantitative models for prediction of strategic mineral resources in China’ (201211022) by China Geological Survey, and by NSERC Discovery Research ‘Research and development of multifractal methods and GIS technology for mineral exploration and environmental assessments’ (ERC-OGP0183993).

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XIE, S. and BAO, Z. 2004. Fractal and multifractal properties of geochemical fields. Mathematical Geology, vol. 36. pp. 847–864. ZUO, R., CHENG, Q., AGTERBERG, F.P., and XIA, Q. 2009. Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration, vol. 101. pp. 225–235. N The Journal of The Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2015/v115n3a10 ISSN:2411-9717/2015/v115/n3/a10

High-order additions to platinum-based alloys for high-temperature applications by B.O. Odera*†‡§, M.J. Papo‡**, R. Couperthwaite**, G.O. Rading†§, ∞ D. Billing , and L.A. Cornish*†‡

Platinum-based alloys are being developed with microstructures similar to nickel-based superalloys for potential high-temperature applications in aggressive environments. Since the chemistries of nickel and platinum are similar, Pt-based alloys can be made with gamma prime ~Pt3Al precipitates in a gamma (Pt) matrix. Currently, the Pt-Al-Cr-Ru system is one of the bases for developing Pt-based alloys, where Al allows the formation of the Pt3Al precipitate and also gives protection from the alumina scale formed, Cr provides oxidation resistance and stabilization for the L12 ~Pt3Al phase, and Ru provides solid solution strengthening in the (Pt) matrix. Four Pt-Al-Cr-Ru-V and two Pt-Al-Cr-Ru-V-Nb alloys were made, with compositions based on a quaternary alloy, ~Pt82:Al12:Cr4:Ru2, which had previously been identified as having optimum properties. Four of the ascast alloys had the targeted two-phase structure of ~Pt3Al and (Pt), and two were single-phase ~Pt3Al. Vanadium partitioned more to (Pt) than to ~Pt3Al. There was an improvement in hardness compared to the quaternary alloys. The best addition of V was ~15 at.%; higher additions resulted in brittle intermetallic phases of the Pt-V system. The effect of Nb could not be ascertained because of its high losses. Keywords high-order Pt-based alloys, scanning electron microscopy, X-ray diffraction, microhardness.

Introduction Although the nickel-based superalloys (NBSAs) have excellent mechanical properties at high temperatures, they are limited by their maximum application temperature, which is dictated by the melting point of the nickel solid-solution matrix. Currently, the maximum temperature at which NBSAs operate is about 1100°C, which is approximately 90% of their melting temperature (Reed, 2004). Although thermal-barrier coatings can be used to increase the application temperature, the component is still restricted by the melting point of the substrate, and for safety reasons the maximum attainable temperatures are still limited (Goward, 1998). If the operating temperatures could be increased, there would be a number of advantages. Higher temperatures improve the efficiency of turbine engines, enabling greater thrust, improved fuel efficiency and thus reduced pollution. There is increasing interest in using a different alloy system with a much higher melting point. Intermetallic compounds have The Journal of The Southern African Institute of Mining and Metallurgy

* School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa. † African Materials Science and Engineering Network (AMSEM), a Carnegie-IAS Network. ‡ DST/NRF Centre of Excellence in Strong Materials, hosted by University of the University of the Witwatersrand, Johannesburg, South Africa. § Department of Mechanical and Manufacturing Engineering, University of Nairobi, Nairobi, Kenya. ** Advanced Materialls Division, Mintek, Randburg, Johannesburg, South Africa. ∞ School of Chemistry, University of the Witwatersrand, Johannesburg, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Oct. 2013; revised paper received Oct. 2014. VOLUME 115

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Synopsis

been considered because of their hightemperature strengths, but the inherent roomtemperature brittleness of these materials remains problematic (Wolff and Sauthoff, 1996). One solution would be to base the new materials on alloys with high melting points and use the naturally-occurring precipitates of that system. This should also help keep the processing cost to a minimum. These systems would be similar in structure to the NBSAs with a matrix and a fine dispersion of small, preferably coherent, and hence stable, precipitates. Face-centred cubic (fcc) structures are advantageous because, being close-packed, they are more creep resistant. Refractory metals (e.g. Nb, Mo, and W) have been considered because of their high melting points (2477°C, 2623°C, and 3422°C respectively), but their more open body-centred cubic structures are more susceptible to creep, as well as being prone to rapid oxidation, even at relatively low temperatures (Briant, 1994). Platinum group metals (platinum, iridium, and rhodium) were targeted because they have high melting points, good environmental resistance, and a mostly fcc structure (Yamabe et al., 1996; Wolff and Hill, 2000).

High-order additions to platinum-based alloys for high-temperature applications Platinum-based alloys are being developed to have microstructures very similar to nickel-based superalloys for potential for high-temperature applications in aggressive environments (Wolff and Hill, 2000; Cornish et al., 2009). Since the chemistries of nickel and platinum are similar, Ptbased alloys can be made with gamma prime ~Pt3Al precipitates in a gamma (Pt) matrix. Although similar (Massalski, 1990), the Pt-Al phase diagram is more complicated than that of Ni-Al in that Pt3Al has at least three variants, depending on the temperature, whereas Ni-Al has only one, the L12 phase, which is ordered fcc. In Pt-Al, the L12 phase is the highest temperature ~Pt3Al phase, whereas the lower temperature ~Pt3Al phases are tetragonal, and so less desirable for components subjected to ranges of temperature, although they can be stabilized by various additions. Currently, the Pt-Al-Cr-Ru system is one of the bases for developing Pt-based alloys, where Al allows the formation of the Pt3Al precipitate and also gives protection by forming alumina scale, Cr provides oxidation resistance and stabilization for the L12 ~Pt3Al phase, and Ru provides solid solution strengthening in the (Pt) matrix. Other researchers (Wenderoth et al., 2005; Völkl et al., 2009) have developed alloys based on Pt-Al-Cr-Ni. In an investigation of some Pt-Al-Cr-Ru alloys, it was found that both Ru and Cr partition preferentially to the (Pt). The solubility ranges of Ru and Cr were found to be 0.5 to 0.7 at.% Ru and 1.3 to 1.8 at.% Cr in the precipitates, and between 1.2 to 2.4 at.% Ru and 2.7 to 4.6 at.% Cr for the (Pt) phase (Shongwe et al., 2008, 2010). The best quaternary alloy in terms of high precipitate volume, microstructure, and hardness to date is ~Pt82:Al12:Cr4:Ru2 (at.%) (Shongwe et al., 2008, 2010). It was postulated that a quinary addition to the optimum Pt-Al-Cr-Ru quaternary alloy could improve the melting temperature and stability and increase the volume fraction of ~Pt3Al. This would improve mechanical properties, such as hardness and strength, at high temperatures while still retaining the oxidation and corrosion resistance in these aggressive environments.

