VOLUME 115
NO. 7
JULY 2015
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The Southern African Institute of Mining and Metallurgy OFFICE BEARERS AND COUNCIL FOR THE 2014/2015 SESSION Honorary President Mike Teke President, Chamber of Mines of South Africa Honorary Vice-Presidents Ngoako Ramathlodi Minister of Mineral Resources, South Africa Rob Davies Minister of Trade and Industry, South Africa Naledi Pando Minister of Science and Technology, South Africa President J.L. Porter President Elect R.T. Jones Vice-Presidents C. Musingwini S. Ndlovu Immediate Past President M. Dworzanowski Honorary Treasurer C. Musingwini Ordinary Members on Council V.G. Duke M.F. Handley A.S. Macfarlane M. Motuku M. Mthenjane D.D. Munro G. Njowa
T. Pegram S. Rupprecht N. Searle A.G. Smith M.H. Solomon D. Tudor D.J. van Niekerk
Past Presidents Serving on Council N.A. Barcza R.D. Beck J.A. Cruise J.R. Dixon F.M.G. Egerton G.V.R. Landman R.P. Mohring
J.C. Ngoma S.J. Ramokgopa M.H. Rogers G.L. Smith J.N. van der Merwe W.H. van Niekerk
Branch Chairmen Botswana
L.E. Dimbungu
DRC
S. Maleba
Johannesburg
I. Ashmole
Namibia
N.M. Namate
Northern Cape
C.A. van Wyk
Pretoria
N. Naude
Western Cape
C. Dorfling
Zambia
D. Muma
Zimbabwe
S. Ndiyamba
Zululand
C.W. Mienie
Corresponding Members of Council Australia: I.J. Corrans, R.J. Dippenaar, A. Croll, C. Workman-Davies Austria: H. Wagner Botswana: S.D. Williams United Kingdom: J.J.L. Cilliers, N.A. Barcza USA: J-M.M. Rendu, P.C. Pistorius
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PAST PRESIDENTS *Deceased * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
W. Bettel (1894–1895) A.F. Crosse (1895–1896) W.R. Feldtmann (1896–1897) C. Butters (1897–1898) J. Loevy (1898–1899) J.R. Williams (1899–1903) S.H. Pearce (1903–1904) W.A. Caldecott (1904–1905) W. Cullen (1905–1906) E.H. Johnson (1906–1907) J. Yates (1907–1908) R.G. Bevington (1908–1909) A. McA. Johnston (1909–1910) J. Moir (1910–1911) C.B. Saner (1911–1912) W.R. Dowling (1912–1913) A. Richardson (1913–1914) G.H. Stanley (1914–1915) J.E. Thomas (1915–1916) J.A. Wilkinson (1916–1917) G. Hildick-Smith (1917–1918) H.S. Meyer (1918–1919) J. Gray (1919–1920) J. Chilton (1920–1921) F. Wartenweiler (1921–1922) G.A. Watermeyer (1922–1923) F.W. Watson (1923–1924) C.J. Gray (1924–1925) H.A. White (1925–1926) H.R. Adam (1926–1927) Sir Robert Kotze (1927–1928) J.A. Woodburn (1928–1929) H. Pirow (1929–1930) J. Henderson (1930–1931) A. King (1931–1932) V. Nimmo-Dewar (1932–1933) P.N. Lategan (1933–1934) E.C. Ranson (1934–1935) R.A. Flugge-De-Smidt (1935–1936) T.K. Prentice (1936–1937) R.S.G. Stokes (1937–1938) P.E. Hall (1938–1939) E.H.A. Joseph (1939–1940) J.H. Dobson (1940–1941) Theo Meyer (1941–1942) John V. Muller (1942–1943) C. Biccard Jeppe (1943–1944) P.J. Louis Bok (1944–1945) J.T. McIntyre (1945–1946) M. Falcon (1946–1947) A. Clemens (1947–1948) F.G. Hill (1948–1949) O.A.E. Jackson (1949–1950) W.E. Gooday (1950–1951) C.J. Irving (1951–1952) D.D. Stitt (1952–1953) M.C.G. Meyer (1953–1954) L.A. Bushell (1954–1955)
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H. Britten (1955–1956) Wm. Bleloch (1956–1957) H. Simon (1957–1958) M. Barcza (1958–1959) R.J. Adamson (1959–1960) W.S. Findlay (1960–1961) D.G. Maxwell (1961–1962) J. de V. Lambrechts (1962–1963) J.F. Reid (1963–1964) D.M. Jamieson (1964–1965) H.E. Cross (1965–1966) D. Gordon Jones (1966–1967) P. Lambooy (1967–1968) R.C.J. Goode (1968–1969) J.K.E. Douglas (1969–1970) V.C. Robinson (1970–1971) D.D. Howat (1971–1972) J.P. Hugo (1972–1973) P.W.J. van Rensburg (1973–1974) R.P. Plewman (1974–1975) R.E. Robinson (1975–1976) M.D.G. Salamon (1976–1977) P.A. Von Wielligh (1977–1978) M.G. Atmore (1978–1979) D.A. Viljoen (1979–1980) P.R. Jochens (1980–1981) G.Y. Nisbet (1981–1982) A.N. Brown (1982–1983) R.P. King (1983–1984) J.D. Austin (1984–1985) H.E. James (1985–1986) H. Wagner (1986–1987) B.C. Alberts (1987–1988) C.E. Fivaz (1988–1989) O.K.H. Steffen (1989–1990) H.G. Mosenthal (1990–1991) R.D. Beck (1991–1992) J.P. Hoffman (1992–1993) H. Scott-Russell (1993–1994) J.A. Cruise (1994–1995) D.A.J. Ross-Watt (1995–1996) N.A. Barcza (1996–1997) R.P. Mohring (1997–1998) J.R. Dixon (1998–1999) M.H. Rogers (1999–2000) L.A. Cramer (2000–2001) A.A.B. Douglas (2001–2002) S.J. Ramokgopa (2002-2003) T.R. Stacey (2003–2004) F.M.G. Egerton (2004–2005) W.H. van Niekerk (2005–2006) R.P.H. Willis (2006–2007) R.G.B. Pickering (2007–2008) A.M. Garbers-Craig (2008–2009) J.C. Ngoma (2009–2010) G.V.R. Landman (2010–2011) J.N. van der Merwe (2011–2012) G.L. Smith (2012–2013) M. Dworzanowski (2013–2014)
Honorary Legal Advisers Van Hulsteyns Attorneys Auditors Messrs R.H. Kitching Secretaries The Southern African Institute of Mining and Metallurgy Fifth Floor, Chamber of Mines Building 5 Hollard Street, Johannesburg 2001 P.O. Box 61127, Marshalltown 2107 Telephone (011) 834-1273/7 Fax (011) 838-5923 or (011) 833-8156 E-mail: journal@saimm.co.za
The Journal of The Southern African Institute of Mining and Metallurgy
Editorial Board R.D. Beck J. Beukes P. den Hoed M. Dworzanowski B. Genc M.F. Handley R.T. Jones W.C. Joughin J.A. Luckmann C. Musingwini J.H. Potgieter 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 Camera Press, Johannesburg
Advertising Representative 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)
VOLUME 115
NO. 7
JULY 2015
Contents Journal Comment by B. Genc and P. den Hoed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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President’s Corner by J.L. Porter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vi–vii
COAL CONFERENCE PAPERS Spontaneous combustion risk in South African coalfields by B. Genc and A. Cook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Processing low-grade coal to produce high-grade products by G.J. de Korte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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IFSA CONFERENCE PAPERS Feasibility study of electricity generation from discard coal by B. North, A. Engelbrecht, and B. Oboirien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
573
The value proposition of circulating fluidized-bed technology for the utility power sector by R. Giglio and N.J. Castilla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
581
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process by D. Toporov and R. Abraham. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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GENERAL PAPERS AND TECHNICAL NOTE
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.
599
An economic risk evaluation approach for pit slope optimization by L.F. Contreras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
607
Investigation of stress in an earthmover bucket using finite element analysis: a generic model for draglines by O. Gölbas, ı and N. Demirel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
623
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine by A. Patri and D. S. Nimaje . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
629
Peak particle velocity prediction using support vector machines: a surface blasting case study by S.R. Dindarloo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
637
Large-scale deformation in underground hard-rock mines by E. Karampinos, J. Hadjigeorgiou, P. Turcotte, and F. Mercier-Langevin . . . . . . . . . . . . . . . .
645
Visions for challenging assets in the South African coal sector by Z. van Zyl. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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VOLUME 115
International Advisory Board
NO. 7
JULY 2015
R. Dimitrakopoulos, McGill University, Canada D. Dreisinger, University of British Columbia, Canada E. Esterhuizen, NIOSH Research Organization, USA H. Mitri, McGill University, Canada M.J. Nicol, Murdoch University, Australia E. Topal, Curtin University, Australia
The Journal of The Southern African Institute of Mining and Metallurgy
JULY 2015
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THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS.
Support stability mechanism in a coal face with large angles in both strike and dip by L.Q. Ma*, Y. Zhang, D.S. Zhang, X.Q. Cao, Q.Q. Li, and Y.B. Zhang. . . . . . . . . . . . . . . . . . .
Journal Comment Heaping coals of fire upon our heads
A
blessing at the best of times, electricity has become the bane of life in South Africa: it adds quality to life, but when supply is erratic, as we all know too well, the effects cripple and evoke anger. Constraints in the supply of electricity are damaging the economy. Some predictions even foretell a crisis of monumental proportions. Eskom can barely meet current demand. It will fall short of growing demand in the near future. There are simply too few power stations to meet the country’s current and growing energy needs. Building new power stations—an obvious solution—takes years, so there is no quick fix. Making matters worse is the sharply rising cost of electricity. South Africans will be digging deeper into their pockets, and industry will see its operating costs increase. There is, in short, a debilitating disparity between the provision of power, on the one hand, and needs and expectations on the other. The disparity raises distressing alarms in many quarters. What, you might ask, does this alarming and embarrassing predicament have to do with coal, the theme (one could say) of the papers appearing in this issue of the journal? Coal is vital to the South African economy. Exported, it exceeds all other commodities in bulk and revenue.1 It is all but integral to the generation of electricity in South Africa: more than 95% of our electricity is generated from coal. Yet some critics, notably the American environmentalist and author Bill McKibben, have argued that— ‘There is an urgent need to stop subsidizing the fossil fuel industry, dramatically reduce wasted energy, and significantly shift our power supplies from oil, coal, and natural gas to wind, solar, geothermal, and other renewable energy sources.’2 South Africa has heeded the call. It, too, is committed to reducing carbon emissions: in the next 50 years coal will drop to 20% of the energy mix; the rest of demand will be met by nuclear and renewable energy.3 But half a century is a long time, and all the while in this country electricity will continue to be generated from coal. Coal mining, processing, and combustion, in all likelihood, will be with us for a long time. This, however, does not mean that the old practices can or should continue as before. Two conferences last year attracted papers that addressed questions of change and challenge in coal mining and power generation. Both conferences were held in Johannesburg. The first one focused on ‘21st century challenges to the southern African coal sector’4. Coal mining, as for mining in any sector, faces new challenges as high-grade, readily accessible seams are mined out, the grade of resources declines, and operations switch to coalfields in more remote locations. Attention and efforts are now directed at mining thin seams, beneficiating fines, and transporting bulk material from locations that lie some distance from available routes. These and other challenges were highlighted and discussed in 22 papers presented at the conference, three of which appear in this issue of the journal. Two of the papers presented at the coal conference discussed a matter at the heart of a sub-theme of the second conference.5 IFSA 2014, a conference on industrial fluidization
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and fluid-bed technologies, was held towards the end of the year. It is held every three years; this one was the fifth in the series. Although ‘industrial’ appears in its title, the papers presented over the two days covered topics both fundamental and applied. A scan of the titles of papers listed in the proceedings, however, reveals a telling bias: only six of the 28 papers covered topics other than the combustion or gasification of a variety of carbonaceous fuels (coal, including discard coal, biomass, and oil). The three papers selected for inclusion in this issue of the journal discuss three elements of the subject: namely, the role of fluid-bed technologies (1) in converting coal of all grades (2) into electricity or syngas (3). Standard textbooks list the advantages of fluidized beds in the design of reactors. Circulating fluidized beds (or CFBs) operate in a regime called fast fluidization: the mixing of particles, which aggregate in clusters that break apart and reform, is extensive and slip velocities (between gas and solids) are an order of magnitude greater than the terminal velocity of the particles. Higher fluidizing velocities lead to the pneumatic transport of particles. CFBs confer advantages on the burning of carbonaceous fuels for power generation:6 a given design can stably burn a variety of fuels in type (coal or biomass) and quality; the solid fuel does not need to be pulverized or necessarily dried; temperatures are uniform and heat transfer is even; limestone captures SO2 in the bed, which does away with the need for flue-gas desulphurization; and because temperatures are lower (850°C, compared with 1500°C in conventional pulverized-fuel boilers) ash does not melt or slag; and the formation of NOx is minimal, which does away with the need for selective catalytic reduction (or SCR). These advantages impart flexibility, save money, and significantly lessen hazardous components in flue-gas emissions. The committee drafting the Integrated Resource Plan (IRP 2010) recognized these advantages when it recommended the procurement of fluid-bed boilers for the burning of high-ash discard coals. Estimates put this resource at about 1.5 billion tons. This measure would go a long way in utilizing a resource that has been cast aside and, in doing so, bring some redress to the energy needs of this country. In the meantime load shedding, higher prices for electricity, and constraints in supply remain a burden. Nevertheless, unless we act now, implementing measures that will work for a country that is rich in coal, the future will be a bleak one.
1This
and many other facts in the editorial piece are quoted from the preface and papers of the proceedings of IFSA 2014. 2W.E. McKibben 3Department of Energy, Integrated Resource Plan for Electricity 2010–2030, Revision 2, Final report, promulgated on 25 March 2011; Integrated Resource Plan for Electricity (IRP) 2010–2030, Update report, 21 November 2013 4This conference styled itself as a symposium. 5‘The future of coal in the low-carbon economy and the impact of natural gas’ by Dave Collins (MAC Consulting) and ‘South Africa’s answers to climate change: challenges and opportunities in clean-coal technologies’ by Professor Rosemary Falcon (University of the Witwatersrand). 6See, for example, ‘The value proposition of circulating fluidized-bed technology for the utility power sector’ in this issue of the Journal.
B. Genc and P. den Hoed The Journal of The Southern African Institute of Mining and Metallurgy
t’s iden Pres er Corn
I
n this month’s article I want to update readers about two important aspects of the Institute – firstly, the launch of the new Botswana Branch, and secondly the Mineral Economics Division of the SAIMM.
SAIMM Botswana Branch The process to set up a Branch in Botswana began in 2006. Alan Clegg, a Council member and the Chairman of the Regional Branch Organizing Committee at the time, was instrumental in establishing and setting the process in motion. The Botswana Government requires professional societies such as ours to be registered with the Registrar of Societies. As with any process that has to be done from a distance without being able to visit the respective departments and people personally, this was quite a lengthy task and took a number of years before it was completed. In the meantime, James Arthur was elected as the Branch Chairman. Although presentations were set up in four main centres, logistics were a problem due to the large geographical area which had to be covered. The Base Metals Conference, organized through the Technical Programme Committee: Metallurgy, was a highlight during 2008. It was held at the Mwana Lodge in Chobe. James Arthur had to return to South Africa, and unfortunately there were no further activities planned. Recently the SAIMM saw the need for a person who would grow the regional Branches, and in October 2014 we employed Malcolm Walker as Regional Development Manager. One of Malcolm’s first tasks was to speak to people in Botswana with a view to resuscitating the Branch. We soon realized that the Branch would need to be started afresh, and Malcolm went to Botswana to personally meet people in the industry and to see if there was an appetite for an SAIMM Branch. In terms of By-law F, which guides the operation of Branches, twelve corporate members are required to submit a written request to the Council of the SAIMM for the establishment of a Branch. Members in Botswana were sent an e-mail to find out if they were interested and at least twenty responses were received. This was seen as a good sign, as there are 77 members of the SAIMM in Botswana. After further planning and arrangements, it was agreed that the Branch would be launched in conjunction with a technical visit. This took place on 5 June, when a visit to the Diamond Trading Company Botswana was arranged, coupled with the launch of the Botswana Branch and the election of the Committee. I am pleased to say that this went well, and a Chairman and Committee were elected as follows: Chairman – Len Dimbungu, Vice Chairman – Andries Bester, Secretary – Craig Robertson, and three Committee Members – Michael Musonda, Omphile Ntabeni, and Wiesiek Masztalerz. Malcolm and I also met with the Chairman of the Botswana Chamber of Mines, Charles Siwawa, to discuss the registration process and the role that the SAIMM can play in the local industry. I am confident that with assistance from Charles and the enthusiasm of the Branch Committee, we will be arranging a number of technical presentations and conferences in Botswana. The Mineral Economics Division The Mineral Economics Division of the SAIMM was established to keep a watching brief on the changing nature of mining and its interface with the political economics of the resource-rich countries in Southern Africa. A workshop was held in February 2012 entitled ‘Towards a Multi-stakeholder Dialogue on Critical Issues facing the Southern African Mining Industry’ to further examine and discuss these issues. A key component of the workshop was the session organized and led by Mike Solomon, the Chairman of the Mineral Economics Division, on the rise of resource nationalism. This resulted in the publication of the report ‘The Rise of Resource Nationalism: a Resurgence of State Control in an Era of Free Markets or the Legitimate Search for a New Equilibrium?’, which was subsequently presented at the Mining Indaba. The dialogues came about as a direct result of a comprehensive, academically sound study consisting of an in-depth look into the issues related to state participation in the mining sector from a global and historical perspective. The objective was to inform national debate through rigorous and exhaustive research that would provide a platform for evidence-based dialogue. Following the inaugural three-day dialogues hosted in 2012, Mining Dialogues 360° (MD360) produced a summary report of the key issues identified by the participants, which is available on their website (www.miningdialogues360.co.za). The organization continued to engage with various constituencies, and in the wake of Marikana hosted further dialogues, most notably with key members from civil society organizations and the various church bodies that are active in the mining communities. The outcome of the one-day dialogue was a set of five key recommendations for King Leruo Molotlegi of the Royal Bafokeng (who provided much of the funding for the work) to present at a meeting of CEOs of the affected platinum companies. In 2013, the Mining Dialogues research team completed the first of a series of in-depth studies into the social and economic footprints of each of South Africa´s major mining sectors. In addition, MD360 participated in think tanks hosted by other industry bodies and has forged co-operative alliances with the ICMM, the Centre for Sustainability in Mining, the Africa Futures Forum, the WEF Global Agenda Council, and the Royal Institute of International Affairs (also known as Chatham House). A full merger of Mining for Change and MD360 took place in early 2014. The consolidation of these entities into a single streamlined platform created a strengthened organization with networks across the South African mining and regulatory sectors, with obvious benefits such as not competing for funding, more efficient staffing, and reduced management and overhead costs.
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The Journal of The Southern African Institute of Mining and Metallurgy
Mining Dialogues 360° is held under the auspices of the SAIMM and enjoys the support of the South African Chamber of Mines, the International Council on Mining and Metals (ICMM), the Centre for Sustainability in Mining and Industry (CSMI), The Royal Institute of International Affairs (Chatham House), the universities of the Witwatersrand and Stellenbosch, the World Economic Forum´s Mining and Minerals Council, the International Institute for Sustainable Development (IISD), the United Nations Environment Programme (UNEP), the mining industry, labour bodies, civil society organizations, government and related institutions, and the investment community. MD360 is a not-for-profit company (NPC) that operates with the support of grants and financial contributions. Key milestones and achievements – 2010 to present 2010 - Research commissioned by the SAIMM and funded by the Royal Bafokeng into resource nationalism in the global context and issues in South Africa. 2011 - Produced an extensive, in-depth report titled ‘The Rise of Resource Nationalism: a Resurgence of State Control in an Era of Free Markets or the Legitimate Search for a New Equilibrium? A Study to Inform Multi-stakeholder Dialogue on State Participation in Mining’. - February: the report is presented at the Mining Indaba to much acclaim from the industry, resulting in calls for the document to form the basis of focused dialogues on the issues. 2012 - April: Mining Dialogues 360 Degrees (MD360) is formed as a not-for-profit company. - July: the inaugural 3-day meeting is held with the dialogues oversubscribed, and the participatory format was received exceptionally well by all stakeholders. - A website was created to form an ongoing communications platform and central repository of information. The platform has been maintained and is updated on a daily basis. http://www.miningdialogues360.co.za - The final meeting report predicts a major industry disaster. Almost exactly one month later, in August, Marikana erupts. - October: in order to provide leadership to tackle the issues, MD360 calls a dialogue between 15 key participants from civil society and faith-based organizations. This think tank resulted in a five-point strategy for Kgosi Leruo to present at a meeting called with the platinum CEOs to identify and seek solutions to the crisis. The points are very well received but again, no action is taken by industry. - November: Lonmin commissions MD360 to do a ‘deep-dive’ research piece on the social and economic footprint of the company at its North West operations. 2013 - September: the report is completed. - Other platinum companies show great interest in participating in the footprint exercise in order to create a wider view and understanding of the landscape. - The independent recommendations of the research team are documented in a paper titled ‘A Platinum Compact’ and shared with the highest levels of government and key advisors, including Pravin Gordhan, Godfrey Oliphant, Musa Mabuza, Roger Baxter, and Gwede Mantashe. 2014 - MD360 is approached by the Farlam Commission to assist with information relating to Lonmin. Owing to an NDA signed with Lonmin, MD360 is limited in what can be shared with the commission. Judge Farlam subpoenas the report from Lonmin and it is widely quoted in the Phase 2 findings of Dr Kally Forrest. Current - In light of the lack of progress with the government’s Framework for Sustainability in Mining agreement, and in response to numerous calls for an industry forum that is properly representative of all stakeholders (not just government, labour, and industry), MD360 has developed the terms of reference for a new three-year research work and dialogue programme. - The programme has received the approval of the SAIMM and has the support of the ICMM, CSMI, the universities of the Witwatersrand and Stellenbosch, the Royal Institute of International Affairs (Chatham House), and the World Economic Forum’s Mining and Minerals Council. - The MD360 Board has appointed a new, influential and high-profile Advisory Council to oversee the programme and provide guidance on the issues. - The Platinum Compact recommendations are under serious consideration by the Emergency Task Response Team for Mining under the oversight of Minister Radebe in the Office of the Presidency. - The organization is currently engaged in fundraising to support the work programme and dialogues. The above information is taken from the various documents available on MD360 and is therefore presented very factually. My own involvement has been as the Chairman of the Advisory Council. I am committed to the work being done by MD360 and I encourage the industry to support the various initiatives. It is only by interested parties’ contributing to the discussions, and where possible funding the initiatives, that we will see a positive change in our industry. You are welcome to send your comments and questions to me so that we can continue meaningful debate.’
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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
Coal Conference Papers Spontaneous combustion risk in South African coalfields by B. Genc and A. Cook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
563
Laboratory tests have been undertaken for five consecutive years in order to determine both the Wits-EHAC index and the crossing point temperature of 119 coal samples.These parameters, when combined, give an indication of the spontaneous combustion propensities of the samples. The database of results, which is continually being updated, provides the basis for an improved risk evaluation methodology for spontaneous combustion. Processing low-grade coal to produce high-grade products by G.J. de Korte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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The coals found in the Waterberg, Soutpansberg, and other coalfields are of relatively low quality compared to the coal mined from the traditional areas of the Witbank, Highveld, and Ermelo coalfields. Processing low-yielding coals into good-quality products while ensuring that coal mining remains economically viable will require the investigation and implementation of more cost-effective coal processing technologies.
IFSA Conference Papers Feasibility study of electricity generation from discard coal by B. North, A. Engelbrecht, and B. Oboirien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
573
A detailed economic assessment of the feasibility of electricity generation from discard coal, comprising material and energy balances and the construction of a discounted cash flow (DCF) table, is presented, showing that the process is potentially attractive from an economic perspective. The value proposition of circulating fluidized-bed technology for the utility power sector by R. Giglio and N.J. Castilla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
581
This paper presents an outlook for future coal supply, quality, and price, as well as a review of the technical and economic benefits of circulating fluidized-bed technology when firing low-quality fuels for utility power generation. Gasification of low-rank coal in the High-Temperature Winkler (HTW) process by D. Toporov and R. Abraham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The High-Temperature Winkler (HTW) gasification process is designed to utilize low-rank feedstock such as coals with a high ash content, lignite, or biomass. The process is characterized by a bubbling fluidized bed, where coal devolatilization and partial oxidation and gasification of coal char and volatiles take place and by a freeboard, where partial combustion and gasification of coal char take place.
These papers will be available on the SAIMM website
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PAPERS IN THIS EDITION These papers have been refereed and edited according to internationally accepted standards and are accredited for rating purposes by the South African Department of Higher Education and Training
General Papers and Technical Note Support stability mechanism in a coal face with large angles in both strike and dip by L.Q. Ma, Y. Zhang, D.S. Zhang, X.Q. Cao, Q.Q. Li, and Y.B. Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
599
A mechanical model has been developed that considers the impact of the coal seam dip angle on the support stability in the strike direction. The research findings were successfully applied to a fully mechanized coal face with large angles both in strike and dip at the Xinji Coal Mine in China. An economic risk evaluation approach for pit slope optimization by L.F. Contreras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
607
An alternative methodology of pit slope design is proposed, where the economic impacts of potential slope failures are calculated and used as the elements on which to apply the acceptability criteria for design. The paper discusses the concepts used for interpreting the probability of slope failure and describes an approach for estimating the economic impacts of slope failure by construction of a ‘risk map’. Investigation of stress in an earthmover bucket using finite element analysis: a generic model for draglines by O. Gölbas, ı and N. Demirel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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This study aims to develop a generic finite-element model of the stress on an operating dragline bucket. Simulation work and sensitivity analyses provide the indicators of failure and stress values. Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine by A. Patri and D. S. Nimaje . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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A novel method is proposed for determining the parameters of a suitable radio propagation model, and is illustrated with the results of a practical experiment carried out in an underground coal mine in Southern India. Peak particle velocity prediction using support vector machines: a surface blasting case study by S.R. Dindarloo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
637
In this study, the support vector machine algorithm was employed for prediction of the peak particle velocity (PPV) induced by surface mine blasting. The major advantages of the method are the very high accuracy of predictions and fast computation times. Large-scale deformation in underground hard-rock mines by E. Karampinos, J. Hadjigeorgiou, P. Turcotte, and F. Mercier-Langevin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
645
Field observations and convergence measurements at two underground mines in Canada were used to provide guidelines of the anticipated squeezing ground levels at these operations. The choice of a favourable angle of interception between the drift and the foliation can result in a more manageable squeezing level and increase the performance of an appropriate support system for squeezing ground conditions. Visions for challenging assets in the South African coal sector by Z. van Zyl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This paper, which is based on research on trends in underground coal mining as well as 16 years’ practical experience in electronic monitoring of mining machinery and productivity optimization, illustrates the benefits of moving from reactive event-based management systems towards a more adaptable and flexible process-based system
These papers will be available on the SAIMM website
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http://dx.doi.org/10.17159/2411-9717/2015/v115n7a1
Spontaneous combustion risk in South African coalfields by B. Genc* and A. Cook†
The risk of spontaneous combustion is well known in the South African coal mining industry. In the coming years it is very possible that the incidence of spontaneous combustion will increase from current levels, due to factors such as an increased rate of mining, re-working of previously mined seams, more stooping and total extraction for underground mines, and higher stripping ratios for surface mines, leading to more spoils. It is also fairly certain that coal mining will face tougher environmental emissions legislation in the near future. To determine the areas where the risks of spontaneous combustion are high, it is necessary to improve on our current laboratory procedures for testing and evaluating coal samples, combining the results with site and field data, and if necessary revising the laboratory rating system to refine our understanding of South African conditions. Currently, laboratory tests are conducted in order to determine both the Wits-EHAC index and the crossing-point temperature which, when combined, give an indication of the spontaneous combustion propensities of the coal samples. This procedure has enabled the establishment of a database of results to review and evaluate South African coal seams. Using this database, the high-risk areas in terms of spontaneous combustion are identified. Tests have been undertaken for five consecutive years, between 2008 and 2012. In total, 119 coal samples from different coal seams and production coalfields have been analysed and classified through a series of laboratory tests. A comprehensive database of these results is available, and is continually being updated as new test results are added. This database will continue to expand, and to provide the basis for an improved risk evaluation methodology for spontaneous combustion. Keywords coal spontaneous combustion, risk assessment, Wits-EHAC liability index, crossing-point temperature.
Introduction The risk of spontaneous combustion is well known in the South African coal mining industry. All major coal producers in South Africa, such as Anglo Thermal Coal, BHP Billiton Energy Coal South Africa (Becsa), Exxaro, and Xstrata Coal or their predecessors have experienced spontaneous combustion incidents in their history. As stated by Phillips et al. (2011) there is always the risk of spontaneous combustion in underground mining (e.g. the Goedehoop fire in 2008), but spontaneous combustion can take place on both underground and surface coal mines. The current problem is in surface mines and nearly always in mines extracting previously worked seams i.e. where old bord and pillar workings are exposed. In the coming years it is very The Journal of The Southern African Institute of Mining and Metallurgy
possible that the rate of spontaneous combustion will increase from its present low levels, due to factors such as higher ventilation pressures, an increased rate of mining, more working of previously mined seams, etc. It is also fairly certain that coal mining will face tougher environmental legislation limiting emissions in the near future. To ascertain the areas where spontaneous combustion risks are high, it is necessary to improve current laboratory procedures for testing and evaluating coal samples, combine the result with site and field data, and if necessary revise the laboratory rating system to better reflect South African conditions. The current laboratory tests are conducted in order to determine both the Wits-EHAC index and the crossing-point temperature, which are combined to obtain the propensities of the coal samples to undergo spontaneous combustion. This has resulted in a database of results to review and evaluate South African coal seams. Using this database, the high-risk areas in terms of spontaneous combustion can be identified. The tests, involving 119 samples, cover five consecutive years, between 2008 and 2012. The samples were from a wide variety of different coal seams and producing coalfields. All samples have been subjected to a series of laboratory tests, and the results analysed. A comprehensive database of these results is available, and is being continually updated as new test results are added.
The spontaneous combustion test At the School of Mining Engineering at the University of the Witwatersrand (Wits), an
* University of the Witwatersrand, Johannesburg. † Latona Consulting Pty. Ltd., Johannesburg. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the 21st Century challenges to the southern African coal sector, 4–5 March 2014, Emperors Palace, Hotel Casino Convention Resort, Johannesburg. VOLUME 115
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Spontaneous combustion risk in South African coalfields apparatus was developed nearly 30 years ago to measure the propensity of coal to undergo spontaneous combustion. This research was funded by the Government Mining Engineer’s Explosion Hazard Advisory Committee (EHAC). The apparatus is used to test coals under predefined conditions, and a combustibility index (Wits-EHAC) is obtained. Although the propensity of coal to combust spontaneously can be determined using various laboratory techniques, ignition temperature tests are commonly used to study spontaneous combustion, as they yield rapid results. Ignition temperature tests use two methods: ➤ Crossing-point temperature (XPT) ➤ Differential thermal analysis (DTA) to determine both the Wits-EHAC index and the XPT. The Wits-EHAC index is defined as: Wits-EHAC index = (Stage II slope/XPT) × 500 (Gouws, 1987) When the temperature differential between a coal sample and an inert sample is plotted against the inert temperature, the portion of the graph where the coal is heating more rapidly than the inert sample, i.e. where an exothermic reaction is taking place, is referred to as Stage II. According to Gouws (1987), the characteristics of the curves plotted using the obtained results (i.e. ignition temperature tests) are used to determine the propensity of coal for self-heating, and this is the basis for the Wits-EHAC liability index. It is important to understand that when an index value of coal is greater than five, there is a high propensity for spontaneous combustion, and when an index value is less than three, there is a low propensity for spontaneous combustion. An index value of between three and five indicates that the coal sample has a relatively medium risk of undergoing spontaneously combustion. As indicated in Table I, a higher index value represents a higher risk of a coal self-heating (Gouws, 1987). The testing apparatus used for the Wits-EHAC index consists of an oil bath, six coal and inert material cell assemblies, an oil circulator, a heater, a flow meter used for air flow monitoring, an air supply compressor, and a computer. The temperatures are recorded every 20 seconds by
the microcomputer during an average of 3–4 hours’ testing time. Detailed information regarding the testing apparatus used, as well as the testing procedure, is well documented by Genc et al. (2013).
Results The tests were done over five consecutive years, between 2008 and 2012, with the spontaneous combustion liability index being obtained for all 119 samples. Table II shows the summary of the results. During this period there were no low-risk samples. Figure 1 represents graphically the total number of tests in terms of medium and high propensity. Table III shows the results for spontaneous combustion tests in 2008 when 15 tests were conducted. The test results include the XPT in degrees Celsius (°C) and the Wits–EHAC index. The names of the mines have been abbreviated. All of the coal samples tested produced results that ranged from medium to high propensity to spontaneous combustion, with an almost 50/50 split between medium (8) and high (7) propensity. The minimum calculated Wits–EHAC index was
Figure 1 – Spontaneous combustion liability test results (2008–2012)
Table I
Table III
Spontaneous combustion liability index
Spontaneous combustion test results (2008)
Index
Mine
Wits-EHAC index
Crossing-point temperature (°C)
Spontaneous combustion liability
GB GB Um At MS MS MS Kl Ma Ge Tw Ui Mb SS SL
4.87 5.03 5.22 4.31 5.41 5.51 5.8 5.55 5.15 3.65 4.09 4.86 4.96 4.55 4.9
127.4 125.1 119.3 126.7 126.7 132.3 129.7 126.7 124.9 153.5 133.6 128 131.6 130.9 110.4
Medium High High Medium High High High High High Medium Medium Medium Medium Medium Medium
Spontaneous combustion liability
0-3 3-5 >5
Low Medium High
Table II
Spontaneous combustion test results between 2008 and 2012
High Medium Total
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7 8 15
13 5 18
20 7 27
6 22 28
6 25 31
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Figure 2 – Spontaneous combustion liability test results, 2008
Figure 3 – Spontaneous combustion liability test results, 2009
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still just above the low range identified by Gouws (1987), while the maximum index was 5.91. Figure 5 shows the 2011 test results for spontaneous combustion liability. Finally, Table VII shows the 2012 tests results of spontaneous combustion, when 31 tests were conducted. The 2012 results showed a very similar trend to 2011, as most of the collieries tested had a medium propensity (25 out of 31). The minimum calculated Wits-EHAC index was 3.71 and the maximum 5.75. Figure 6 shows the 2012 test results for spontaneous combustion liability.