It is well recognized (Cornish et al., 2009) that Pt-based alloys have serious disadvantages due to their price and their density. Attempts are ongoing to substitute some of the Pt with another element, which is less expensive and less dense, but still retaining the high-temperature capabilities and the required microstructure. Addition of niobium to Pt-based alloys resulted in elevated strengths at high temperatures through precipitation-hardening (Wenderoth et al., 2008), and since vanadium is near Nb in the periodic table and has a smaller atomic radius than Nb, it may also act as a precipitation strengthener in addition to solid solution strengthening. The binary phase diagrams of Nb-Pt and Pt-V have the melting point of (Pt) increasing with increasing additions of Nb and V respectively (Massalski, 1990). This could counteract the effect of the Al-Pt eutectic with the decreasing (Pt) melting point. In order to predict the effect of Nb and V on the Pt-based alloys, phase diagrams were studied for (which had not been reported before): Pt-Al-Nb (Ndlovu, 2006; Samal and Cornish, 2010) and Pt-Cr-Nb (Mulaudzi, 2009), as well as Pt-Al-V (Odera et al., 2012a; Odera, 2013a) and Pt-Cr-V (Odera et al., 2012b; Odera, 2013a). The results indicate how the additions affect the microstructure and give an idea of the maximum additions without losing the two-phase structure, or forming other phases.

Experimental procedure Alloy buttons weighing ~2 g each were prepared for compositions chosen (Tables I and II) from previous work (Ndlovu, 2006; Shongwe et al., 2008, 2010; Mulaudzi, 2009; Samal and Cornish, 2010; Odera et al., 2012a, 2012b; Odera, 2013) for a series of alloys based on Pt-Al-Cr-Ru with additions of Nb and/or V. The elemental components had 99.9% purity, except for V (99.6% purity). The samples were manufactured by arc-melting under argon, on a water-cooled copper hearth, with Ti as an oxygen-getter, and each was turned over and re-melted three times in an attempt to

Table I

Composition analyses (at.%) for the as-cast alloys Alloy

Phase

1 Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9

63.9±1.0 65.2±0.7 62.3±1.2

12.2±0.4 5.0±1.1 27.4±1.9

4.3±0.5 4.9±0.2 2.5±0.3

0.7±0.4 1.4±0.3 0

18.9±0.9 23.5±0.7 7.8±0.7

(Pt) ~Pt3Al

2 Pt69.5:Al11.5:Cr4.2:Ru0.6:V14.2

69.5±0.5 70.2±1.4 66.9±1.6

11.5±0.5 8.4±1.1 22.5±3.7

4.2±0.3 4.4±0.3 3.2±0.3

0.6±0.3 1.0±0.6 0

14.2±0.5 16.1±1.1 7.5±2.1

(Pt) ~Pt3Al

74.7±0.6

11.2±0.4

4.0±0.2

0.6±0.4

9.5±0.2

~Pt3Al

78.2±0.9

12.2±0.7

3.8±0.2

0.6±0.1

5.2±0.3

~Pt3Al

5 Pt63.2:Al12.9:Cr4.0:Ru0.7:V19.0:Nb0.6

63.4±1.1 62.7±0.6 56.1±0.8

12.9±1.7 6.3±0.6 31.1±0.9

4.0±0.1 5.1±0.4 3.9±0.3

0.7±0.3 1.0±0.4 0.1±0.1

19.0±0.9 23.3±0.9 8.8±0.4

0.6±0.4 1.6±0.7 0

(Pt) ~Pt3Al + (Pt) eutectic

6 Pt71.7:Al12.8:Cr4.9:Ru1.1:V9.9:Nb0.3

71.0±2.1 73.8±1.6 64.4±1.2

12.8±1.8 8.8±2.0 29.4±1.0

4.9±0.4 4.5±0.4 2.7±0.2

1.1±0.8 1.0±0.8 0

9.9±0.5 10.9±1.3 3.6±0.5

0.3±0.1 1.1±0.8 0

(Pt) ~Pt3Al

Pt75.2:Al11.2:Cr4.0:Ru0.6:V9.5 4 Pt78.7:Al12.2:Cr3.8:Ru0.6:V5.2

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High-order additions to platinum-based alloys for high-temperature applications Table II

Composition analyses (at.%) for the alloys heat treated at 1000°C for 1500h Pt

Phase

1H Pt61.7:Al13.9:Cr4.1:Ru0.6:V19.6

61.8±0.7 61.8±1.3 57.9±0.4

13.9±0.9 7.1±1.3 37.6±0.8

4.1±0.3 4.7±0.2 1.8±0.3

0.6±0.2 1.4±0.4 0

19.6±0.4 25.0±1.4 2.7±0.4

~Pt2V ~Pt3Al

2H Pt69.8:Al11.4:Cr4.4:Ru0.5:V14.1

69.6±1.3

11.4±1.0

4.4±0.2

0.5±0.4

14.1±0.7

~Pt3Al

3H Pt83.0:Al10.1:Cr3.7:Ru0.8:V9.0

76.4±1.0 75.8±1.0 75.8±2.0

10.1±0.3 10.4±0.6 10.5±1.4

3.7±0.6 4.3±0.3 4.2±0.5

0.8±0.1 0.8±0.2 0.5±0.1

9.0±0.4 8.7±0.3 9.0±0.4

~Pt3Al ~Pt3Al

4H Pt83.0:Al8.7:Cr3.7:Ru0.8:V4.3

82.5±0.7 82.6±2.0

8.7±0.6 6.8±1.5

3.7±0.4 4.1±0.5

0.8 1.6±1.3

4.3±0.6 4.9±0.4

(Pt) with ~Pt3Al pptn.

80.4±2.0

10.1±1.7

3.9±0.4

1.2±0.8

4.4±0.5

5H Pt53.9:Al18.0:Cr4.3:Ru1.4:V21.9:Nb1.0

53.4±1.8 56.4±1.6 57.3±1.3 47.8±1.9 55.6±0.3

18.0±1.4 8.1±1.9 24.8±1.6 7.7±2.4 17.0±1.3

4.3±0.3 5.1±0.5 2.4±0.3 7.6±0.9 4.5±0.5

1.4±0.9 2.2±0.6 0.2 2.6±0.1 1.3±0.3

21.9±1.3 26.9±0.3 14.8±1.7 34.3±3.4 21.6±0.9

1.0±0.8 1.3±0.8 0.5±0.1 0 0

~Pt2V ~Pt3Al ~PtV ~PtV + ~Pt2V

6H Pt74.1:Al11.5:Cr4.5:Ru0.5:V9.3:Nb0.3

73.9±0.6

11.5±0.6

4.5±0.3

0.5±0.2

9.3±0.3

0.3±0.1

~Pt3Al

achieve homogeneity. The buttons were halved, and one half was prepared metallographically in the as-cast condition, with the other half being sealed in an evacuated ampoule, then annealed at 1000°C for 1500 hours and quenched in water, then similarly prepared. The microstructures of all the alloys were analysed using a scanning electron microscope (SEM) model HR-NovaNano SEM200, using both secondary (SE) and backscattered electron (BSE) modes. An accelerating voltage of 20.0 kV was used on all the samples and the working distance ranged from 5.0 to 5.5 mm. Area and spot phase compositions were obtained from energy dispersive X-ray spectroscopy (EDX), taking an average of at least five different measurements in different places. The overall area from which composition measurements were taken was 1600 μm2. The interaction volume of the X-rays can be as much as 3 μm across and deep (especially for higher atomic number elements and the necessary high accelerating voltage). Usually an accuracy of ±1 at.% would be expected for this technique. A Bruker D2 Phaser Diffractometer with Lynxeye detector using Co Kα radiation of wavelength 1.78897 Å was used for X-ray diffraction (XRD) to confirm the phase identities. The diffraction angles ranged between 2θ = 20° to 2θ = 100°. The generator settings were 30 kV and 10 mA and the step size was 0.0260°. Microhardness tests were performed after etching in a solution of 10 g NaCl in 100 cm3 HCl (32% vol. concentration), using a DC power supply and a voltage range of 9 V to 12 V (Odera et al., 2012c). A Vickers microhardness tester was used with a load of 300 g, and at least five measurements taken.

Results There were losses during melting, especially of Ru (up to 1.5 at.%) and Nb (up to 1.7 at.%), even higher than before (Cornish et al., 2009). The EDX results of the as-cast alloys The Journal of The Southern African Institute of Mining and Metallurgy

~Pt3Al with (Pt) pptn.

are shown in Table I, whereas those of the heat-treated alloys are given in Table II. The last columns of Tables I and II show the phases that were confirmed by XRD analysis. Table III is a summary of the data from Rietveld analysis, while Table IV contains the hardness data.

Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9 (at.%) (alloys 1 and 1H) The as-cast Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9 (at.%) alloy (Figure 1a) had dendrites of (Pt) in a matrix of ~Pt3Al. The expected eutectic was not visible, but it could have been too fine to resolve. Vanadium partitioned preferentially to the (Pt) at ~23.5 at.% V compared to ~7.8 at.% V in ~Pt3Al. After heat treatment, the (Pt) had transformed to ~Pt2V (Figure 1b) and the overall composition changed slightly, with 0.1 at.% Ru lower content and 0.7 at.% V higher content (although the limit of the detector was ±1 at.%). The phase transformation was confirmed by XRD, as shown in Figure 2.

Pt69.5:Al11.5:Cr4.2:Ru0.6:V14.2 (at.%) (alloys 2 and 2H) The microstructure of as-cast Pt69.5:Al11.5:Cr4.2:Ru0.6:V14.2 (at.%) was very similar to the previous alloy, and the grain boundaries were irregular (Figure 3a). Most of the V went into solution in the (Pt). After heat treatment, the alloy became mostly single-phase ~Pt3Al, with very little change in overall composition, with Ru and V decreasing by 0.1 at.% as measured by EDX (Tables I and II), and the grain boundaries had become much smoother, indicating that the heat treatment temperature and duration had an effect on the microstructure (Figure 3b).

Pt75.2:Al11.2:Cr4.0:Ru0.6:V9.5 (at.%) (alloys 3 and 3H) The as-cast Pt75.2:Al11.2:Cr4.0:Ru0.6:V9.5 (at.%) alloy differed from the other as-cast alloys in that it was single-phase ~Pt3Al, which was confirmed by XRD. After heat treatment, the alloy was still single-phase and the grain boundaries had also become more regular and shorter. VOLUME 115

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Alloy

High-order additions to platinum-based alloys for high-temperature applications Table III

Rietveld analyses of selected samples Alloy

Phase

Proportion, %

Pt3Al

53.5

(Pt)

0.1

Pt3V

46.4

RuAl

0.2

Space group

Pt3Al

59.5

– Pm 3 m – Fm 3 m – Pm 3 m – Pm 3 m – Pm 3 m

Pt61.7:Al13.9:Cr4.1:Ru0.6:V19.6

Pt2V

36.2

Immm

CrPt

4.3

Pt3Al

79.7

(Pt)

0.1

Pt3V

19.5

RuAl

0.7

P213 – Pm 3 m – Fm 3 m – Pm 3 m – Pm 3 m – Pm 3 m – Pm 3 m – Pm 3 m – Pm 3 m – P 4/mbm – Fm 3 m – I 4 /mmm

Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9

Pt69.5:Al11.5:Cr4.2:Ru0.6:V14.2

Pt3Al

66.6

Pt69.8:Al11.4:Cr4.4:Ru0.5:V14.1

Pt3V

18.6

Cr3Pt

14.8

Pt3Al

90.28

Pt3Al

95.7

(Pt)

1.4

Pt3V

2.1

Pt63.2:Al12.9:Cr4.0:Ru0.7:V19.0:Nb0.6

– Pm 3 m

RuAl

0.8

Pt3Al

62.0

– P 4/mbm

PtV

34.8

P mma

Pt2V

3.2

Immm

Pt3Al

94.1

Pt71.7:Al12.8:Cr4.9:Ru1.1:V9.9:Nb0.3

Pt3V

2.7

RuAl

3.2

Lattice parameter, Å

Pt53.9:Al18.0:Cr4.3:Ru1.4:V21.9:Nb1.0

Figure 1a—SEM-BSE image of as-cast Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9 (at.%), showing dark (Pt) and light ~Pt3Al

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– Pm 3 m – Pm 3 m – Pm 3 m

a = 3.86255 a = 3.96781 a = 3.87000 a = 3.01516 a = 3.86742 a = 2.73018 b = 8.27452 c = 3.83065 a = 4.85507 a = 3.86255 a = 3.96796 a = 3.87000 a = 3.01639 a = 3.86362 a = 3.81112 a = 3.87362 a = 3.87750 a = 5.47216 c = 7.74843 a = 3.96532 a = 3.87182 c = 8.08972 a = 3.02034 a = 5.39536 c = 8.12855 a = 3.41466 b = 2.70016 c = 4.76353 a = 2.71709 b = 8.32022 c = 3.79396 a = 3.88863 a = 3.89561 a = 3.03984

Figure 1b—SEM-BSE image of annealed Pt61.7:Al13.9:Cr4.1:Ru0.6:V19.6 (at.%), showing a twophase structure of dark ~Pt2V and light ~Pt3Al The Journal of The Southern African Institute of Mining and Metallurgy

High-order additions to platinum-based alloys for high-temperature applications

Figure 3a—SEM-BSE image of as-cast Pt69.5:Al11.5:Cr4.2:Ru0.6:V14.2 (at.%), showing dark ~Pt3Al and light (Pt), with irregular grain boundaries

Pt78.7:Al12.2:Cr3.8:Ru0.6:V5.2 (at.%) (alloys 4 and 4H) Similar to the previous alloy, Figure 4a shows that as-cast Pt78.7:Al12.2:Cr3.8:Ru0.6:V5.2 (at.%) was single-phase ~Pt3Al, as confirmed by XRD. The different contrasts are due to orientation (Cornish et al., 2008), since the analyses were within 2 at.% of each other. After heat treatment, the alloy had lost some Cr and V and was two-phase, with each phase containing precipitates of the other phase, which brought the analysed compositions closer together (Figure 4b). However, the low contrast between the phases made the microstructure difficult to discern. The (Pt) phase had higher Pt, Ru, and V with lower Al, and unlike the other samples, (Pt) was the lighter phase because it contained less V. The solid-state precipitations indicated retreating solvi of ~Pt3Al and (Pt) with decreasing temperature.