Table IV
Spontaneous combustion test results (2009) Mine
ND Sl Bo Mo M1 Po Op Sp Sp Sp Bw Pa DR Ss M2 M3 M4 M5
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Wits-EHAC index
Crossing-point temperature (°C)
Spontaneous combustion liability
4.24 5.72 4.79 5.09 5.01 5.27 5.41 5.31 4.88 5.46 5.35 4.14 5.31 4.53 5.62 5.02 5.18 5.33
127.6 120.2 115.3 123.3 108.7 136.4 119.3 111.9 120.8 119.9 111.4 133.4 121.9 130.9 119.2 116.4 121 118.4
Medium High Medium High High High High High Medium High High Medium High Medium High High High High
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3.65, and the maximum 5.8. Figure 2 shows the 2008 test results for spontaneous combustion liability. The Wits-EHAC index margins are indicated using colours. The yellow part shading indicates the coal samples that have a value of more than 3 but less than 5, which are thought to possess a medium risk to spontaneous combustion. The red shading indicates samples that have a value of more than 5 and are, therefore, thought to have a high risk of spontaneous combustion. Table IV shows the 2009 tests results for spontaneous combustion, when 18 tests were conducted. All of the tested coal samples showed medium to high propensity to spontaneously combust, and most of the collieries have high propensity (13 out of 18). The minimum calculated Wits–EHAC index was 4.14, and the maximum 5.72. Figure 3 shows the 2009 test results for spontaneous combustion liability. Table V shows the 2010 tests results for spontaneous combustion, when 27 tests were conducted. The 2010 results show a very similar trend to the 2009 results, as most of the collieries tested had results in the high propensity range (20 out of 27). The minimum calculated Wits–EHAC index was 4.64 and the maximum 5.64. Figure 4 shows the 2010 test results for spontaneous combustion liability. Table VI shows the 2011 tests results for spontaneous combustion, when 28 tests were conducted. Although the range was similar to that previously observed (i.e. medium to high), most of the collieries tested had results in the medium propensity range (22 out of 28). The reason for the difference in the test results from one year to the next is because every coal seam has different physical and chemical properties, and these impact on its propensity for spontaneous combustion. The minimum calculated Wits-EHAC index was 3.1, which is
Spontaneous combustion risk in South African coalfields
Figure 4 – Spontaneous combustion liability test results, 2010
Figure 5 – Spontaneous combustion liability test results, 2011
Table V
Table VI
Spontaneous combustion test results (2010)
Spontaneous combustion test results (2011)
Mine
Wits-EHAC index
Crossing-point temperature (°C)
Spontaneous combustion liability
Mine
Wits-EHAC index
Crossing-point temperature (°C)
Spontaneous combustion liability
Gr4 Gr5 Xs5 Ma Ta SW Sl D16 D15 Si M1 NC nK Op Op Op Op
5.64 5.44 5.31 5.14 5.47 5.26 5.58 4.91 5.51 5.32 4.64 5.02 5.38 5.49 5.23 5.1 5.52
98.6 102.6 105.2 109.3 110.8 117.3 104.8 118.8 102.9 113.7 117.8 119.3 108 108.2 120.9 110.8 113.6
High High High High High High High Medium High High Medium High High High High High High
Op Wy DE VaA KE KW Xs2 SW VaG Mo
5.27 5.24 5.6 5.58 4.86 4.71 4.91 4.92 4.86 5.33
117 114.7 121.2 95.2 120.2 113.9 116.7 121.9 108.5 124.7
High High High High Medium Medium Medium Medium Medium High
COA COA COA TshM TshV TshG KD M3 KG Sl Sp SW GGV4 Kr Ta Tu Gr4 Sp Kr Kr COA Gr5 Dr GGV2 M2 M1 M1 M1
3.14 3.46 3.58 3.63 3.65 3.76 4.21 4.21 4.3 4.41 4.56 4.62 4.66 4.67 4.73 4.73 4.74 4.79 4.81 4.81 3.86 4.9 5.01 5.12 5.18 5.33 5.36 5.91
161.9 150.5 154.3 145.8 144.8 137.4 133.2 134.6 130.3 129.4 121.2 126.9 128.1 126.2 124.3 129.3 118.7 117.3 114.2 114.2 131.3 112.9 118.7 123.2 125.9 123.4 126.8 127.8
Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium medium Medium Medium Medium Medium Medium Medium medium Medium Medium Medium Medium High High High High High High
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Figure 6 – Spontaneous combustion liability test results, 2012
Analysis To address spontaneous combustion problems in South African collieries, 119 tests were conducted over five consecutive years and the results were rated according to the Wits-EHAC index. The analysis shows that none of the samples fell in the low-risk category. Sixty-seven out of 119 tested collieries possess a medium risk of spontaneous combustion (almost 56.3 per cent), while the remaining collieries (43.7 per cent) possess a high risk of spontaneous combustion (about a 13 per cent difference). Figure 7 shows the propensity for spontaneous combustion percentages for all 119 tests.
Figure 7 – Spontaneous combustion liability test results in percentage, 2008–2012
Table VII
Spontaneous combustion test results (2012)
AA AA AA G8 Gr4 Kr Kh5 Ar8 Ar8 Ar8 Ar8 Ar10 KiD KiD KiD KiD VeD VeC VeT VeR MoW Vu Mo TCM DCM FZN FZS WKC KE KW Tu
Wits-EHAC index
Crossing-point temperature (°C)
Spontaneous combustion liability
4.23 4.29 4.15 4.53 4.73 5.5 4.84 4.78 4.41 4.06 5.75 5.7 5.05 4.25 4.8 4.3 4.43 4.49 4.62 4.19 4.75 5.26 3.93 5.11 4.56 4.78 4.7 3.71 4.04 4.83 4.15
139.6 141 142.9 131.4 132.5 128.2 133.7 132.1 127.2 138.3 126 127.9 109 137.8 125.1 139.6 117.9 128.2 119.3 134.8 119.9 124.8 138.3 128.1 125.1 127.8 124.9 153.8 143.3 120.4 142.9
Medium Medium Medium Medium Medium High Medium Medium Medium Medium High High High Medium Medium Medium Medium Medium Medium Medium Medium High Medium High Medium Medium Medium Medium Medium Medium Medium
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The results indicate that spontaneous combustion propensity is dependent on the properties of each coal seam. In 2010, when the largest high-risk rating percentage was recorded, test results showed that more than 74 per cent of the mines were in this high-risk category, compared with about 21 per cent in 2011 and 19 per cent in 2012. In 2009, similar to 2010, 72 per cent of the mines had high risk ratings. In 2008, the high and medium rating percentages were very close; 47 and 53 per cent, respectively. The results show that, given the right environmental conditions, most of the collieries located in the Witbank and Highveld coalfields have a high risk of spontaneous combustion. The high-risk areas also include the northeastern part of Ogies. The southern parts of Witbank coalfield, in the Ermelo area, are also rated high in terms of risk. Although 52 out of 119 collieries have a high inherent risk of spontaneous combustion, most of the selected collieries possess medium risk ratings. Medium risk ratings can be seen around the northern parts of Ermelo, as well as in KwaZulu-Natal Province where anthracite coal is mined. It is interesting that there were no low-range results recorded during the five-year testing period from 2008 to 2012. This finding indicates that there is a need to re-visit the current definition of the spontaneous combustion liability index within the ranges of low, medium, or high; but this requires a further study as to how Gouws (1987) defined these ranges and, if it is necessary to change the current definitions, at what levels should the new criteria should be set. VOLUME 115
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Spontaneous combustion risk in South African coalfields Conclusion The inherent propensity for spontaneous combustion to occur at the selected South African collieries was analysed and classified through a series of laboratory tests. It is evident that, despite the low frequency of underground incidents, South African collieries do have the risk of spontaneous combustion. In all, 119 tests were conducted between 2008 and 2012. The test results indicated that the majority of South African collieries have medium risk ratings (56.3 per cent), and the propensity of spontaneous combustion of the collieries ranges from medium to high. There results and the subsequent analysis highlight a significant concern – that there are no low-range results , and this emphasizes the importance of monitoring the early signs of spontaneous combustion in the collieries. However, there is also the need to re-visit the definitions of low, medium, and high risk for the spontaneous combustion liability index, and this will require further research. There is already a considerable body of evidence that the seams of the Waterberg coalfield are particularly prone to spontaneous combustion, and there will be a definite need to incorporate those results into any new research.
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Based on the tests results, it was found that there are high spontaneous combustion risks in the Witbank and Highveld coalfields. Relatively low spontaneous combustion risk was found in the KwaZulu-Natal coalfield, where anthracite coal is mined, as well as the northern parts of Ermelo coalfield.
References GENC, B. and COOK, A. 2013. Determination of spontaneous combustion risk in the South African coalfields. 23rd International Mining Congress and Fair, Antalya, Turkey, 16–19 April 2013.
GOUWS, M.J. 1987. Crossing point characteristics and differential thermal analysis of South African coals. MSc dissertation, University of the Witwatersrand, Johannesburg.
PHILLIPS, H., CHABEDI, K., and ULUDAG, S. 2011. Best Practice Guidelines for South African Collieries. http://www.coaltech.co.za/Annual_Colloquium/Colloquim%202011/Spont aneous%20Combustion%20Prevention%20and%20Control%20by%20Hu w%20Phillips,%20Kelello%20Chabedi%20&%20Sezer%20Uludag.pdf [Accessed 5 January 2014]. ◆
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http://dx.doi.org/10.17159/2411-9717/2015/v115n7a2
Processing low-grade coal to produce high-grade products by G.J. de Korte*
History of coal processing in South Africa
South Africa’s best-quality coal, located in the central Highveld basin, is becoming depleted and alternative sources of coal, such as the Waterberg coalfield, will have to be developed to supply the country with coal in the future. The quality of the coal being mined in the central basin is gradually becoming poorer. This necessitates that more of the coal be processed to improve the quality to meet customer requirements. The challenge to the coal processing industry is to process low-yielding coals to produce goodquality products and at the same time ensure that coal mining remains economically viable. This requires that more cost-effective coal processing technologies be investigated and implemented. Keywords low-grade raw coal, new developments, low-cost coal processing technologies, dry processing of raw coal.
Introduction South Africa’s main sources of coal supply for the last century have been the Witbank, Highveld, and Ermelo coalfields. These coalfields have been extensively mined to produce the coal required to satisfy the needs for power generation and other industrial requirements, and also to establish a competitive share for South African coal in the international export market. The remaining reserves of coal in these coalfields will eventually become depleted – it is predicted that this will happen by about 2040. South Africa does, however, still have extensive reserves of coal in other areas, namely the Waterberg and Soutpansberg coalfields, while coalfields located in the Springbok Flats, the Free State, and Molteno area remain largely unexploited. The coal from some of these areas is, however, of relatively low quality compared to the coal from the Witbank area. Since the coal found in the Waterberg, Soutpansberg, and other coalfields differs from the coal traditionally mined, new techniques will be required in the future to mine, process, and utilize the coal. It is expected that the quality of the coal as-mined will become increasingly poorer while the coal market will become increasingly more demanding in terms of the quality of the product. This will therefore present significant challenges to the coal industry. The Journal of The Southern African Institute of Mining and Metallurgy
In the early days of coal mining in South Africa, coal was selectively mined to satisfy the requirements of local industries – mainly the gold and diamond mines and the transport industry. The requirement was for coarse coal and it was customary to screen the coal finer than about 6 mm, which was termed ‘duff’, from the coal as- mined. The coarse coal was supplied to the end-users while the duff, which comprised a significant portion of the run-ofmine coal, was discarded. Selective mining resulted in the sub-optimum utilization of coal reserves, and mine owners realized that whole-seam mining would be a more sustainable option. Mining the complete coal seam, however, resulted in lower quality coal and hence some form of upgrading of the coal was needed. Hand-picking (Figure 1) was the method first employed to achieve this objective, but improved coal processing techniques eventually followed. The first coal preparation plant in South Africa was a jig plant constructed in the Witbank area in 1909 (Coulter, 1957). The next major advance in coal processing was the commissioning of a Chance washer in the Vereeniging area in about 1935 (Coulter, 1957). Other coal processing plants in the Witbank area and in the former Natal province followed. In response to a growing demand for coal and the ever-increasing pressure to supply good-quality coal, jig washers were installed at a number of coal mines. Following the introduction of dense medium processing using magnetite as the medium during the 1950s, the jigs were gradually replaced by the more efficient dense medium process. The
* CSIR, Pretoria. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at the 21st Century challenges to the southern African coal sector, 4–5 March 2014, Emperors Palace, Hotel Casino Convention Resort, Johannesburg. VOLUME 115
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Processing low-grade coal to produce high-grade products
Figure 1 – Hand-picking at Springbok Colliery
major change in the South African coal processing industry came about as a result of a contract concluded with a consortium of Japanese steel mills during the early 1970s for the supply of 2.5 Mt of low-ash coal per annum to be used as a blend coking coal. The duration of the contract was 10 years, and the coal was to be produced from the Witbank No. 2 seam. This contract led to the establishment of the dedicated coal railway line from the Highveld to Richards Bay and the Richards Bay Coal Terminal (RBCT). Very efficient coal processing techniques were required to process the difficult-to-wash No. 2 Seam coal in order to produce the lowash coal required for the Japanese steel industry. Research conducted by the Fuel Research Institute of South Africa (FRI) during the late 1950s and 1960s was successfully implemented to satisfy this requirement. The initial focus of the FRI research was not the Japanese market, but was aimed at extracting coking coal from the Waterberg coalfield for use by the local iron and steel industry. This know-how came in handy for the Japanese contract and was also successfully implemented when Grootegeluk Mine came into production in the late 1970s. South Africa’s coal processing industry therefore became equipped to effectively process difficult raw coals to produce high-quality products.
Current coal processing practice South Africa today has approximately 60 coal preparation plants, most of which are located in the Witbank area. Many of these plants produce export thermal coal, which is exported via RBCT – currently a total of some 70 Mt/a. The export coal typically has a heat value of 6000 kcal/kg, which
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requires the raw coal to be processed at a low relative density. Most of the mines employ two-stage processing plants, with the first stage processing the raw coal to yield an export product and the second stage re-processing the rejects from the first stage at a higher relative density to produce a thermal coal for Eskom. There are also a growing number of small plants that only produce coal for Eskom. Most of the export plants, as well as the Eskom-only plants, use dense medium drums and/or cyclones for processing coarse coal and spirals to process fine coal. There are a number of smaller plants in the Witbank area, and also a few in KwaZulu-Natal, that produce sized products for the inland market. These plants tend to be equipped with a single Wemco drum to process coarse coal, dense medium cyclones to process the small coal, and spirals to process the fine coal. The drum product is usually screened to produce large and small nuts, while the cyclone product is screened into peas and duff. The spiral product is usually added to the duff.
New developments in coal processing As mentioned previously, the quality of raw coal being mined continues to decrease, and several mines extract coal pillars left from previous bord and pillar mining operations. The product yields obtained from the raw coal are lower than in the past and processing of the coal is becoming more of a necessity. Since the coal is becoming more difficult to process and product yields are low, there is increasing pressure on the profitability of mines and as a result, low-cost processing techniques are being evaluated and implemented. Some of the The Journal of The Southern African Institute of Mining and Metallurgy
Processing low-grade coal to produce high-grade products
Figure 2 – 3-product cyclone at Umlalazi Mine The Journal of The Southern African Institute of Mining and Metallurgy
beneficiation of coarse coal. Further advantages of dry processing are that the capital and operating costs are much lower than dense medium processing; the product coal stays dry, which effectively increases the heat value of the coal; and no slurry is produced, which lowers the environmental impact of coal processing. Unfortunately, the separation efficiency of the available dry processing technologies is inferior to that of dense medium separation, and these technologies are not generally applicable to all raw coals. The FGX plant at Middelkraal Colliery is shown in Figure 4. Some of the mines that exclusively process coal for Eskom need not process the complete size range of raw coal and employ partial washing. In partial washing, the finer sizes of coal are dry-screened from the plant feed and report directly to the product conveyor. The coarser coal is processed and the resulting product blended with the fine raw coal to constitute the final Eskom product. The size at which the coal is dry-screened depends on the specific quality of the raw coal and can vary between 4 mm and 40 mm. Dry screening at small aperture sizes is not easy, but the Bivitec and Liwell Flip-Flo screens have proven capable of this duty.
Future needs It is expected that coal processing will become more difficult in future as the quality of raw coal mined continues to decline. Coal processing plants will have to contend with lower yields and more difficult-to-process coal. At the same time, strict product quality specifications will have to be maintained. The separation efficiency of the processes employed will become even more important and it will be necessary to balance separation efficiency against capital and operating costs. The low cost of dry processes make them very attractive, especially for small mining companies, but the low separation efficiency of these processes may make them uneconomical in the long run. An efficient dry process is therefore required. Dry dense medium separation offers good efficiency but is still unproven in practice. A pilot-scale dry dense medium plant is in operation in China and the South African coal industry, through the Coaltech research programme, plans to evaluate this technology in the near future.
Figure 3 – Filter press at Hakhano Mine VOLUME 115
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technologies recently implemented in South Africa include the 3-product dense medium cyclone and dry beneficiation. The 3-product dense medium cyclone was developed in Russia during the 1970s, but only found widespread application in China in the past 10 or so years. The unit is essentially a Larcodems-like cylindrical cyclone with a conventional conical cyclone attached to the sinks outlet. The raw coal and medium is pumped to the primary cylindrical cyclone, where a low-density separation is effected to yield an exportquality coal. The sinks and part of the medium then enters the conical cyclone, where a high-density separation is effected on the coal to yield an Eskom product and a final reject. The unit therefore allows two separations to be carried out with a single medium circuit, which results in significantly lower capital and operating costs. The 3-product cyclone in operation at Umlalazi Mine is shown in Figure 2. The capital cost of new plants is an important consideration and has to be kept as low as possible while still maintaining efficient processing of difficult raw coals. This is achieved by simplifying plant configuration through the use of large, high-capacity processing units such as largediameter dense medium cyclones and large-capacity screens. This reduces the number of equipment items in a plant and still enables effective separation of coal. South Africa is a water-scarce country and the coal industry is under pressure to reduce the amount of water consumed for coal processing. In this regard, a number of coal processing plants have installed filter presses to close their water circuits. By filtering the slurry produced during coal processing rather than disposing of it in slurry ponds, water consumption is reduced by a factor of about three. An added advantage is that the product obtained from the filter may be saleable. The filter press in operation at Hakhano Mine can be seen in Figure 3. Dry processing of coal requires no water and dry processing techniques are therefore very attractive considering our climatic conditions. Two dry processing technologies have been evaluated and implemented in South Africa, namely the FGX dry coal separator and X-ray sorting. The FGX unit is suited to processing of -80 mm raw coal while the X-ray sorter is well suited to de-stoning or pre-
Processing low-grade coal to produce high-grade products
Figure 4 – FGX plant at Middelkraal Colliery
Due to low-yielding raw coal and resulting low product yields, it will be necessary for coal processing equipment to be able to cope with high amounts of reject coal. High spigotcapacity dense medium cyclones or suitable substitutes will be required. Improved fine coal beneficiation and dewatering techniques will be needed as the amount of fine coal in runof-mine coals is expected to increase further – especially in those mining operations where remnant pillars are being remined. An additional factor to contend with in the case of pillar re-mining operations is the influence of weathering and spontaneous heating of coal. It is further anticipated that more fine coal will have to be utilized in power generation, and methods to improve the transport characteristics of fine coal will therefore have to be investigated. The effective recovery and re-use of water in coal processing plants will become even more important. It is also anticipated that the cost of water, especially in the Waterberg area, will increase significantly in future. Improved methods for the disposal and/or use of discards and slurry will be required to ensure that coal mines comply with ever-increasing environmental concerns.
Africa. Fuel Research Institute Symposium, Pretoria, June 1957.
FRASER, T. and YANCEY, H.F. 1926. Artificial storm of air-sand floats coal on its upper surface, leaving refuse to sink. Coal Age, March. pp 325–327.
HALL, I. 2013. South African Coal Roadmap. Presentation to FFF Council Meeting, SRK Offices, Johannesburg, 27 February 2013.
HONAKER, R.Q., LUTTRELL, G.H., BRATTON, R., SARACOGLU, M., THOMPSON, E., and RICHARDSON, V. 2007a. Dry coal cleaning using the FGX separator. SA Coal Preparation Conference and Exhibition. Sandton, 10–14 September 2007.
HONAKER, R.Q., LUTTRELL, G.H., BRATTON, R., SARACOGLU, M., THOMPSON, E., and RICHARDSON, V. 2007b. Dry coal cleaning using the FGX separator. Coal Preparation Conference and Exhibition. Lexington, KY, 30 April 30–3 May 2007.
LITH, A. 2003. Scheiding van kool en schalie met het fluïdebed. Study report, Technical University of Delft, Netherlands.
SHUYAN ZHAO, S. and YU, J. 2012. Novel efficient and simplified coal preparation
Conclusion
process. International Coal Preparation 2012. Lexington, KY. 30 April
Mining conditions in the traditional mining areas will become more demanding in future and mining operations from new coalfields will have to commence. This will require coal processing engineers to find new and improved methods to process low-grade raw coals to yield high-grade products within ever-increasing economic and environmental challenges.
30–3 May 2012. Paper 9.
TAKO, P.R. DE JONG, VAN HOUWELINGEN, J.A., and KUILMAN, W. 2004. Automatic sorting and control in solid fuel processing: opportunities in European perspective. Geologica Belgica, vol. 7, no. 3-4. pp. 325–333.
YELL, A. 2007. Problems associated with dry screening of coal. Presentation to the South African Coal Processing Society, 31 January 2007.
References QINGRU, C. and YUFEN, Y. 2002. Current status in the development of dry beneficiation technology of coal with air-dense medium fluidized bed in China. XIV International Coal Preparation Congress and Exhibition, Sandton, South Africa, 11–15 March 2002.. South African Institute of Mining and Metallurgy, Johannesburg.
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COULTER, T. 1957. The history and development of coal washing in South
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ZHAO, S., ZHANG, C., XU, X., YAO, W. CHEN, J., YUAN, Z., and ZHANG, H. 2010. Super-large gravity-fed three-product heavy medium cyclone. Proceedings of the XVI International Coal Preparation Congress, Lexington, KY, May 2010. pp 296–305.
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The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n7a3
Feasibility study of electricity generation from discard coal by B. North*, A. Engelbrecht*, and B. Oboirien*
There is large electricity generation potential in discard coal, both in stockpiles and current arisings. Power stations with a combined capacity of up to 18 GW electrical (GWe) could be fuelled by discard coal. Modern circulating fluidized bed combustion (CFBC) boilers, with capital costs comparable to equivalent pulverized fuel (PF) boilers, are capable of utilizing discard coal at a high efficiency while reducing sulphur dioxide (SO2) emissions though the use of limestone sorbent for ‘in-situ’ capture. A detailed economic assessment of the feasibility of electricity generation from discard coal, comprising material and energy balances and the construction of a discounted cash flow (DCF) table, shows that it is also potentially attractive from an economic perspective. A base case analysis shows positive net present values (NPVs) and an internal rate of return (IRR) of 21.4%. Sensitivity analyses on critical parameters show that the economic viability is heavily dependent on parameters such as coal cost and the value of electricity. The project becomes unattractive above a coal price of approximately R300 per ton and at an electricity value below approximately 59c per kilowatt-hour (kWh). Site- and project-specific information such as the delivered cost of coal, location and efficacy of sorbents, and effective value of the electricity produced can be used as input to the economic analysis to evaluate siting options and sorbent source options for such a power station. Keywords fluidized bed, discard coal, electricity generation, techno-economics, sulphur capture.
Introduction South Africa has large resources of coal. Prévost (2010) reported that South Africa has coal reserves of 33 000 Mt. Annual production is 250 Mt, over 70% of which is utilized in the domestic market, mostly for electricity and synthetic fuels production. Annual exports total 61 Mt, which generates a large foreign income stream for South Africa. However, the export market demands coal of a high quality. For many producers to meet this quality (and indeed, to meet quality requirements for domestic use), often the coal must be beneficiated to reduce the ash content and increase the calorific value (CV). This results in the generation of waste coal. This waste coal can be categorized into three main streams – discards, duff, and slurries. Discards are the high-ash fraction coal. These often also contain relatively high levels of sulphur. Discards have been reported to have a CV in the range of 11 to 15 MJ/kg (Pinheiro, Pretorius, and Boshoff, 1999; Du The Journal of The Southern African Institute of Mining and Metallurgy
Potential value of discard coal As a form of screening exercise, the value of discard coal, in terms of the amount of electricity that could be generated from it, was assessed. This was carried out on both the existing stockpiles of discards and the current arisings.
* CSIR Materials Science and Manufacturing (Energy Materials), Pretoria, South Africa. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at IFSA 2014, Industrial Fluidization South Africa, Glenburn Lodge, Cradle of Humankind, 19–20 November 2014 VOLUME 115
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Synopsis
Preez, 2001) The amount of discard coal currently stockpiled on the surface is estimated at 1500 Mt, and the amount of discard coal generated in 2009 was reported to be approximately 67 Mt (Prévost, 2010). This discarded coal represents both a loss of potentially usable energy and an environmental threat due to occasional spontaneous combustion of the heaps. Stockpiles of discard coal can also become a source of acid rock drainage (ARD). High-ash discard coal cannot be utilized in pulverized fuel (PF) boilers, but can be successfully utilized in circulating fluidized bed combustion (CFBC) boilers. Although CFBC technology has lagged behind PF technology in terms of steam conditions, with the commissioning of supercritical CFBC boilers this ‘disadvantage’ for CFBC has largely been overcome (Utt and Giglio, 2011). Additionally, capital costs for the two technologies have converged, making CFBC cost-competitive with PF (Utt and Giglio, 2011; Aziz and Dittus, 2011; Haripersad, 2010). The South African Department of Energy has released a call for 2500 MW of coal-fired base load electricity to assist in addressing the current electricity supply deficit in the country (South African Department of Energy, n.d.). It is against this background that an assessment was made of the economic merit of generating electricity from discard coal in a CFBC power station.
Feasibility study of electricity generation from discard coal Electricity generation from stockpiled discard coal Table I shows the amount of electricity that could be generated from existing discard stockpiles. It was assumed that the discards would be utilized over a 40-year period (the assumed lifetime of a power station). This shows that a significant amount of electricity (6.5 GWe of installed capacity, or approximately 16% of South Africa’s current generating capacity) could potentially be generated from existing discard coal stockpiles throughout the 40-year lifetime of the power station.
Electricity generation from discard coal arisings Based on estimates of the amount of discard coal generated on an annual basis, a similar exercise to the above was undertaken to estimate how much electricity could potentially be generated from this source. The results are shown in Table II. A power station with a capacity in excess of 11 GWe could be fuelled by the discard coal arisings. The total amount of electricity that could be generated from existing stockpiles and arisings, in terms of installed capacity and generation per year (in gigawatt-hours per year), is shown in Table III. It is clear that a significant amount of electricity could potentially be generated from existing stockpiles and current arisings of discard coal. But, would this be an economically viable undertaking? An economic analysis was undertaken to determine the economic indicators of an FBC power station.
Economic analysis of a discard coal-fired FBC power station A case study of a 450 MWe station was considered. This is in line with the size of FBC power stations envisaged in the South African Integrated Resource Plan (South African
Department of Energy, 2010a) and plants being considered by industry (Hall, Eslait, and Den Hoed, 2011), and is within the proven capacity of efficient, supercritical FBC plants (Utt and Giglio, 2011). The analysis was undertaken in two components, both of which utilized Excel® spreadsheets. The first is essentially a material and energy balance, in which fuel and sorbent requirements are calculated using input data such as plant size, plant efficiency, fuel CV, calcium to sulphur (Ca/S) ratios etc. Additionally, in this component, operating costs, fuel and sorbent transport costs, and revenue (from the sale of electricity) are calculated. The figures calculated in the first component are then used to construct the second component, a discounted cash flow (DCF) analysis. This is used to run sensitivity analyses and to calculate economic indicators such as the net present value (NPV) and the internal rate of return (IRR). The IRR is the discount rate at which a zero NPV is seen, and is essentially a measure, as its name would suggest, of the return that could be made on the investment. Most companies have a ‘hurdle rate’, and will not consider projects returning an IRR that falls below this. The IRRs (and NPVs) of various projects are also often compared to select the optimal investment out of many possible investments. Definitions of, and example calculations of, DCF, IRR, and NPV can be found in any standard economics or finance book, e.g. Correia et al. (1989). A list of input parameters, with a discussion and references (if available) follows. These values are used as a base case, and different scenarios are evaluated and sensitivity analyses presented.
Assumptions and input to economic analysis Plant size: 450 MWe
Table I
Electricity generation potential from existing discard coal stockpiles Input data
Value
Discard coal stockpiled Utilization period Average CV of discards Efficiency (coal to electricity) Output Rate of use of discards Power plant capacity
1500 Mt 40 years 13 MJ/kg 40%
Source Prevost, 2010 Assumption Du Preez, 2001 Estimate (SC)
37.5 Mt/y 6.5 GWe
This is in line with the size of FBC power stations envisaged in the South African Integrated Resource Plan (South African Department of Energy, 2010a) and plants being considered by industry (Hall, Eslait, and Den Hoed, 2011), and is within the proven capacity of efficient, supercritical FBC plants (Utt and Giglio, 2011).
Plant efficiency: 40% Utt and Giglio (2011) assumed 40% efficiency for a supercritical CFB. In a prior publication, Utt, Hotta, and Goidich, (2009) reported an efficiency of 41.6% for the Łagisza power station. Jantti (2011) later reported that an efficiency of 43.3% was being achieved at Łagisza; however, it appears that this may have been calculated on the lower
Table II
Electricity generation potential from discard coal arisings Input data
Value
Discard arisings Lifetime of plant Average CV of discards Efficiency (coal to electricity) Output Power plant capacity
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67 Mt/a 40 years 13 MJ/kg 40 % 11.6 GWe
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Source Prevost, 2010 Assumption Du Preez, 2001 Estimate (SC)
Table III
Electricity generation potential from both discard coal stockpiles and arisings Source
GWe
GWh/a
Stockpiles Arisings Total
6.5 11.6 18.1
54 167 96 778 150 944
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Feasibility study of electricity generation from discard coal heating value (LHV) (or net calorific value, NCV) rather than the higher heating value (HHV) (or gross calorific value, GCV). It was decided therefore, keeping in mind the lowquality discard coal that would be utilized, to assume the relatively conservative figure of 40%.
This is drawn from studies undertaken to assess the inventory of duff and discard coal (Pinheiro, Pretorius, and Boshoff, 1999; Du Preez, 2001).
sorbent plays a large role. A figure of 2.9 was derived from data quoted by Aziz and Dittus (2011) for limestone. Utt, Hotta, and Goidich (2009) indicate that 94% of the sulphur could be removed from a fuel containing 0.6% to 1.4% sulphur at a Ca/S ratio of 2.0 to 2.4. It was decided to use the figure of 2.9 quoted by Aziz and Dittus (2011), as a conservative approach. For dolomite (the rationale for use of which is explained below), a Ca/S ratio of 5.3 was used. This is based on the relative performance of Lyttelton dolomite versus Union lime shown in the research on the NFBC. This is an estimate, but it is intended to show the effect of sorbent type and source on financial viability. The Ca/S ratio is an important parameter, as it dictates the amount of sorbent that will be required, which is a significant operating cost for the plant. It would be of great value if the economic assessment developed here could be linked to a sorbent efficacy model, so that the required Ca/S ratio for a given sorbent can be input, rather than estimated.
Fuel ash content: 45%
Calcium carbonate content of sorbent: 30–96%
Discard coal in both dumps and current arisings has a wide range of ash contents (Pinheiro, Pretorius, and Boshoff, 1999; Du Preez, 2001). A figure of 45% was used, This is in good agreement with figures quoted by Hall, Eslait, and Den Hoed (2011), and is similar to the ash content of the Greenside discards tested by the CSIR in the National Fluidized Bed Combustion Boiler (NFBC) project (Eleftheriades and North, 1987). (Note, this was a bubbling FBC.) The economic calculations are not, however, very sensitive to the coal ash content, as for the purposes of this analysis the coal requirements are calculated from the calorific value of the coal rather than the ash content.
While the selected Ca/S ratio drives the calculation of how much calcium is required, the calcium content of the sorbent then dictates how much sorbent is required. This has implications for both the base cost of the limestone and the transport cost. South African limestones typically have a calcium carbonate content in the range of 85% to 95% (Agnello, 2005). The limestone chosen for this analysis is supplied by Idwala Lime from the limestone quarry in Danielskuil, approximately 700 km from the Witbank area. Idwala Lime currently supplies the limestone for the CSIRdesigned FBC high-sulphur pitch incinerator operating at Sasol in Sasolburg (North et al., 1999). This limestone has a high calcium carbonate content, at 96%. This equates to a calcium content of 38.4%, as the molecular weight of calcium carbonate is 100, whereas that of calcium is 40. An advantage of in-situ sulphur capture in FBC over flue gas desulphurization (FGD) in pulverized fuel (PF)-fired boilers is that FBC can utilize relatively poor sorbents, including dolomite. Haripersad (2010), drawing heavily on Agnello (2005), concluded that the ability of FBC to utilize these lower grade sorbents was a driver towards the adoption of FBC technology. There would be competition with the gold mining industry and the cement industry for the high-grade limestone required for FGD on PF plants, whereas there is little competition for low-grade limestone and dolomite. Further, he concluded that PF with FGD would become resource-constrained in terms of both sorbent and water by 2025. A scenario of using dolomite was therefore also considered in this current assessment.
This is the electricity that is actually produced in a year as a percentage of the electricity that could be produced. It takes into account load-following and planned and unscheduled maintenance. The US Electrical Power Research Institute (EPRI) assumed 85% in a study undertaken as input to the South African IRP, and this was adopted in the current analysis (South African Department of Energy, 2010b).
Fuel calorific value: 13 MJ/kg
Sulphur content: 2.77% Again, there is a wide range of sulphur contents in both arising discards and in dumps. A value of 2.77% was used, this being the sulphur content of the Greenside discards tested in the NFBC (Eleftheriades and North, 1987). This figure is also in agreement with sulphur contents reported by Hall, Eslait, and Den Hoed, (2011). Aziz and Dittus (2011) reported a significantly lower sulphur content of 1.5% in their study of a CFB power station utilizing discard coal from the Delmas coal mine. The economic study is sensitive to the sulphur content of the coal because this dictates the amount of sorbent required to reduce the sulphur oxide emissions. Not considered here, but of merit to consider in a real application, is the possibility of beneficiating the discards, particularly those recovered from dumps, to reduce the sulphur content and therefore sorbent requirements (discussed below). Hall, Eslait, and Den Hoed, (2011) considered this option, whereas Aziz and Dittus (2011) did not.
Required Ca/S ratio: 2.9, 5.3 This is the molar ratio of calcium in the sorbent to sulphur in the coal, with a stoichiometric (1:1) ratio theoretically (but not in practice) being able to remove all the sulphur. As shown by the research in the NFBC, the calcium content of a sorbent is not necessarily a good indication of the efficacy of the sorbent, and therefore of the amount required (Eleftheriades and North, 1987). The physical nature of the The Journal of The Southern African Institute of Mining and Metallurgy
Fixed operational costs: R202 million per year This was calculated from the figures quoted by EPRI for fixed costs of an FBC power station (with limestone addition) as a factor of the installed capacity (South African Department of Energy, 2010b). (R404 per kW per year, escalated by the consumer price index).
Variable operational costs: R258 million per year This was calculated from the figure quoted by EPRI (South African Department of Energy, 2010b) for variable operating costs for an FBC power station as a factor of power sent out VOLUME 115
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Capacity factor: 85%
Feasibility study of electricity generation from discard coal in the year (R69.1 per MWh) (escalated by CPI). The costs for FBC without limestone addition were used, as in this current analysis the limestone costs are split out in order to assess their contribution to the costs, and to enable sensitivity analyses to be carried out on the delivered cost of limestone.
Water cost: R434 000 per year This was derived from the water consumption indicated by EPRI (South African Department of Energy, 2010b) (33.3 l/MWh) and an assumed cost of water of R3.50 per megalitre (escalated by CPI). Water costs appear to be a relatively small component of the total annual operating costs.
Fuel cost: R129 per ton This value was essentially ‘reverse engineered’ from the current electricity price and the indication by Koornneef, Junginger, and Faaij (2006) that the fuel component of the cost of electricity for ’waste coal’ is 15%. Again, in reality, there could be a great range to this value. From experience, when a waste begins to be used the owner of that waste starts to ascribe increasing value to it. If the power station developer is also the owner of the mine, this effect will largely be negated. Utt and Giglio (2011) used a value of $100 per ton for a 25 MJ/kg coal, and EPRI (South African Department of Energy, 2010b) used approximately R288 per ton for a 19.2 MJ/kg coal. The cost of the fuel needs to be determined/ negotiated and contracted in order to conduct an accurate economic viability assessment. For the purposes of this study, where the specific intent is to show the potential advantage of using waste coal, we believe the approach of using the fuel cost component indicated by Koornneef, Junginger, and Faaij (2006) is valid. A wide range of fuel costs is considered in the sensitivity analyses.
Fuel transport cost: R0.93 per kilometer per ton It proved difficult to obtain transport costs from the transport industry itself. An indication of road transport costs was obtained from Blenkinsop (2012). Although not in the transport industry, Blenkinsop is assessing the viability of utility-scale FBC projects in southern Africa, and is therefore regarded as a reliable source of information. He indicated a range of between R0.90 and R1.30 per kilometre per ton (including escalation by CPI). The lower limit was taken, this being the transport cost indicated by Idwala Lime (below).