Figure 2b—XRD pattern for annealed Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9 (at.%), showing peaks for Pt and ~Pt2V

Figure 3b—SEM-BSE image of annealed Pt69.8:Al11.4:Cr4.4:Ru0.5:V14.1 (at.%), showing mainly singlephase ~Pt3Al with grains at different orientations

ternary alloy of average composition Pt59.1:Al23.1:V17.8 (at.%) (reported as alloy 2 (Odera, 2013)), with the dendrites being (Pt) of higher Pt, Ru, and V and lower Al content, with a ~Pt3Al + (Pt) eutectic. Although the targeted Nb content was 5 at.%, Nb was lost during melting, resulting in an average content of only 0.6 at.%. After annealing, the structure was much more complex (Figure 5b), and the composition was also slightly different. The (Pt) phase had transformed to ~Pt2V and ~PtV (although its composition was fairly similar to the τ1 ternary phase of Pt-Al-V (Odera, 2013). These phases were interpreted as a coarsened eutectoid. The XRD patterns of the as-cast and annealed samples are shown in Figure 6, and Table III contains the data for Rietveld refinement. The pattern after Rietveld refinement is shown in Figure 6c.

Pt63.2:Al12.9:Cr4.0:Ru0.7:V19.0:Nb0.6 (at.%) (alloys 5

Pt71.7:Al12.8:Cr4.9:Ru1.1:V9.9:Nb0.3 (at.%) (alloys 6

and 5H)

and 6H)

The microstructure of as-cast Pt63.2:Al12.9:Cr4.0:Ru0.7: V19.0:Nb0.6 (at.%) (Figure 5a) was similar to that of the

Figure 7a shows the two-phase microstructure of as-cast Pt71.7:Al12.8:Cr4.9:Ru1.1:V9.9:Nb0.3 (at.%). After annealing (Figure 7b), the alloy was single-phase ~Pt3Al.

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Figure 2a—XRD pattern for as-cast Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9 (at.%), showing peaks for Pt and Pt3Al

High-order additions to platinum-based alloys for high-temperature applications

Figure 4a—SEM-BSE image of as-cast Pt78.7:Al12.2:Cr3.8:Ru0.6:V5.2 (at.%), showing single- phase ~Pt3Al, with irregular grains at different orientations

Figure 5a—SEM-BSE image of as-cast Pt63.2:Al12.9:Cr4.0:Ru0.7:V19.0:Nb0.6 (at.%), showing dark (Pt) dendrites and a eutectic of ~Pt3Al + (Pt)

Figure 6a—XRD pattern of Pt63.2:Al12.9:Cr4.0: Ru0.7:V19.0:Nb0.6 (at.%), showing peaks for (Pt) and ~Pt3Al

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Figure 4b—SEM-BSE image of annealed Pt83.0:Al8.7:Cr3.7:Ru0.8:V4.3 (at.%), showing light (Pt) and dark ~Pt3Al

Figure 5b—SEM-BSE image of annealed Pt53.9:Al18.0:Cr4.3: Ru1.4:V21.9:Nb1.0 (at.%), showing medium ~Pt2V with a light solidstate precipitate, dark ~PtV, coarsened eutectoid of ~PtV + ~Pt2V

Figure 6b—XRD pattern of annealed Pt53.9:Al18.0:Cr4.3:Ru1.4:V21.9:Nb1.0 (at.%), showing peaks for Pt3Al, PtV, and Pt2V The Journal of The Southern African Institute of Mining and Metallurgy

High-order additions to platinum-based alloys for high-temperature applications

Figure 6c—The pattern after Rietveld refinement for annealed alloy 5H, Pt53.9:Al18.0:Cr4.3:Ru1.4:V21.9:Nb1.0 (at.%), confirming the presence of ~Pt3Al, ~PtV, and ~Pt2V

Rietveld Analyses Due to the complexity of the XRD spectra and the difficulty in resolving peaks of the different phases, which were very close, Rietveld analysis was done. The phases identified by XRD are shown in Tables I and II, and the Rietveld analysis data is given in Table III. Obviously, the Rietveld data was more accurate (even though alloy 4 contained only one phase, and this was not 100%), and sometimes detected phases that had been overlooked by XRD. This was particularly useful for the lower temperature phases that form from (Pt) and have similar peaks. In alloy 1, XRD analysis confirmed the presence of ~Pt3Al and (Pt). After Rietveld refinement, the phases identified were ~Pt3Al, ~Pt3V, and traces of (Pt) and RuAl. The solid solution must have gone through a solid-state transformation to form ~Pt3V, which could not be identified by XRD. In Alloy 1H, both XRD and Rietveld analyses identified both ~Pt3Al and ~Pt2V. However, Rietveld also indicated the presence of a small amount of CrPt. In alloy 2, XRD analysis identified ~Pt3Al and (Pt) while Rietveld analysis identified ~Pt3Al, and ~Pt3V with only The Journal of The Southern African Institute of Mining and Metallurgy

Figure 7b—SEM-BSE image of annealed Pt74.1:Al11.5:Cr4.5:Ru0.5:V9.3:Nb0.3 (at.%), showing singlephase ~Pt3Al with grains at different orientations

traces of (Pt) and RuAl. The (Pt) identified by XRD must have gone through solid-state transformation to form ~Pt3V. In alloy 2H, XRD analysis identified only ~Pt3Al peaks. Rietveld analysis confirmed the presence of ~Pt3Al and also identified smaller amounts of ~Pt3V and ~Cr3Pt. In alloy 4, XRD identified ~Pt3Al and this was confirmed by Rietveld analysis. There were smaller amounts of other phases, since Rietveld analysis gave the proportion of the ~Pt3Al as 90.28%. In Alloy 5, XRD analysis identified ~Pt3Al and (Pt). Rietveld analysis confirmed the presence of ~Pt3Al and also identified smaller amounts of ~Pt3V and (Pt). This shows that the alloy was predominantly ~Pt3Al and some of the (Pt) went through solid-state transformation to form ~Pt3V. In alloy 5H, XRD analysis identified ~Pt3Al, ~PtV, and ~Pt2V, and the same phases were found by Rietveld analysis. In alloy 6, XRD analysis identified ~Pt3Al and (Pt) while Rietveld analysis found that the alloy was predominantly ~Pt3Al (94.1%) with small amounts of ~Pt3V (2.7%) and RuAl (3.2%). The solid solution identified by XRD analysis must have gone through solid-state transformation to form ~Pt3V. VOLUME 115

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Figure 7a—SEM-BSE image of as-cast Pt71.7:Al12.8:Cr4.9:Ru1.1:V9.9:Nb0.3 (at.%) showing dark ~Pt3Al and light (Pt), with variations in contrast also due to orientation