Fuel transport distance: zero As the intent is to operate a mine-mouth power station, this will be zero for this current assessment. It has, however, been included in the calculations in order that sensitivity to this figure can be assessed should a potential application be located away from the mine. Alternatively, there could be multiple fuel feeds from multiple mines.
Sorbent cost: R449 per ton This cost was obtained from Idwala Lime. The price has been escalated by CPI.
Sorbent transport cost: R0.93 per ton per kilometre Idwala Lime indicated that the transport cost of their product from Danielskuil to Witbank is R650 per ton (after escalation by CPI). With the distance being approximately 700 km, this equates to approximately 93c per kilometre per ton.
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Sorbent transport distance: 700 km A distance of 700 km was used for the analysis, this being the distance from the Idwala Lime mine in Danielskuil to Witbank. The sorbent transport distance is, however, varied in order to gauge the sensitivity of the project viability to this parameter.
Electricity value: R0.5982 per kWh The tariff at which Eskom is allowed to sell electricity is currently a hotly debated subject in South Africa. Proposed tariffs are set out in a Multi-Year Pricing Determination (MYPD) document. The National Energy Regulator of South Africa (NERSA) reviews this, and makes a decision on what it believes is a reasonable tariff increase, based on considerations of the cost of producing electricity and the impact that increase power tariffs could have on the economy of South Africa. For each of the years 2010/2011, 2011/2012, and 2012/2013, an increase of 25.9% was approved by NERSA (Eskom, 2012). However, following a ‘... combined effort by Government and Eskom to lessen the impact of higher tariff increases on consumers ...’, the increase for 2012/2013 was reduced to 16% (Eskom, 2012). The revenue reported in 2013/2014 was 62.82 cents per kWh, which includes a 3 cent per kWh environmental levy (Eskom, 2014). A figure of 59.82 cents per kWh was therefore used. It is not clear, however, how much of this value could be realized by an independent power producer (IPP). If the electricity is to be used elsewhere (but possibly within the same company or group), there will be costs associated with transporting the electricity through the Eskom grid. An analysis was therefore run to estimate the lower limit for the electricity value that still results in a viable project. The value of the product, electricity, does of course have a major impact on viability. In the case of generation of electricity for self-use, the electricity will not result in true revenue, but will be an avoided cost. An alternative approach was also taken, i.e. to calculate the cost that electricity would need to be sold at in order to realize an acceptable IRR (the hurdle rate of 20%).
Plant capital cost: R20 490 per kWe This is a very important parameter, and unfortunately estimates of this varied. Utt and Giglio (2011) indicate a specific plant cost of $2000 to $2100 per kWe installed capacity for supercritical FBC. Tidball et al. (2010) showed a range of between approximately $1700 and $2600 per kWe (reported in 2007). This was a subcontract report written for a National Renewable Energy Laboratory (NREL) contract. EPRI (South African Department of Energy, 2010b) indicate a specific plant cost of R16 540 per kWe. This is quoted in South African rands rather than US dollars because the analysis was conducted as input to the South African Integrated Resource Plan. It was decided to use this value (corrected for four years of inflation at the average South African inflation rate of 5.5%, giving R20 490 per kWe) because this (a) was specifically carried out for a South African scenario and (b) specifically considered FBC power stations.
Depreciation period: 5 years This is included in the discounted cash flow as a ‘wear-andtear’ tax allowance that is allowed on capital expenditure. The The Journal of The Southern African Institute of Mining and Metallurgy
Feasibility study of electricity generation from discard coal allowable depreciation was assumed to be straight-line over 5 years. This approach is explained in Correia et al. (1989).
requirements), and transport obviously requires fuel and/or electricity.
Plant lifetime: 30 years
Limestone, fixed operational and variable operational costs: 5.5% (equal to CPI)
Discount rate: 9% This is a key parameter in an economic analysis. Unfortunately, again, there is a range of values suggested. The discount rate is essentially a return that an investor would have to receive on the investment to warrant it. Generally, the value used here is the weighted average cost of capital (WACC) (or weighted marginal cost of capital, WMCC). This is (simplistically) calculated from the relative weights and contributions of equity, debt, and shares that are used to finance the project (Correia et al. , 1989). The accurate calculation of the WACC is in itself a science, and can involve the application of a capital asset pricing model (CAPM) (Nell, 2011). Power (2004) asserts that ‘The cost of capital is a price, a price for a “share” of risk sold by a company.’ As such, factors such as where a company’s head office is listed can significantly affect it. For the purposes of this analysis it was decided to use available figures for the WACC for the only current electricity utility in South Africa, Eskom. However, even with this narrowed focus, a range was obtained. BUSA states that the WACC proposed by Eskom (10.3%) was possibly high, and a value of 8% may be more realistic (BUSA, 2009). Mokoena states that Eskom’s WACC is 8.16% (Mokoena, 2010). Mining Weekly quoted Dick Kruger, SA Chamber of Mines techno-economic assistant adviser, as saying that ‘... the 10.3% applied by the utility ... should be as much as three percentage points lower ...’ (Mining Weekly, 2012). It was decided to adopt a figure towards the middle of this range, namely 9%.
Tax rate: 28% This is the standard tax levied on companies by the South African Revenue Service (2012).
Inflation: 5.5% Inflation is a variable figure. Historically South Africa has seen periods of high inflation, whereas more recently inflation has been lower and more stable. Bruggemans (2011) shows a current inflation rate (2012) of 5.6%, and forecasts 5.5% and 5.9% respectively for 2013 and 2014. The figure of 5.5% forecast for 2013 was assumed for this study. It was further assumed that this would hold steady over the analysis period. An inflation rate for each future year could be incorporated into the DCF, but this would complicate the analysis, with uncertain added value. In any event, the more important consideration is how much more or less than the CPI inflation rate other parameters will be, such as fuel price, transport price etc.
Coal, water, and transport cost inflation: 7.5% (2% above CPI) An assumption was made that energy-related costs would rise at a rate above inflation. Coal is an energy product, water has a high electricity component to its price (due to pumping The Journal of The Southern African Institute of Mining and Metallurgy
These commodity or equipment-type costs are assumed to inflate in line with the CPI.
Electricity price inflation (5-year 16%, CPI + 5%) The general belief that electricity price increases would continue to be well above inflation has proven to be valid, with the release of Eskom’s Multi-year Price Determination 3 (MYPD3) document. Engineering News reports that increases of 16% have been requested in MYPD3, which was released on 22 October 2012 (Engineering News, 2012). As with previous MYPD submissions this will still need to be reviewed by NERSA, but for the purposes of this analysis an increase of 16% per year was assumed for the first 5 years, with increases of CPI plus 5% thereafter.
Discussion on material and energy balance and DCF The material and energy balance, including calculation of costs, of the base case is shown in Table IV. In order to test the material and energy balance, input data was derived from the information presented by Aziz and Dittus (2011) and the same output in terms of fuel and sorbent requirements etc. was obtained. It was therefore concluded that the material and energy balances were sound. The DCF table produced from this data (plus additional input such as inflation estimates) is presented in Table V. From these ‘input parameters’, a DCF table was constructed. The cash flow was calculated per year for 30 years. A summarized form of the DCF for the base case is given in Table V. A summary of the financial indicators (NPV at 10, 20, and 30 years, and the IRR) is given in Table VI. With an IRR of 21.4%, this appears to be a potentially worthwhile investment opportunity, warranting further investigation (and refinement of figures). As discussed above, investors would adopt a hurdle rate of about 20%.
Minimum value of electricity for financial viability (to achieve 20% IRR) The DCF was used to calculate the value of electricity (in cents per kWh) that would deliver the adopted hurdle rate of 20% (with all other parameters as per the base case). This was calculated at 55.42 cents per kWh.
Table IV
Fuel and sorbent requirements and costs, and revenue Parameter
Value
Fuel required Sorbent required Fuel cost Fuel transport Sorbent cost Sorbent transport Total sorbent cost Electricity value Electricity revenue
2.3 Mt/a 0.6 Mt/a 300.0 Rm/a 0.0 Rm/a 272.0 Rm/a 393.0 Rm/a 665.0 Rm/a 59.5 c/kWh 2000.0 Rm/a
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Although a power station may be kept operating for 40 years or more, the assumption made by EPRI (South African Department of Energy, 2010b) of 30 years’ plant life was also used in this analysis.
Feasibility study of electricity generation from discard coal Table V
Discounted cash flow for the base case (all values in millions of rands) Year
0
1
2
3
4
5
10
15
20
25
30
300 0 272 393 0 202 258 1425
323 0 287 422 0 213 272 1518
347 0 303 454 1 225 287 1616
373 0 320 488 1 238 303 1721
401 0 337 524 1 251 319 1833
575 0 441 753 1 328 417 2515
826 0 576 1080 1 428 545 3457
1186 0 753 1551 2 560 713 4764
1702 0 984 2227 2 731 931 6579
2444 0 1287 3197 4 956 1217 9104
2000 575 161 414 516 853 –8367
2320 803 225 578 516 921 –7446
2692 1076 301 774 516 997 –6449
3122 1401 392 1009 516 1081 –5369
3622 1789 501 1288 516 1173 –4196
5967 3452 967 2486
9830 6373 1784 4588
16195 11431 3201 8230
26680 20101 5628 14473
43954 34850 9758 25092
1050 630
1260 6510
1469 13435
1678 21406
1891 30435
Costs Capital 9221 Coal Coal Xport Limestone LS Xport Water Fixed opex Var. opex Total costs Revenue Electricity Pre-tax profit Tax Post-tax profit Depreciation DCF NPV
–9221 –9221 –9221
Table VI
Financial indicators for the base case Indicator NPV (10 years) NPV (20 years) NPV (30 years) IRR (30 years)
Value
Units
630.0 13 435.0 30 435.0 21.4
Rm Rm Rm %
fuel cost component of the cost of electricity for ’waste coal’ is 15%. However, estimates varied, as indicated previously, with Utt and Giglio taking $100 per ton as a value (Utt and Giglio, 2011). In this current analysis, the coal will be purchased in South African rands. The cost of the coal was varied from zero to R900 per ton. Figure 2 shows the trend of NPV and IRR with coal price.
This price is very sensitive to the chosen hurdle rate. For example, should an investor adopt a hurdle rate of 22%, an electricity price of 61.54 cents per kWh would be required. An even more conservative investor, adopting a hurdle rate of 24%, would require 67.88 cents per kWh.
Sensitivity analysis Financial indicators were calculated using the DCF. These were calculated for the base case and also used to run sensitivity analyses on the following parameters: ➤ Plant capital cost ➤ Cost of coal ➤ Transport distance of sorbent ➤ Electricity price (at project start).
Figure 1 – Effect of specific plant capital cost
Plant capital cost To assess the sensitivity of the project to plant capital cost, this was varied from $1600 to $2800 per kWe installed capacity. The results are shown in Figure 1. The IRR is sensitive to the specific plant capital cost, and falls from 26.1% to 19.74% as the specific plant cost rises from $1600 to $2800 per kWe. At a hurdle rate of 20%, the project would be considered marginal at a capital cost in excess of $2600 per kWe.
Cost of coal The cost of coal was calculated using information from Koornneef, Junginger, and Faaij (2006) indicating that the Figure 2 – Effect of coal cost
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Transport distance of sorbent In order to evaluate the sourcing of sorbent, the effect of transport distance (and therefore cost) was assessed. For the base case, a distance of 700 km was taken. For this sensitivity analysis a range of zero to 1000 km was used. Figure 3 shows the trend of NPV and IRR with sorbent transport distance. This analysis could also be used to assess the options of sourcing a low-grade sorbent near to the power station or a high-grade sorbent further away. For this to be of value, however, a full understanding of the efficacy of the sorbents would be needed. Although the IRR at a transport distance of 1000 km, at 20.3%, is still above the hurdle rate, an investor should investigate sorbent sourcing options. The limestone and dolomite deposits in South Africa are well known, but the efficacy of these sorbents in CFBCs has not been fully determined.
Effect of electricity price (at start of project) An electricity value of 59.82 cents per kWh was used for the base case analysis as described above. There is, however, significant doubt as to the accuracy of that figure, as it depends on factors such as charges to ‘wheel’ the electricity through the existing grid, which would lower the effective revenue earned. There are also indications that it could be higher. Tore Horvei (2012), who was involved in feasibility studies of this kind in southern Africa, indicated that the value of electricity could be 85 cents per kWh. In order to gauge the sensitivity of the project to the electricity price it was varied from 30 cents to 90 cents per kWh. Figure 4 shows the trends of NPV and IRR with electricity price. The electricity price has a marked effect on the viability of the project. At 30 cents per kWh to 50 cents per kWh the project shows a negative NPV after 10 years. The IRR hurdle
Figure 3 – Effect of sorbent transport distance The Journal of The Southern African Institute of Mining and Metallurgy
rate of 20% is achieved only at approximately 59 cents per kWh. At the higher electricity prices, a high IRR is seen, in excess of 30%. The conclusion that can be drawn from this is that a potential IPP needs to understand clearly how much revenue will be effectively gained through the sale of electricity, as project viability is very sensitive to this parameter.
Conclusions and recommendations There is significant electricity generation potential in discard coal. A combined total of approximately 18 GWe installed capacity could be fed with discard coal stockpiles and arisings. CFBC technology has developed to the point where it is on a par with PF technology in terms of both efficiency and cost, and the ability of CFBC to utilize discard coal has been proven. An economic analysis indicates that generating electricity from discard coal via CFBC is potentially favourable. The base case shows an IRR of 21.4%, which is above the hurdle rate adopted in this study of 20%. However, there are many factors to consider that affect the return on investment. The major elements affecting the IRR are the cost of the coal and the value of the electricity. For a given project, the analysis (in particular ash content, sulphur content, and CV) and amount of discard coal and the logistics around getting it to the power station must be fully understood, so that the effective cost of the ‘free’ fuel is known. The true value of the electricity, or the avoided cost if the electricity is generated for self-use, must be ascertained. If possible, updated figures on the size and analysis of both discard stockpiles and arisings should be generated. This is because the dumps are being reprocessed, and modern coal beneficiation technologies are resulting in reduced carbon content of the arisings. Unbeneficiated run-of-mine coal could also be considered as a feed to a CFBC power station. The cost and efficacy of sorbent also affects the viability of the project. South African sorbent resources are well known, but sorbent efficacy in CFBCs is not. An efficacy database, perhaps linked to a GIS database, would enable an accurate determination of the cost of sorbent to be made.
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The cost of coal has a large effect on the viability of the project. From zero cost up to R200 per ton, the project still shows an IRR above the hurdle rate of 20%. At R300 per ton, the IRR is 19.63%, marginally below the hurdle rate. The 10year NPV also becomes negative. At R700 per ton, the 20year NPV also becomes negative. The indication is that this project (with the assumptions on other costs and revenue) will not be viable at a coal price in excess of approximately R300 per ton.
Feasibility study of electricity generation from discard coal References AGNELLO, V. 2005. Dolomite and Limestone in South Africa: Supply and Demand 2005. Report no. R49/2005. Department of Minerals and Energy, South Africa. AZIZ, T. and DITTUS, M. 2011. Kuyasa mine-mouth coal-fired power project: Evaluation of circulating fluidized-bed technology. Proceedings of Industrial Fluidization South Africa, 2011. pp. 11–29. BLENKINSOP, M. 2012. 11 October 2012. Personal communication. BRUGGEMANS, C. 2011. First National Bank Five Year Economic Forward Look. https://www.fnb.co.za/economics/econhtml/forecast/fc_5yearview_new.htm. [Accessed 17 October 2012]. BUSINESS UNITY SOUTH AFRICA. 2009. Preliminary response to the Eskom Revenue Application for the Multi Year Price Determination for the period 2010/11 to 2012.13 (MYPD 2). http://www.busa.org.za/docs/PRELIMINARY%20SUBMISSION%20ESKOM %20APP LICATIONfinal.pdf [Accessed 17 October 2012]. CORREIA, C., FLYNN, D., ULIANA, E., and WORMALD, M. 1989. Financial Management. 2nd edn. Juta, Johannesburg, South Africa. DU PREEZ, I. 2001. National Inventory of Discard and Duff Coal. Badger Mining. Confidential report prepared for the SA Department of Minerals and Energy. ELEFTHERIADES, C.M. and NORTH, B.C. 1987. Special plant features and their effect on combustion of waste coals in a fluidized bed combustor. Proceedings of the 9th International Conference on Fluidized Bed Combustion, Boston, 3–7 May 1987, Mustonen, J.P. (ed.). ASME New York. pp. 353–359. ENGINEERING NEWS. 2012. Eskom seeks yearly increases of 16% to 2018. http://m.engineeringnews.co.za/article/eskom-seeks-yearly-increases-of16-to-2018-2012-10-22 [Accessed 23 October 2012]. ESKOM. 2012. Tariffs and Charges Booklet 2012/2013. http://www.eskom.co.za/content/ESKOM%20TC%20BOOKLET%20201213%20(FINAL)~2.pdf [Accessed 16 October 2012]. ESKOM. 2014. Tariffs and Charges Booklet 2014/2015. HALL, I., ESLAIT, J., and DEN HOED, P. 2011. Khanyisa IPP – a 450 MWe FBC project: Practical challenges. Proceedings of Industrial Fluidization South Africa 2011. pp. 47–55. HARIPERSAD, N. 2010. Clean Coal Technologies for Eskom. MSc thesis. Da Vinci Institute of Technology Management, Johannesburg. HORVEI, T. 2012. 22 October 2012. Personal communication. JANTTI, T, 2011. Lagisza 450 MWe supercritical CFB – operating experience during first two years after start of commercial operation. Proceedings of Coal-Gen Europe 2011, Prague, Czech Republic, 15–17 February 2011. KOORNNEEF, J., JUNGINGER, M., and FAAIJ, A. 2006. Development of fluidized bed combustion – An overview of trends, performance and cost. Progress in Energy and Combustion Science, vol. 22, no. 1. pp. 19–55.
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MINING WEEKLY. 2012. Big electricity hikes will be “materially damaging” to SA mines. http://www.miningweekly.com/article/big-electricity-hikes- willbe-materially-damaging-to-sa-mines-2010-01-22 [Accessed 10 October 2012]. MOKOENA, S. 2010. Guideline on municipal electricity price increase for 2011/12. http://www.busa.org.za/docs/PRELIMINARY%20SUBMISSION%20ESKOM %20APP LICATIONfinal.pdf [Accessed 16 October 2012]. NEL, S. 2011. The application of the capital asset pricing model (CAPM): a South African Perspective. African Journal of Business Management, vol. 5, no. 13. pp. 5336–5347. NORTH, B.C., ELEFTHERIADES, C.E., ENGELBRECHT, A.D., and RUTHERFORD-JONES, J. 1999. Destruction of a high sulphur pitch in an industrial scale fluidized bed combustor. Proceedings of 15th International Conference on Fluidized Bed Combustion, Savannah, Georgia, 16–19 May 1999. PINHEIRO, H.J., PRETORIUS, C.C., and BOSHOFF, H.P. 1999. Analysis of discard coal samples of producing South African collieries. Confidential unpublished report for the South African Department of Minerals and Energy. PRÉVOST, X.M. 2010. Personal communication. 14 October. POWER, M. 2004. How has South Africa Inc sought to reduce its high cost of capital? OECD Development Centre Seminar: ‘Cheaper Money for Southern Africa – Unlocking Growth’. Paris, 7 October 2004. SOUTH AFRICAN DEPARTMENT OF ENERGY. Not dated. Coal Baseload call. https://www.ipp-coal.co.za/Home/About [Accessed 7 May 2015]. SOUTH AFRICAN DEPARTMENT OF ENERGY. 2010a. Integrated Resource Plan. http://www.energy.gov.za/IRP/2010/IRP2010.pdf [Accessed 12 October 2011]. SOUTH AFRICAN DEPARTMENT OF ENERGY. 2010b. Power Generation Technology Data for Integrated Resource Plan of South Africa. http://www.energy.gov.za/ – Programmes and Projects - Integrated Resource Plan – EPRI report on supply side cost) [Accessed 12 October 2012]. SOUTH AFRICAN REVENUE SERVICE. 2012. SARS pocket tax guide, budget 2012. http://www.treasury.gov.za/documents/national%20budget/2012/sars/Bu dget%202 012%20Pocket%20Guide.pdf [Accessed 17 October 2012]. TIDBALL, R., BLUESTEIN, J., RODRIGUEZ, N., and KNOKE, S. 2010. Cost and performance assumptions for modelling electricity generating technologies. NREL subcontract report NREL/SR-6A20-48595. http://www.nrel.gov/docs/fy11osti/48595.pdf [Accessed 11 October 2012]. UTT, J. and GIGLIO, R. 2011. Technology comparison of CFB versus pulverizedfuel firing for utility power generation. Proceedings of IFSA 2011: Industrial Fluidization South Africa, Johannesburg, 16–17 November 2011. pp. 91–99. UTT, J., HOTTA, A., and GOIDICH, S. 2009. Utility CFB goes “supercritical” – Foster Wheeler’s Lagisza 460 MWe operating experience and 600-800 MWe designs. Proceedings of Coal-Gen 2009, Charlotte, North Carolina, 18–21 August 2009. ◆
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The value proposition of circulating fluidized-bed technology for the utility power sector by R. Giglio* and N.J. Castilla*
Circulating fluidized-bed (CFB) combustion technology has been around for over 40 years, but over the last 6 years it has been commercially demonstrated at the 500 MWe scale at the Łagisza plant located in Będzin, Poland. The CFB at the Łagisza plant has unique first-of-a-kind design features, such as vertical-tube supercritical steam technology and a lowtemperature flue-gas heat extraction that allows the plant to achieve high plant efficiencies of over 43% (net lower heating value). Another unusual feature for a coal power plant is that this plant meets all its atmospheric emission permit levels without any post-combustion de-NOx or de-SOx equipment such as selective catalytic reduction (SCR) or flue-gas desulphurization (FGD). CFB clean coal power technology is entering the utility power sector just in time to help deal with declining quality in internationally traded coals and to promote the large-scale use of economic, low-quality domestic fuels. Owing to the very attractive price discounts, growing supplies of low-quality Indonesian coals are outpacing the supply of high-quality Australian, Russian, and US coals. In Germany and Turkey, the use of domestic lignites for power production provides a secure and economic energy solution while creating domestic jobs. Conventional pulverized coal (PC) boilers will have trouble accepting these off-specification coals because of their narrow fuel specifications; they typically call for heating values above 5500 kcal/kg. This limitation is not an issue for CFB technology because of its ability to burn the worst and best of coals with heating values ranging from 3900 to 8000 kcal/kg. This paper provides an outlook for future coal supply, quality, and price, as well as a review of the technical and economic benefits of CFB technology firing low-quality fuels for utility power generation. Keywords value proposition, CFB, flexibility, lignite, Łagisza, circulating fluidizedbed, power generation.
Advanced CFB boilers for utility power generation When the Łagisza power plant (Figure 1), located in the Katowice area of southern Poland, began commercial operation in June 2009, it marked a new era in the evolution of circulating fluidized-bed (CFB) technology. The plant is now celebrating its sixth year of successful commercial operation. Besides being the most advanced operating CFB steam generator in the world, the CFB at the Łagisza plant has unique first-of-a-kind design features, such as vertical-tube supercritical steam technology and lowtemperature flue-gas heat recovery system that allows the plant to achieve a very high net plant efficiency of 43.3% (based on the fuel’s The Journal of The Southern African Institute of Mining and Metallurgy
lower heating value). A notable feature of the Łagisza CFB is that it meets all atmospheric emission permit levels without postcombustion de-NOx or de-SOx equipment such as selective catalytic reduction (SCR) or fluegas desulphurization (FGD). Like the Łagisza plant owners (PKE), Korean Southern Power Company (KOSPO) also saw value in CFB technology when it chose the technology for its 2200 MWe Green Power Plant project in Samcheok, Korea (Figure 2). The Samcheok plant, which is now under construction, will utilize four larger 550 MWe CFB boilers featuring ultrasupercritical steam conditions (257 barg, 603/603°C). These CFB boilers will be the most advanced units in the world when the plant comes on line as expected in 2016. Both PKE and KOSPO first considered conventional pulverized coal (PC) technology for their projects, but after studying the additional technical and economic benefits that a CFB brings, they ultimately chose CFB technology. The CFB boilers offer many benefits, but two in particular played a big role in their decision. They were: ➤ The CFB’s ability to reliably burn both low-rank and high-quality coals besides biomass and waste coal slurries (Łagisza only) dramatically improved the potential for huge fuel cost savings and high fuel procurement security ➤ The CFB’s ability to meet atmospheric emission goals without FGD or SCR technology saved on capital, operating costs and water.
* Amec Foster Wheeler Global Power Group, Hampton, NJA, USA © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at IFSA 2014, Industrial Fluidization South Africa, Glenburn Lodge, Cradle of Humankind, 19–20 November 2014 VOLUME 115
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Synopsis
The value proposition of circulating fluidized-bed technology for the utility power sector CFB’s benefits are rooted in its unique combustion process The CFB’s advantages of reliability, low maintenance, a wide fuel range, and smaller and less costly boilers are rooted in its unique flameless, low-temperature combustion process. As Figure 3 summarizes, unlike conventional PC or oil/gas boilers, the fuel’s ash does not melt or soften in a CFB, which allows the CFB to avoid many of the fouling and corrosion problems encountered in conventional boilers with an open flame.
Supercritical boiler design considerations
Figure 1 – Łagisza CFB power plant located in Będzin, Poland
Figure 2 – 2200 MWe Green Power CFB plant located in Samcheok, Korea
For once-through supercritical boiler designs (Figure 4), the low, even combustion temperature and heat flux throughout the CFB’s furnace minimizes the risk of uneven tube-to-tube temperature variations, which permit the furnace walls to be constructed with cost-effective and easy-to-maintain smooth vertical tubes. For additional protection, Amec Foster Wheeler’s once-through CFB boilers utilize a patented lowsteam mass flux design providing a natural self-cooling characteristic that uses buoyancy forces to increase the water/steam flow in a tube proportionate to the amount of heat it receives. This further minimizes tube-to-tube temperature variations and ensures low mechanical stresses across the furnace, thereby extending furnace life. To cope with the uneven temperatures and heat absorption in the furnace, most conventional PC and oil or gas once-through boilers incline and wrap the furnace wall tubes around the lower section of the furnace to even out tube-to-tube heat absorption and temperatures. Although this solves the heat imbalance problem, the spiral design has several disadvantages compared with Amec Foster Wheeler’s CFB vertical-tube design. The spiral design requires a heavier, more complicated boiler and boiler support system
Figure 3 – Comparison of conventional versus CFB boiler technology
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Figure 4 – Comparison of spiral versus vertical-tube once-through furnace design
Figure 5 – Impact on furnace size as fuel quality degrades: PC versus CFB
Furthermore, unlike a PC boiler, a CFB boiler does not need soot blowers to control the build-up of deposits and slag in the furnace as the ash does not soften and the circulating solids themselves remove deposits and minimize their buildup on the furnace wall, panels and coils.
Furnace size versus fuel quality
Superheater and reheater design considerations
As ash does not soften or melt in a CFB, the size of the furnace does not increase as much as conventional boilers when firing lower quality fuels. As can be seen in Figure 5, in order to control fouling, slagging, and corrosion, the furnace height of a PC boiler doubles and its footprint increases by over 60% when firing a low-quality fuel such as high-sodium lignite, whereas the CFB boiler height increases by only 8% and its footprint increases by only 20%. This results in a CFB boiler that is smaller and costs less that a PC boiler.
Another very important feature of a CFB boiler involves the final superheat and reheat steam coils. These coils operate at the highest metal temperatures in the boiler, which makes them vulnerable to corrosion and fouling. This vulnerability increases significantly for supercritical boilers with high steam temperatures. As shown in Figure 6, in a conventional PC or oil/gas boiler these coils are suspended from the furnace ceiling and are directly exposed to the slagging ash and corrosive gases
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which makes furnace tube repairs more difficult. Furthermore, the loss in steam pressure is high because the steam path is long and the ledge formed at the interface between the spiral and the vertical tube header is a natural location for build-up of slag.
The value proposition of circulating fluidized-bed technology for the utility power sector
Figure 6 – A comparison of boiler design features: PC boilers versus CFB boiler
Availabilty (%)
increases dramatically and the fuel delivery system requires more maintenance as its reliability declines. A CFB boiler does not require pulverizers as its fuel is only coarsely crushed and fed to the CFB boiler directly from the fuel silos via a simple gravity feed system.
Overall plant reliability
Figure 7 – Availabilities* of PC and CFB power plants. *Availability means total time plant is available to run accounting for both planned and unplanned downtime. The Amec Foster Wheeler CFB plant availability derives from client-supplied data reported over the period 2000–2008 for CFB plants located mainly in Europe. The PC values derive from client-supplied data over the period 2002–2011 for PC units that are mainly located in Europe ¹
(sodium and potassium chlorides) in the hot furnace fluegas. To cope with this undesirable situation, boiler designers use expensive alloys and recommend a high level of cleaning and maintenance for these coils. This design weakness is avoided in Amec Foster Wheeler’s CFB boilers by submerging these coils in hot solids fluidized by clean air in heat exchangers called INTREX®, which protects them from the corrosive flue-gas (see Figure 6). The bubbling solids efficiently conduct their heat to the steam contained in the coils and as the solids never melt or soften, fouling and corrosion of these coils are minimal. Furthermore, because the high heat transfer rates of the solids (by conduction), the coil size is many times smaller than those in conventional boilers.
Fuel delivery system A final important design issue involves the fuel delivery system to the boiler. A PC boiler requires the fuel to be finely ground and pneumatically transported and distributed to many burners. For low-quality, high-ash fuels such as brown coals and lignite, the power consumption of fuel pulverizers
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Based on these process and design differences, CFB power plants have demonstrated plant availabilities well above conventional PC boilers, as shown by a recent study comparing PC plant availability to Amec Foster Wheeler CFB boilers (see Figure 7). Availability is defined as a percentage of 8760 hours, the total number of hours that a plant can be operationally available. The total includes both planned and unplanned downtime. Power plants with CFB boilers had about a 5% (absolute) higher availability than PC plants, and this higher availability is maintained even for brown coals and lignites. For a 1000 MWe supercritical coal power plant, this 5% difference in plant availability can translate into a $160 million increase in power plant net income on a 10-year net present value (NPV) (see Figure 8).
Environmental performance and equipment requirements: PC versus CFB From an environmental aspect, the low-temperature CFB combustion process (850°C for CFB versus 1500°C for PC/oil/gas) produces less NOx and allows limestone to be fed directly into the furnace to capture SOx as the fuel burns. In most cases SCR or a FGD is not needed, which dramatically reduces the plant installed and operating cost and water consumption while improving plant reliability and efficiency. For a 1000 MWe power plant, the savings alone on the costs of SCR and FGD would be in the range of $250 million–$300 million.
1 VGB PowerTech 2012. Availability of Thermal Power Plants 2002-2011 – Report VGB-TW103Ve The Journal of The Southern African Institute of Mining and Metallurgy
Net income ($/m)
The value proposition of circulating fluidized-bed technology for the utility power sector
Figure 10 – Average gross heating value of Indonesian export coal. Source: marketing, sales and logistics analyst, Banpu PCL
Export volume
Figure 8 – Impact of plant utilization factor on annual plant net income for a 1000 MWe supercritical steam power plant operating at a utilization factor of 90% and receiving a $100 per MWe electricity tariff based on buying coal at $100 per ton
Figure 9 – Global coal exports. Source: historical data and Amec Foster Wheeler projections Figure 11 – Difference in prices between Indonesian Ecocoal and Australian thermal coal delivered (CIF) to the coast of South Korea. Prices shown are nominal. Source: Amec Foster Wheeler forecast
A permanent change to the global coal market
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heating value, which amounts to 30% on a comparative energy basis, translates into a very attractive net 18% discount for the Ecocoal, a benefit that goes right to the bottom line of a power plant’s balance sheet. Since fuel cost makes up about 85–90% of the total operating cost of a large power plant, it would be foolish to ignore the economic benefits of using low-quality fuels. We can see this in several domestic markets, where low-quality coals and lignites play a major role in power production. For example, 77% of Germany’s solid fuel power is produced from lignite; only 23% is produced from hard coal. In the USA, 54% of the solid fuel power comes from low-quality sub-bituminous coals. Use of low-rank coals and lignites for power production is growing in Turkey, India, China, Indonesia, Australia, South Africa, and Mozambique, a trend driven by the very low cost of these fuels relative to premium coals. Until recently, low-quality coals and lignites have been confined to domestic markets and have not been part of the international coal market. This is because their economic benefit is quickly eroded by their transportation costs, owing to the lower energy contents of the coals. But today we see more low-quality coals and even lignites coming into the global coal market, a move that is driven by steep price discounts in a tight market for premium coals. From 2001 to 2010 for example, Korean imports of Indonesian coals (mostly sub-bituminous) increased sevenfold by 38 Mt, while imports from Australia coal grew by only 13 Mt. VOLUME 115
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Since 2005, Indonesian coal exports have grown faster than all other countries combined; nearly quadrupling to 400 Mt in 2013 (see Figure 9). Projections into the future predict Indonesian exports reaching nearly 500 Mt by 2030, about twice that of Australia, the world’s second largest exporter of coal. Today about 50% of the coal exported from Indonesia is low-quality, high-moisture sub-bituminous in quality with gross-as-received (GAR) higher heating values ranging from 3900 to 4200 kcal/kg, well below the 6000 kcal/kg benchmark used in the international coal market for the last 50 years. Over the last 3 years, the quality of Indonesia’s export coal has been declining, and this trend is expected to continue well into the future. Today about 60% of Indonesia’s coal mines hold low-rank sub-bituminous coals. The other 40% hold bituminous coals estimated to have heating values less than 5200 kcal/kg. The heating value of Indonesia’s export coals has been steadily declining and is forecast to continue, a trend that reflects the impact of mining this lower quality coal. The primary driver for the ballooning share of Indonesian coal in the international coal market is simple economics. The current and forecast price discount between Indonesia’s subbituminous 4200 kcal/kg Ecocoal and a 6000 kcal/kg Australian thermal coal, both on a net-as-received basis (NAR), shows a steady pattern: a 48% or $55 per metric ton average discount for the lower quality Indonesia coal over the period from 2012 to 2020 (see Figure 11). The difference in
The value proposition of circulating fluidized-bed technology for the utility power sector This trend is not expected to change any time soon. Instead, it looks to be a permanent shift towards a more flexible coal market, where buyers and sellers trade price for coal quality, similar to markets in many other commodities and finished goods.
The impact of the changing coal market on coal boiler technology This price versus quality shift in the global coal market will likely be viewed as good news by some observers and bad news by others, with the responses depending on their power plant technology position. PC power plants with tight coal specifications (here one thinks of supercritical designs) will have a limited ability to use the discounted coals. These plants will have to choose either to stay within the tightening premium coal market or to venture into the broader coal market and trade lower plant outputs, reduced reliability, and higher maintenance costs for discounts in the cost of fuel. On the other hand, the shift will come as good news for power generators utilizing CFB technology. Owing to the CFB’s fuel flexibility, plant owners can access the full range of discount coals (even for ultra-supercritical designs), buying fuels for maximum economic benefit while avoiding the high-priced premium coals. Furthermore, the impact of declining coal quality on plant output, reliability, and maintenance is minimized with a CFB, and the risk of future carbon regulation is lessened because of the CFB’s ability to utilize biomass and other carbon-neutral fuels. For new power plants, this trend clearly increases the value of fuel-flexible coal plants such as those utilizing CFB technology and will likely push towards (if not accelerate) the adoption of CFB technology in large coal-fired utility plants. The timing seems right, as CFB technology has demonstrated its capabilities in serving the utility power sector. This is not to say that new PC boiler power plants cannot be designed to burn low-rank fuels. They can. The point for consideration is that once a PC is designed to use a specific low-rank fuel, the
plant has difficulty burning other fuels without adversely affecting plant performance, reliability, and maintenance.
The economic benefits of CFB technology at the utility scale To quantify the benefits of CFB technology on a large utilityplant scale, Amec Foster Wheeler conducted a study comparing both the technical and economic performances of two supercritical 1100 MWe (gross) power plants. One of the plants used conventional PC technology and the other CFB technology. The study involved the development of full power-plant financial models, heat and material balances, as well as conceptual plant designs for plant layout, sizing, and cost estimation purposes. For the purpose of comparison, a number of performance metrics were evaluated. They included plant capital and operating costs, plant height and footprint, reliability, atmospheric emissions, solid and liquid inputs, and waste streams. The PC plant was configured with a single 1100 MWe ultra-supercritical boiler that provided its steam to a single 1100 MWe steam turbine generator. The plant fired an Australian bituminous thermal coal with an NAR heating value of 5500 kcal/kg and a sulphur content of 0.35%. The coal was priced at $95 per metric ton. SCR was installed in the boiler to control stack NOx emissions to 50 ppmv (6% O2 dry) and wet limestone FGD was installed behind the boiler to control stack SOx to 50 ppmv (6% O2 dry). The CFB plant was configured with two 550 MWe ultrasupercritical boilers that provided steam to a single 1100 MWe steam turbine generator. The CFB plant fired an Indonesian sub-bituminous thermal coal (Ecocoal) with an NAR heating value of 4200 kcal/kg and sulphur content of 0.27%. The coal was priced at $55 per metric ton. SCR was installed in the boiler to control NOx emissions to 50 ppmv (6% O2 dry), but no separate FGD was installed behind the CFB boiler, for the boiler itself used limestone to control stack SOx to 50 ppmv (6% O2 dry).
Table I
A comparison of capital costs of 1100 MWe supercritical PC and CFB power plants Note: Absolute design and supply boiler cost depends on scope. Source: Amec Foster Wheeler study
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The value proposition of circulating fluidized-bed technology for the utility power sector Table II
A comparison of costs of 1100 MWe supercritical PC and CFB power plants. Source: Amec Foster Wheeler study
Table III
A comparison of annual and NPV electricity production costs for 1100 MWe supercritical PC and CFB power plants. Source: Amec Foster Wheeler study
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A full financial proforma model for both the PC and CFB plant configurations was developed to calculate the levelized electricity production cost for each plant configuration. In addition to total capital and operating costs, the proforma analysis takes into account plant utilization, financing conditions and terms. Figure 12 compares the proforma analyses and the components that make up electricity production costs. The smaller capital and fuel cost components for the CFB plant results in a net savings of $10 per megawatt-hour of electricity produced. This translates into $82 million annually based on 90% plant utilization: the savings are worth $503 million NPV over a 10-year period (see Table III). VOLUME 115
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We compare the capital costs of boiler and pollution control equipment on a design and supply basis, excluding erection (see Table I). Even though the cost of two CFB boilers burning a low-rank coal is about 11% higher than the cost of a single large PC boiler burning a high-quality coal, meeting emission targets without installing an FGD for the CFB boilers resulted in a net $93 million savings in capital for the CFB plant configuration. As for operating costs (see Table II), using the discounted Indonesian coal, the CFB plant saves $66 million annually in fuel costs. Adding in other operating costs such as limestone, ash disposal, gypsum sales, and maintenance increases savings to $69 million. These savings are worth $424 million in NPV over a 10-year period.
The value proposition of circulating fluidized-bed technology for the utility power sector Table IV
A comparison of emissions, plant efficiency, fuel, limestone, ash, and FGD water flow in 1100 MWe supercritical PC and CFB power plants. Source: Amec Foster Wheeler study
Figure 12 – A comparison of levelized electricity production costs for 1100 MWe supercritical PC and CFB power plants. Source: Amec Foster Wheeler study
Finally, Table IV compares other plant parameters and performance metrics, highlighting that both the CFB and PC plants meet the same stack emission limits, but as the CFB plant does not have a separate wet FGD for SOx control, it saves about 2 × 106 m3 of water annually.
Conclusions and observations Six years of successful operation of the large supercritical once-through CFB boiler at the Łagisza power plant in Poland has demonstrated CFB technology for utility power generation. KOSPO reinforces this conclusion by selecting Amec Foster Wheeler CFB technology for its 2200 MWe Green Power Project in Samcheok, Korea. Because combustion is flameless and occurs at low temperatures, CFB technology offers many benefits for utility power generation. Its fuel flexibility, reliability, and ability to meet strict environmental standards with minimal post-
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combustion pollution control equipment are highly valued benefits for utilities. Additionally, the CFB’s load-following flexibility (CFB has the same load ramp rates as a PC, but better turndown) is another important value for grids containing a high level of intermittent renewable power. As an example, the Łagisza unit cycles daily between 40 and 100% MCR to meet the requirements of the Polish national grid. The CFB benefits become more compelling when considering low-quality fuels. The technology is able to provide smaller, less costly boilers as fuel quality declines, while achieving plant availabilities well beyond conventional PC boiler technology. The global coal market is moving away from traditionally rigid, single-specification coal towards a more flexible pricefor-coal-quality market. The convergence of the coal market shift with the CFB’s entry into utility power application is expected to speed up the adoption of CFB technology in the large utility power sector. Owing to the large economic benefit, the use of domestic brown coal and lignite for utility power generation is growing in Germany, Turkey, and Indonesia, all of which have abundant supplies of economical low-quality coal and lignites. It is expected that CFBs will be utilized more in these markets. A technical and economic study conducted by Amec Foster Wheeler showed that a large utility CFB power plant has a compelling economic advantage over a traditional PC power plant, mainly because the CFB plant does not require post-combustion FGD equipment and can utilize a low-quality Indonesian coal. The numbers indicate that a 1100 MWe CFB power plant would cost $93 million less to build and would produce a net saving in the cost of producing electricity of about $82 million annually, worth $503 million on a 10-year NPV basis. In today’s price-sensitive global utility market these numbers deserve serious consideration. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n7a5
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process by D. Toporov* and R. Abraham*
Gasification is a process of thermal conversion of solid carbonaceous materials into a gaseous fuel called syngas. Coal gasification is an efficient technology for a range of systems for producing low-emission electricity and other high-value products such as chemicals, synthetic fuels, etc. The paper presents the High-Temperature Winkler (HTW) gasification process, which is designed to utilize low-rank feedstock such as coals with high ash content, lignite, biomass etc. The process is characterized by a bubbling fluidized bed, where coal devolatilization and partial oxidation and gasification of coal char and volatiles take place, and by a freeboard where partial combustion and gasification of coal char take place. The recent development of the high-pressure HTW process is reviewed. Gasification of low-rank, high-ash coals with respect to gasification temperatures, conversion rates, and syngas quality is also discussed. The main HTW design steps required for an industrial-scale design are presented. Special attention is given to the process modelling, including global thermodynamic calculation as well as detailed CFD-based simulation of a reacting fluidized bed. Three-dimensional numerical results of the HTW process are also provided and discussed. Keywords coal gasification, high-temperature Winkler process, HTW, reacting fluidized bed simulation, high-ash coal, low-rank coal, biomass, peat, municipal solid waste.
Introduction During the last three decades, primary energy consumption has increased worldwide by about 70% (Figure 1), reaching 11 Gt oil equivalent (Gtoe) at the end of 2009. There was a rapid increase in oil and natural gas consumption, sharing 35% and 25% respectively of the total consumption. Global coal demand growth under the New Policies Scenario (International Energy Agency, 2010) will be around 20% between 2008 and 2035, with 100% of this increase occurring in non-OECD countries. Global coal demand is expected to peak around 2025 and begin to decline slowly, returning to 2003 levels by 2035 due to the restrictions imposed by climate policy measures. Coal possesses the largest potential of all non-renewable fuels and provides 56% of the reserves and 89% of the resources worldwide (Andruleit et al., 2013) (Figure 2). Coal, being the most abundant, available, and affordable fuel, has the potential to become the most reliable and easily accessible energy source The Journal of The Southern African Institute of Mining and Metallurgy
* Gas Technology Division, ThyssenKrupp Industrial Solutions AG, Dortmund, Germany. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. This paper was first presented at IFSA 2014, Industrial Fluidization South Africa, Glenburn Lodge, Cradle of Humankind, 19–20 November 2014. VOLUME 115
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Synopsis
and thus to provide a crucial contribution to world energy security. The major challenges facing coal are concerned with its environmental impacts both in production and in use. Various pollutant control systems have been developed over the past few decades and are continually evolving. These new technologies, which facilitate the use of coal in a more environmentally friendly way by drastically reducing pollutant emissions, are commonly known as clean coal technologies (CCTs). Within this concept two different approaches can be considered, namely (i) reducing emissions by reducing the formation of pollutants during the coal conversion process, and (ii) developing systems with higher thermal efficiency, so that less coal is consumed per unit power generated, together with improved techniques for gas cleaning and for residues use or disposal. Nowadays, state-of-the-art combustion systems can reach plant net efficiencies of 43–45% (LHV) (Klauke, 2006; ABB Ltd, n.d) utilizing high-rank coals. With some exceptions (Germany), the use of low-rank coals is still problematic due to the low plant efficiency and high pollution potential. Coal gasification, being a CCT, provides an environmentally friendly and efficient solution not only for power production, but also for the production of a variety of chemicals such as methanol, ammonia, and hydrogen, as well as synthetic fuels such as synthetic natural gas (SNG), gasoline, and Fischer-Tropsch liquids. Gasification of low-rank coals is even more attractive due to the low prices of coal, its local availability, and high prices or even nonavailability of other resources such as natural gas and oil.
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process
Figure 1 – Development of primary energy consumption worldwide (cumulative) and projections of IEA until 2035 (International Energy Agency, 2010)
The High-Temperature Winkler (HTW) gasification process was specially developed for utilization of low-rank feedstock such as lignite, biomass, sub-bituminous coals with high ash content etc. The technology development steps, the process description, as well as the design steps are discussed in detail in the following sections.
Description of the High-Temperature Winkler (HTWTM) gasification process Historical The HTWTM fluidized-bed gasification process is based on the Winkler generator, which was developed in the 1920s in Germany by Industrie Gewerkschaft (IG). From 1920 to 1930 IG investigated the possibility of using low-rank local coals, such as brown coal, instead of expensive coke, for synthesis gas production and subsequent production of ammonia and methanol. Dr. Winkler in 1921 conceived the idea of using a
‘boiling’ bed, i.e. using particles of fuel small enough to be almost gas-borne and hence comparatively mobile. Under such conditions the fuel bed behaves very much like a liquid; the gas passing through the fuel gives an appearance as if the bed were boiling, the bed finds its own level, as does a liquid, and circulation of particles within the bed is such as to give substantially equal temperatures throughout the bed. This is what we nowadays call a fluidized bed. The first Winkler generator was put into operation at Leuna, Germany in 1926, making power gas and having a capacity of 40 000 Nm3/h. In 1930 the production of nitrogen-free water gas began, which was obtained by continuous blast of pure oxygen with steam (Figure 3). Commercial-scale Winkler gasifiers were operated at atmospheric pressure in over 40 applications around the world. Since 2000 more than 40 new atmospheric units have been built in China alone. Thus, the Winkler gasification process became a widely used technology, characterized by the following advantages: ➤ Low oxygen consumption due to moderate temperatures ➤ Optional use of air or pure oxygen as an oxidant ➤ Simple coal preparation ➤ Good partial load behaviour over a wide range of operating conditions ➤ Simple start-up and shut-down procedure ➤ High operational reliability ➤ No by-products in the raw gas, such as tars, phenols, and liquid hydrocarbons, etc. In the 1970s, ThyssenKrupp Industrial Solutions (former Uhde) together with Rheinische Braunkohlenwerke AG (now RWE AG) commenced with the development of a pressurized version of the Winkler gasifier – the High-Temperature Winkler (HTWTM) gasification process. The development process went through several steps that involved building and operating pilot, demonstration, and commercial plants operating at increased pressure, as shown in Figure 4.
Figure 2 – Global share of all energy resources in terms of consumption as well as the production, reserves, and resources of non-renewable energy resources as at the end of 2012 (Andruleit et al., 2013)
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Gasification of low-rank coal in the High-Temperature Winkler (HTW) process
Figure 3 – Earlier Winkler generators (i) with traveling grate, 1930s (left) and (ii) grateless modification, 1940s (right) (Von Alberti and Rammler, 1962)
This development led to several major enhancements to the advantages of the atmospheric Winkler gasifier. ➤ By increasing the pressure to 10 bar, the reaction rates were increased and thus the specific performance per unit cross-sectional area of the gasifier was increased, while the compressive energy required for the subsequent chemical synthesis was reduced ➤ By increasing the temperature, the methane content in the raw gas was reduced and the carbon conversion rate, and thus the gas yields, increased ➤ By recirculating the dust fines entrained from the fluidized bed it was possible to increase the carbon conversion rate ➤ Inclusion of proven and robust systems such as dry dust filtration and waste heat recovery
➤ Ability to handle a great variety of feedstock (coal, peat, biomass, municipal solid waste (MSW) etc.) and high flexibility regarding particle size of the feedstocks ➤ High cold gas efficiency ➤ Stable and smooth gasifier performance with great inherent safety due to the large carbon inventory. HTW gasification plants, like the Oulu plant (Finland) gasifiying peat for ammonia, the Niihama plant (Japan) gasifying MSW for power, and the Berrenrath plant (Germany) gasifying German brown coal for methanol production, have been operated on a commercial basis, which has resulted in the technology attaining industrial maturity. The Berrenrath plant was in operation for more than 12 years and is an excellent reference for the HTWTM gasification technology (shown in Figure 5).
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Figure 4 – Stages in the development of HTW gasification
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process Some typical operating figures, obtained for industrialscale HTWTM gasification of different feedstock are listed in Table I.
The HTWTM process The HTWTM process involves (Figure 6) a gasification unit consisting of a feeding system, the gasifier itself, a bottom ash removal system located below the gasifier, and a gas exit in the head of the gasifier with a cyclone. In the subsequent steps the raw syngas is cooled and de-dusted and then further treated in accordance to the needs of the downstream processes. Screw conveyors or gravity pipes (according to the feedstock) supply the feedstock to the HTWTM gasifier. Due to the gasifier pressure, feeding and bottom ash removal have to be performed by lock-hopper systems. The gasification is controlled using the gasification agents steam and oxygen (or air), which are injected into the gasifier via separate nozzles. The nozzles are arranged in several levels which are located in both the fluidized bed (FB) zone and the freeboard zone (also called the post-gasification zone). A high material and energy transfer rate is achieved in the FB and this ensures a uniform temperature distribution throughout the fluidized bed. In order to avoid the formation of particle agglomerations the temperature is maintained below the ash softening point. Additionally, the gasification agents are injected into the post-gasification zone in order to improve the syngas quality and the conversion rate by increasing the temperature. In summary, the industrial-scale pressurized HTWTM
Figure 5 – HTW demonstration plant, Berrenrath, Germany. Production rates: 300 t /d methanol, total opperating hours: 67 000 h (Renzenbrink et al., n.d)
process is characterized by two temperature zones, namely the fluidized bed with an operating window between 800–1000°C and a post-gasification zone with temperature levels between 900 and 1200°C. The cyclone separates approximately 95% of the entrained solids from the syngas and returns them to the FB of the gasifier, thus increasing the overall carbon conversion
Table I
HTWTM gasification process data for industrial-scale plant Parameter Feed C H O N S ID.T. reduced Ash content Particle size range** Moisture content of the feed, as gasified*** Thermal input Operating pressure Fluid bed temperature Free board temperature Syngas quality CO H2 CO2 CH4 Carbon conversion efficiency Synthesis gas (CO+H2) yields Specific oxygen consumption Cold gas efficiency
German lignite
Finnish peat
High-ash hard coal*
Dimensions
23.2 68 4.9 25.7 0.7 0.6 >1 150 4.0 0-6 12
21 58 6.0 33 1.9 0.3 7.0 0-4 15
76.2 70.8 6.0 20.7 1.7 0.8 1 270 48 0-3 3
t/h (d.a.f.) wt.% d.a.f. wt.% d.a.f. wt-% d.a.f. wt.% d.a.f. wt.% d.a.f. °C wt.% dry mm wt.%
140 10 810 900
140 10 720 1030
600 30 870 1,100
MW bar °C °C
45 34 17 4 95.5 1 500 0.39 85
35 33 27 5 90 1 000 0.36 75
48 28 21 3 93 1 440 0.55 75
Vol. % (N2 and H2O free) Vol. % (N2 and H2O free) Vol. % (N2 and H2O free) Vol. % (N2 and H2O free) Carbon in dry gas / carbon in feed, % Nm3/t of feed, d.a.f. O2 Nm3/kg of feed, d.a.f. % (100 x Heating value of product gas, MWHHV / Heating value in feedstock, MWHHV)
* Estimated for coals of Indian origin ** Typical grain size for HTW process ranges between 0 and 10 mm. Coal fines can be used *** Before the gasification process the feed has to be dried to about the inherent moisture of the feed in order to improve the flow behaviour and due to economics (usually the content of lignite is 10 to 20% by weight).
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Gasification of low-rank coal in the High-Temperature Winkler (HTW) process
Figure 6 – Schematic of the HTWTM process
rate. Downstream of the gasifier, the raw syngas is cooled in the raw gas cooler and the heat is used to produce saturated steam that can be exported to external steam consumers. After the raw gas cooler the remaining fine ash particles are removed from the syngas in the ceramic filter. The fly ash is further cooled and then discharged from the pressurized system using a lock-hopper system. Subsequently, the syngas is sent to the scrubbing system, where it is quenched with water to remove the chlorides. The syngas is saturated, thus making further chemical treatment like the CO-shift easier.
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[7]
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[9]
Mathematical modelling of the HTWTM gasification process
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When heated, coal decomposes into char and volatile material (Equation [1]), the former reacting slowly in the fluidized bed and in the post-gasification zone (Equations [7]–[10]), while the volatile material, consisting mainly of water, CO, CO2, CH4, tars, H2, and some other light hydrocarbons conditionally named as CNHMOL, is assumed to rapidly form CO and H2 (Equations [2], [3], and [4]) as the most simple reaction mechanism. Gasification temperatures are normally so high that no hydrocarbons other than methane can be present in any appreciable quantity (Equations [2], [6], and [10]). Numerical simulation of a reacting fluidized bed reactor is not a trivial task. Prediction tools based on three different model approaches for simulation of gasification of solid fuel in a fluidized bed are used at ThyssenKrupp Industrial Solutions AG. These are based on the following methods: ➤ Black-box methods (BBM): a zero-dimensional model resolving the overall mass and heat balances over the entire gasification reactor ➤ Fluidization methods (FM): a one-dimensional VOLUME 115
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The gasification of solid fuel (coal, peat, biomass, MSW etc.) is a complex process governed by a number of physical and chemical phenomena. The principal steps by which the reaction progresses are the thermal decomposition of the raw fuel and the subsequent burnout of the char and the volatile matter. The following main reaction steps (Equations [1]–[10]) typically summarize the process of coal gasification:
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process steady-state model, which avoids the details of complex gas-solid dynamics but still maintains the fluid dynamic effects by assuming (using externally derived empirical correlations) a multiphase pattern in the bed. Here the particle-particle interactions are not accounted ➤ Computational fluid dynamics methods (CFDM): a three-dimensional unsteady model, which considers the fluid dynamics, gas-particle, and the particle-particle interactions entirely.
HTWTM process simulation as a design tool The BBM approach Normally the BBM approach is based on the equilibrium model, assuming that equilibrium is attained in the outlet streams. Equilibrium simulations yield almost no oxygen, solid carbon (above 800°C), or tar. In practice, however, the contents of hydrocarbons and char in the gas are far from zero, thus showing a strong kinetic limitation. Therefore, for calculation of the HTWTH process the so-called ‘pseudoequilibrium’ approach, implemented in HTW-specialized inhouse software, is used. This method ‘supports’ the equilibrium method with empirical correlations accumulated during long-term operation of the HTW gasifier. A simplified schematic diagram of this approach is shown in Figure 7. The pseudo-equilibrium approach allows solid carbon (present in the bottom ash product and in the dust), H2S, COS, HCN, NH3, and some heavy hydrocarbons like C6H6 and C10H8 to be contained in the outlet gas and the corresponding quantities of carbon, sulphur, nitrogen, and hydrogen are discounted from the feedstock. This approach is supported by a large empirical database containing operational data from different HTW gasifiers. Thus the remaining feedstock elements and the gasification agents react to attain equilibrium. The outlet gas is then obtained by summing the gas components given by the equilibrium and those taken off initially. The underlying reason for this approach is that the decomposition of tar and the char conversion by gasification, as well as the sulphur and nitrogen chemistries, are mainly kinetically limited.
For this reason the reaction mechanism and the reacting species participating in this mechanism are pre-defined based on experimental data obtained at the HTW pilot or demonstration plant. Additionally, the equilibrium of the reactions is evaluated at a lower temperature, a ‘quasi-equilibrium temperature’, than the actual process temperature. In this way the discrepancies of the equilibrium predictions are attributed to temperature gradients from the bubbling fluidized bed to the post-gasification zones by which the HTW gasifier is characterized. Therefore, the temperature is modified to obtain a reasonable correlation with the existing HTWTM empirical data. Furthermore, the split between bottom ash and dust as well as their elemental compositions is made on the basis of empirical correlations taken from real operating conditions. In case there is no empirical data available for a specific feedstock quality, the following pre-design steps are required: ➤ Laboratory determination of key feedstock parameters, such as ultimate and proximate analysis; coal ash analysis; ash softening temperature in a reducing atmosphere; coal char reactivity; physical properties (such as bulk density and true density); bulk fluidization behaviour; calorific values etc. ➤ Determination of the key operational parameters. Real gasification tests are performed at the state-of-the-art HTWTM pilot plant (0.5 MW thermal input) shown in Figure 8. These tests are required in order to obtain real data about the gasification temperature in both the fluidized bed and in the post-gasification zones, the composition of syngas, bottom ash, and dust (including trace elements, tars etc.), carbon conversion, agglomeration limits, fluidization behaviour etc. After obtaining the key feedstock and operational parameters, the HTW-specialized in-house software using the quasi-equilibrium approach as described above can be used for obtaining information about: ➤ Syngas composition, production rates, and HHVs ➤ Bottom ash and dust composition, production rates, and HHVs ➤ Cold gas efficiency, carbon conversion ➤ Utilities (air or oxygen, steam, carbon dioxide, water, etc.) for a given industrial-scale HTW geometry, operating pressure, and temperature. In practice, this relatively simple approach is very helpful for quick estimation of the performance of an industrial-scale HTW gasifier for a given feedstock, load, gasification agents, pressure, and temperature.
The FM approach
Figure 7 – A simplified schematic diagram of the HTWTM quasiequilibrium model
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The FM-based Pressurised Fluidised Bed Gasification (PFBG) program was developed at Siegen University, Germany (Hamel , 2001; Dersch and Fett, 1997) in a research programme with ThyssenKrupp Industrial Solutions (TKIS) AG. The program divides the computational domain into several zones arranged in series (cells) along the main gasparticle flow path. Each cell is subdivided into a solid-free bubble phase and an emulsion phase. The emulsion phase is assumed to contain some gas and all the solid; the gas and particles are perfectly mixed in this phase, whereas the gas The Journal of The Southern African Institute of Mining and Metallurgy
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process
Figure 8 – State-of-the-art HTWTM pilot plant (0.5 MW thermal input), located at the Technical University of Darmstadt, Germany
phase is considered to be in plug-flow. The flow pattern in each cell is defined using semi-empirical correlations to determine the distribution of gas species and reacting particles, the rising velocity, minimum fluidization velocity, terminal velocity, bubble size, bubble void fraction, bubble velocity, bubble–to-emulsion mass transfer, starting particle velocity and other relevant variables. A system of onedimensional conservation equations for each species is solved by neglecting diffusion. The cell temperature is calculated from the energy balance for each cell, whereas the mass balance equations are formulated separately for bubble and emulsion phases in each cell as shown in Figure 9. The program uses kinetic data for drying, devolatilization, char conversion, and homogeneous reactions. The parameters for these reactions can be changed according to the specific feedstock. Normally the kinetic data is taken from the literature or (better) from experiments performed at the pilot plant or in similar operating conditions to those in the gasifier. The program is validated using the composition of the gas at the gasifier outlet. The freeboard temperature profile was validated against measurements in laboratory-scale and fullscale gasifiers, as can be seen in Figure 10. The validations show reasonably good agreement for the main species at the outlet. The deviations between the measured and the calculated values for the CO, H2, H2O, and CO2 mole fractions point to an insufficiency of the implemented reaction models and can be attributed to the kinetic data available, in particular for the water-shift reaction. According to the literature (Gomez-Barea and Lekner, 2010), the model from Siegen is among the most advanced FM models developed to date. In practice, this much more complex approach compared to the BBM approach is helpful for estimation of the influence of the kinetic data on the performance of an industrial-scale HTW gasifier for a given feedstock, load, gasification agent, pressure, and temperature.
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Figure 9 – Simplified schematic diagram of the FM model from the University of Siegen (Hamel, 2001; Dersch and Fett, 1997)
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process
Figure 10 – Example of HTW Berrenrath simulation and validation of the FM model from the University of Siegen
The CFD approach Three-dimensional simulation of coal combustion/gasification systems is based on a complex mathematical model, which includes modelling of the fluid flow, turbulence, chemical reactions, and heat transfer in an Eulerian framework, and modelling of the coal particles transport, heterogeneous reactions, and the associated momentum, heat, and mass transfer with the surrounding reacting fluid in a Lagrangian framework. This approach is widely used for pulverized fuel combustion and gasification systems. However, it cannot be applied for flows with a high volume fraction of solid matter, which are typical for fluidized bed systems. Modelling of particle-dense flows such as in fluidized beds requires the introduction of new model approaches that consider the particle-particle interactions. Therefore in comparison with other applications such as entrained flow combustion and gasification, three-dimensional simulation of a reacting fluidized bed is still in the very early stage of development and application. TKIS AG is currently using two commercial CFD software packages and also develops open-source code for 3D simulations of the HTW gasification process. The models are based on a simplified discrete element method (DEM) approach assuming particles that have similar physical properties to form a cluster of a so-called ‘numerical’ particle. Thus the total number of the simulated particles can be reduced and simulations can be performed in an acceptable time schedule. Such simulations are unsteady by their nature and therefore unsteady Reynolds-averaged Navier-Stokes (URANS) or large eddy simulation (LES) methods are used for turbulence modelling. The chemistry is modelled using kinetic data for both homogeneous and heterogeneous reactions.
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TKIS AG is actively using the CFD technique for design, assessment, and optimization of the operating conditions of new HTW gasifiers. Some preliminary CFD results, obtained by the authors, for the HTW gasifier in Berrenrath can be seen in Figure 11. As can be seen from the distribution of the particle volume fraction, shown in Figure 11 (left), the coal after entering the gasifier undergoes fast devolatilization. Small particles are entrained to the freeboard where they have a residence time of approximately 10-15 seconds and can react with gasification agents before being separated by the cyclone and returned to the fluidized bed. The larger particles, together with the bed material (typically coal ash), form a fluidized bed with a height about the same as the height of the conical section of the gasifier. The residence time of the particles inside the fluidized bed is long enough to achieve high carbon conversion rates. The gas temperature distribution in the middle plane of the gasifier (Figure 11, right) clearly shows two temperature zones: (i) lower temperature and uniform temperature distribution inside the fluidized bed, and (ii) higher temperature in the post-gasification zone above the fluidized bed. The higher temperature is achieved with a controlled supply of oxygen just above the fluid bed. Thus several effects are achieved, namely (i) the volatiles (hydrocarbons such as tars), which are released during the devolatilisation inside the fluid bed, are oxidized and/or cracked, and (ii) faster endothermic gasification reactions take place in the post-gasification zone. In practice, the CFD predictions of the HTW process can be used for estimation of the influence of kinetic data on the performance of an industrial-scale HTW gasifier for a given feedstock, load, gasification agent, pressure, and temperature. Furthermore, the 3D information obtained from The Journal of The Southern African Institute of Mining and Metallurgy
Gasification of low-rank coal in the High-Temperature Winkler (HTW) process
Figure 11 – Example of CFD simulations of the HTW Berrenrath; particle (left) and gas temperature distribution in the middle plane (right)
such predictions can be successfully used for process optimization, improved design of the cyclone, finding the optimum number of nozzles and their position, local temperature control inside the fluidized bed and also in the post-gasification zone, etc.
ADLHOCH W., SATO, K., WOLFF, J., and RADTKE, K. 2000. High Temperature Winkler gasification of municipal solid waste. Gasification Technologies Conference, San Francisco, CA, 8–11 October 2000. ANDRULEIT H., BAHR, A., BABIES, H-G., FRANKE, D., MESSNER, J., PIERAU, R., SCHAUER, M., SCHMIDT, S., and WEIHMANN, S. 2013. Reserves, Resources and
Conclusions The High-Temperature Winkler (HTWTM) gasification process is characterized by a reacting bubbling fluidized bed operated at elevated pressure and temperatures, thus achieving high efficiency and high flexibility in terms of feedstock quality, reaction conditions, throughput, and syngas quality. The use of nozzles for supplying the gasification agents (oxygen, steam, and CO2) provides the opportunity to achieve uniform fluidization conditions and high flexibility in temperature and stoichiometric conditions along the gasifier height. HTWTM is a mature gasification technology for utilization of low-rank solid feedstocks such as high-ash subbituminous coals, lignite, peat, biomass, and MSW. More than 30 years of intensive R&D has led to building and operation of several industrial-scale gasifiers producing syngas on a commercial basis for many years. Recent developments made by ThyssenKrupp Industrial Solutions AG are focused on widening of the feedstock portfolio and improving the design by an intensive research and development programme based on both experiments at the HTW pilot plant and numerical simulations using the newest achievements in modelling of reacting fluidized bed processes.
Availability of Energy Resources 2013. Energy Study, Bundesanstalt für Geowissenschaften und Rohstoffe (BGR), Hannover. DERSCH J., and FETT, F. 1997. Anleitung zur Benutzung des Simulationsprogramms PFBG „Pressurised Fluidised Bed Gasifier“. University of Siegen. GOMEZ-BAREA, A. and LEKNER, B. 2010. Modelling of biomass gasification in fluidised bed. Progress in Energy and Combustion Science, vol. 36. pp. 444–509. HAMEL, S. 2001. Mathematische Modellierung und experimentelle Untersuchung der Vergasung verschiedener fester Brennstoffe in atmosphärischen und druckaufgeladenen stationären Wirbelschichten. PhD thesis, University of Siegen. INTERNATIONAL ENERGY AGENCY. 2010. World Energy Outlook 2010. KLAUKE, F. 2006. Moderne und umweltfreundliche Kohlekraftwerke als essentieller Baustein zur globalen CO2-Reduktion. Workshop at RWTH Aachen University, 13 July, 2006. RENZENBRINK W., WISCHNEWSKI, R., ENGELHARD J., and MITTELSTADT, A. Not dated. High Temperature Winkler (HTW) Coal Gasification – A Fully Developed Process for Methanol and Electricity Production. Rheinbrawn AG.
References
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VON ALBERTI, H-J. and RAMMLER, E. 1962. Technologie und Chemie der Braunkohleverwertung. er Deutsche Verlag für Grundstoffindustrie, Leipzig.
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ABB LTD. Not dated. The State of Global Energy Efficiency. Global and Sectional Energy Efficiency Trends. Corporate Communications Report, ABB Ltd, Zurich, Switzerland. www.abb/energyefficiency
http://dx.doi.org/10.17159/2411-9717/2015/v115n7a6
Support stability mechanism in a coal face with large angles in both strike and dip by L.Q. Ma*†, Y. Zhang*†, D.S. Zhang*†, X.Q. Cao*†, Q.Q. Li*†, and Y.B. Zhang‡
To solve the support stability control problem for a coal face with large angles along both strike and dip (CLSD), the ’support-surrounding rock’ mechanical model has been developed, which takes into account the impact of the dip angle of the seam on the stability of the support in the strike direction. The mechanical relationships of the critical topple angle and critical slip angle of the support along the strike of the coal face with large dip angle and the support height, support resistance, friction coefficient, and other factors have been derived through the mechanical analysis of support stability in the strike direction of CLSDs in the free state, the operating state, and the special state. The research findings were applied to a fully mechanized CLSD in Xinji Coal Mine. The maximum underhand angle and overhand angle in strike are 42° and 25° respectively, and the maximum dip is 39°. It is calculated that during underhand mining and overhand mining, the critical support resistances for avoiding support toppling are 3723 kN and 1714 kN respectively, and the critical support resistances for avoiding slipping of the support are 7405 kN and 6606 kN respectively. Thus, the selection of type ZZ7600/18/38 hydraulic roof support for the coal face is justified. Measures to prevent sliding of the support and the installation of a limiting stop maintain the support runs in good condition and ensure safe and efficient mining of CLSD. Keywords coal mining, support stability, dip angle, strike angle, critical topple, critical slip.
Introduction A coal seam with a large dip angle (CLDA) is a seam that dips at 35°–55°. CLDAs account for about 15–20% of China’s coal reserves and 5–10% of the output. More than 50% of the coal seams comprise scarce coal varieties under protective mining. CLDA mining is challenging due to the difficulty in controlling the stability of the roof, floor, and the coal face equipment, the difficult operating environment for workers, frequent accidents, and low extraction rates. CLSD refers to a coal face having a large underhand angle or large overhand angle in the strike direction, as well as a large dip angle. To ensure safe production at the coal face, more stringent requirements for the stability of the support and other equipment, as well as roof control, have been proposed. The mining of CLDA occurs mainly in the region of the former Soviet Union, and relevant reports are also available from Germany, France, Spain, and India. The study The Journal of The Southern African Institute of Mining and Metallurgy
* School of Mines, China University of Mining & Technology, Xuzhou, China. † Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, Xuzhou, China. ‡ State Development & Investment Corporation Xinji Energy Company Limited, Huainan, China. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received June 2012 and revised paper received May 2015. VOLUME 115
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Synopsis
of CLDA mining includes the mining method, strata control, and equipment development. In recent years, the study of CLDA mining has focused mainly on surface subsidence and its prediction. Kulakov (1995a and 1995b) made a systematic study of the rock pressure in a coal face with steep dip (large dip angle). Rafael and Javier (2000) investigated subsidence phenomena caused by CLDA mining and established a subsidence prediction model for CLDA mining. Chinese scholars have focused on the support stability control mechanism in a coal face with large dip angle. Wu (2006, 2005) analysed the varieties of instability of a roofsupport-floor (R-S-F) system under diverse conditions, established a R-S-F system dynamic model, and determined the control mode for R-S-F system dynamic stability. Lin et al. (2004) analysed anti-topple, anti-slip, and skew stability of hydraulic support for fully mechanized caving mining under the condition of large dip angle based on statics. Combined with field investigations, they studied three kinds of stability of hydraulic support for fully mechanized caving mining with large dip angles. However, all the research findings to date that analyse the support stability start from the point of view of the coal face dip angle. The research involving underhand/overhand mining has also focused merely on the characteristic analysis of roof-breaking (Zhang et al., 2010; Tian et al., 1994). There are few reports in the literature on research into support stability for CLSD.
Support stability mechanism in a coal face with large angles in both strike and dip The current investigation examines the underhand and overhand mining of CLSD at State Development & Investment Corporation’s Xinji Coal Mine, in the Anhui Province of China. The impact of the dip angle of the coal seam on support stability in the strike direction is considered for the first time, based on the actual conditions for underhand mining and overhand mining. The dip angle is introduced as an important parameter in the ‘support-surrounding rock’ mechanical model to study the instability of the support in the strike direction. Mechanical parameters of the support in the free state, the operating state, and the special state are calculated for underhand mining and overhand mining. The factors that impact on support stability in the strike direction are analysed, and methods are proposed to solve the stability control problem in CLSD.
strike direction of the coal face (kN), α is the dip angle of the coal face, L is the base length of the support (m), h is the support height (m), λ1 is the height coefficient of the gravitational centre of the support (the ratio between the height of gravitational centre y and the support height h), and λ2 is the length coefficient of the gravitational centre of the support (the ratio between the tail length of the support base away from the gravitational centre x and the base length of the support L). The slip mechanical model of the support in the free state is shown in Figure 2 (b), and its stress state is analysed in Equation [3]:
[3]
CLSD ‘support-surrounding rock’ mechanical model The critical slip angle β2 is:
Support stability in the strike direction in the free state Support stability depends on the interaction between the angle of strike and dip angle of the coal face. The analysis of deadweight of the support is shown in Figure 1 (Cao et al., 2010; Ma et al., 2010; Zhang, 2010; Li, 2009; Ostayen et al., 2004).