High-order additions to platinum-based alloys for high-temperature applications Hardness There were no cracks or noticeable slip lines around the edges of the indentations (probably due to the low load of 300 g), although pincushioning was observed for as-cast alloys 2 and 5. Pincushioning results from sinking of the metal around the flat faces of the pyramid indenter, and gives an overestimate of the diagonal length, hence a lower hardness value. Alloy 1H had slight pincushioning in the annealed condition and again, there were no cracks or noticeable slip lines around the edges of the indentations. The hardness values are given in Table IV. Half of the as-cast alloys had hardnesses above 500 HV0.3. Alloy 5 had the highest hardness: 603 HV0.3. As-cast alloys with ~Pt3Al and (Pt) were generally harder than single-phase ~Pt3Al, and these had higher V contents. The higher hardnesses could not be attributed to (Pt), which is softer, and pure platinum has a hardness of only ~50 HV (Murakami et al., 2008). Only the alloys with higher V content had a two-phase structure of (Pt) and ~Pt3Al. Thus, for the alloys studied, higher V content was the major contributing factor to higher hardness. Figure 8 shows the variation of the hardnesses with vanadium content, indicating a general trend of increased hardness with vanadium content, for the four alloy compositions (four as-cast and three annealed alloys). Also, the hardnesses for the annealed alloys were generally higher than the as-cast alloys.

occurred, since the hardnesses were higher than for the ternary alloys with the (Pt) and ~Pt3Al phases (Hill, 2001; Süss, 2007; Odera, 2013). The alloy compositions were chosen to target two-phase microstructure analogues to NBSAs, although some of the results were unexpected. The (Pt) phase in as-cast alloy 1 transformed to ~Pt2V during heat treatment (alloy 1H). Alloy 2H was two-phase (Pt) + ~Pt3Al in the as-cast state (alloy 2), but became single-phase ~Pt3Al after heat treatment. Alloy 3H remained single-phase ~Pt3Al after heat treatment, while alloy 4H changed from single-phase ~Pt3Al (alloy 4) in the as-cast condition to two-phase ~Pt3Al and (Pt), both with solid-state precipitation of the other phase, indicating sloping solvi for both phases. This would be beneficial for precipitation strengthening because it would potentially allow more precipitation and a higher volume fraction of precipitate to form. The structure of alloy 5H changed substantially from that in the as-cast condition. The solid-solution (Pt), which had been part of a eutectic in the as-cast condition, transformed to two phases, ~PtV and ~Pt2V, during heat treatment. There was also solid-state precipitation in the ~Pt3Al dendrites. The morphology of the ~Pt3Al component of the eutectic also became more rounded. Alloy 6H, which had a two-phase structure in the as-cast condition, became single-phase ~Pt3Al after heat treatment. The required microstructure of fine precipitates of ~Pt3Al

Discussion As-cast alloys 1 and 2 both contained (Pt) dendrites and ~Pt3Al, and V partitioned preferentially to the (Pt), with alloy 1 having ~23.5 at.% V in (Pt) compared to ~7.8 at.% V in ~Pt3Al, and alloy 2 having ~16 at.% V in (Pt) compared to ~10 at.% V in ~Pt3Al. Alloy 6 also contained (Pt) dendrites and ~Pt3Al, with the V partitioned preferentially to (Pt). There was ~11 at.% V in (Pt) and ~3.5 at.% V in ~Pt3Al. All of the Nb went into solution in (Pt). Alloy 5 had (Pt) dendrites and a ~Pt3Al + (Pt) eutectic, with ~23 at.% V in the (Pt) dendrites and ~9 at.% V in the eutectic, and again, all of the Nb was in solution in the (Pt). Alloys 3 and 4 were both single-phase ~Pt3Al, with all the vanadium being in solution in ~Pt3Al (~10 at.% V in alloy 3 and ~5 at.% V in alloy 4). One reason why V was selected as an addition to Pt-Al-based alloys was its high solubility in the solid-solution (Pt). It was hoped that this would increase the solid solution strengthening, and this

Figure 8—Hardness values (HV0.3) plotted against vanadium content (at.%)

Table IV

Hardness values of as-cast and annealed alloys Alloy no.

Average composition (at.%)

As-cast hardness (HV0.3)

Annealed hardness (HV0.3)

Phases present in the as-cast alloys

Phases present in the annealed alloys

1 & 1H

Pt63.9:Al12.2:Cr4.3:Ru0.7:V18.9

537±13

700±20

~Pt3Al, (Pt)

~Pt3Al, ~Pt2V

2 & 2H

Pt69.5:Al11.5:Cr4.2:Ru0.6:V14.2

428±11

482±24

~Pt3Al, (Pt)

~Pt3Al

3 & 3H

Pt75.2:Al11.2:Cr4.0:Ru0.6:V9.5

377±8

359±9

~Pt3Al

4 & 4H

Pt78.7:Al12.2:Cr3.8:Ru0.6:V5.2

422±9

~Pt3Al

~Pt3Al, (Pt)

5 & 5H

Pt63.2:Al12.9:Cr4.0:Ru0.7:V19.0:Nb0.6

603±21

821±32

~Pt3Al, (Pt)

~Pt3Al, ~PtV, ~Pt2V

6 & 6H

Pt71.7:Al12.8:Cr4.9:Ru1.1:V9.9:Nb0.3

545±12

535±15

~Pt3Al, (Pt)

~Pt3Al

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transformed to ~PtV and ~Pt2V. Consequently, the hardness increased from 603 HV0.3 to 821 HV0.3 because of the two Pt-V intermetallic compounds. The as-cast alloy 6 had two phases, ~Pt3Al and (Pt), which changed to single-phase ~Pt3Al during annealing, although the hardness remained the same, taking the errors in the hardnesses into account (Table IV). The as-cast alloys did not have the expected microstructure, although they were generally harder than the quaternary alloys (Shongwe et al., 2008, 2010; Odera, 2013), where the hardest alloy was Pt84:Al11:Cr3:Ru2 (at.%) with a hardness of 472 HV10. They were also harder than the eight ternary Pt-Al based alloys investigated by Hill (2001), where the hardest alloy had a Vickers hardness of 530 HV. However, Pt-Al-V (Odera et al., 2012a; Odera, 2013) and PtCr-V alloys (Odera et al., 2012b; Odera, 2013) and Pt-Al-Cr alloys (Süss, 2007) were generally much harder than the current as-cast alloys, because many of the Pt-Al-V and PtCr-V alloys contained the hard Pt-V intermetallic phases. The high hardness of some of the Pt-Al-Cr alloys was attributed to ~PtAl2, ~PtAl, and ~Pt2Al3. The Pt-Cr-Ru system (Süss, 2004) exhibited hardnesses between 225±13 for (Pt) and 1013±68 for an alloy with A15 Cr3Pt. The latter sample showed cracking around the indentations, which is detrimental – however, this was not seen in the current alloys. Thus, although the current alloys did not all attain the desired ~Pt3Al - (Pt) microstructure, the phases were such that the alloys had reasonably high hardnesses without being brittle. El-Bagoury (2011) measured hardness of aged experimental NBSAs, solution-treated at 1120°C and 1180°C then aged at 845°C for 24 hours; the Vickers hardness for the specimens solution treated at 1120°C was 472 HV and for those at 1180°C were 450 HV. These values compare very well with those of the current alloys. The heat treatment at 1000˚C for 1500 hours did not transform the higher order alloys to the intended structure of fine ~Pt3Al precipitates in a matrix of (Pt), and in some cases full homogenization was not achieved, as shown by the large errors in the EDX analyses. It is suggested that a heat treatment similar to that of Wenderoth et al. (2005) be used, with two stages (under flowing argon). Thus, homogenization would be more achievable at a higher temperature of 1500˚C for a shorter period of 12 hours, followed by water quenching. The second step would result in precipitation: 1000˚C for 120 hours, also followed by water quenching.