Underhand mining stage The mechanical model of the support in the free state is shown in Figure 2. The topple mechanical model of the support in the free state is shown in Figure 2(a), and its stress state is given by: [1]
[4] where f21 is the frictional resistance provided by the floor to the support (kN), R21 is the reaction force between the floor and the support (kN), and μ is the frictional coefficient between the support and the roof/floor.
Overhand mining stage The mechanical model of the support in the free state is shown in Figure 3. The topple mechanical model of the support in the free state is shown in Figure 3 (a) and its stress state is analysed in Equation [5]: [5]
where
The critical topple angle β1 is shown by Equation [6]:
The critical topple angle β1 is: [6] [2] where G is the support deadweight (kN), G2 is the component of the support deadweight perpendicular to the floor (kN), G3 is the component force of the gravity of support along the
The slip mechanical model of the support in the free state is shown in Figure 3 (b), and its stress state is analysed in Equation [7]:
Figure 1 – Component analysis of deadweight of the support
Figure 2 – Mechanical model for support in the free state during underhand mining
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Support stability mechanism in a coal face with large angles in both strike and dip where f22 is the frictional resistance provided to the roof by the support (kN), Le is the distance between the tail of the support canopy and the tail of the support base (m), and R22 is the reaction force between the roof and the support (kN). The critical topple angle β1 is given by: [10]
where M1 = LR22 + R22μh – R22Le The slip mechanical model of the support in the operating state is shown in Figure 4 (b) and its stress state is analysed in Equation [11]: Figure 3 – Mechanical model of support in the free state during overhand mining
[11]
The critical slip angle β2 is given by: [12]
Overhand mining stage The mechanical model of the support in the operating state is shown in Figure 5. The topple mechanical model of the support in the operating state is shown in Figure 5 (a) and its stress state is analysed in Equation [13]:
Figure 4 – Mechanical model of support in the operating state during underhand mining
[13] [7] The critical topple angle β1 is given by:
The critical slip angle β2 is given by Equation [8]:
[14] [8]
Support stability in the strike direction in the operating state Underhand mining stage The mechanical model of the support in the operating state is shown in Figure 4. The topple mechanical model of the support in the operating state is shown in Figure 4 (a), and its stress state is analysed in Equation [9]:
[9]
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Figure 5 – Mechanical model of support in the operating state during overhand mining
Support stability mechanism in a coal face with large angles in both strike and dip The slip mechanical model of the support in the operating state is shown in Figure 5 (b) and its stress state is analysed in Equation [15]:
When hK is reached or exceeded, the last layer will be the top layer in the caving zone. The total thickness from the bottom layer to the top layer is the actual thickness of strata in the caving zone, as shown in Figure 7 (Dou et al., 2009). The total weight of the rock in the caving zone P (kN) is given by:
[15] [18]
The critical slip angle β2 is given by: [16]
Support stability in the strike direction in the special state The stress state of the support in the strike direction for the coal face in the special state is shown in Figure 6. When roof weighting occurs in the coal face, the support stability is subjected to a larger lateral force, due to roof fracture, which is generated in the upper part of the support along the strike direction. The impacts of faulting, roof falls, and other factors on the support are consistent with that of roof weighting in the coal face in the strike direction.
The caving zone After the coal has been extracted, the roof strata will fail from bottom to top, layer by layer. When a stable geometry is formed in the strata above the caving zone, the lateral force on the support is mainly from the weight of the rock within the caving zone (Qian and Miao, 1995). The theoretical thickness of strata in the caving zone, hK (m), is shown in Equation [17]: [17] where M is the mining height (m), KK is the bulking factor, and α is the dip angle of the coal face. The thickness of each layer of the immediate roof and the main roof is accumulated from bottom to top to evaluate hK.
Figure 6 – The stress state of the support in the strike direction for the coal face in the special state
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where b is the support width (centre to centre) (m) γz is the average body force of the immediate roof in the caving zone (kN/m3) hz is the thickness of the immediate roof (m) Lz is the rock canopy length of the immediate roof (m). Lz=Ld+Lh+Lzx (Ld is the tip-to-face distance, which is about 1.0 m, Lh is the sum of the support canopy and front canopy lengths (m), Lzx is the maximum hanging length of the immediate roof behind the support (for the mudstone, Lzx is about 1.0 m) n is the number of the main roof layers in the caving zone γi is the average body force of the ith layer of the main roof in the caving zone (kN/m3) hi is the thickness of the ith layer of upper roof in the caving zone (m) Li is the length of the ith layer of rock in the main roof in the caving zone. The actual measured data for rock length of each main roof should be used for the calculation. If there is no actual data, the rock length of each main roof can be assumed to be the same as the rock length of the first layer of the main roof, due to few main roof layers collapsed in the actual caving zone. The rock length of the first layer of the main roof is the average periodic weighting interval at the coal face (m). The lateral force on the support from the rock in the caving zone F1 (kN) is shown in Equation [19]. [19]
Underhand mining stage The mechanical model of the support in the special state is shown in Figure 8.
Figure 7 – Schematic of the caving zone
The Journal of The Southern African Institute of Mining and Metallurgy
Support stability mechanism in a coal face with large angles in both strike and dip
Figure 9 – Mechanical model of support in the special state during overhand mining
Figure 8 – Mechanical model of support in the special state during underhand mining
The topple mechanical model of the support in the special state is shown in Figure 8 (a) and its stress state is analysed in Equation [20]:
The critical topple angle β1 is given by: [25]
where [20] The slip mechanical model of the support in the special state is shown in Figure 9 (b) and its stress state is analysed in Equation [26]:
The critical topple angle β1 is given by: [21] The slip mechanical model of the support in the special state is shown in Figure 8 (b) and its stress state is analysed in Equation [22]:
[26]
The critical slip angle β2 is given by: [22]
The critical slip angle β2 is given by: [23]
[27] Increasing the support resistance, increasing the frictional coefficient between the support and the roof/floor, and reducing the support deadweight (while ensuring the support has sufficient strength) can be conducive to preventing the support from slipping in the strike direction.
Engineering projects Mining geological conditions
The mechanical model of the support in the special state is shown in Figure 9. The topple mechanical model of the support in the special state is shown in Figure 9 (a) and its stress state is analysed in Equation [24]:
In Xinji Coal Mine, the E1108 coal face has a length of 877 m in the strike direction and 115 m in the dip direction. The thickness of the seam is 2.2–3.6 m, with an average thickness of 2.83 m. The conditions in the roof of the seam are shown in Table I. The dip angle of the coal seam is 22–39°, with an average of 30°. Because the strike direction of the coal seam at the coal face varies, underhand mining is adopted in the inner segment of the coal face, and overhand mining in the outer segment. The dip angle of the seam in the underhand mining section is 22–35° and the maximum underhand mining angle is 42°. The dip angle of the seam in the overhand mining section is 28–39° and the maximum
[24]
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Overhand mining stage
Support stability mechanism in a coal face with large angles in both strike and dip Table I
Characteristics of the roof of the coal seam Roof 2nd layer of the main roof 1st layer of the main roof Immediate roof
Lithology
Thickness (m)
Body force (kN.m-3)
Bulking factor
Medium sandstone Sandstone Mudstone
6.2 7.5 2.0
25
1.4
Table II
Main technical parameters of the support Model number
Nominal working resistance (kN)
Support height (mm)
Canopy length (mm)
Front canopy length (mm)
Base length (mm)
Centre distance (mm)
Weight (t)
ZZ7600/18/38
7600
1800-3800
2767
1740
3050
1500
25
overhand mining angle is 25°. The layout and the crosssection of the E1108 coal face are shown in Figure 10. ZZ7600/18/38 chock-shield support is utilized for the coal face. At the mining height of 2.8 m, the height coefficient and the length coefficient of the gravitational centre of the support are 0.5 and 0.4 respectively. The distance between the tail of the support canopy and the tail of the support base is 0.95 m. The main technical parameters of the support are shown in Table II.
Table III
Support stability in the strike direction in the free state
Underhand mining Overhand mining
Critical topple angle (°)
Critical slip angle (°)
Maximum dip angle
42.15 14.12
0 0
35° 39°
Back-analysis of the support selection The friction coefficient is considered to be 0.3 (Hu et al., 2008) and technical parameters of the support are put into the mechanical model in the free state. The critical topple angle and the critical slip angle of the support for the coal face in the free state can be calculated for the maximum dip angle, which is 35° during underhand mining and 39° during overhand mining. The results are shown in Table III. If the working resistance of the support (7600 kN) and the maximum dip angle of the coal face are entered into Equations [10], [12], [14], and [16], neither the critical topple angle nor the critical slip angle of the support in the operating state are reached at the stage of underhand mining or overhand mining. If the maximum dip angles of 35° during underhand mining and 39° during overhand mining are entered into Equation [17], the corresponding thicknesses of the theoretical caving zone are 8.54 m and 9 m. The immediate roof and the first layer of the main roof slice can completely fill the gob area. Therefore, the highest slices of the caving zone during underhand mining and overhand mining are both the first layer of the main roof slice. If the parameters are entered into Equation [18], the total weight of the rock in the caving zone, P, can be calculated as 5410 kN. If the working resistance of the support is 7600 kN, and P and the maximum dip angle of the coal face during underhand mining and overhand mining are entered into Equations [21], [23], [25], and [27], the critical topple angle and the critical slip angle of the support in the special state can be obtained for underhand mining and overhand mining. The results are shown in Table IV. The critical support resistance at the maximum underhand mining angle and maximum overhand mining
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Table IV
Support stability in the strike direction in the special state
Underhand mining Overhand mining
Critical topple angle (°)
Critical slip angle (°)
Maximum dip angle
-
44.78 41.57
35° 39°
angle of the coal face in the special state can be calculated using Equations [21], [23], [25], and [27]. The results are shown in Table V. It can be seen from the results that the working resistance of the support meets the requirements not only in the operating state, but also in the special state. During
Figure 10 – Layout of the E1108 coal face The Journal of The Southern African Institute of Mining and Metallurgy
Support stability mechanism in a coal face with large angles in both strike and dip Table V
Critical support resistance of support stability in the strike direction in the special state Strike angle
42° (underhand mining) 25° (overhand mining)
Critical support resistance Avoid toppling (kN)
Avoid slipping(kN)
Maximum dip angle
3732 1714
7405 6606
35° 39°
underhand mining and overhand mining at large angles, the support is prone to slip and topple in the free state, and this is much more likely to occur during overhand mining than during underhand mining. Therefore, some auxiliary measures are required in order to ensure support stability in the free state.
Auxiliary measures Prevention of sliding of the support It can be seen from mechanical analysis that the support can easily slip in the free state. Therefore, measures must be introduced to increase the frictional force between the support base and the floor and between the support canopy and the roof at all times, thus changing the support action from the free state into the operating state.
Installation of the limiting stop In order to prevent the sliding of the scraper conveyor and the swinging of the support during support advance, limiting
stops are installed on the support base (see Figure 11) to restrict the swing range of the push-pull rod and to ensure support stability.
Practical effect During underhand mining at a large angle, the support has a maximum support resistance of 7489 kN, reaching 98.5% of the working resistance; and the average support resistance during roof weighting is 4354 kN, and 3175 kN without roof weighting. During overhand mining at a large angle, the support has a maximum support resistance of 6535 kN, reaching 86.0% of the working resistance; and the average support resistance during roof weighting is 3461 kN, and 2676 kN without roof weighting. It can be seen that the support resistance meets the requirements for roof control. Assisted by technical enhancements, including anti-topple and anti-slip measures, the support provides good operating conditions, and ensures normal mining of CLSD. The daily average coal cutting production is 2080 t. The operating states of the support for the coal face during underhand mining are shown in Figure 12, and during overhand mining in Figure 13.
Discussion
Figure 11 – Location of the limiting stop
According to the mechanical model, by reducing the height of the gravitational centre of the support, reducing the weight of the support, increasing the base length of the support, and increasing the support’s resistance, the anti-topple and antislip capacity of the support can be significantly improved. Therefore, in the support design, the established mechanical model can be adjusted to optimize the structure of the support and the dimensions of each part of the support, so as to enable it to be best suited for CLSD and to have a strong anti-topple and anti-slip capacity. This mechanical model is
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Figure 12 – Operating state of the support
Support stability mechanism in a coal face with large angles in both strike and dip Acknowledgements We thank State Development & Investment Corporation Xinji Energy Company Limited for their assistance. We also thank Dr. F.T. Wang and the Fundamental Research Funds for the Central Universities (2014YC01). This work was supported by Qing Lan Project, and the Priority Academic Program Development of Jiangsu Higher Education Institution.
References CAO, S.G., XU, J., LEI C.G., PENG, Y., and LIU, H.L. 2010. The stent adaptability of steep fully mechanized working face under the complex conditions. Journal of China Coal Society, vol. 35, no. 10. pp. 1599–1603 (in Chinese).
Figure 13 – Operating state of the support in dip direction
applicable not only to support selection for Xinji Coal Mine’s E1108 coal face, but also to other CLSD situations. The established mechanical model described above does not cater for some special cases yet. For example, during underhand mining at a large angle, the waste within the gob area may surge onto the support, causing a thrust on the support, and increasing the possibility of slipping and toppling. The limiting stops installed on the support restrict the swing range of the push-pull rod. As the push-pull rod is rigidly connected, it may break when the sliding impulsive force of the scraper conveyor is very large. To avoid this, a hydraulic jack can substitute for the limiting stop, with a safety valve installed to ensure the normal use of the pushpull rod.
Key conclusions ➤ The ‘support-surrounding rock’ mechanical model has been developed considering the impact of the dip angle of the coal seam on the support stability in the strike direction. The mechanical analysis of the support is divided into the free state, the operating state, and the special state. Various forces have been adopted as the boundary conditions, so as to obtain the critical topple angle and the critical slip angle of the support in different states. The key factors that impact on the support stability of the coal face with a large dip angle have been analysed ➤ The research findings have been applied to the E1108 fully mechanized coal face in Xinji Coal Mine, and the critical support resistances required to ensure that the support neither topples nor slips during underhand and overhand mining have been calculated. It has been verified that the working resistance of the support meets requirements for support in the special state ➤ During underhand mining and overhand mining of the E1108 coal face with a large angle, the resistance of the support meets the requirements for roof control and the selection of the coal face support is relatively reasonable. Assisted by technical enhancements, such as prevention of sliding of the support and the installation of a limiting stop, the support has achieved good operating conditions, and ensured normal mining of CLSD.
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DOU, L.M., LU, C.P., and MOU, Z.L. 2009. The stope roof control and monitoring technology. China University of Mining and Technology Press, Xuzhou (in Chinese). HU, M. and CAO, B.D. 2008. Analysis for lateral stability of ZY10800/28/63 powered support. Coal Mine Machinery, vol. 29, no. 8. pp. 61–63 (in Chinese). KULAKOV, V.N. 1995a. Stress state in the face region of a steep coal bed. Journal of Mining Science, vol. 31, no. 3. pp. 161–168. KULAKOV, V.N. 1995b. Geomechanical conditions of mining steep coal beds. Journal of Mining Science, vol. 31, no. 2. pp. 136–143. LI, H.C. 2009. Force analysis of hydraulic support. Coal Mine Machinery, vol. 30, no. 7. pp. 69–71 (in Chinese). LIN, Z.M., CHEN, Z.H., XIE, J.W., and XIE, H.P. 2004. Stability analysis and control measures of powered supports in greater inclined full mechanized coal seam. Journal of China Coal Society, vol. 29, no. 3. pp. 264–268 (in Chinese). MA, L.Q., ZHANG, D.S., REN, T.X., ZHANG C.G., and LI, Y.S. 2010. Support design and strata control of coal face with deep dip angle and large mining height. ICMHPC-2010 International Conference on Mine Hazards Prevention and Control, Qingdao, China, 15–17 October 2010. Atlantis Press, Paris, France. pp. 460–467. OSTAYEN, R.A.J.V., BEEK, A.V., and ROS, M. 2004. A parametric study of the hydro-support. Tribology International, vol. 37, no. 8. pp. 617–625. QIAN, M.G. and MIAO, X.X. 1995. Theoretical analysis on the structural from and stability of overlying strata in longwall mining. Chinese. Journal of Rock Mechanics and Engineering, vol. 14, no. 2. pp. 97–106 (in Chinese). RAFAEL, R.D. and JAVIER, T.A. 2000. Hypothesis of the multiple subsidence trough related to very steep and vertical coal seams and its prediction through profile functions. Geotechnical and Geological Engineering, vol. 18, no. 4. pp. 289–311. TIAN, Q.Z., LIU, J.C., and ZHANG, Y.Q. 1994. Fracturing characteristics of main roof strata when mining uphill and downhill and its effect on stability of immediate roof. Journal of China Coal Society, vol. 19, no. 2. pp. 140–150 (in Chinese). WU, Y.P. 2006. Keys to dynamic equations of system R-S-F and determination on working resistance of face support in steeply dipping seam mining. Journal of China Coal Society, vol. 31, no. 6. pp. 736–741 (in Chinese). WU, Y.P. 2005. Dynamic equation of system ‘roof (R)-support (S)-floor (F)’ in steeply dipping seam mining. Journal of China Coal Society, vol. 30, no. 6. pp. 685–689 (in Chinese). ZHANG, Y.D., CHENG, J.Y., WANG, X.X., FENG, Z.J., and JI, M. 2010. Thin plate model analysis on roof break of up-dip or down-dip mining stope. Journal of Mining and Safety Engineering, vol. 27, no. 4. pp. 487–493 (in Chinese). ZHANG, W.D. 2010. Research on the stability of hydraulic support with the large mining height under conditions of the big inclination and the large depression angle. Mining and Processing Equipment, vol. 38, no. 1. pp. 21–24 (in Chinese). ◆ The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n7a7
An economic risk evaluation approach for pit slope optimization by L.F. Contreras*
Synopsis In open pit mine design, it is customary for geotechnical engineers to define the appropriate slope design angles within practical limits. The conventional approach to slope angle design is based on the comparison of calculated stability indicators, such as the factor of safety (FS) and the probability of failure (PF), with generic acceptability criteria not directly related to the impacts of failure. A major drawback of this type of approach is related to the difficulty of defining meaningful acceptability criteria. An alternative methodology of pit slope design is proposed, where the economic impacts of potential slope failures are calculated and used as the elements on which to apply the acceptability criteria for design. The methodology is based on the construction of a graph, referred to as a risk map, that relates the probability of exceeding the economic impact of slope failure to the magnitude of the impact measured in monetary terms. The process includes the analysis of a selected number of representative years of the mine plan and slope sections of the pit areas to define the required inputs for the construction of the risk map. The paper discusses the concepts used in interpreting the probability of slope failure, and describes the approach followed for the estimation of the economic impacts of slope failure and the construction of the risk map. Finally, the two main uses of the risk map are discussed, including the comparison with acceptability criteria for the evaluation of a specific open pit design and the comparative analysis of open pit design options in terms of value and risk to identify optimum pit layouts. Keywords risk evaluation, economic risk map, slope design, slope failure, probability of failure.
avoid this drawback. The methodology is based on a quantitative risk evaluation of the slopes, which has as a central element the construction of a risk map that relates the probability of the impact to its magnitude. In this process the economic impacts of slope failure are calculated and used as the elements on which to apply the acceptability criteria for design. The proposed methodology is an evolution of the approach described by Tapia et al. (2007) and Steffen et al. (2008), where event tree analysis similar to that used for safety risk evaluations was applied to the economic assessment of slope failures. This approach was superseded by a probabilistic method with a less subjective basis, as described by Contreras and Steffen (2012). The method was still in a development phase at the time of the latter publication, and was due to be applied to actual projects. Since then, the methodology has been used to evaluate two open pit mine projects, and as a result of that work some improvements have been implemented, particularly in terms of the concepts of probability used for the construction of the risk map. The graphs and data used in this paper to present the methodology are derived from these two previous studies.
Introduction
Background
The open pit mine design process seeks to define the optimum pit limits and sequence of mining, in order to derive the maximum benefit from the exploitation of a mineral resource given its spatial distribution and the particular geological, economic, and mine settings. Pit slope angles are determined using the conventional approach, whereby slope stability indicators such as the factor of safety (FS) or the probability of failure (PF) are calculated and compared with generic acceptability criteria to define the values to be used in the mine design process. The main drawback of this approach is that in spite of the effect that the slope angle has on the economics of the mine plan, its definition is based on criteria not directly related to this aspect of the design. The pit slope design process described in this paper attempts to
The optimum design of a pit requires the determination of the most economic pit limit, which normally results in steep slope angles as in this way the excavation of waste is minimized. In general, as the slope angle becomes steeper, the stripping ratio (waste to ore ratio) is reduced and the mining economics improve. However, these benefits are counteracted by an increased risk to the
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* SRK Consulting, Johannesburg, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Jul. 2014 and revised paper received Jan. 2015.
An economic risk evaluation approach for pit slope optimization operation. Thus, the determination of the acceptable slope angle is a key aspect of the mining business. The difficulty in determining the acceptable slope angle stems from the uncertainties associated with slope stability. Typical uncertainties encountered in the pit slope design process are discussed by Tapia et al. (2007) with reference to the Chuquicamata open pit. There are three main approaches commonly used to account for the uncertainties in slope design: factor of safety, probability of failure, and risk analysis.
Factor of safety approach The oldest approach to slope design is based on the calculation of the factor of safety (FS). The FS can be defined as the ratio between the resisting forces (strength) and the driving forces (loading) along a potential failure surface. If the FS has a value of unity, the slope is said to be in a limit equilibrium condition, whereas values larger than unity correspond to stable slopes. The FS approach is a deterministic design technique as a point estimate of each variable is assumed to represent the variable with certainty. The uncertainties implicit in the stability evaluation are accounted for through the use of a FS for design larger than unity. This acceptability criterion is intended to ensure that the slope will be stable enough to ensure a safe mining operation. Acceptable FS values in mining applications range between 1.2 and 2.0 according to Priest and Brown (1983), as indicated in Wesseloo and Read (2009). Acceptable values are based on observations of the performance of slopes at specific sites and experience accumulated over time. There are two main disadvantages in the FS approach for slope design. Firstly, the acceptability criterion is based on a limited number of cases and combines the effect of many factors that make it difficult to judge its applicability in a specific geomechanical environment. Secondly, the FS does not provide a linear scale of the likelihood of slope failure.
Probability of failure approach In recent years, probabilistic methods have been increasingly used in slope design. These methods are based on the calculation of the probability of failure (PF) of the slope. A probabilistic approach requires that a deterministic model exists. In this case the input parameters are described as probability distributions rather than point estimates of the values. By combining these distributions within the deterministic model used to calculate the FS, the probability of failure of the slope can be estimated. A technique commonly used to combine the distributions is the Monte Carlo simulation. In this case, each input parameter value is sampled randomly from its distribution, and for each set of random input values a FS is calculated. By repeating this process many times, a distribution of the FS is obtained. The PF can be calculated as the ratio between the number of cases that represent failure (FS<1) and the total number of simulations. The advantage of the PF over the FS as a stability indicator is based on the fact that there is a linear relationship between the PF value and the likelihood of failure1, whereas the same is not true for the FS. A larger FS does not necessarily represent a safer slope, as the magnitude of the implicit uncertainties is not captured by the FS value. A slope with a FS of 3 is not twice as stable as one with a FS of
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1.5, whereas a slope with a PF of 5% is twice as stable as one with a PF of 10%. Some drawbacks of the FS methodology that persist in the PF approach are the difficulties in defining an adequate acceptability criterion for design and the limitations in predicting failure with the underlying deterministic model. Acceptability criteria for PF have been defined by different authors and organizations, and a summary of this information is presented in Wesseloo and Read (2009). However, the actual criteria to be used in a specific mine cannot be determined from general guidelines like these, and should be subjected to a more thorough analysis of the consequences of failure (Sjoberg, 1999).
Risk analysis approach The risk analysis approach tries to solve the main drawback of the previous methodologies with regard to the selection of the appropriate acceptability criteria. Risk can be defined as the probability of occurrence of an event combined with the consequence or potential loss associated with that event: Risk = P(event) × Consequence of the event In the case of slopes, the P(event) is the PF of the slope and the consequences can be two-fold: personnel impact and economic impact. The PF calculated as part of the design process is normally based on a slope stability model calculation and accounts only for part of the uncertainties of the slope. Because risk analysis sets the acceptability criteria on the consequences rather than on the likelihood of the event, a thorough evaluation of the PF of the slope is required, incorporating other sources of uncertainty not accounted for with the slope stability model. For this purpose and for the analysis of consequences of slope failure, non-formal sources of information (engineering judgment, expert knowledge) are incorporated into the process with the aid of methods such as development of logic diagrams and event tree analysis. These techniques are described in detail by Baecher and Christian (2003) with reference to geotechnical engineering problems, more commonly in the disciplines of dam and foundation engineering. However, the use of risk methods in open pit mining focuses on safety applications, based on qualitative approaches to assess operational aspects. In the following sections, a description of the proposed risk methodology for slope design optimization is presented.
Methodology The proposed methodology uses the framework described by the Australian Geomechanics Society (2000) with reference to the landslide risk management process, characterized by the following main steps: ➤ Identify the event generating hazards
1
In non-technical literature, ‘likelihood’ is usually a synonym for ’probability’, but in statistical usage, a clear technical distinction is made. Here, probability of failure refers to the estimated frequency of FS<1 cases with the model assuming that this conditions represents failure. PF can take values only between 0 and 1. The likelihood of failure is a quantity not constrained and refers to the chances of actual failure, given the results of the stability analysis. The Journal of The Southern African Institute of Mining and Metallurgy
An economic risk evaluation approach for pit slope optimization ➤ Assess the likelihood or probability of occurrence of these events ➤ Assess the impact of the hazard ➤ Combine the probability and impact to calculate the risk ➤ Compare the calculated risk with benchmark criteria to produce an assessment of risk ➤ Use the assessment of risk as an aid to decision-making. The methodology described in this paper refers mainly to steps 2 to 5 as applied to the risk evaluation of pit slopes. The proposed risk evaluation process for slope design is intended to quantify the impact of potential slope failures on the economic performance of open pit mines. Figure 1 illustrates the risk evaluation process and depicts the main elements of the methodology, which are described in detail in this paper. The diagram includes the main components of the conventional geotechnical slope design process as described in Stacey (2009) and incorporates the additional elements required from the mine design process. The main objective of the methodology is the definition of the pit slope angles for mine design by applying project specific criteria to the quantified risk costs. The approach includes the following main steps: ➤ Definition of the set of slope sections for analysis covering key and critical pit areas during the mine life to provide representative cases of potential risks of slope failure within the mine plan ➤ Calculation of the probability of failure (PF) of the slopes from the analysis of stability of the selected slope sections ➤ Quantification of the economic impacts of slope failure with reference to the loss of annual profit or total project value as measured by the NPV ➤ Integration of the results of probability of failure and economic impact on an annual basis to define the economic risk map per year and for the life of mine
➤ Comparison of the risk map with criteria to assess acceptability of the design and to define risk mitigation options as required ➤ If the analysis is intended for the comparison of alternative slope design options, the process is repeated for each alternative pit layout and the results are collated in a graph of slope angle versus value and risk cost where the optimum slope angles can be defined. A complete risk evaluation process should also include the evaluation of the safety impact of slope failures. Safety risk evaluation is discussed by Contreras et al. (2006), Terbrugge et al. (2006), Tapia et al. (2007), and Steffen et al. (2008), and is not covered in this paper.
Slope sections for analysis The risk evaluation process requires a programme of slope stability analyses, including the critical pit areas and years in terms of potential economic impacts of eventual slope failures. This means that besides adequate information on geotechnical conditions defining the likelihood of failures, a good understanding of the mine plan is required to identify those areas and years in which the impacts of failure are likely to be greater. The selection of the sections for stability analysis starts with the selection of the years of the mine life that represent development periods in the mine plan with similar characteristics in terms of pit geometry, production profile, and economic scenario. Figure 2 shows an example of the cumulative discounted profit of a mine plan, which is a representation of the realization of value with time. This graph facilitates the definition of the appropriate periods and representative years of mine development for the risk model analysis, which in this example corresponds to the six years defining the stepped curve.
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Figure 1 – Risk-based slope design approach
An economic risk evaluation approach for pit slope optimization In general, probabilities of failure increase through the mine life, whereas impacts tend to maintain their levels or even decrease as mining progresses. The assumption that risk conditions of a later year (2027) represent those of early years (2025/2026) is therefore reasonable, with a minor
effect on the results or (more commonly) on the conservative side. The graph in Figure 2 implies that there is a trade-off between rigour and practicality when selecting the years for analysis. Ideally, every year would have to be analysed, although this would not be practical and is probably unnecessary in the majority of cases. The appropriate slope sections for analysis can be selected by examination of the mine plan in the identified key years. The criterion used for this selection is based on covering the anticipated higher risk areas of the mine, which include locations where the likelihood of slope failure or the associated impact is expected to be high. Examples of the preferred locations for analysis include areas with higher or steeper slopes, sites with unfavourable geological conditions, areas with distinct characteristics such as those defined by the geotechnical domains, critical access points to mining faces, areas close to key infrastructure, and so forth. The pit development plan sketched in Figure 3 shows an example with the selection of 42 sections used in this paper to illustrate the risk process.
Slope stability analysis Figure 2 â&#x20AC;&#x201C; Realization of value with time as a criterion for defining years of risk model analysis
The results of the slope stability analyses are reported in terms of PF values, which are calculated with the appropriate
Figure 3 â&#x20AC;&#x201C; Example of selection of slope sections for risk analysis
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An economic risk evaluation approach for pit slope optimization slope stability models in accordance with the relevant failure mechanisms in each domain. The methodology used for the calculation of the PF is in part determined by the type of deterministic model used for the calculation of the FS of the slope. A compilation of the methods commonly used in slope design can be found in Lorig et al. (2009). In a probabilistic stability analysis, the input parameters that represent the uncertainties are described by probability distributions. These distributions are combined within the deterministic model to define the distribution of the FS, which is used to estimate the PF of the slope. The PF is calculated as the ratio between the number of cases representing failure (FS<1) and the total number of cases of FS described by the distribution. Simple models, such as those based on the limit equilibrium method, can incorporate built-in routines to perform Monte Carlo simulations that enable the PF to be calculated relatively quickly. However, the use of more elaborate models based on stress-deformation analysis, with higher computational demands, restricts the calculation of the PF to those methods requiring a reduced number of FS entries to define its variability. Examples of such methods include those based on Taylor series expansions, the point estimate method, and the response surface methodology. Descriptions of these methods in terms of their conceptual basis are given by Baecher and Christian (2003) and Morgan and Henrion (1990). The response surface method has been used in risk-based slope design applications as described by Steffen et al. (2008). This approach has the advantage of combining the rigour of a Monte Carlo simulation with the practicality of requiring fewer FS calculations with the geotechnical model to construct the response surface used as a surrogate model in the process. Due to practical limitations, the PF values calculated with slope models are typically the result of considering the uncertainty of the strength properties of rock masses and structures, without consideration of any other potential factors contributing to slope instability. Therefore, these PF values are incomplete representations of the likelihood of failure, and need to be adjusted as discussed later for the purpose of a risk consequence analysis.
Strictly speaking, a Bernoulli trial refers to a discrete independent event, which is not exactly the case of the continuous process in time or space that characterizes the excavation of a pit slope. However, the consideration of the slope excavation process as a series of discrete situations, for example, excavation of consecutive slope lengths along a pit wall or annual exposure of slopes through the mine life, is a valid assumption within the framework of the risk model for slope failure, as failure events are associated with specific slope sections that are selected precisely to represent distinct conditions in terms of time of exposure and location within the pit. The association of open pit slope failure events with a Bernoulli process enables the following interpretations based on the number of trials of the process.
Bench slope failure in a homogeneous domain A bench slope failure in an open pit situation could be seen as a Bernoulli process involving many trials. The probability of bench failure in a benched slope within a homogeneous structural domain corresponds approximately to the ratio between the cumulative length of failed benches and the total length of constructed benches in that domain. In this case, the entire slope could be considered as a series of consecutive realizations of a unitary slope with a length given by the typical failure width. This case is illustrated in the sketch in Figure 4 and is comparable with the situation of rolling a dice many times to verify the probability of getting a ‘one’. In fact, the bench slope case can be seen as if a bench of length ‘b’ is constructed many times, with a percentage of those corresponding with failure situations.
Inter-ramp slope failure in a homogeneous domain The case of a hangingwall in an open pit mine located within a homogeneous geotechnical domain could be loosely associated with a Bernoulli process with several trials. In this case the probability of failure of the inter-ramp slopes for the life of mine could be approximated by the ratio between the cumulative volume of inter-ramp slope failures having
Interpretation of probability of failure (PF) of the slope
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A slope failure event could be regarded as a Bernoulli trial (also called binomial trial), which is defined as a random experiment with only two possible outcomes, success or failure, and in which the probability of success (or failure) is the same every time the experiment is conducted. According to this definition, and considering failure as the target event of analysis, if p is defined as the probability of failure, then q = (1-p) corresponds to the probability of no failure. Examples of Bernoulli trials include a ‘head’ after tossing a coin (p=50%, q=50%), a ‘one’ after rolling a dice (p=16.7%, q=83.3%) and, under certain assumptions as explained below, a failure after excavating a slope (p=PF, q=1-PF). The successive repetition of Bernoulli trials constitutes a Bernoulli process. The probability of success (or failure) is revealed in a Bernoulli process with a large number of trials. It is possible to verify that after rolling the dice a hundred times, the number of ‘one’ cases will be close to 17 and as more trials are considered, the better the approximation will be to the ‘one in six’ probability of getting a ‘one’.
An economic risk evaluation approach for pit slope optimization occurred and the total volume of rock excavated during the life of mine in the hangingwall, as illustrated in Figure 5.
Overall slope failure in a heterogeneous domain The case of overall slopes in open pits in heterogeneous geotechnical domains could be associated with a Bernoulli process with few trials or even with a single Bernoulli trial. The probability of failure of these slopes is not revealed in a physical manner and the estimation can be based only on simulation trials with geomechanical models representing the slopes. In this case the slope could be seen as a unique realization or trial that is not repeated in time or space, similar to the situation of a dice rolled once with two possible outcomes in terms of getting a ‘one’, success or failure. The overall slope failure case as a Bernoulli trial is illustrated in Figure 6.
Estimation of PF values for risk analysis The PF values to be used in a risk evaluation process need to represent all the exposed areas of the pit in the year of analysis, and to account for all possible uncertain factors that may lead to slope failures. The PF values calculated with slope stability models refer to specific sections of the slopes and typically account only for the uncertainties associated with variability of geotechnical properties. Therefore, these limitations need to be accounted for in the set of PF values resulting from the geotechnical analysis, such that they are truly representative of the likelihood of failures in the pit areas and mine plan years of analysis. For this purpose, two types of adjustments are required to the PF values calculated with the geotechnical models: one related to the estimation of the PF of the pit wall as opposed to that of the section of analysis; and the other to the estimation of the total PF as opposed to the model PF.
Section and slope wall PF Figure 7 shows the difference between the PF resulting from a stability analysis with a representative section of the slope and the PF value reflecting the likelihood of slope failure in a pit wall with a length greater than the expected width of the failure. It is clear that the PF of the longer slope wall in Figure 7 is greater than that of the shorter slope shown. Considering the shorter slope as a unitary slope with a length comparable to the expected width (d) of the failure, then the longer wall with length (L) could be seen as a series of consecutive realizations of the unitary slope (Bernoulli trials). If the PF of the shorter slope is given by the probability of failure (ps) resulting from the analysis of a typical section of the slope, then the PF of the longer wall (PFw) can be estimated with the following expression: PFw ≈ 1 – (1 – ps)L/d Figure 5 – Interpretation of the inter-ramp slope failure case as a Bernoulli process with several trials
Figure 6 – Interpretation of the overall slope failure case as a Bernoulli trial
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[1]
This consideration is useful to ensure that the possibility of failure of every exposed slope in the pit is included in the
Figure 7 – Interpretation of the probability of failure of a slope wall in a homogeneous rock mass as a function of its length The Journal of The Southern African Institute of Mining and Metallurgy
An economic risk evaluation approach for pit slope optimization
Model and total PF The analysis of consequences of slope failure requires that the PF of the slopes be a true reflection of the likelihood of occurrence; therefore, all possible situations leading to slope failures need to be included in the analysis. Due to practical limitations, the PF calculated with the geotechnical slope model typically accounts only for the uncertainty of the material properties, and is referred to as the model probability of failure (PFmodel) in the following discussion. The estimation of the PF incorporating other sources of uncertainty not accounted for with slope models was discussed by Contreras et al. (2006) and by Steffen et al. (2008) using a methodology based on the analysis of source response diagrams (SRDs). The methodology is based on concepts presented by Chapman and Ward (2003) with reference to project risk management processes used in a wide range of industries. The method enabled the quantification of the contributions to the PF caused by departures from the normal conditions assumed for the design of the slopes. These variations were evaluated within various categories such as groundwater conditions, geological features, operational factors, or occurrence of seismic events. The estimated contributions were added to the PF value resulting from assuming normal conditions of design to calculate the total probability of failure (PFtotal) to be used in a risk analysis. The methodology presented in this paper is analogous to the SRD approach described by Steffen et al. (2008), but adds some considerations regarding time in order to reflect the gradual increase, with time, of exposure to the atypical conditions evaluated. The method is appropriate for the assessment of types of uncertainties characterized by an aleatory nature. Other uncertainties not associated with frequency of events would be better treated with an expert opinion approach, with a greater reliance on experience and intuition. There are two main types of uncertainty in geotechnical engineering – aleatory and epistemic. The former is due to the random variation of the aspect under analysis, and the latter to the lack of knowledge of the aspect. Uncertainties are quantified with probabilities, which in turn can be interpreted as frequencies in series of similar trials or as degrees of belief. Baecher and Christian (2003) provide a detailed discussion on the topic of this duality in the interpretation of uncertainty and probability in geotechnical engineering, indicating that both types of probabilities are present in risk and reliability analysis and pointing out that the separation between them is a modelling artifact rather than an immutable property of nature. Some aspects of geotechnical engineering can be treated as random entities represented by The Journal of The Southern African Institute of Mining and Metallurgy
relative frequencies, and others may correspond to unique unknown events better treated as a degree of belief represented by expert opinion. Subjectivity associated with probability estimates is a way of capturing and integrating expert judgment, only some of which may be based on hard data, and is what formal modeling of uncertainty and risk is about. Analysis, which must be based on hard data, is inherently partial and weak. The topic of subjectivity and expert opinion as a key element of risk and reliability analysis in geotechnical engineering is discussed in detail by Vick (2002) and by Baecher and Christian (2003). The atypical conditions treated with this methodology are analysed on an annual basis, therefore each year they either occur or do not, and their annual occurrence is determined by the same underlying probability derived from a common set of conditions judged for the life of mine, either from hard data or from expert opinion or from a combination of both. These conditions suit those of a Bernoulli process and support the gradual increase of likelihood of occurrence with time estimated with the approach. Given the probability of occurrence of a particular uncertain atypical situation leading to slope failure (Patypical) associated with a defined mine life duration in years (n), the annual probability of occurrence of this situation (patypical) can be calculated with the following expression: patypical = 1 – (1 – Patypical)1/n
[2]
The probability of failure of the slope, given that the atypical conditions occur (PFmodel│atypical), could be evaluated with the slope stability model. The results of such analysis could be expressed as a factor (fatypical) of the model probability of failure evaluated under normal conditions. This factor could be the result of sensitivity analysis where different scenarios of the atypical condition are evaluated. Therefore: PFmodel│atypical = PFmodel × fatypical
[3]
Finally, the probability of failure of the slope due to atypical conditions (PFatypical) can be calculated for a particular year (i) of the mine plan as follows: (PFatypical)i = PFmodel│atypical × (1 – (1 – patypical)i )
[4]
The probability of failure of the slope due to atypical conditions (PFatypical) is added to the model probability of failure (PFmodel) from the geotechnical analysis under normal conditions of design to define the total probability of failure (PFtotal) appropriate for the risk evaluation process. The addition of the probability values is carried out with the following generic expression, which is based on the concept of system reliability: PFtotal = 1 - (1 – PFmodel) × (1 – PFatypical)
[5]
The method of calculation of PFtotal from PFmodel is illustrated with an example where the contributions from uncertainties related to groundwater, geology, and mining are added to the PF calculated with the geotechnical model for the slope represented by Section 3 of the mine case shown in Figure 3. Equation [5] can be extended to account for these three aspects as follows: PFtotal = 1 - (1 – PFmodel) × (1 – PFgroundwater) (1 – PFgeology) × (1 – PFmining) VOLUME 115
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risk analysis. However, the applicability of this adjustment is restricted to those situations where the assumption of homogeneity of the wall represented by the section analysed is reasonable; otherwise, the analysis of additional sections needs to be implemented. This consideration is also important in conventional geotechnical design procedures where PF values from geotechnical analysis are compared with acceptability criteria of reference, since complying with the specified criteria on a section basis does not necessarily guarantee that the criteria are met for the slope wall.
An economic risk evaluation approach for pit slope optimization The results of this analysis are indicated in Table I and Figure 8, and the input probabilities and factors are indicated in the footnotes to the table. Equations [2], [3], and [4] are used to calculate the terms in Equation [6]. The calculated contributions of each uncertain aspect to the PFtotal are shown by the curves in Figure 8. The uncertainties considered in the example of Table I and Figure 8 are intended to present the concept of adding uncertainties of random character not included in the geotechnical models for slope analysis. However, the relevant uncertainties not included in the models need to be identified and assessed on a project-specific basis. It may be that factors such as unknown stress conditions, actual pit geometry variations, or other specific situations are the more relevant aspects that would contribute to the overall PF in a given project. Also, the best way to treat a particular uncertainty needs to be defined based on its prevalent nature (i.e. aleatory or epistemic). In the slope stability evaluation process, the consideration of the potential effect of atypical situations leading to failure means that no matter how stable a slope might appear in terms of the calculated stability indicators, the probability of failure for the risk analysis is never zero and therefore the risk of failure is always present.
for the risk study of the Chuquicamata pit as described by Tapia et al. (2009). Unfortunately, this approach requires local historic records, which are not always available; therefore, judgement as well as reference to similar projects is the only practical option left to account for this uncertainty.
Model uncertainty
Estimation of economic impact of slope failure events
Model uncertainty in the slope stability analysis can be evaluated through the critical FS value (FScritical) used to define failure with the model. This type of uncertainty arises through systematic biases in input parameter determinations and idealizations in the calculation process, leading to the result that failure occurs for some FScritical value that may not be unity, as commonly assumed. Bias in parameter determination is inevitable, and is handled by calibration to slope performance. Model idealizations arise from simplifications required to represent the geometry, material behaviour, etc. Some aspects of model idealizations will tend to reduce FScritical, while others might raise FScritical. The effect of the parameter bias and model uncertainty is to produce an uncertainty band that is centred on the underlying bias. An evaluation of FScritical based on the comparison of actuarial failure rates versus nominal factor of safety was carried out
The economic impact of a slope failure can be measured through the quantification of the effect of this event on the value of the mine plan as measured by the NPV. The NPV corresponds to the cumulative discounted annual profits during the life of the mine and is normally defined as the result of a mining scheduling and optimization process carried out with specialized software. In general, the economic impact of a slope failure is a result of the disruption of the planned ore feed during the time required to restore the site, and the additional costs caused by these activities. Figure 9 illustrates the conceptual basis for the estimation of impacts of slope failures. The economic impact of a slope failure is defined as the difference between the NPV of reference (mine plan without failures) and the re-calculated NPV incorporating the effects of the failure on production and cost components.
Figure 8 – Calculated contributions to the PFtotal due to uncertain atypical conditions not included in PFmodel
Table I
Example of estimation of PFtotal Year Mine plan Year Section 3
PFmodel PFgroundwater PFgeology PF mining PFtotal
2
4
6
8
11
14
2015
2017
2019
2021
2024
2027
1.0% 0.0% 0.1% 0.0% 1.2%
1.3% 0.1% 0.3% 0.0% 1.7%
1.8% 0.2% 0.6% 0.1% 2.6%
2.3% 0.4% 1.0% 0.1% 3.7%
2.8% 0.6% 1.6% 0.2% 5.1%
3.0% 0.8% 2.1% 0.3% 6.1%
Notes: Input data on uncertainties: Groundwater: P 10% in 15 years (p annual = 0.70%) fgroundwater = PFmodel⏐ groundwater/PFmodel = 3 Geology: P 15% in 15 years (p annual = 1.08%) fgeology = PFmodel⏐ geology/PFmodel = 5 Mining: P 5% in 15 years (p annual = 0.34%) fmining= PFmodel⏐ mining/PFMODEL = 2
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An economic risk evaluation approach for pit slope optimization
Figure 9 – Conceptual basis for estimation of the economic impact of slope failure
Production may be disrupted by different factors such as interrupted access to the mining faces, covered ore, variations of grade when alternative sources of ore are used to mitigate the effects of the failure, and so forth. The additional costs are caused by the additional material handling and rescheduling of equipment required to restore the site affected by the failure. A simplified approach to quantifying the impact of a failure consists of calculating the differential NPV due to the failure, using a cash flow model that includes the estimated effects of the failure on production and costs. The impact on production is simulated by means of a reduction factor of the mined tons, which is estimated by considering aspects such as the magnitude, location, and time of occurrence of the failure and the flexibility of the mine plan to provide alternative ore feed sources. Engineering judgment and supporting reference data are normally used to estimate the impact factors from each failure event. The simplified cash flow model should include production data per mining phase, revenue calculations, as well as operating and capital costs, and needs to be calibrated against the reference NPV in the mine plan. An example of the structure of the simplified cash flow model used for the calculation of economic impact of slope failures is shown in Figure 10. The example illustrated shows that the impact on production affects the plant product tons and the revenue, which, together with the additional costs of restoring the site, ultimately reduces the net benefit and consequently the NPV. One drawback of the simplified approach is that the complex effect of variations of the planned grade feed when drawing from stockpiles cannot be simulated accurately. For this reason, the calculated impacts need to be validated with results derived from a thorough evaluation of selected key
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Figure 10 – Structure of simplified cash flow model for slope failure impact assessment
An economic risk evaluation approach for pit slope optimization events in a similar manner as they would be evaluated in a real-life situation, where specific re-designs of the plan would be carried out to minimize the impact of the slope failure.
Risk map for economic impact analysis of slope failure The results of probability of failure and economic impact calculations for individual failure events are used to construct the economic risk map per year and for the mine life. The risk map defines the relationship between the probability of a particular economic impact and the magnitude of that impact; and accounts for different situations of occurrence of events in a year, including isolated occurrences, concurrent occurrences of the different possible combinations of the events, and no occurrence of any event. The risk map construction process is based on the concept of event tree analysis. The event tree is a diagram that connects a starting event with the ultimate consequence under evaluation through a series of intermediate events based on a cause-effect relation. The events are quantified in terms of their likelihood of occurrence, thus enabling the assessment of the final outcomes in terms of their probabilities of occurrence. The event tree methodology for economic impact, originally described by Tapia et al. (2007) with reference to the case of the Chuquicamata mine and later discussed by Steffen et al. (2008) and in Wesseloo and Read (2009), relies on subjective inputs of probability for the events in the tree to produce an assessment of the expected likelihood of three categories of economic impact (force majeure, loss of profit, and minor impact). The main drawbacks of this methodology are that there is no consideration of the possible occurrence of various events in a year and that the impacts are assessed only in terms of likelihood, without a clear definition of the magnitude of these impacts. The combined analysis of probability and economic impacts with event trees is discussed in detail by Baecher and Christian (2003), including examples of consequence analysis where the probabilities of events and the respective impacts in monetary terms are multiplied to produce risk cost values used as a measure of the risks. One drawback of this approach is that the outcomes of the analysis do not represent actual possible impacts, but rather amounts weighted by the respective probabilities. This characteristic of the risk calculation is referred to by Baecher and Chirstian (2003) as ‘risk neutrality’, where high-probability lowconsequence outcomes are treated as equivalent to lowprobability high-consequence outcomes, as long as the product is the same. The reality is that the events either do or do not occur and consequently the impacts will either be caused or not – intermediate results are not possible. The proposed risk evaluation approach is carried out with a separate accounting for probabilities and impacts and the end results from the event tree branches are used to construct the risk map. The method is illustrated in Figure 11 for the simple case of a pit with two major slopes named East and West, with PF values of 5% and 10% and impacts of 100 and 50, respectively. The sum of the probabilities of the four possible outcomes depicted with the tree is 100%, indicating that all the possible combinations of events have been adequately accounted. The risk map constructed with the results of the event tree is shown in Figure 12. The
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cumulative probability curve of particular impacts constitutes the economic risk envelope of the pit. The risk map of a more realistic case, such as the mine plan described in Figures 2 and 3, is constructed for the individual key years selected to represent the various periods of the mine plan, which are then used to define the overall risk map for the life of mine, as shown in Figure 13. The graph at the top shows the various risk envelopes and the graph at the bottom shows the details of the failure events of year 2019 used to construct the envelope. The risk envelopes are cumulative probability distributions of impacts and are interpreted as indicated in the graph at the top of Figure 13 for the case of impacts with a 10% probability of exceedance. The result for the indicated case would be a 20% probability of having an annual impact of at least $160 million over a period of 15 years. The display of the individual events in the risk chart is useful to identify critical events causing an increase of the risk level as measured by the envelope, as depicted in the example shown on the graph at the bottom of Figure 13.
Probability concepts for construction of risk map The slope failure events considered for the construction of risk maps correspond to large-scale failures and are analysed
Figure 11 – Event tree for economic impact of slope failure of pit with two major slopes
Figure 12 – Risk map from results of event tree analysis in Figure 11 The Journal of The Southern African Institute of Mining and Metallurgy
An economic risk evaluation approach for pit slope optimization event tree, and is given by the following expression: T = 2n
[7]
From this number, n cases correspond with the occurrence of isolated events and one case to the nonoccurrence of any of the events. The remaining N cases correspond with the occurrence of combinations of two or more events. The generic expression to calculate the number of combinations (N) of 2 or more events that can be obtained with (n) events is: [8] or N = 2n – (n + 1)
[9]
The calculation of all possible probability and impact pairs can be done without constructing the respective event tree, which would be a cumbersome task as the number of branches of the tree increases exponentially with the number of annual events. A summary of the probabilities and impacts of the different possible combinations of 7 events per year is presented in Table II. In this table, p corresponds with the probability of occurrence (failure) and q with the probability of no occurrence (no failure) of the respective events. The number of cases in Table II is calculated with Equation [8] and the total number of possible occurrences of the 7 events is 128. This is the number of data points available to construct the risk map as described in the following section.
on a year-by-year basis. The events are treated as Bernoulli trials and are characterized by a probability of occurrence (p) given by the calculated probability of failure of the slope (PF) and the respective impact (i) estimated in monetary terms. The risk map construction is based on the calculation of the probability (P) of having an economic impact (I) considering different possible situations of occurrence of the events as explained below. In the following expressions, the terms with sub-indices i, j, and k (in bold) represent the occurring events, and those with sub-indices r, s, and t (in italic), refer to the nonoccurring events: ➤ Occurrence of single events: Pi = pi × (1 - pr) × … × (1 – pt) Ii = ii ➤ Multiple occurrence of events: Pi…k = pi × … × pk × (1 - pr) × … × (1 – pt) Ii…k = ii + … + ik ➤ A particular case of the multiple occurrence described in (2) is the occurrence of all the events in a year: Pi…k = pi × pj × … × pk Ii…k = ii + ij + … + ik ➤ No occurrence of any of the events: Pr…t = (1 – pr) × (1 – ps) × … × (1 – pt) Ir…t = 0 The total number of possible cases of occurrence of events (T) for (n) independent events in a year effectively corresponds to the number of branches of the respective The Journal of The Southern African Institute of Mining and Metallurgy
Construction of the risk map An example of the input data required for the construction of the risk map is presented in Table III. The data includes the probability of slope failure and the associated impact of seven sections per year and six years of analysis, on the mine plan of 15 years’ duration, as described in Figures 2 and 3. The PF values in Table III are based on the results of the geotechnical analysis of the respective sections and cater for the atypical conditions leading to failure discussed previously. The data in Table III is shown in graphic form in Figure 14 to illustrate the variations of the probability of failure and associated impacts with pit development. The graph at the left of Figure 14 is consistent with the increasing likelihood of failure of the slopes expected as the pit grows deeper. The curves in the graph at the right of Figure 14 do not show a unique trend in the variation of impact with pit growth, as impacts are dependent on the particular characteristics of ore exposure and ore access during the development of the mining phases. The risk map construction is carried out per year and the data is used to calculate the pairs of values of probability and impact associated with all possible combinations of failure events using the expressions in Table II. The 128 data pairs for each year of analysis are sorted and used to construct the respective probability distribution graphs of impacts. These graphs include a frequency distribution histogram and the corresponding cumulative frequency curve as shown in the graph at the top of Figure 15 for the year 2019 of the example in Table III. The risk map result is shown in the graph at the bottom of Figure 15. The graph contains the probability distribution plots with the axes swapped to conform with the typical way VOLUME 115
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Figure 13 – Example of a risk map for economic impact of slope failure, showing risk envelopes for key years and for life of mine (top), and details of events shaping the risk envelope for year 2019 (bottom)
An economic risk evaluation approach for pit slope optimization Table II
Number of possible cases of occurrence for the situation of 7 events per year Probabilities and impacts of combination of events Description
No cases
P
I
Isolated events 7 p1.q1.q2.q3.q4.q5.q6 i1 2 events 21 p1.p2.q1.q2.q3.q4.q5 i1+i2 3 events 35 p1.p2.p3.q1.q2.q3.q4 i1+i2+i3 4 events 35 p1.p2.p3.p4.q1.q2.q3 i1+i2+i3+i4 5 events 21 p1.p2.p3.p4.p5.q1.q2 i1+i2+i3+i4+i5 6 events 7 p1.p2.p3.p4.p5.p6.q1 i1+i2+i3+i4+i5+i6 7 events 1 p1.p2.p3.p4.p5.p6.p7 i1+i2+i3+i4+i5+i6+i7 No event 1 q1.q2.q3.q4.q5.q6.q7 0 Total 128 Notes: Numbers identifying the p, q and i terms in the expressions to calculate P and I should be interpreted as indices that are cycled through the 7 individual events to generate the number of cases indicated in column 2. p = probability of failure q = probability of no failure = (1 –p) i = economic impact of individual event P = probability of occurrence of combination of events I = cumulative impact of combination of events
Table III
Example of data for construction of the risk map for impact on NPV Year LOM year Section
2015
2017
2019
2021
2024
2
4
6
8
11
PF %
1 0.1 2 0.1 3 1.2 4 3.7 5 0.6 6 0.1 7 0.1 Note: – NPV of reference M$ 5.000
2027 14
Impact M$
PF %
Impact M$
PF %
Impact M$
PF %
Impact M$
PF %
Impact M$
PF %
Impact M$
109 78 25 41 16 18 14
0.2 0.9 1.7 5.9 5.2 0.4 0.8
72 96 70 36 166 92 83
0.4 5.8 2.6 8.0 9.5 1.2 2.9
55 26 34 12 155 47 42
0.7 11.1 3.7 10.3 12.0 2.9 6.4
52 64 27 15 65 14 11
1.7 17.6 5.1 13.9 15.4 7.3 9.4
54 62 35 43 68 48 43
3.7 23.0 6.1 16.1 19.4 10.1 12.0
59 52 29 60 44 40 34
Figure 14 – Input data for construction of economic risk map, probability of failure of the slopes (left) and impact on NPV (right)
in which risk acceptability criteria is presented, as discussed in the following section. The graph also includes the data points representing the various possible occurrences of the events. The blue data points correspond with isolated events, the green points with the concurrent occurrence of
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combinations of events, and the red point on the horizontal axis represents the particular situation of no occurrence of any of the events. Not all the data points are visible because many of them correspond with low probability values outside the range of the logarithmic scale used in the graph. The Journal of The Southern African Institute of Mining and Metallurgy
An economic risk evaluation approach for pit slope optimization mine (PLOM) for a given impact is calculated from the corresponding annual probabilities using the following expression: PLOM = 1 - (1-P2015)2 x (1-P2017)2 x (1-P2019)2 x (1-P2021)2 x (1-P2024)3 x (1-P2027)4
[10]
The exponents in this equation correspond to the number of years represented by the probability value in the respective term. The sum of these exponents is 15 and corresponds with the total number of years of the mine plan. A different perspective of the economic risk could be provided by the analysis of impacts on annual profits, because in this way, future amounts are not discounted to present values, which in some cases causes a perceived distortion of value. Risk maps based on the impacts on annual profits can be calculated following a similar process to that described for impacts on NPV. Furthermore, the analysis can be carried out with impacts measured in terms of commodity product rather than monetary units, in order to avoid possible distortions caused by the assumptions on commodity prices.
Uses of the risk map
Nevertheless, these low-probability events have an influence on the final result, which is captured by the cumulative distribution curve. Typically the risk map excludes the frequency distribution histogram in order to avoid an overcrowded graph. A practical way of defining the cumulative distribution curve of impacts is through a Monte Carlo simulation where the seven failure events are modelled with Bernoulli distributions (also called yes-no distributions) and the impacts calculated accordingly. The probability values given by the risk envelope should be interpreted as probabilities of exceedance of the respective value, as this curve corresponds to a cumulative probability distribution associated with all possible combinations of events considered. The risk envelope defines the economic risk profile for the respective year. The analysis of the patterns shown by the data points representing the occurrence of individual events is valuable for identifying critical events that push the risk envelope towards the upper right side of the graph. One example of such an event would be the slope failure associated with Section 5 in year 2019 as shown in Figure 15. The risk envelopes of the six representative years included in Table III were used to construct the economic risk map for the life of mine as shown in the graph at the top of Figure 13. The procedure is based on compounding the probabilities of the various years for fixed values of impact, considering the periods of the mine life represented by each year as shown in Figure 2. The probability values are added using the concept of reliability of a system. In this particular example the probability of an economic impact for the life of The Journal of The Southern African Institute of Mining and Metallurgy
Comparison with acceptability criteria The risk map can be used to assess a specific pit design by comparing this result with acceptability criteria specifically defined for the project. The result of this analysis enables the identification of the more appropriate risk treatment strategies to advance the project. In particular, the comparison with acceptability criteria is useful for the identification of those years of more relevance in terms of potential economic impacts and the respective critical pit areas causing those risks. This information is valuable for the definition of the areas requiring more investigation in further stages of study and for the evaluation of mitigation strategies to reduce the risks. Risk acceptability criteria are normally described in the form of a matrix in which risk is categorized in terms of likelihood of occurrence along the horizontal axis and severity of the impact up the vertical axis, to define high (H), medium (M), and low (L) risk levels. This type of matrix was originally developed for use in qualitative methods of risk analysis, with the scales adapted or adjusted to suit different types of application (Joy and Griffiths, 2005). However, a more precise definition of the scales of likelihood and severity results in acceptability matrices especially suited for the use in quantitative risk evaluation methods such as that based on the risk map construction described in this paper. An example of a risk acceptability matrix is shown in Figure 16, where likelihood and impact categories are defined specifically for the project setting at hand. The risk matrix also provides guidelines for risk treatment actions to follow, based on the risk results. The use of the risk acceptability matrix in Figure 16 is illustrated in Figure 17, where the risk map results shown in VOLUME 115
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Figure 15 â&#x20AC;&#x201C; Construction of the economic risk envelope for year 2019 in example from Table III; probability distribution graphs (top) and risk map result (bottom)
There are two main uses of the risk map described in this paper; one is for the evaluation of a specific open pit design in terms of economic risk by comparing the result with acceptability criteria, and the second refers to the comparative analysis of open pit design options, in terms of value and risk, to identify optimum pit layouts.
An economic risk evaluation approach for pit slope optimization Figure 13 are compared with the acceptability criteria. The criteria presented in Figure 16 are intended to adjudicate risk envelopes of individual years and need to be converted to the appropriate values for the analysis of the LOM envelope. The conversion is carried out with the same approach used to calculate the LOM envelope from the annual curves. This involves adding the annual probabilities using the concept of system reliability, considering a 15-year time span. In the example presented in Figure 17 the grey curves are included for reference but are not intended to be compared with the displayed risk zone categories. The evaluation of the individual years (top graph) indicates a low to moderate risk profile for all years, with the envelope of year 2019 showing a local elevated risk associated with conditions of Section 5, as depicted in Figure 15. This finding constitutes a pit optimization opportunity and illustrates the way in which the risk envelopes can be used to identify areas requiring attention in further stages of study. The evaluation of the LOM risk envelope illustrated in the graph at the bottom of Figure 17 suggests a moderate risk level of the overall mine plan.
Value and risk analysis of design options
Figure 16 – Example of risk acceptability matrix for economic impact (top) and the appropriate risk treatment options (bottom)
Figure 17 – Comparison of risk map in Figure 13 with acceptability criteria in Figure 16, for the evaluation of results of individual years (top) and LOM (bottom)
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The risk map can also be used to define risk cost values of alternative pit slope design options that need to be compared in terms of economic risk performance. Risk cost values are used to construct the value and risk profile for changing slope geometries, which provides the elements for screening of options in an early design stage and facilitates the identification of the main features of pit geometry for an optimum design. Generally, a base case pit slope design is available, which is the result of conventional slope design methods based on FS or PF criteria, or local experience in terms of slope performance in particular geological settings. The base case mine plan typically corresponds with a balanced risk condition, therefore slope design options on both sides of the base case are required to define the relationship between the slope angle and the value and risk condition of the pit layout. An example of the construction of alternative pit slope geometries for the risk analysis from the base case layout is illustrated in Figure 18. In this case the alternative slope designs are generated by flattening the base case by 5° and steepening by 5° and 10°, resulting in nominal slope design angles of 35°, 40°, 45°, and 50° for the east wall and 40°, 45°, 50°, and 55° for the west wall.
Figure 18 – Example of definition of alternative slope design options The Journal of The Southern African Institute of Mining and Metallurgy
An economic risk evaluation approach for pit slope optimization
Figure 19 – Example of risk envelopes of alternative pit design options compared with acceptability criteria and risk cost values indicated for three levels of likelihood
The risk maps for the four alternative pit design options are constructed using the respective slope stability results and economic impact assessment of slope failures. An example of the risk envelopes for the life of mine of the four slope design options shown in Figure 18 is presented in Figure 19. The risk envelopes are compared with the acceptability criteria (Figure 16), adjusted for a life of mine of 15 years. The graph also includes the risk cost values read from the envelopes for probabilities of exceedance of 10%, 50%, and 90%, which are used to assess the options in terms of value and risk. The risk envelopes in Figure 19 indicate that the base case -05° (BC-05) is in the low to moderate risk threshold, the base case (BC) and base case +05° (BC +05) are in the moderate risk area, and the base case +10° (BC+10) option falls in the high risk area. The comparison with the acceptability criteria does not provide sufficient elements to establish a clear contrast between the options in terms of their risk performance. The risk cost values indicated in Figure 19 are used to construct the value and risk profiles of the slope design as shown in Table IV and Figure 20. These results show the variation of value in terms of NPV and risk cost for the various slope design angles. The design options have been categorized in terms of the risk results as conservative, balanced, aggressive, and maximum, for the slope design cases of BC -05, BC, BC +05, and BC +10, respectively. The risk cost or costs of impact of slope failures have an inverse relationship with the probability of incurring those costs, with higher probabilities of small impacts and lower probabilities associated with large impacts.
Figure 20 – Risk cost (top) and project value (bottom) variations with slope design angle for risk levels of 10%, 50%, and 90%
The graph at the top of Figure 20 shows the typical increase of risk cost with increasing slope angle for various levels of likelihood of impacts. The risk cost values were used to construct the NPV with risk curves shown in the graph at the bottom of Figure 20. This graph shows a steady increase in NPV with increasing slope angle when no risk aspects are considered. However, once the risk cost is included in the analysis, the curve of value shows an inflexion point as the slope steepens, defining the angle that represents the optimum balance between value and risk. The results in Figure 20 would serve to confirm the adequacy of the base case design, and would suggest a possible optimization opportunity by steepening the slopes by up to 3 degrees. Information such as that included in Figure 20 constitutes a valuable tool to optimize the pit design and to bracket the overall slope angles for further phases of study.
Conclusions The methodology presented provides a rational approach to defining, at an early stage of a mine, the main features of pit geometry reflecting the appropriate balance between value
Table IV
Value and risk cost of the pit design options Slope angle
Design class
NPV (M$)
option (°) 1 2 3 4
BC –05 BC BC +05 BC + 10
conservative balanced aggressive maximum
4.935 5.000 5.050 5.090
The Journal of The Southern African Institute of Mining and Metallurgy
Risk costs (M$)
NPV with risk (M$)
P 10 %
P 50 %
P 90 %
P 10 %
P 50 %
P 90 %
157 170 205 275
88 115 160 230
53 70 112 198
4.778 4.830 4.845 4.815
4.847 4.885 4.890 4.860
4.882 4.930 4.938 4.892
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Case no.
An economic risk evaluation approach for pit slope optimization and risk, in accordance with the specific conditions of the project. The process considers both the likelihood of occurrence of individual slope failure events and the resulting economic impacts from all possible combinations of occurrence of these events on an annual basis and for the mine life. The economic risk map constructed for a particular pit slope layout can be used in an optimization process by comparing this result with project-specific acceptability criteria. When the process is used for the evaluation of alternative design options, the risk maps can be used to make risk cost estimations to calculate the variation of project value with slope angle. These results enable the definition of the main features of pit geometry reflecting the appropriate balance between value and risk, in accordance with the specific conditions of the mine, which allows the rationalization of requirements of geotechnical information at different stages of project development, once risk criteria have been defined.
JOY, J. and GRIFFITHS, D. 2005. National Minerals Industry Safety and Health Risk Assessment Guideline. Minerals Council of Australia. Version 4, Jan. 2005. LORIG, L., STACEY, P., and READ, J. 2009. Slope design methods. Guidelines for Open Pit Slope Design. Read, J. and Stacey, P. (eds). CSIRO Publishing, Collingwood, Victoria. pp. 237–264. MORGAN, M.G. and HENRION, M. 1990. Uncertainty: a Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press. READ, J. 2009. Data Uncertainty. Guidelines for Open Pit Slope Design, Read, J. and Stacey, P. (eds). CSIRO Publishing, Collingwood, Victoria. pp. 214–220. ROSS, S. 2010. A First Course in Probability. 8th edn. Pearson Prentice Hall, New Jersey. SJOBERG, J. 1999. Analysis of large scale rock slopes. Doctoral thesis, Division of Rock Mechanics, Lulea University of Technology, Lulea, Sweden. STACEY, P. 2009. Fundamentals of slope design. Guidelines for Open Pit Slope Design. Read, J. and Stacey, P. (eds). CSIRO Publishing, Collingwood, Victoria. pp. 1–14.
References
STEFFEN, O.K.H. 1997. Planning of open pit mines on a risk basis. Journal of the Southern African Institute of Mining and Metallurgy, vol 97, no. 1. pp. 47–56.
AUSTRALIAN GEOMECHANICS SOCIETY. 2000. Landslide risk management concepts and guidelines. AGS Sub-Committee on Landslide Risk Management, Sydney, Australia.
TERBRUGGE, P.J., WESSELOO, J., and VENTER, J. 2006. A risk consequence approach to open pit slope design. Journal of the South African Institute of Mining and Metallurgy, vol. 106, no. 7. pp. 503–511.
BAECHER, G.B., and CHRISTIAN, J.T. 2003. Reliability and Statistics in Geotechnical Engineering. Wiley, Chichester, UK.
STEFFEN, O.K.H., CONTRERAS, L.F., TERBRUGGE, P.J. and VENTER, J. 2008. A risk evaluation approach for pit slope design. Proceedings of the 42nd US Rock Mechanics Symposium, 2nd US-Canada Rock Mechanics Symposium, ARMA, San Francisco, USA, June 30 – July 2, 2008.
CHAPMAN, C. and WARD, S. 2003. Project Risk Management: Processes, Techniques and Insights. 2nd edn. Wiley, Chichester, UK. pp. 148–150. CONTRERAS, L.F., LESUEUR, R., and MARAN, J. 2006. A case study of risk evaluation at Cerrejon Mine. Proceedings of the International Symposium on Stability of Rock Slopes in Open Pit Mining and Civil Engineering Situations, Cape Town, South Africa, 3-6 April 2006. Symposium Series S44. Southern African Institute of Mining and Metallurgy, Johannesburg.
TAPIA, A., CONTRERAS, L.F., JEFFERIES, M., and STEFFEN, O.K.H. 2007. Risk evaluation of slope failure at the Chuquicamata Mine. Proceedings of the 2007 International Symposium on Rock Slope Stability in Open Pit Mining and Civil Engineering, Perth, Australia, 12-14 September 2007. Potvin, Y. (ed.). Australian Centre for Geomechanics.
CONTRERAS, Lf. and STEFFEN, O.K.H. 2012. An economic risk-based methodology for pit slope design. Newsletter of the Australian Centre for Geomechanics (ACG), vol. 39, December 2012.
VICK, S.G. 2002. Degrees of Belief: Subjective Probability and Engineering Judgment. American Society of Civil Engineers, Reston, Virginia.
HARR, M.E. 1996. Reliability-based Design in Civil Engineering. Dover Publications, Mineola, New York.
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WESSELOO, J. and READ, J. 2009. Acceptance criteria. Guidelines for Open Pit Slope Design. Read, J. and Stacey, P. (eds). CSIRO Publishing, Collingwood, Victoria. pp. 221–236. ◆
The Journal of The Southern African Institute of Mining and Metallurgy
http://dx.doi.org/10.17159/2411-9717/2015/v115n7a8
Investigation of stress in an earthmover bucket using finite element analysis: a generic model for draglines by O. Gölbaşı* and N. Demirel*
Draglines are massive machines extensively utilized in opencast mines for overburden stripping. The demanding working environment induces fractures, wear and tear, and fatigue failures in dragline components and eventuates in extended maintenance, lengthy downtimes, and loss of production. The bucket is the main source of external loads on the machinery, since interactions with ground materials take place in this region. This study aims to develop a generic finite element model of the stress on an operating bucket. This entails (i) three-dimensional modelling of a dragline bucket, (ii) analytical estimation of resistive forces in the bucket movement, (iii) three-dimensional simulation of the moving bucket using finite element analysis (FEA), and (iv) sensitivity analysis to examine the effect of formation characteristics on stress variation. Simulation results imply that the drag hitch and digging teeth are the elements of the bucket that are most prone to failure. In addition, sensitivity analysis indicates that internal friction angle of the formation is the dominant parameter leading fluctuations in stress values. Changes in stress level are least influenced by formation density. Keywords dragline bucket, formation-bucket interaction, stress distribution, finite element analysis, sensitivity analysis.