Conclusions There were some losses of Ru and Nb, so not all of the targeted compositions were made. It is possible to obtain two-phase structures of (Pt) and ~Pt3Al with additions of V and Nb, although the microstructures still need to be optimized. The optimum addition of V is ~15 at.%; higher additions would result in the formation of the brittle intermetallic phases of the Pt-V system, but the effect of Nb could not be evaluated since the losses were too high. The hardnesses of the alloys investigated were higher than those of the quaternary Pt-based alloys previously investigated. The alloys therefore show promise in terms of both microstructure and hardness, and future work on alloys of different compositions would be beneficial.

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in a matrix of (Pt) is obtained only when an alloy solidifies to (Pt), and solid-state precipitation of ~Pt3Al follows upon cooling, because the alloy is then in the (Pt) + ~Pt3Al twophase region, due to the sloping (Pt) solvus. Any other phases, especially a eutectic, that could also be associated with shrinkage porosity, could be detrimental, leading to reduced strength and toughness (Cornish et al., 2009). All the alloys solidified with either (Pt) dendrites or single-phase ~Pt3Al, and annealing did not always produce the required (Pt) + ~Pt3Al precipitates. Thus, the compositions and/or the annealing procedure were unsuitable, because single-phase (Pt) was not formed on casting, although it might be expected that some eutectic would form because of the high cooling rates from arc-melting. Alloy 1 had the highest V content, 19 at.%, and although it retained a two-phase structure after annealing, the (Pt) transformed to ~Pt2V, which is not desirable. The V content was therefore too high. The next highest V content was in alloy 27, at ~15 at.%. This was also two-phase, with ~Pt3Al dendrites and (Pt), but transformed to single-phase ~Pt3Al on annealing, showing that the composition was too close to ~Pt3Al. It is possible that a V content around ~15 at.% would produce the targeted microstructure. For the alloys that had single-phase ~Pt3Al, rather than (Pt) dendrites, it was apparent that V additions effectively moved the overall alloy composition to the ~Pt3Al liquidus surface, so that future higher order alloys should have lower Al contents to position them on the (Pt) liquidus surface. The six alloys were based on the quaternary alloy Pt82:Al12:Cr4:Ru2 (at.%), and both V and Nb were added to replace Pt, while the contents of the other elements were meant to remain constant. However, because of losses during melting, the alloy compositions were not necessarily those targeted (hence the actual average compositions were used to designate the alloys). After heat treatment at 1500°C for 18 hours followed by water quenching, then further heat treatment at 1100°C for 12 hours followed by air cooling, the hardness of alloy Pt82:Al12:Cr4:Ru2 (at.%) was 378 HV10 with a 10±5% precipitate volume (Shongwe et al., 2008, 2010) (measured by the grid method on enlarged micrographs). This shows that the addition of V, and in the case of alloys 5 and 6, V and Nb, substantially increased the hardness and possibly the precipitate volume fraction, even in the as-cast condition. There was a general increase in the hardnesses after annealing, except for alloy 6H, which had the same value statistically, and alloy 3H, the hardness of which decreased marginally. The hardness of alloy 1 increased from 537 HV0.3 to 700 HV0.3 (alloy 1H), which was expected since the (Pt) solid solution in the as-cast alloy transformed to ~Pt2V during annealing. As-cast alloy 2 was two-phase ~Pt3Al and (Pt), but became single-phase ~Pt3Al on annealing (alloy 2H), and the hardness increased from 428 HV0.3 to 482 HV0.3. As-cast alloy 3 was single-phase ~Pt3Al, and remained single-phase after annealing (alloy 3H), with the hardness remaining the same. As-cast alloy 4 was single-phase ~Pt3Al, but became two-phase ~Pt3Al and (Pt) after annealing and disintegrated on further metallographic preparation, indicating brittleness and probable associated increased hardness. Alloy 5 had two phases, ~Pt3Al and (Pt), in the ascast condition but during annealing (alloy 5H), (Pt)

High-order additions to platinum-based alloys for high-temperature applications ODERA, B.O., CORNISH, L.A., SHONGWE, M.B., RADING, G.O., and PAPO, M.J. 2012a.

Recommendations It is recommended that the Al content be reduced with addition of V to the higher order alloys to achieve the required microstructure of ~Pt3Al precipitates within (Pt). A two-step heat treatment, with a shorter time at 1500˚C for solution treatment, and a longer time at 1000˚C, is recommended.

As-cast and heat-treated alloys of the Pt-Al-V system at the Pt-rich corner. Journal of the Southern African Institute of Mining and Metallurgy, vol. 112, no. 7. pp. 505–515. REED, R.C. 2008. The Superalloys: Fundamentals and Applications. Cambridge University Press. SAMAL, S. and CORNISH, L.A. 2010. Characterisation of the Pt-rich alloys in the

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Witwatersrand, South Africa. VÖLKL, R., WENDEROTH, M., PREUSSNER, J., VORBERG, S., FISCHER, B., YAMABEMITARAI, Y., HARADA, H., and GLATZEL, U. 2009. Development of a precipi-

MASSALSKI, T.B. 1990. Binary Alloy Phase Diagrams. vol. 1, 2nd edn. ASM International, Materials Park, OH. MULAUDZI, F.M. 2009, Constitution of the Pt-Cr-Nb system. MSc dissertation,

tation-strengthened Pt-based superalloy. Materials Science and Engineering A, vol. 510–511A. pp. 328–331. WENDEROTH M., CORNISH L.A., SÜSS R., VORBERG S., FISCHER B., GLATZEL U., and VÖLKL, R. 2005. On the development and investigation of quaternary Pt-

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2008. Effect of solute elements on hardness and grain size in platinum based binary alloys. Materials Transactions, vol. 49, no. 3. pp. 538–547.

WENDEROTH, M., VOBERG, S., FISCHER, B., YAMABE-MITARAI, Y., HARADA, H., GLATZEL, U., and VÖLKL, R. 2008. Influence of Nb, Ta, and Ti on

NDLOVU, G.F. 2006. Microstructural investigation of the Pt-Al-Nb system. MSc dissertation, University of the Western Cape, South Africa.

microstructure and high-temperature strength of precipitation-hardened Pt-based alloys. Materials Science and Engineering A, vol. 483–484A. pp. 509–511.

ODERA, B.O. 2013. Addition of vanadium and niobium to platinum-based alloys. PhD thesis, University of the Witwatersrand, South Africa. ODERA, B.O., CORNISH, L.A., PAPO M.J., and RADING G.O. 2012c. Electrolytic etching of platinum-aluminium based alloys. Platinum Metals Review, vol. 56, no. 4. pp. 257–261.

investigation of some as-cast alloys of the Pt-Cr-V system. Proceedings of the Ferrous and Base Metals Development Network Conference, Magaliesburg, South Africa, 15–17 October 2012. pp. 291–308.