Introduction Draglines are self-operated stripping machines employed for removing of overburden material in opencast mines without assistance from a haulage machine (Figure 1). These earthmovers can be more than 4000 t overall weight, with bucket capacities commonly 90–120 m3, and have a capital cost up to US$100 million (Townson, Murthy, and Gurgenci, 2003). The productivity of a dragline is influenced by various considerations arising from operational, environmental, and humanbased issues. Irregularities and inhomogeneities in the environment of operation and the resultant stress variations are the main issues that cause unsteady loading of the front-end components of a dragline. Stress accumulation and induced damage to mechanical elements results in downtime, delays in the production schedule, and increased maintenance costs and contractor expenses. During stripping, the resistance encountered by the bucket is absorbed and transmitted to other components of the The Journal of The Southern African Institute of Mining and Metallurgy
* Department of Mining Engineering, Middle East Technical University, Ankara, Turkey. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received July 2013 and revised paper received Mar. 2015. VOLUME 115
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Synopsis
dragline such as the drag chain, hoist chain, rigging, and boom. The bucket is the source of external forces during operation. Therefore, investigation of the locations of stress concentration on the bucket is of paramount concern for better clarification of potential deficiencies in the bucket. Finite element analysis (FEA) can be effectively utilized to simulate actual cases of the dragline earthmoving process. FEA has been extensively utilized in many studies to develop models of the interaction between formation and digging tool. Mouazen and Nemenyi (1999) developed a FEA model to simulate the formation cutting process in sub-layers with various geometries. Fielke (1999) presented a model revealing the effect of cutting edge geometry on the required cutting force. Davoudi et al. (2008) generated a model capable of estimating draft forces during tillage operation. Frimpong and Demirel (2009) examined the stress distribution along a dragline boom using FEA together with results acquired by kinematic and dynamic modelling of the boom. Brittle and plastic deformability of the formation subjected to the earthmoving process have also been investigated (Chi and Kushwaha, 1989; Raper and Erbach, 1990; Aluko and Chandler, 2004; Aluko, 2008). The current study intends to bring innovation to dragline productivity by examining stress distributions on the bucket body. The development of a 3D solid modeling of a dragline bucket is described. This is followed by an estimation of the resistive force exerted by the formation encountered in earthmoving activity. The procedures and assumptions regarding FEA within the scope of study are
Investigation of stress in an earthmover bucket using finite element analysis
Figure 1 – (a) Dragline in operation and (b) schematic view of dragline components
Figure 2 – Research methodology
discussed, an investigation of the released stress distribution and sensitivity analysis is presented, and conclusions are drawn from the study. The framework of the research methodology followed is illustrated in Figure 2.
Solid modelling of dragline bucket A dragline bucket body is composed of a back wall, two sidewalls, a floor, an arch, a bucket lip, and teeth that create space to gather unconsolidated or soft material during excavation. The sidewalls of the bucket are slightly inclined
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outward. Borders outlined by sidewalls provide rearward space with an upward tapering. The back wall has a convex configuration with oblique extension. The anterior sections of the sidewalls and the floor are integrated with bucket lip at the front. A rope and chain assembly moves the bucket vertically, and digging teeth are attached to the bucket lip using connection links. An example of a dragline bucket with 50 m3 capacity modelled in Solidworks (Dassault Systèmes SolidWorks Corporation, 2009) can be seen in Figure 3. The bucket model has a mouth opening of 4.32 m and six digging The Journal of The Southern African Institute of Mining and Metallurgy
Investigation of stress in an earthmover bucket using finite element analysis
Figure 3 – Dragline bucket views from different perspectives
Prediction of formation resistive force An earthmoving process is a succession of consecutive formation failures due to the interaction between the formation and excavation tool. Estimation of resistive forces resulting from action-reaction behaviour of the formation is essential to calculate the opposing stresses during the stripping movement of a dragline bucket. There are various empirical and analytical methods available for analysing the forces that are generated during formation cutting. Some researchers have observed the performance of various earthmoving machines to predict the cutting resistance of the formation empirically (Alekseeva et al., 1995; Zelenin, Balovnev, and Kerov, 1986; Nedoredzov, 1992, Hemami, Goulet, and Aubertin, 1994). In addition, there are many other studies that have investigated the formation-tool interaction in 3D or 2D perspective using analytical methods. Since empirical definitions are constructed from specific field observations, these methods are not able to offer representative estimations for other sites. Analytical methods, however, can be more objective in defining earthmoving processes by a holistic approach. This research study utilizes an analytical approach to calculate the approximate resistive forces imposed on a dragline bucket in operation. Analytical techniques can be handled as 2D or 3D according to the area of utilization. As cited in Blouin’s review study (Blouin, Hemami, and Lipsett, 2001), 3D models (McKyes, 1985; Swick and Perumpral, 1988; Boccafogli et al., 1992) incorporate the effect of accumulated The Journal of The Southern African Institute of Mining and Metallurgy
material at the edges of the digging tool during operation. 2D approaches, on the other hand, do not consider the side effect of the formation resistance in modelling (Osman, 1964; Gill and Vanden Berg, 1968; McKyes, 1985; Swick and Perumpral, 1988). The shape of the excavation tool can be used in decision-making to designate the dimensional type of process. Excavation tool shapes are generally classified as bucket and blade types. 2D resistance models are convenient for bucket movement since the sidewalls of the body ensure the direct passage of cut material to the inside and, unlike scraper blades, accumulation of material is restricted, (Blouin, Hemami, and Lipsett, 2001; McKyes, 1985). This paper utilizes McKyes’s 2D model (McKyes, 1985) as given in Equation [1] to estimate the forces due to weight, cohesion, adhesion, overloading, and inertia to express the resistance of a formation to earthmoving. T = w(γgd2Nγ + cdNc + CadNca + qdNq + γv2dNa)
[1]
where T is the resultant cutting force w is the cutting width γ is the density of the formation g is the gravitational acceleration d is the tool depth c is the cohesion Ca is the adhesion q is the overload v is the formation cutting velocity Nγ is the weight coefficient Nc is the cohesion coefficient Nca is the adhesion coefficien Nq is the overload coefficient Na is the inertia coefficient. VOLUME 115
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teeth 0.35 m in width. The bucket body extends 4.88 m in length, 3.05 m in height; and the width tapers from front to back.
Investigation of stress in an earthmover bucket using finite element analysis McKyes’s model covers many parameters that can contribute to variations in formation resistance. This research study neglects the overload pressure due to additional load on the formation surface and leading to increased compaction of the formation. Adhesion force is considered to be negligible in overburden stripping since this kind of force is encountered only in presence of frictional interaction between two heterogeneous materials. The metal composition and smooth surface of digging teeth minimizes such interaction. In addition, the inertial effect of the formation is not included in the formula since inertial fluctuations come into play only while the formation particles are being accelerated from rest to a certain velocity (Abo-Elnor, Hamilton, and Boyle, 2003). However, this study handles the model in terms of cutting the formation with constant velocity by means of a dragging action. Eventually, the general form of McKyes’s equation is reduced to Equation [2]. T = w(γgd2Nγ + cdNc)
[2]
The formula parameters obtain their values from both the cutting geometry and the deforming medium. Geometrical values can be acquired from Figure 4, which illustrates the interaction between the solid model and the medium. The total width of cutting medium (w) is 4295 mm and depth of the interaction (d) is 512 mm. N coefficients for weight (γ) and cohesion (c) are acquired using Equation [3] and friction angle charts by Hettiaratch and Reece (1974), where δ and φ denote external and internal friction angles, respectively. [3] Medium parameters required for Equation [2] and Equation [3] are obtained from a tillage research study by Mouazen and Nemenyi (1999). The calculated weight and cohesion coefficient and resultant cutting force are presented in Table I. Effective resistance of the medium against the cutting force is estimated to be about 154 kN.
Table I
Input parameters and cutting force Parameter
Value
Formation cohesion strength, c (kPa) Density of formation, γ (t/m3) Internal friction angle, φ (°) External friction angle, δ (°) Weight coefficient, Nγ Cohesion coefficient, Nc Resultant cutting force, T (kN)
20.40 1.84 34.00 25.00 1.73 2.65 154.0
Figure 4 – Area of interaction between the formation and the solid model
Finite element mesh and boundary conditions Finite element analysis (FEA) constitutes a virtual environment to measure the reaction of a solid model under external and internal loads using nodal displacement of solid elements. Prior to implementing the analysis, pre-processing items such as material and element type, loading, and boundary conditions should be satisfied to ensure the authenticity of the model under the prescribed limits. FEA modelling and all simulation in this research study are executed in Abaqus 6.9-2 (Dassault Systèmes Simulia Corporation, 2010). Materials are assigned to solid models using characteristics of two metals as given in Table II. The material specifications are for two casting metals with strengths of 510 and 410 MPa, exhibiting elastic-perfectly plastic behaviour. Meshing of the solid bodies is carried out using a four-node linear tetrahedron continuum element denoted as C3D4. Figure 5 illustrates the resultant meshing body, which incorporates 199 062 solid elements and 45 318 connection nodes. One important issue in FEA pre-processing is the designation of loading and boundary conditions in a simulation ensuring the cutting movement of dragline bucket. Dragline buckets are filled by a pull-back motion of the bucket toward the machinery housing over a distance from two to three times the bucket length (Demirel, 2011). The bucket initially penetrates the formation with the digging
Table II
Material characteristics of the constituent parts (Matbase, 2010) Part Teeth Main bucket body
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Density (kg/m3)
Young’s modulus (N/m2)
Poisson’s ratio
Yield stress (N/m2)
7800 7850
205 x 109 200 x 109
0.30 0.29
510 x 106 410 x 106
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Investigation of stress in an earthmover bucket using finite element analysis stress is between 0.013 and 0.3 MPa. Thematic results indicate that the concerning medium sample cannot afford to fail bucket elements. However, any stress fluctuations or overloading initially induce fractures or fatigue on the redyellow zone of the solid model as shown in Figure 8.
Sensitivity analysis results
Figure 5 – Meshing of dragline bucket using C3D4 continuum element
The sensitivity of stress value to variations in formation properties such as density, cohesion, internal friction angle, and external friction angle was examined to determine the formation properties that have the most influence on the stress distribution along the bucket. The effect of each parameter was measured by changing the value by ±20 per cent and determining the resultant changes in resistance forces exerted by the medium. Best-fit lines of the simulation outcomes for the modified loading conditions for a representative solid element, coded as 24753, in the tooth body are illustrated in Figure 9. The graphs indicate that fluctuations in internal friction angle have the greatest effect on the range of stress concentration on the element. Density, on the other hand, has a minimal effect on the stress value variance for the solid element.
Conclusions Figure 6 – Interaction of dragline bucket with formation during the filling process
Severe operation conditions on draglines, coupled with pressure for continual production and a high utilization, lead to frequent breakdowns of dragline components and ensuing
teeth using its own weight (Figure 6a) and then proceeds to cut the formation by means of the dragging force transmitted along the drag rope during whole filling cycle (Figure 6b and Figure 6c). The cutting action of the bucket dominates the filling cycle and leads to stripping of the formation at a velocity between 0.5–0.7 m/s (Frimpong and Demirel, 2009). Simulation in this investigation uses two external loads: the dragging force applied at the drag hitch element of bucket (Figure 3) and the formation resistance applied on the digging teeth as a distributed load.
Results and discussion Stress distribution on the bucket
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Figure 7 – Failure of the medium at the initial contact of the dragline bucket
Figure 8 – Stress distribution on dragline bucket VOLUME 115
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The developed model simulates the formation cutting action of the dragline bucket. Stress accumulates between the surfaces of the bucket and the formation, and the resultant failure of the formation initiates the earthmoving process. Exposure of the bucket to continuous resistance by the medium can also lead to surface fractures on the bucket body. A non-homogeneous medium and irregularities in the area being excavated may initiate stress growth and cause mechanical failure. Detection of the zones that are most prone to failure in these conditions is essential for planning preventive maintenance. The Von Mises stress distribution in the formation at 200 mm bucket movement is illustrated in Figure 7. It was observed that the Von Mises stress on the medium can reach up to 100 MPa. The output of finite element analysis of stress distribution on the bucket is presented in Figure 8. Von Mises stress is mostly accumulated on the front-end elements of the bucket such as the digging teeth and drag hitch element. The maximum stress on the bucket is 3.85 MPa, and the general
Investigation of stress in an earthmover bucket using finite element analysis BLOUIN, S., HEMAMI, A., and LIPSETT, M. 2001. Review of resistive force models for earthmoving processes. Journal of Aerospace Engineering, vol. 14, no. 3. pp. 102–111. BOCCAFOGLI, A., BUSATTI, G., GHERARDI, F., MALAGUTI, F., and PAOLUZZI, R. 1992. Experimental evaluation of cutting dynamic models in soil bin facility. Journal of Terramechanics, vol. 29, no. 1. pp. 95–105. CHI, L. and KUSHWAHA, R. L. 1989. Finite element analysis of forces on a plane formation blade. Canadian Agricultural Engineering, vol. 31, no. 2. pp. 135–140. DASSAULT SYSTÈMES SIMULIA CORPORATION. 2010. Abaqus 6.9-2. Rhode Island, USA. DEMIREL, N. 2011. Effects of the rock mass parameters on the dragline excavation performance. Journal of Mining Science, vol. 47. pp. 442–450.
Figure 9 – Effects of formation properties on stress values
pauses in production. These capital-intensive earthmovers should be operated with high reliability and availability, as well as longevity, to sustain effective production scheduling. Deterioration of the mechanical elements of draglines can be reduced by minimizing the factors that have an adverse impact on the condition of the machinery. The bucket is the source of external loads on the dragline, which are transmitted along the chain and rope assemblies. The resistive force that is generated during the dragging action of the bucket in the formation can increase the stress intensity in various parts of the bucket. Detection of areas of stress concentration in the bucket is vital to identify possible overloading states in bucket-filling operation. This study integrates solid modelling, FEA, and the analytical resistance approach to build up a generic model for the investigation of stress distribution during the dragging movement of a dragline bucket. The simulation results indicated that the tips of the digging teeth and drag hitch elements are the most stressintensive points and therefore most prone to failure. The highest stress value obtained on the bucket was 3.85 MPa at the prevailing boundary conditions in the simulation. Although this stress value is not sufficient to cause failure of the entire bucket body, any overloading situation may induce fractures or fatigue. Sensitivity analysis revealed that internal friction of the medium has the greatest effect on stress distribution in the bucket, whereas the density of the medium has the least influence.
References ABO-ELNOR, M., HAMILTON, R., and BOYLE, J.T. 2003. 3D dynamic analysis of formation–tool interaction using the finite element method. Journal of Terramechanics, vol. 40. pp. 51–62. ALEKSEEVA, T.V., ARTEM'EV, K.A., BROMBERG, A.A., VOITSEKHOVSKII, R.I., and UL'YANOV, N.A. 1985. Machines for Earthmoving Work: Theory and Calculations. Balkema, Rotterdam. ALUKO, O.B. and CHANDLER, H.W. 2004. A fracture strength parameter for brittle agricultural formations. Biosystems Engineering, vol. 88, no. 3. pp. 369–381. ALUKO, O.B. 2008. Finite element aided brittle fracture force estimation during two-dimensional formation cutting. International Agrophysics, vol. 22. pp. 5–15.
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DAVOUDI, S., ALIMARDANI, R., KEYHANI, A., and ATARNEJAD, R. 2008. A two dimensional finite element analysis of a plane tillage tool in formation using a non-linear elasto-plastic model. American-Eurasian Journal of Agricultural and Environmental Science, vol. 3, no. 3. pp. 498–505. FIELKE, J.M. 1999. Finite element modelling of the interaction of the cutting edge of tillage implements with formation. Journal of Agricultural Engineering Research, vol. 74. pp. 91–101. FRIMPONG, S. and DEMIREL, N. 2009. Case study: planar kinematics of dragline for efficient machine control. Journal of Aerospace Engineering, vol. 22, no. 2. pp. 112–122. GILL, W.R. and VAN DEN BERG, G.E. 1968. Formation Dynamics in Tillage and Traction. Agricultural Research Service, Washington, USA. HEMAMI, A., GOULET, S., and AUBERTIN, M. 1994. Resistance of particulate media to excavation: application to bucket loading. International Journal of Surface Mining, Reclamation and Environment, vol. 8. pp. 125–129. HETTIARATCHI, D. and REECE, A. 1974. The calculation of passive formation resistance. Geotechnique, vol. 24, no. 3. pp. 289–310. MATBASE. 2010. Material Property Database. http://www.matbase.com/material/ferrous-metals/cast-steel/ [Accessed 20 June 2010]. MCKYES, E. 1985. Formation Cutting and Tillage. McGill Bioresource Engineering. http://www.mcgill.ca/files/bioeng/BREE512_part1.pdf MOUAZEN, A.M. and NEMENYI, M. 1999. Finite element analysis of subsoiler cutting in non-homogeneous sandy loam soil. Formation and Tillage Research, vol. 51. pp. 1–15. NEDOREDZOV, I. 1992. Forces prediction of underwater formation cutting by excavating robots. 9th International Symposium on Automation and Construction, Tokyo. OSMAN, M.S. 1964. The mechanics of formation cutting blades. Journal of Agricultural Engineering Research, vol. 9, no. 4. pp. 313–328. RAPER, R.L. and ERBACH, D.C. 1990). Prediction of formation stresses using the finite element method. Transactions of the ASAE, vol. 33, no. 3. pp. 725–730. DASSAULT SYSTÈMES SOLIDWORKS CORPORATION. 2009. Solidworks. © Concord, Massachusetts, USA. SWICK, W.C. and PERUMPRAL, J.V. 1988. A model for predicting formation-tool interaction. Journal of Terramechanics, vol. 25, no. 1. pp. 43–56. TOWNSON, P.G., MURTHY, D.N., and GURGENCI, H. 2003. Optimization of dragline load. Case Studies in Reliability and Maintenance. Blischke, E.W. and Murthy, D.N. (eds). Wiley. pp. 517–544. ZELENIN, AN., BALOVNEV, V.I., and KEROV, L.P. 1986. Machines for Moving the Earth. Balkema. Rotterdam. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
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Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine by A. Patri* and D. S. Nimaje*
Wireless sensor networks and wireless communication systems have become indispensable in underground mines. Wireless sensor networks are being used for better real-time data acquisition from ground monitoring devices, gas sensors, and mining equipment, whereas wireless communication systems are needed for locating and communicating with workers. Conventional methods like wireline communication have proved to be ineffective in the event of mine hazards such as roof falls, fires etc. Before implementation of any wireless system, the variable path loss indices for different workplaces should be determined. This helps in better signal reception and sensor node localization, and also improves the method by which miners carrying the wireless devices are tracked. This paper proposes a novel method for determining the parameters of a suitable radio propagation model, which is illustrated with the results of a practical experiment carried out in an underground coal mine in southern India. The path loss indices, along with other essential parameters for accurate localization, have been determined using the XBee modules and ZigBee protocol at 2.4 GHz frequency. Keywords WSN, RSSI, path loss index, miner localization, underground coal mining, ZigBee.
Introduction Advancements in the mining industry in the last three decades have primarily focused on improvements in heavy machinery, support systems, and safety equipment. Recently the focus has shifted towards development of communication systems for better safety and connectivity. In this context, the wireless sensor network (WSN) technology, owing to its efficiency, speed, and applicability in emergency conditions, has come out on top (Fiscor, 2011; Liu, 1996; Patri, Nayak, and Jayanthu, 2013). The current need is for a reliable wireless system in the harsh underground mine environment (Bandyopadhyay et al., 2009), in which radio propagation models are playing a vital role. Recent studies have considered the underground mine as a hybrid case of regular and harsh environments and shown that the signal propagation models and critical parameters of wireless channel propagation for an indoor environment are similar to an underground mine scenario at 900 MHz, indicating that the wireless nodes used in the The Journal of The Southern African Institute of Mining and Metallurgy
* Department of Mining Engineering, National Institute of Technology Rourkela, India. Š The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Feb. 2014 and revised paper received Mar. 2015. VOLUME 115
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indoor environment can be modified for use in mines (Qaraqea et al., 2013; Murphy et al., 2008). Zhang et al. (2001) experimented at 900 MHz with two different scenarios, namely the gateroad and working face of a longwall coal mine, in order to evaluate the additional losses due to gateroad curvature and the presence of mining equipment, and subsequently modified the wave guide propagation model. The hybrid tunnel propagation model developed by Zhang et al. uses both a free space propagation model and a modified waveguide propagation model to describe the propagation characteristics. Some simulation tools have also been developed for path loss calculation and propagation modelling by taking into account the effects of barriers. The simulations were carried out by varying the frequency with standard tunnel dimension, shape, and material properties. Comparison with an actual scenario proved that the path loss is mostly dependent on tunnel dimension, and signal frequency (Hrovat, Kandus, and Javornik, 2012). With advances in micro-electro mechanical systems (MEMS), transceivers working at 2.4GHz are now available at a reasonable price (Wurneke and Pister, 2002). The better performance of such transceivers in localization within a small range is due to highly directional antennae and a very high operational frequency, resulting in less noise. Liu et al. (2009) studied the transmission performance of WSN near a mine working face at 2.4 GHz frequency, incorporating all the electromagnetic properties in their theoretical model and comparing it with experimental results. The effective transmission distance was studied for IEEE 802.15.4, known as the ZigBee protocol (Liu et al., 2009; IEEE Std 802.15.4. 2011).
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine In this paper, the radio frequency (RF) propagation model has been developed and the path loss of wireless signal at 2.4 GHz was experimentally derived for the GDK 10A incline, a longwall underground mine of Singareni Collieries Company Limited (SCCL). Before implementing WSN, the path loss index and other parameters should be calculated to perform better localization, base-station placement and optimization, improve receiver design, and combat the fading of signal (Iskander and Yun, 2002). The reception distance was determined by utilizing the path loss of the signal, which determines the energy loss factor. The repeaters should be placed accordingly and their amplification factors should be set to different values to achieve a high-efficiency wireless communication system for different environments. The performance of ZigBee protocol using the XBee modules was experimentally studied for the mine.
Radio frequency propagation models A wireless propagation model can be defined as a mathematical expression or an algorithm for predicting the radio characteristics of a particular type of environment. There are two types of wireless propagation model: deterministic models and empirical models (Iskander and Yun, 2002; Rappaport, 2002). The deterministic model does not fit into the real environment properly; however, for lowfrequency waves, the results produced by the deterministic model are approximately equal to the actual result, with a very low rounding error. Since the operating range is much less, elements present in the surroundings have a significant effect on propagation in the high-frequency channel while variations due to environmental effects are largely insignificant in the low-frequency channel. The aforementioned propagation models are again subcategorized into three types, i.e. free space propagation models, two-ray ground models, and lognormal models. These models are deterministic with the exception of the lognormal model, which is empirical.
where, Ct is the constant representing transceiver characteristic in the two-ray ground model.
Log-distance model The log-distance model is an analytical and empirical model which can be mathematically represented as [3] where, Ρ represents the path loss factor or distance power gradient. The actual results vary from the results derived using the log-distance model. Hence, for hostile environments like underground mines, models have to be developed by using shadow-fading phenomena. At high frequencies, power loss is different for different locations owing to obstructions in the path between two communicating devices. Figure 1 shows a typical example of this phenomenon, where the dotted circle shows the ideal boundary of operation for an omnidirectional antenna placed at the centre, and the bold line shows the actual boundary of operation with a minimum and maximum range of R1 and R2 respectively due to presence of various obstructions. For this purpose, the empirical model is chosen over the deterministic model to predict or calculate power received at a particular distance from the transmitter (Pahlavan and Levesque, 2005). Moreover, the power loss can be subdivided into two parts on the basis of fluctuation around the average path loss, i.e. multi-path fading and shadow fading. In case of multi-path fading, the transmitted signal reaches the receiver through two or more paths, causing both constructive and destructive interferences near the receiver which in turn leads to phase shifting and addition of noise. Therefore in a dynamic environment, where both the transmitter and receiver are stationary, the received signal strength (RSS value) varies randomly due to the movement of objects and
Free space propagation model The free space propagation model is a simplified model that assumes line-of-sight communication between the transmitter-receiver pair and that there is no intervening obstruction. The mathematical representation of the model can be written as [1] where, Pr and Pt represent the power received and power transmitted respectively, CT is a constant that depends on the transceiver, and d is the distance between the transmitterreceiver pair.
Two-ray ground model This model is obtained by modifying the free space propagation model after taking into account the effect of reflection of signals. It is also assumed that both the direct and the reflected ray are used for communication. In this model the distance between the transmitter-receiver pair is much greater than their individual heights, and it can be represented as [2]
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Figure 1 â&#x20AC;&#x201C; Variation in operation range due to fading of signal radiated from the omnidirectional antenna The Journal of The Southern African Institute of Mining and Metallurgy
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine small changes in the environment. The long-term average of RSS values represents the effect of shadow fading of signal that is caused by the presence of a constant barrier between the transceivers (Pahlavan and Levesque, 2005). Although time of arrival (TOA), angle of arrival (AOA), and time difference of arrival (TDOA) provide higher accuracy in most cases, they fail in a harsh mining environment (Gentile et al., 2013; Sahoo and Hwang, 2011). Therefore, the received signal strength index (RSSI)-based model for localization has been developed. This low-cost RSSI-based localization provides less communication overhead with lower complexity of implementation. The distance or range of the signal can be calculated accordingly by the loss factor of the environment from the RSSI-based equations [4] and [7].
Shadow-fading model and proposed scheme for parameter determination The log-distance model can be represented more accurately by introducing a Gaussian distribution variable to represent the fading or fluctuation of received signal strength. The modified model is called the lognormal shadowing model and it is most appropriate for wireless sensor networks since it is all-inclusive in nature and can be easily configured according to the target environment (Nafarieh and Ilow, 2008). The mathematical equation for the above relation can be defined as
[8] Assuming maximum error with 95% confidence interval, the σψ1 value can be replaced by 1.96 σ, which gives
[9]
However, observational analysis shows that the standard deviation varies as a function of distance, and on the basis of considerable experimental evidence, we claim it to be a fourth-degree polynomial function: [10] Now the observational error ε can be defined as the difference of these two terms, i.e. experimental and observational σ. [11] In order to avoid negative error and for solving this expression, the objective function ∈ can be written as
[4]
[12]
[5]
To obtain the values of the coefficients of the polynomial, i.e. a, b, c, e, and f, a partial derivative method is adopted, and it can be mathematically represented as the following set of equations:
where,
and d0 is the near-earth reference distance. The random variable ψ is the zero-mean Gaussian random noise, the probability distribution function of which is given by
[13.1] [6]
The value of η depends on the surrounding or propagation environment as per Equation [4]. The distance d0 is taken to be one metre for simplicity of calculation, and it can also be represented in the terms of received power or RSSI as
[13.2]
[13.3]
[7]
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[13.4]
[13.5]
The above set of equations can be solved in matrix form, to obtain the coefficients VOLUME 115
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In Equation [4], there are two unknown terms, η and ψ, which should be determined experimentally. The linear regression analysis for the data-set with distance and received power as attributes gives the η value, which can be further used for that particular place with unknown distance and known received power to localize a wireless node. In Equation [4], Var(ψ) = σ2 and E(ψ) = 0. Therefore, it can be mathematically proven that Var(σψ1) = σ2 and E(σψ1) = 0. This relationship shows that the ψ function has the same distribution as ψ1, where ψ1 represents the zero-mean Gaussian distribution with unit variance. Equation [4] can be modified as
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine
[14]
If the coefficients and path loss index for a particular place are known, the standard deviation and the power loss due to fading can be calculated for the new set of data with known RSSI and unknown distance, for accurate localization.
Mining conditions at GDK 10A mine The GDK 10A incline of SCCL is situated at Ramagundam in Telengana, India, in the Godavari valley coalfield. Figure 2 shows a schematic layout of a longwall mine. The minimum and maximum depths of Seam 1, where the experiments were carried out, are 175 m and 310 m respectively, and the seam thickness is 6.5 m. The surface area is flat with undulating terrain having a gentle slope towards northeast and south. The coal seam is accessed via two tunnels, with lengths of 450 m and 500 m at a gradient of 1 in 4.5 and 1 in 5, for haulage and manway respectively. The mine floor is mainly grey sandstone and the roof is coal with a 0.30 m clay band. The length and width of the longwall face are around 150 m and 1 km respectively, with an average depth of 350 m from surface. Coal cutting is by means of an Anderson double-ended ranging drum shearer with a diameter of 1.83 m and a web width of 0.85 m. Caterpillar independent front suspension-based hydraulic powered roof supports are provided with 101 PMC-R controlled hydraulic chocks. Anderson bridge-type stage loaders are used in the gateroad to transport the coal from the armoured face conveyor (AFC) to the belt conveyor. The 260 m long DBT-manufactured AFC is used in the face, with a pan size of 232 × 844 × 1500 mm and deck plate thickness of 35 mm at an average chain speed of 1 m s-1.
The head and tail gateroads are driven in parallel through Seam 1. The gateroad wall surface is rough and water percolates from the strata and the gateroads. The gateroad bearing the belt conveyor system has an average height and width of 3.6 and 4.2 m respectively. The conveyor belt, supported by a steel structure, is at a height of 1.32 m to 1.4 m from the floor and carries an average lump size of 200 × 200 × 200 mm. The belt has a width of 0.8 m to 1.2 m, and is made mainly of rubber. The roof supports are generally wire mesh type with bolts and girders. The material properties, dimensions, and other features of the equipment described have a major influence on signal propagation, together with mine dimension, rock properties, slope, and other geo-mining conditions.
Experimental set-up and procedure Instruments and set-up A pair of XBee series-1 modules, one being used as a transmitter and the other as a receiver, which implement the ZigBee protocol, each capable of transmission or reception, were used for wireless communication at 2.4 GHz. The specifications of the XBee module are given in Table I. Each of the XBee modules is configured by setting the preferred data rate, modulation technique, lapse rate between packets, and other parameters using X-CTU software by mounting the modules on the XBee USB adapter (which has an onboard 3.3 V low-drop voltage regulator and light-emitting diode (LED) indicators for RSSI, associate, and power), and then connecting to a computer’s universal serial bus (USB) port through a FT232 USB-to-serial converter. There are two modes of operation for the XBee module; in transparent data mode (AT) the signal coming to the Data IN (DIN) pin is sent directly to the receivers, while in application programming interface mode (API) (which was used in this study), the data is sent in the form of packets that include the receiver address along with a feedback for the delivered packets, payload information, and various parameter settings to increase the reliability of the network and to send the signal safely over the wireless network (Hebel, Bricker, and Harris, 2010). The module has a mounted rubber-duck wire antenna or whip antenna, which radiates in a nearly omnidirectional pattern. As there is very little distortion in radiation pattern, the antenna is considered to radiate equal power in all azimuthal directions (Bandyopadhyay, Chaulia, and Mishra, 2010).
Table I
Specifications of XBee module
Figure 2 – Schematic layout of longwall mining method
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Parameter
Property
Raw data rate Maximum range Receiver sensitivity Channels Addressing Temperature Channel access
2.4 GHz: 250 kbps (ISM band) Indoor: 30 m; outdoor (line of sight): 100 m -92 dBm (1% packet error rate) 16 channels Short 8-bit or 64-bit IEEE -40 to +85°C CSMA-CA (Carrier Sense Multi Access-Collision Avoidance)
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Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine This module also supports universal asynchronous receiver/transmitter (UART) interface, which is beneficial for clock-setting and connecting it to a microcontroller. The ATMEL Atmega-32 microcontroller (14.7456 MHz crystal) development board was used, which has a compatible UART serial communication integrated circuit together with electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), and an in-system self-programmable flash memory of 1024, 2k, and 32k bytes respectively. It has an inbuilt reverse polarity protection and the 7805 voltage regulator has a heat sink for continuous dissipation to supply 1 A current constantly without overheating. The Request to Send (RTS) and Clear to Send (CTS) module pins can be used to provide flow control. CTS flow control provides an indication to the host to stop sending serial data to the module. RTS flow control allows the host to signal the module not to send data in the serialtransmit buffer through the UART. Data in the serial-transmit buffer will not be sent out through the Data OUT (DOUT) pin as long as RTS is de-asserted or set high. The UART connections for the transmitter and receiver module are shown in Figure 3. The module operates in a low-voltage range of 2.8–3.4 V, but for the whole set-up, a pair of 12 V 1.3 A.h DC batteries of lead-acid type was used, one for each node. This battery can be replaced by a cap-lamp battery used in underground mines in compliance with Directorate General of Mine Safety India (DGMS) standard. A liquid crystal display (LCD) is programmed and connected to the microcontroller unit at the receiver to display the desired output. The transmitter and receiver units are shown in Figure 4.
Figure 3 – UART connections for the transmitter and receiver module
Table II
Parameters required for an intrinsically safe instrument (source: Digi International, n.d.) XBee Series 1 IEEE 802.1.5.4 Properties
Values
Maximum power at antenna connector Maximum current at antenna connector
2 mW 7 mA (AC current at 2.4 GHz) 757 pF 60 nH 220 pF 56 nH
Sum total of all capacitance on PCB Sum total of all inductance on PCB Largest capacitor on PCB Largest inductor on PCB
For use in underground mines, the electronic instrument must be intrinsically safe to avoid any fire hazard. Since ZigBee protocol-based wireless modules have been used in underground mines worldwide, they can be considered as intrinsically safe for most of the underground mining scenarios in India (Bandyopadhyay, Chaulia, and Mishra, 2010; Chen, Shen, and Zhou, 2009). Parameters required for the XBee module to be intrinsically safe are specified in Table II. The ZigBee protocol is based on the carrier sense multiple access (CSMA) with collision avoidance (CA) channel access to provide energy saving, latency, and negligible error in the received data packet. Direct sequence spread spectrum (DSSS) modulation is used in the PHY layer, which has high resistance to noise or jamming. The ZigBee standard supports star, tree, and mesh networks, thus permitting numerous applications. In sleep mode it uses only 0.1 μA which helps in energy saving during idle periods. It supports AES-128 encryption that converts a 128-bit plain text to a 128-bit cipher text. It has a capacity to acquire more than 256 peer-to-peer connections in a master-slave configuration; which is very high compared to other wireless protocols used in day-to-day life. The experiment was divided into two parts, namely an RSSI test and a range test. The RSSI test provides the data for determining path loss index and various parameters affecting the localization and fading of power, and the range test gives the operation range of the module in different underground mine scenarios.
RSSI test The first set of readings was taken at the longwall face with shearer, hydraulic power supports, AFC, stage loader, and other machinery which obstructed the wireless signal. To avoid fast fading of the signal, the readings were taken in a static environment free from moving machinery or men between the transmitter-receiver pair. A second set of readings was taken beside the belt conveyor system, in running condition, installed in the gateroad, which would have created some fast fading.
Range test The range test was conducted in three different places – near the longwall face, the belt conveyor system, and in the inclined mine car pathway.
Experimental procedure
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Figure 4 – Transmitter and receiver unit
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine longwall face close to the hydraulic powered roof support at a height of 1.5 m from the floor. The transmitter and receiver set-ups were kept at a distance of 1 m and 2 m from the chocks and the working face respectively. The transmitter node was programmed to send 100 packets with a delay interval of 500 ms between two subsequent packets, and LCD showed the average RSSI over these 100 packets. Twenty RSSI readings were taken at each position of the receiver node and the same procedure was repeated up to a distance of 20 m with a 1 m step size. The packet received rate (PRR) was also calculated and displayed on the LCD at distance intervals of 1 m, and all the readings were taken in line-ofsight conditions. The second set of readings was taken on the gateroad near the belt conveyor system. The transmitter node was fixed at a location exactly 1 m above the floor, 0.5 m from the belt conveyor, and the receiver node was kept at varying distances (1–20 m) from the transmitter node along the passage. The range test for the XBee module was then carried out sequentially in all the three areas by fixing the transmitter node at a particular location and moving the receiver node away until the LCD showed a ‘zero’ value for the RSSI and indicated that the packet sent by the transmitter could not be received beyond that particular distance.