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temperature service. Platinum Metals Review, vol. 44, no. 4. pp. 158–166. WOLFF, I.M. and SAUTHOFF, G. 1996. High temperature behaviour of precious metal base composites. Metallurgical and Materials Transactions A,

ODERA, B.O., CORNISH, L.A., PAPO, M.J., and RADING G.O. 2012b. Microstructural

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vol. 27A. pp. 2642–2652. YAMABE, Y., KOIZUMI, Y., MURAKAMI, H., RO, Y., MARUKO, T., and HARADA, H. 1996. Development of Ir-base refractory superalloys. Scripta Materialia, vol. 35, no. 2. pp. 211–215.

The Journal of The Southern African Institute of Mining and Metallurgy

THE DANIE KRIGE GEOSTATISTICAL CONFERENCE GEOSTATISTICAL GEOVALUE REWARDS AND RETURNS FOR SPATIAL MODELLING

Crown Plaza, Johannesburg · 19–20 August 2015 THEME The theme of the conference is ‘Geostatistical Geovalue—Rewards and Returns for Spatial Modelling’, a theme which emphasizes the improvement in, or addition to, value that spatial modelling can bring to the process of mine evaluation and mineral resource and reserve estimation. Spatial modelling of earth related data to estimate or enhance attributed value is the principle domain of geostatistics, the broad content of the Danie Krige Commemorative Volumes, and the focus of this conference.

OBJECTIVES The conference provides authors who have recently published papers in the SAIMM’s Danie Krige Commemorative Volumes, a platform to present their research. In addition an invitation to geostatisticians, resource estimation practitioners, and those with an interest in geostatistics to present new papers for inclusion in the proceedings is now open. The conference will explore advances in technology and methodologies, and case studies demonstrating the application of geostatistics. It will cross the commodity boundaries, with applications presented from precious to base metals, and diamonds. This is a valuable opportunity to be involved in constructive dialogue and debate, and to keep abreast with the best practice in this specialist field.

WHO SHOULD ATTEND The conference provides a platform for: • local and international geostatisticians • geologists • engineers • researchers • software vendors • mineral resource managers and practitioners, across the mining industry • consultancy and academia, to present their work and contribute to the advancement of this field.

BACKGROUND Geostatistics constitutes a globally accepted technical approach to mineral resource-reserve estimation and the basic toolkit for mine evaluation practitioners. Following the call for papers and the publication of the Danie Krige Commemorative Volumes, the SAIMM invites submission of papers for the Danie Krige Geostatistical Conference to be held in Johannesburg, South Africa, 19–20 August 2015.

CONFERENCE SUPPORTER

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

Sponsorship opportunities are available. Companies wishing to sponsor or exhibit should contact the Conference co-ordinator.

Conference Announcement

INTERNATIONAL ACTIVITIES 2015 8–10 April 2015 — 5th Sulphur and Sulphuric Acid 2015 Conference Southern Sun Elangeni Maharani KwaZulu-Natal, South Africa Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 23–25 April 2015 — SANCOT Conference 2015 Mechanised Underground Excavation in Mining and Civil Engineering Elangeni Maharani Hotel, Durban Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za Website: http://www.saimm.co.za 12–13 May 2015 — Mining, Environment and Society Conference: Beyond sustainability—Building resilience Mintek, Randburg, South Africa Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za Website: http://www.saimm.co.za 10-11 June 2015 — Risks in Mining 2015 Conference Emperors Palace Hotel Casino, Convention Resort, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 14–17 June 2015 — European Metallurgical Conference Dusseldorf, Germany, Website: http://www.emc.gdmb.de 14–17 June 2015 — Lead Zinc Symposium 2015 Dusseldorf, Germany, Website: http://www.pbzn.gdmb.de 16–20 June 2015 — International Trade Fair for Metallurgical Technology 2015 Dusseldorf, Germany Website: http://www.metec-tradefair.com

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6–8 July 2015 — Copper Cobalt Africa Incorporating The 8th Southern African Base Metals Conference Zambezi Sun Hotel, Victoria Falls, Livingstone, Zambia Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 13–14 July 2015 — School Production of Clean Steel Emperors Palace, Johannesburg Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za Website: http://www.saimm.co.za 15–17 July 2015 — Virtual Reality and spatial information applications in the mining industry Conference 2015 University of Pretoria Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 19–20 August 2015 — The Danie Krige Geostatistical Conference: Geostatistical geovalue —rewards and returns for spatial modelling Crown Plaza, Johannesburg Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za Website: http://www.saimm.co.za 25–27 August 2015 — Coal Processing – Unlocking Southern Africa’s Coal Potential Graceland Hotel Casino and Country Club Secunda Contact: Ann Robertson Tel: +27 11 433-0063 26–28 August 2015 — MINESafe 2015—Sustaining Zero Harm: Technical Conference and Industry day Emperors Palace Hotel Casino, Convention Resort, Johannesburg Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za

The Journal of The Southern African Institute of Mining and Metallurgy

INTERNATIONAL ACTIVITIES 2015

28 September-2 October 2015 — WorldGold Conference 2015 Misty Hills Country Hotel and Conference Centre, Cradle of Humankind Gauteng, South Africa Contact: Camielah Jardine, Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.z 12–14 October 2015 — Slope Stability 2015: International Symposium on slope stability in open pit mining and civil engineering In association with the Surface Blasting School 15–16 October 2015 Cape Town Convention Centre, Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 21–22 October 2015 — Young Professionals 2015 Conference Making your own way in the minerals industry Mintek, Randburg, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 28–30 October 2015 — AMI: Nuclear Materials Development Network Conference Nelson Mandela Metropolitan University, North Campus Conference Centre, Port Elizabeth Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za

The Journal of The Southern African Institute of Mining and Metallurgy

8–13 November 2015 — MPES 2015: Twenty Third International Symposium on Mine Planning & Equipment Selection Sandton Convention Centre, Johannesburg, South Africa Contact: Raj Singhal E-mail: singhal@shaw.ca or E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za

2016 11–13 March 2016 — Diamonds Conference 2016 South Africa Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 13–14 April 2016 — Mine to Market Conference 2016 South Africa Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za Website: http://www.saimm.co.za 17–18 May 2016 — The SAMREC/SAMVAL Companion Volume Conference Johannesburg Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 9–10 June 2016 — 1st International Conference on Solids Handling and Processing A Mineral Processing Perspective South Africa Contact: Raymond van der Berg Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za 16–19 August 2016 — The Tenth International Heavy Minerals Conference ‘Expanding the horizon’ Sun City, South Africa Contact: Camielah Jardine, Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za

MARCH 2015

15–17 September 2015 — Formability, microstructure and texture in metal alloys Conference Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za Website: http://www.saimm.co.za

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

Engineering and Project Company Ltd

Namakwa Sands (Pty) Ltd

AEL Mining Services Limited

eThekwini Municipality

New Concept Mining (Pty) Limited

Air Liquide (PTY) Ltd

Evraz Highveld Steel and Vanadium Corp Ltd

Northam Platinum Ltd - Zondereinde

AMEC Mining and Metals AMIRA International Africa (Pty) Ltd

Exxaro Coal (Pty) Ltd

Outotec (RSA) (Proprietary) Limited

Exxaro Resources Limited

ANDRITZ Delkor(Pty) Ltd

PANalytical (Pty) Ltd

Fasken Martineau

Anglo Platinum Management Services (Pty) Ltd

Osborn Engineered Products SA (Pty) Ltd

Paterson and Cooke Consulting Engineers (Pty) Ltd

FLSmidth Minerals (Pty) Ltd

Anglo Operations Ltd

Fluor Daniel SA (Pty) Ltd

Anglogold Ashanti Ltd

Franki Africa (Pty) Ltd Johannesburg

Polysius A Division Of Thyssenkrupp Industrial Solutions (Pty) Ltd

Atlas Copco Holdings South Africa (Pty) Limited

Fraser Alexander Group

Precious Metals Refiners

Glencore

Rand Refinery Limited

Aurecon South Africa (Pty) Ltd

Goba (Pty) Ltd

Redpath Mining (South Africa) (Pty) Ltd

Aveng Moolmans (Pty) Ltd

Hall Core Drilling (Pty) Ltd

Rosond (Pty) Ltd

Axis House (Pty) Ltd

Hatch (Pty) Ltd

Royal Bafokeng Platinum

Bafokeng Rasimone Platinum Mine

Herrenknecht AG

Roymec Tecvhnologies (Pty) Ltd

Barloworld Equipment -Mining

HPE Hydro Power Equipment (Pty) Ltd

RSV Misym Engineering Services (Pty) Ltd

BASF Holdings SA (Pty) Ltd

Impala Platinum Limited

Rustenburg Platinum Mines Limited

Bateman Minerals and Metals (Pty) Ltd

IMS Engineering (Pty) Ltd

SAIEG

BCL Limited

JENNMAR South Africa

Salene Mining (Pty) Ltd

Becker Mining (Pty) Ltd

Joy Global Inc. (Africa)

BedRock Mining Support (Pty) Ltd

Leco Africa (Pty) Limited

Sandvik Mining and Construction Delmas (Pty) Ltd

Bell Equipment Company (Pty) Ltd

Longyear South Africa (Pty) Ltd

BHP Billiton Energy Coal SA Ltd

Lonmin Plc

Blue Cube Systems (Pty) Ltd

Ludowici Africa

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

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

BSC Resources

Magnetech (Pty) Ltd

Sebilo Resources (Pty) Ltd

CAE Mining (Pty) Limited

Magotteaux(PTY) LTD

SENET

Caledonia Mining Corporation

MBE Minerals SA Pty Ltd

Senmin International (Pty) Ltd

CDM Group

MCC Contracts (Pty) Ltd

Shaft Sinkers (Pty) Limited

CGG Services SA

MDM Technical Africa (Pty) Ltd

Sibanye Gold (Pty) Ltd

Chamber of Mines

Metalock Industrial Services Africa (Pty)Ltd

Smec SA

Concor Mining

Metorex Limited

SMS Siemag South Africa (Pty) Ltd

Concor Technicrete

Metso Minerals (South Africa) (Pty) Ltd

SNC Lavalin (Pty) Ltd

Council for Geoscience Library

Minerals Operations Executive (Pty) Ltd

Sound Mining Solutions (Pty) Ltd

CSIR-Natural Resources and the Environment

MineRP Holding (Pty) Ltd

SRK Consulting SA (Pty) Ltd

Mintek

Time Mining and Processing (Pty) Ltd

Department of Water Affairs and Forestry

MIP Process Technologies

Tomra Sorting Solutions Mining (Pty) Ltd

Deutsche Securities (Pty) Ltd

Modular Mining Systems Africa (Pty) Ltd

TWP Projects (Pty) Ltd

Digby Wells and Associates

Runge Pincock Minarco Limited

Ukwazi Mining Solutions (Pty) Ltd

Downer EDI Mining

MSA Group (Pty) Ltd

Umgeni Water

DRA Mineral Projects (Pty) Ltd

Multotec (Pty) Ltd

VBKOM Consulting Engineers

Duraset

Murray and Roberts Cementation

Webber Wentzel

Elbroc Mining Products (Pty) Ltd

Nalco Africa (Pty) Ltd

Weir Minerals Africa

MARCH 2015

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

The Journal of The Southern African Institute of Mining and Metallurgy

IP PONSORSH EXHIBITS/S ng to sponsor ishi Companies w ese t at any of th bi hi ex or and/ th t contac e events should rdinator -o co conference ssible po as on so as

SAIMM DIARY 2015 N CONFERENCE 5th Sulphur and Sulphuric Acid 2015 Conference 8–10 April 2015, Southern Sun Elangeni Maharani KwaZulu-Natal

or the past 120 years, the Southern African Institute of Mining and Metallurgy, has promoted technical excellence in the minerals industry. We strive to continuously stay at the cutting edge of new developments in the mining and metallurgy industry. The SAIMM acts as the corporate voice for the mining and metallurgy industry in the South African economy. We actively encourage contact and networking between members and the strengthening of ties. The SAIMM offers a variety of conferences that are designed to bring you technical knowledge and information of interest for the good of the industry. Here is a glimpse of the events we have lined up for 2015. Visit our website for more information.

N CONFERENCE SANCOT Conference 2015: Mechanised Underground Excavation in Mining and Civil Engineering 23–25 April 2015, Elangeni Maharani Hotel, Durban N CONFERENCE Mining, Environment and Society Conference 12–13 May 2015, Mintek, Randburg, Johannesburg N CONFERENCE Risks in Mining 2015 Conference 10–11 June 2015, Emperors Palace Hotel Casino, Convention Resort, Johannesburg N CONFERENCE Copper Cobalt Africa Incorporating The 8th Southern African Base Metals Conference 6–8 July 2015, Zambezi Sun Hotel, Victoria Falls, Livingstone, Zambia N SCHOOL Production of Clean Steel 13–14 July 2015, Emperors Palace, Johannesburg N CONFERENCE Virtual Reality and spatial information applications in the mining industry Conference 2015 15–17 July 2015, University of Pretoria, Pretoria N CONFERENCE The Danie Krige Geostatistical Conference 2015 19–20 August 2015, Crown Plaza, Johannesburg N CONFERENCE MINESafe 2015—Sustaining Zero Harm: Technical Conference and Industry day 26–28 August 2015, Emperors Palace Hotel Casino, Convention Resort, Johannesburg N CONFERENCE Formability, microstructure and texture in metal alloys Conference 2015 15–17 September 2015

For further information contact: Conferencing, SAIMM P O Box 61127, Marshalltown 2107 Tel: (011) 834-1273/7 Fax: (011) 833-8156 or (011) 838-5923 E-mail: raymond@saimm.co.za

N CONFERENCE World Gold Conference 2015 28 September–2 October 2015, Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Muldersdrift N SYMPOSIUM International Symposium on slope stability in open pit mining and civil engineering 12–14– October 2015 In association with the

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

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

Integrated systems of support

Applying Poka Yokes in the mining industry

Patents Pending

+27 11 494 6000 www.ncm.co.za

ÂŠ New Concept Mining 2015