Table III
Data collected near the longwall face of GDK 10A Distance (m)
M (dBm)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-51.65 -57.65 -71.5 -69.8 -73.95 -76.1 -76.85 -78.45 -80.25 -76.55 -76.8 -81.15 -80.95 -81.85 -79.35 -80.95 -82.6 -81.6 -84.15 -86.85
SD (dBm) 0.48936 2.00722 4.54799 3.67924 5.78996 4.93004 5.83343 6.88665 6.04261 6.60522 5.94491 4.56828 3.64872 4.22119 3.54334 4.20443 4.87097 3.93901 4.51051 4.88041
PRR (%) 100 100 96.59 96.76 96.29 95.83 95.7 95.07 95.08 95.45 95.65 93.92 93.89 93.9 94.2 93.77 92.71 93.85 90.05 86.2
Table IV
Results and analysis
Data collected near the belt conveyor gateroad
The data collected near the working face and the belt conveyor gateroad is represented in Tables III and IV respectively. The standard deviation was calculated for each set of RSSI values on every location. The standard deviation (SD) can be calculated as
Distance (m)
[15]
where, SDi is same as Yi in Equation [9] for a particular distance di, Xj represents the different RSSI values recorded at each distance di, M is the mean RSSI, and n is the total number of observations (i.e. 20). The integer variables i and j both vary from 1 to 20. MATLAB version 7.6.0.324 r2008a was used for the linear regression analysis model. The slope of the fitted gradient line denotes the path loss index for the place of the experiment, for longwall working face the value was found to be 2.14. Figure 5 (A) depicts the scatter plot of the received signal for the longwall face corresponding to the logarithmic distance. The higher value of path loss index indicates that fading of the signal was due to the presence of more obstructions than in the normal outdoor scenario. Moreover, it also implies that more repeaters should be placed and the internode distance should be kept small compared to typical outdoor scenario (for which the index is 2). More fading and gradual degradation of power transmitted was due to the presence of metallic bodies; homogenous obstructions present in the surroundings and the static nature of the environment resulted in less standard deviation (more concentrated in the region of 3.5 to 6) from the mean RSSI values. The values of PRR show a dependency on both standard deviation and received power, with a higher correlation with the former. The signal is marginally affected by the waveguide property of the tunnel for the first 3–4 m, after which the effect
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
M (dBm) -54.2857 -60.0952 -68.5714 -67.0476 -67 -73 -73.6667 -70.6191 -73.1905 -68.2381 -66.1905 -69.5714 -69 -75 -75.3333 -79.8095 -75.5714 -76.5714 -74.5455 -83
SD (dBm) 3.48056 1.92106 7.59402 7.89087 7.75887 4.12311 6.5904 5.45414 6.14261 5.76052 4.44491 3.35517 3.6606 5.12119 4.23478 4.7394 3.99464 5.59081 5.41363 5.54076
PRR (%) 99.37 99.3 95.73 95.22 96.19 96.04 95.98 96.53 95.9 96.3 97.24 96.83 96.89 95.5 95.81 94 95.14 94.63 94.99 92.8
increases gradually. A trade-off is observed between distance covered and the wave guide effect, leading to a fluctuation of RSSI over a small range. As discussed previously, the curve fitting was done to find a relationship between the standard deviation and distance to determine the coefficients for the longwall mining area as shown in Figure 5 (B). The coefficients a, b, c, e, and f of the fourth-degree polynomial are found to be 2.626 × 10−6, 6.176 × 10−3, -0.2276, 2.403, and -1.721 respectively. R2 and root mean square error (RMSE) were 0.8332 and 0.6958 respectively. For the belt conveyor gateroad, the path loss index was found to be 1.568, using linear regression analysis. Figure 6 The Journal of The Southern African Institute of Mining and Metallurgy
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine
(A)
(B) Figure 5 – (A) Variation of RSSI with respect to distance near the longwall face, (B) relationship between standard deviation and distance from the longwall face
(A)
(B)
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Figure 6 – (A) Variation of RSSI with respect to distance in the belt conveyor gateroad, (B) relationship between standard deviation and distance for the belt conveyor gateroad
Radio frequency propagation model and fading of wireless signal at 2.4 GHz in an underground coal mine (A) depicts the scatter plot of RSSI vs. the logarithmic distance. The lower value of power loss compared to the longwall face was due to the predominant effect of the waveguide property of the tunnel. The standard deviations (more concentrated in the region of 4–7.5 m) from the mean RSSI values were high compared to the longwall face area due to inhomogeneous surroundings like different support systems, material, and spacing, machinery, variable coal lump size carried by the belt, and other distributive obstructions. Due to movement of the belt conveyor carrying coal lumps of various sizes, some fast fading was observed, as indicated by the dispersal of data from the fitted line. The signal loss for a particular place was found to be greater than its consecutive place readings, each taken at 1 m distance, due to presence of girders over the receiver. The presence of fewer metallic bodies in the gateroad compared to the longwall face reduced the fading effects. The signal propagation was mildly affected by the steel structure because the nodes were located higher than the belt conveyor support structure. Figure 6 (B) depicts the curve fitting for the fourth-degree polynomial. The coefficients for determining the standard deviation as a function of distance were found to be -6.685 × 10-4, 0.3418 × 10-1, -0.5813, 3.599 and -0.4563 for a, b, c, e, and f respectively. The R2 value of 0.474 and RMSE value of 1.281 indicate the fluctuation of standard deviation due to fast fading. From the range test, it was found that the XBee module provides satisfactory results up to a range of 40–45 m, 60–65 m, and 75–85 m for the longwall face, belt conveyor gateroad, and mine car pathway respectively.
References BANDYOPADHYAY, L.K., CHAULIA, S.K., and MISHRA, P.K. 2010. Wireless Communication in Underground Mines. Springer Science. BANDYOPADHYAY, L.K., CHAULIA, S.K., MISHRA, P.K., CHOURE, A., and BAVEJA, B.M. 2009. A wireless information and safety system for mines. Journal of Industrial and Scientific Research, vol. 68, no. 2. pp. 107–117. CHEN, G., SHEN, C., and ZHOU, L. 2009. Design and performance analysis of wireless sensor network location node system for underground mine. Journal of Mining Science and Technology, vol. 19, no. 6. pp. 813–818. DIGI INTERNATIONAL. Not dated. Intrinsically Safety Certifications. http://www.digi.com/support/kbase/kbaseresultdetl?id=2128 [Accessed January 2014]. FISCOR, S. 2011. Miners quickly adopt new communication systems. Coal Age, January 2011. pp. 2–6. Gentile, C., Alsindi, N., Raulefs, R., and Teolis, C. 2013. Geolocation Techniques: Principles and Applications. Springer Science. HEBEL, M., BRICKER, G., and HARRIS, D. 2010. Getting Started with XBee RF Modules Version 1.0. (Parallax Inc.). HROVAT, A., KANDUS, G., and JAVORNIK, T. 2012. Path loss analyses in tunnels and underground corridors. International Journal of Communication, vol. 6, no. 3. pp. 136–144. IEEE Std 802.15.4. 2011. IEEE Standard for Local and Metropolitan Area Networks. Part 15.4: Low-Rate Wireless Personal Area Networks (LRWPANs). ISKANDER, M.F. and YUN, Z. 2002. Propagation prediction models for wireless communication systems. IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 3. pp. 662–673. LIU, S.Y. 1996. Advances in longwall coal mining techniques. Seminar on High Productivity and High Efficiency Coal Mining Technology, Beijing.
Conclusion This study reveals that the efficiency of an underground mine communication system is dependent on the environment. Before implementing any wireless system in underground mines, the path loss index and the variance of Gaussian distribution representing the shadow fading effect should be determined. This helps in determining the distance at which repeaters should be placed in order to enhance the signal and localize the sensor node from its received signal strength. With an increasing number of physical obstructions, the path loss index increases, resulting in the total loss of signal beyond a particular range. The XBee module facilitates satisfactory wireless communication over an adequate range of operation with a negligible packet error rate. The PRR depends upon transmitter distance and dynamic behaviour of the surroundings. These intrinsically safe modules are economic, energy-efficient, and enhance the mine safety system by facilitating tracking of miners and real-time data acquisition from sensors. The experiment was carried out in a hazard-prone underground coal mine. The experimental results may vary for underground mines other than coal mines, due to the variation in the rock mass properties and dimensions of tunnels, passages, galleries, and working areas, depending on the mining method. In our current work, two nodes were used for experimentation. To ensure the viability of the ZigBee protocol, further studies could be carried out to analyse the network performance using more than two nodes.
Acknowledgement We wish to express our sincere gratitude to the authorities of
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SCCL for permission and assistance in carrying out the experiment and collect valuable data at GDK 10A. We thank the anonymous reviewers for their valuable comments.
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LIU, X., WANG, M., WEN, J., and ZHAO, Z. 2009. Transmission performance of 2.4 GHz wireless sensor nodes when used in a working-face environment. Journal of Mining Science and Technology, vol. 19, no. 2. pp. 185–188. MURPHY, R.R., SHOURESHI, R., ARNOLD, H.W., ARSLAN, H., BURKE. J., GREENSTEIN, L.J., KILLINGER, D.K., LUNDGREN, C.W., RUSTAKO, A.J., and STOVER, S. 2008. Analysis of Viability and Feasibility of Current and Emerging Mining Communication and Mine Rescue Technologies. Final Report, Institute for Safety, Security, Rescue Technology, University of South Florida, Tampa, FL, USA. NAFARIEH, A. and ILOW, J. 2008. A testbed for localizing wireless LAN devices using received signal strength. Proceedings of the 6th Annual Communication Networks and Services Research Conference, Halifax, Canada, May 2008. pp. 681–687. PAHLAVAN, K. and LEVESQUE, A.H. 2005. Wireless Information Network. 2nd edn. Wiley-Interscience. PATRI, A., Nayak, A., and Jayanthu, S.J. 2013. Wireless communication systems for underground mines – a critical appraisal. International Journal of Engineering Trends and Technology, vol. 4, no. 7. pp. 3149–3153. QARAQEA, K.A., YARKANB, S., GÜZELGÖZC, S., and ARSLAN, H. 2013. Statistical wireless channel propagation characteristics in underground mines at 900 MHz: a comparative analysis with indoor channels. Journal of Ad Hoc Networks, vol. 11, no. 4. pp. 1472–1483. RAPPAPORT, T.S. 2002. Wireless Communications: Principles and Practice. 2nd edn. Prentice Hall. SAHOO, P.K. and HWANG, I. 2011. Collaborative localization algorithms for wireless sensor networks with reduced localization error. Sensors, vol. 11, no. 10. pp. 9989–10009. WURNEKE, B.A. and PISTER, K.S.J. 2002. MEMS for distributed wireless sensor networks. Proceedings of the 9th International Conference on Electronics, Circuits and System, Dubrovnik, Croatia, September 2002. pp. 291–294. ZHANG, Y.P., ZHENG, G.X., and SHENG, J.H. 2001. Radio propagation at 900 MHz in underground coal mines. IEEE Transactions on Antennas and Propagation, vol. 49, no. 5. pp. 757–762. ◆ The Journal of The Southern African Institute of Mining and Metallurgy
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Peak particle velocity prediction using support vector machines: a surface blasting case study by S.R. Dindarloo*
Although blasting is one of the most widely used methods for rock fragmentation, it has a major disadvantage in that it causes adjacent ground vibrations. Excessive ground vibrations can cause a wide range of problems, from nearby residents complaining to ecological damage. Prediction of blast-induced ground vibration is essential for evaluating and controlling the many adverse consequences of surface blasting. Since there are several effective variables with highly nonlinear interactions, no comprehensive model of blast-induced vibrations is available. In this study, the support vector machine (SMV) algorithm was employed for prediction of the peak particle velocity (PPV) induced by blasting at a surface mine. Twelve input variables in three categories of rock mass, blast pattern, and explosives were used for prediction of the PPV at different distances from the blast face. The results of 100 experiments were used for model-building, and 20 for testing. A high coefficient of determination with low mean absolute percentage error (MAPE) was achieved, which demonstrates the suitability of the algorithm in this case. The very high accuracy of prediction and fast computation are the two major advantages of the method. Although the case study was for a large surface mining operation, the methodology is applicable to all other surface blasting projects that involve a similar procedure. Keywords blast-induced ground vibration, peak particle velocity, support vector machine, surface mining.
Introduction Blasting is one of the most economical and energy-efficient methods of rock fragmentation, and is widely used in mining, civil, construction, and environmental projects around the world. However, there are several drawbacks, including (but not limited to) complaints from nearby residents (Kahriman, 2001), damage to residential structures (Singh et al., 1997; Gad et al., 2005; Nateghi et al., 2009), damage to adjacent rock masses and slopes (Villaescusa et al., 2004; Yi and Lu, 2006; Singh et al., 2005), damage to existing groundwater conduits, and damage to the ecology of the nearby area (Khandelwal and Singh, 2007). The main cause of these undesirable effects is excessive blast-induced ground vibrations. Thus, predicting the adjacent ground vibrations is essential for safe, environmentally responsible, and sustainable blasting operations. Ground vibrations can be defined and measured in terms of peak particle displacement, velocity, The Journal of The Southern African Institute of Mining and Metallurgy
* Department of Mining and Nuclear Engineering, Missouri University of Science and Technology, Rolla, MO, USA. Š The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Dec. 2014 and revised paper received March 2015. VOLUME 115
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acceleration, and frequency. The peak particle velocity (PPV) has been used by many researchers as a versatile metric for both predicting and controlling the blast-induced ground vibrations. There are three major methods cited in the literature for PPV prediction, including empirical, theoretical, and artificial intelligence techniques. Conventionally, there are some widely used empirical predictors for estimation of the blast-induced ground vibrations. The US Bureau of Mines proposed the first ground vibration predictor (Duvall et al., 1959). Subsequently, other empirical predictors were proposed (Langefors and Kihlstrom, 1963; Ambraseys and Hendron, 1968; Ghosh and Daemen; 1983; Pal Roy, 1993). These methods consider two main input parameters â&#x20AC;&#x201C; maximum charge used per delay and distance between the blast face and the monitoring points. Despite the simplicity and fast application of these methods, several recent studies have shown their shortcomings in rendering acceptable predictions (Khandelwal and Singh, 2007). More recently, Chen and Huang (2001) conducted a seismic survey to predict blast-induced vibrations and PPV empirically. Ozer et al. (2008) examined the results of some 500 blasts in a limestone quarry in Turkey for an experimental analysis of PPV. Ak et al. (2009) performed a series of ground vibration tests in a surface mine in Turkey in order to measure PPV. Aldas (2010) proposed an empirical relationship between the explosive charge mass and PPV. Deb and Jha (2010) examined the effects of surface blasting on adjacent underground workings, using PPV measurements. Mesec et al. (2010) proposed an empirical relationship between PPV and distance for a series of vibration tests in some sedimentary rock deposits, comprising
Peak particle velocity prediction using support vector machines mainly limestone and dolomite. Nateghi (2011) examined the effects of different rock formations, different detonators, and explosives on ground vibrations induced by blasting at a dam site. Generally, empirical methods have two major limitations: lack of generalizability and limited number of input variables. Some researchers have proposed theoretical models based on the physics of blasting. For instance, Sambuelli (2009) proposed a theoretical model for prediction of PPV on the basis of some blast design and rock parameters. However, because of the complicated nature of the blasting process and its highly nonlinear interaction with the non-homogeneous and non-isotropic ground, a closed form mathematical model is almost impossible. Recently, following the rapid growth in soft computing methods, including artificial intelligence, several researchers have tried to benefit from these newly emerging techniques. In this category, artificial neural networks (ANNs) might be the most widely used method for prediction of the ground vibrations. ANNs are among the techniques that map input variables into the output(s). The technique is capable of handling extremely nonlinear interactions between different variables through assigning and adjusting proper weights. However, no functional relationship is proposed (‘black-box’ modelling). Khandelwal and Singh (2006) used ANNs for prediction of PPV in a large mine in India. Iphar et al. (2008) employed an adaptive neural-fuzzy inference system (ANFIS) for prediction of PPV in a mine in Turkey. Dehghani and Ataee-pour (2011) employed ANNs for prediction of PPV in a large open pit copper mine. Monjezi et al. (2011) used ANNS to predict blast-induced ground vibrations in an underground project. Bakhshandeh et al. (2012) used ANNs to adjust burden, spacing, and total weight of explosive used in order to minimize PPV. The support vector machine (SVM) is a relatively new computational learning method for solving classification and nonlinear function estimation, which is based on statistical learning theory. The SVM has been adopted rapidly by many researchers in different fields of geology, geotechnical, and environmental engineering (Brenning 2005; Yu et al., 2006; Samui 2008; Mountrakis et al., 2011; Dindarloo, 2014). Experimental results have revealed the superior performance of SVMs with respect to other techniques. The reasons behind the successful performance of SVMs, compared to other powerful approaches like ANNs, are twofold. Firstly, rather than being based on empirical risk minimization (ERM) as ANNs, which only minimizes the training errors, a SVM makes use of structural risk minimization (SRM), which seeks to minimize an upper bound on the generalization error. Secondly, finding a SVM solution corresponds to dealing with a convex quadratic optimization problem. Thus, the Karush-Kuhn-Tucker (KKT) statements determine the necessary and sufficient conditions for a global optimum (Scholkoff and Smola 2002). For ANNs, however, it is not guaranteed that even a well-selected optimization algorithm will achieve the global minimum in finite computation time (Moura et al., 2011). In this study, the SVM was used for analysis of the blastinduced ground vibration by prediction of PPV. A large iron ore mine in Iran was selected as a case study. After obtaining different input variables, a SVM model was constructed and tested.
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Methods Developed by Boser, Guyon, and Vapnik (Boser, Guyon, and Vapnik, 1992; Vapnik, 1995, 1998), support vector machine (SVM) is a relatively new computational learning method for solving classification and nonlinear function estimation, which is based on statistical learning theory. SVM is based on Vapnik-Chervonenkis theory (VC theory), which recently emerged as a general mathematical framework for estimating (learning) dependencies from finite samples. This theory combines fundamental concepts and principles related to learning, well-defined formulation, and self-consistent mathematical theory. Moreover, the conceptual framework of VC theory can be used for improved understanding of various learning methods developed in statistics, neural networks, fuzzy systems, signal processing, etc. (Widodo and Yang, 2007). LIBSVM is a library of SVM algorithms (Chang and Lin, 2011) that was used along with Rapidminer, a data mining (DM) software package (Hofmann and Klinkenberg, 2013). The theory of SVM regression, used in LIBSVM, is presented in the following section.
Support vector regression Consider a set of training points, {(x1, z1), . . . , (xL, zL)}, where xi ∈ Rn is a feature vector and zi ∈ RL is the target output. Under given parameters C > 0 and ∈ > 0, the standard form of the support vector regression (SVR) (Equation [1]) with constraints (Equations [2]-[4]) are as follows (Chang and Lin, 2011): [1]
subject to [2] [3] [4] The dual problem (Equation [5]) is
[5]
subject to constraints (Equations [6]-[7]) [6]
[7] where [8] After solving Equation [5], the approximate function is: The Journal of The Southern African Institute of Mining and Metallurgy
Peak particle velocity prediction using support vector machines Table I
[9] The nomenclature is presented in Table I. For more detailed information about the theory and applications of SVR, see Burges (1998), Müller et al. (2001), Hsu and Lin (2002), Chapelle et al. (2002), and Smola and Scholkoff (2004).
Case study Golegohar iron ore mine is located in southern Iran, 50 km from Sirjan, in the southwest of Kerman Province. This iron ore complex includes six known orebodies and is one of the largest producers and exporters of iron concentrate in the country. Mining is by open pit methods, and the measured and indicated reserves of over 1.1 billion tons of ore. The Golegohar deposits are situated in a metamorphic complex of probable Paleozoic age with a northwest-southeast trend, known as the Sanandaj-Sirjan zone, which is parallel to the Zagros thrust belt on the southwest and is bounded on the northeast by the Urmieh-Dukhtar volcanic belt (Moxham and McKee, 1990). The deposits are considered to be of sedimentary or volcano-sedimentary origin, laid down in deltaic or near-shore environments that resulted in abrupt lateral and vertical changes in the sedimentary facies. Subsequent deep burial, folding, metamorphism, and erosion left a group of folded or down-faulted magnetite-rich deposits as elongated remnants of an iron formation that originally had a broader, perhaps more continuous extent. The mine’s metamorphic rocks consist mostly of gneiss, mica schist, amphibolite, quartz schist, marble, dolomite, and calcite (Karimi Nasab et al., 2011). Figure 1 illustrates one of the operating pits. The geometry and slope stability factors of the mine are summarized in Table II.
SVR notations b C
Intercept A parameter representing the compromise between machine capacity and training error Weight vector Mapping function Function parameter Regression function Slack variables Kernel function Number of observations
w ϕ α Q β,β* K l
Table II
Geometric parameters of pit No.1, Golegohar. Final wall slopes in ore and waste Slopes in overburden Safety bench height Safety bench width Safety bench slope Working bench height
45 degrees 38 degrees 30 m 10 m 65 degrees 15 m
Parameter selection Rock mass, blast pattern and explosives, and distance from the face are the three major parameters in blast-induced ground vibrations, and hence the measured PPV. The dominant rock types at Golegohar include amphibolite schist, quartz schist, chlorite schist, haematite, and magnetite. Density (t/m3), Young’s modulus (Gpa), uniaxial compression strength (Mpa), and tensile strength (Mpa) of representative samples of all the rock types were measured in the rock and soil mechanics laboratory at the mine site (Table IIIa). The major discontinuities have a significant influence on blast wave propagation in the rock mass. The
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Figure 1 – Open pit mining at Golegohar (CNES/Astrium image on Google Earth, 29°05’15.21” N and 55° 19’ 03.24” E. Retrieved 3 April 2015)
Peak particle velocity prediction using support vector machines Table III
Mechanica l and physical properties. (a) Intact rock, (b) discontinuities Petrology: (a) Intact rocks Item Density
(t/m3)
Young’s modulus (Gpa) Uniaxial compressive strength (Mpa) Tensile strength (Mpa)
Amphibolite schist
Quartz schist
Chlorite schist
Haematite
Magnetite
Mean Range Mean Range Mean Range Mean Range
2.81 2.76-3.02 34.8 19.6-47.1 42.8 18.6-77.3 15.4 12.1-17.8
2.69 2.63-2.84 52.7 18.6-77.3 112.5 35.2-176.2 7.54 6.99-9.42
2.84 2.76-2.95 37.6 15.7-40.3 105.9 33.7-155.1 13.47 8.24-18.42
4.02 3.65-4.35 29.7 14.9-41.2 66.8 30.8-114.8 6.95 4.63-10.52
4.41 4.15-4.62 42.6 33-55.9 121.4 35.2-176.2 9.24 5.5-14.62
Major joints
(b) Discontinuities Spacing (m) Dip (degree)
Set 1
1.1
45
Set 2
0.8
75
spacing, dip, and direction of the two major joint sets are presented in Table IIIb (Dindarloo et al., 2015). The second group of important parameters is related to the drilling pattern and explosives used. A typical production, buffer, and pre-split pattern are illustrated in Figure 2. The main explosive is ANFO, and a blast delay of 15–75 ms between rows is used. The descriptive statistics of the pattern geometry, including burden, spacing, hole depth to burden ratio, specific charge, and stemming are presented in Table IV. Thus, the 12 input variables include: density, Young’s modulus, uniaxial compression strength, tensile strength, joint spacing, burden, spacing, hole depth to burden
Direction Northeastsouthwest North-south
ratio, specific charge, stemming, delay per row, and distance between the measurement point and the blasting face. Since the main charge for all holes was ANFO, the parameter for type of explosive was omitted, as it was the same for all tests.
Results and discussions One hundred and twenty experiments were conducted at different distances, 15 m to 7500 m, from the blasting faces. The PPV was measured using the procedures described by Dowding (1992). One hundred data-sets, including the 12 input variables and one output (PPV), were used in the SVR model. The results of 20 randomly selected experiments were
Figure 2 – Blast pattern (red: pre-splitting hole, yellow: ANFO, brown: stemming/crushed rock, white: no stemming/charging). Distances are in metres, and angles in degrees
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Peak particle velocity prediction using support vector machines Table IV
Descriptive statistics of the collected data. No 1 2 3 4 5
Parameter
Symbol
Unit
Min.
Max.
Mean
DS
Burden Spacing Hole depth – burden ratio Stemming Powder factor
B S H/B
metre metre
3.83 4.37 2.04
5.88 7.11 4.44
4.81 6.14 3.40
0.68 0.91 0.62
ST PF
metre kg/t
3.86 0.21
7.95 0.47
5.19 0.32
0.79 0.07
used for model testing. Figure 3 depicts a scattergram of the predicted SVR versus the measured PPVs for the 20 testing data-sets. The coefficient of determination (Equation [10]), root mean squared error (RMSE, Equation [11]), and mean absolute percentage error (MAPE, Equation [12]) were used as the statistical metrics for evaluation of the SVR model (Table V). The obtained R2 value of 0.99 shows a very good correlation between the predicted and measured PPVs. The obtained MAPE value of less than 10% demonstrates the high accuracy and applicability of the method in PPV estimation, using the 12 input variables.
[10]
Table V
Statistics of SVM in PPV prediction R2 0.99
MAPE (%)
RMSE (mm/s)
8.5
3.45
The optimized SVM parameters were kept the same for twelve sensitivity analysis runs. In each run, one of the input variables was omitted and its effect on prediction accuracy was examined. The results showed that omission of distance, specific charge, delay per row, and joints spacing had the highest negative effects on SVM predictions. Hence the method is more sensitive to these variables. The results of sensitivity analysis for other variables are shown in Figure 4.
Comparison with traditional methods [11]
[12] where ymeas and ypred are the observed and predicted values, respectively ymeas and ypred and are mean observed and predicted values, respectively.
The partial least-square regression (PLSR) method is mainly used for modelling linear regression between multiple dependent variables and multiple independent variables. An advantage of this method over linear and nonlinear multiple regressions is that PLSR combines the basic functions of regressing models, principal component analysis, and canonical correlation analysis (Zhang et al., 2009). In addition, PLSR avoids the harmful effect of multi-collinearity and regressing when the number of observations is less than the number of variables. In the context of linear MR, the least-squares solution for Equation [13] is given by Equation [14].
Sensitivity analysis In order to analyse the effect of each individual variable on the SVM prediction accuracy, a sensitivity analysis was performed.
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Figure 3 – SVM predicted vs. measured PPV (mm/s)
Peak particle velocity prediction using support vector machines Y = XB + ε T
[13]
–1 T
B = (X X) X Y
[14] T
Often, the problem is that X X is singular, either because the number of variables (columns) in X exceeds the number of objects (rows), or because of collinearities. PLSR circumvents this by decomposing X into orthogonal scores (T) and loadings (P) (Mevik and Wehrens, 2007): X = TP
[15]
Furthemore, PLSR regresses Y, not on X, but on the first α columns of the scores. The goal of PLSR is to incorporate information on both X and Y in the definition of the scores and loadings. The scores and loadings are chosen in such a way to describe as much as possible of the covariance between X and Y. The result of the prediction of PPV by the PSLR technique is illustrated in Figure 5. Statistics of the predictions, for the same testing data-set as SVM, are summarized in Table VI. The R2 value in PLSR decreased to 94% (i.e., the PLSR can model 94% of the variability in PPV based on the 12 independent variables). Furthermore, both the obtained RMSE and MAPE values in PLSR (see Table VI) were poorer than the SVM (see Table V).
Conclusions Blast-induced ground vibration control is a major challenge in construction projects that employ blasting. Peak particle velocity (PPV) is a widely used metric for evaluation of the magnitude and severity of the possible inconvenience to people and damage to adjacent structures and the environment. This study demonstrates that the support vector machine (SVM) approach is a versatile tool for prediction of PPV based on the 12 input variables used. The very high accuracy of prediction and fast computation are the two major advantages of the method. Results of the sensitivity analysis demonstrated the considerable effect of distance, specific charge, delay per row, and joint spacing on PPV. Thus, in specific instances where the level of PPV is higher than a prespecified threshold, appropriate remedies can be applied. Modification of the specific charge and the amount of delay per row are expected to have direct effects on PPV reduction. Although the SVM was used in a large surface mining case study, it is applicable to all other surface blasting projects with a similar procedure.
Table VI
Statistics of PLSR in PPV prediction R2 0.94
MAPE (%)
RMSE (mm/s)
16.7
8.43
Acknowledgements We would like to thank two anonymous reviewers for their critical reviews and constructive comments. Golegohar mine management and staff are acknowledged for their support.
References ALDAS, G.G.U. 2010. Explosive charge mass and peak particle velocity (PPV)frequency relation in mining blast. Journal of Geophysics and Engineering, vol. 7, no. 3. pp. 223–231. AK, H., IPHAR, M., YAVUZ, M., and KONUK, A. 2009. Evaluation of ground vibration effect of blasting operations in a magnesite mine. Soil Dynamics and Earthquake Engineering, vol. 29, no. 4. pp. 669–676. AMBRASEYS, N.R. and HENDRON, A.J. 1968. Dynamic Behaviour of Rock Masses: Rock Mechanics in Engineering Practices. Wiley, London. BAKHSHANDEH AMNIEH, H., SIAMAKI, A., and SOLTANI, S. 2012. Design of blasting pattern in proportion to the peak particle velocity (PPV): artificial neural networks approach. Safety Science, vol. 50, no. 9. pp. 1913–1916. BOSER, B., GUYON, I., and VAPNIK, V. 1992. A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM, Pittsburgh. pp. 144–152. BRENNING, A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Science, vol. 5, no. 6. pp. 853–862. BURGES, C.J.C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, vol. 2, no. 2. pp. 121–167. CHANG, C.-C. and LIN, C.-J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3. Article no. 27. CHAPELLE, O., VAPNIK, V., BOUSQUET, O., and MUKHERJEE, S. 2002. Choosing multiple parameters for support vector machines. Machine Learning, vol. 46, no. 1-3. pp. 131–159. CHEN, G. and HUANG, S.L. 2001. Analysis of ground vibrations caused by open pit production blasts - a case study. FRAGBLAST, vol. 5, no. 1–2. pp. 91–107. DINDARLOO, S.R. 2014. Support vector machine regression analysis of LHD failures. International Journal of Mining, Reclamation and Environment. [doi: 10.1080/17480930.2014.973637]. DINDARLOO, S.R., ASKARNEJAD, N-A., and ATAEI, M. 2015. Design of controlled blasting (pre-splitting) in Golegohar iron ore mine, Iran. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, vol. 124, no. 1. pp. 64–68. DEB, D. and JHA, A.K. 2010. Estimation of blast induced peak particle velocity at underground mine structures originating from neighbouring surface mine. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, vol. 119, no. 1. pp. 14–21. DEHGHANI, H. and ATAEE-POUR, M. 2011. Development of a model to predict peak particle velocity in a blasting operation. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, vol. 48, no. 1. pp. 51–58.
Figure 5 – PLSR predicted vs. measured PPV (mm/s)
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DOWDING, C.H. 1992. Suggested method for blast vibration monitoring. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, vol. 29, no. 2. pp. 145–156. The Journal of The Southern African Institute of Mining and Metallurgy
Peak particle velocity prediction using support vector machines DUVALL, W.I. and PETKOF, B. 1959. Spherical propagation of explosion generated strain pulses in rock. Report of Investigation 5483. US Bureau of Mines. 21 pp. GAD, E.F., WILSON, J.L., MOORE, A.J., and RICHARDS, A.B. 2005. Effects of mine
NATEGHI, R., KIANY, M., and GHOLIPOURI, O. 2009. Control negative effects of blasting waves on concrete of the structures by analyzing of parameters of ground vibration. Tunnelling and Underground Space Technology, vol. 24, no. 6. pp. 608–616.
blasting on residential structures. Journal of Performance of Constructed Facilities, vol. 19, no. 3. pp. 222–228. GHOSH, A. and DAEMEN, J.K. 1983. A simple new blast vibration predictor. Proceedings of the 24th US Symposium on Rock Mechanics, College
OZER, U., KAHRIMAN, A., AKSOY, M., ADIGUZEL, D., and KARADOGAN, A. 2008. The analysis of ground vibrations induced by bench blasting at Akyol quarry and practical blasting charts. Environmental Geology, vol. 54, no. 4. pp. 737–743.
Station, Texas, USA, 20–23 June 1983. pp. 151–161. HOFMANN, M. and KLINKENBERG, R. 2013. RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman & Hall/CRC Data Mining
PAL ROY, P., 1993. Putting ground vibration predictors into practice. Colliery Guardian, vol. 241. pp. 63–67.
and Knowledge Discovery Series. CRC Press, Boca Raton, FL. HSU, C-W. and LIN, C-J. 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, vol. 13, no. 2. pp. 415–425. IPHAR, M., YAVUZ, M., and AK, H. 2008. Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environmental Geology, vol. 56, no. 1. pp.
SAMBUELLI, L. 2009. Theoretical derivation of a peak particle velocity-distance law for the prediction of vibrations from blasting. Rock Mechanics and Rock Engineering, vol. 42, no. 3. pp. 547–556.
SAMUI, P. 2008. Slope stability analysis: a support vector machine approach. Environmental Geology, vol. 56, no 2. pp. 255–267.
97–107. KAHRIMAN, A. 2001. Prediction of particle velocity caused by blasting for an infrastructure excavation covering granite bedrock. Mineral Resources
SCHOLKOFF, B. and SMOLA, A.J. 2002. Learning with kernels—support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge MA.
Engineering, vol. 10, no. 2. pp. 205–218. KARIMI NASAB, S., HOJAT, A., KAMKAR-ROUHANI, A., AKBARI JAVAR, H., and MAKNOONI, S. 2011. Successful use of geoelectrical surveys in Area 3 of the Gol-e-Gohar iron ore mine, Iran. Mine Water and the Environment, vol.
SINGH, P.K., VOGT, W., SINGH, R.B., SINGH, M.M., and SINGH, D.P. 1997. Response of surface structures to rock blasting. Mineral Resources Engineering, vol. 6, no. 4. pp. 185–194.
30, no. 3. pp. 208–215. KHANDELWAL, M. and SINGH, T.N. 2006. Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. Journal of Sound and Vibration, vol. 289, no. 4–5. pp. 711–725. KHANDELWAL, M. and SINGH, T.N. 2007. Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering, vol. 27, no. 2. pp. 116–125. LANGEFORS, U. and KIHLSTROM, B. 1963. The Modern Technique of Rock Blasting. Wiley, New York.
SINGH, P.K., ROY, M.P., and SINGH, R.K. 2005. Responses of roof and pillars of underground coal mines to vibration induced by adjacent open-pit blasting. Environmental Geology, vol. 47, no. 2. pp. 205–214.
SMOLA, A.J. and SCHOLKOFF, B. 2004. A tutorial on support vector regression. Statistics and Computing, vol. 14. pp. 199–222.
VAPNIK, V. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
MESEC, J., KOVAČ, I., and SOLDO, B. 2010. Estimation of particle velocity based on blast event measurements at different rock units. Soil Dynamics and Earthquake Engineering, vol. 30, no. 10. pp. 1004–1009. MEVIK, B-H. and WEHRENS, R. 2007. The pls package: principal component and partial least squares regression in R. Journal of Statistical Software, vol. 18, no. 2. pp. 1–23. MONJEZI, M., GHAFURIKALAJAHI, M., and BAHRAMI, A. 2011. Prediction of blast-
VAPNIK, V. 1998. The support vector method of function estimation. Nonlinear Modeling: Advanced Black-box Techniques. Suykens, J.A.K. and Vandewalle, J. (eds). Kluwer Academic Publishers, Boston. pp.55–85.
VILLAESCUSA, E., ONEDERRA, I., and SCOTT, C. 2004. Blast induced damage and dynamic behaviour of hangingwalls in bench stoping. Fragblast, vol. 8, no. 1. pp. 23–40.
induced ground vibration using artificial neural network. Tunnelling and Underground Space Technology, vol. 26, no. 1. pp. 46–50. MOUNTRAKIS, G., IM, J., and OGOLE, C. 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing,
WIDODO, A. and YANG, B-S. 2007. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, vol. 21, no. 6. pp. 2560–2574.
vol. 66, no. 3. pp. 247–259. MOURA, M.D.C., ZIO, E., LINS, I.D., and DROGUETT, E. 2011. Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering and System Safety, vol. 96, no. 11. pp.
YI, C-P. and LU, W-B. 2006. Research on influence of blasting vibration on grouted rockbolt. Yantu Lixue/Rock and Soil Mechanics, vol. 27, no. 8. pp. 1312–1316.
1527–1534. MÜLLER, K-R., MIKA, S., RÄTSCH, G., TSUDA, K., and SCHÖLKOPF, B. 001. An introduction to kernel-based learning algorithms. IEEE Transactions on
YU, P-S., CHEN, S-T., and CHANG, I-F. 2006. Support vector regression for realtime flood stage forecasting. Journal of Hydrology, vol. 328, no. 3–4. pp. 704–716.
Neural Networks, vol. 12, no. 2. pp. 181–201.
different rock units. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, vol. 48, no. 6. pp. 899–908. The Journal of The Southern African Institute of Mining and Metallurgy
ZHANG, M., MU, H., LI, G., and NING, Y. 2009. Forecasting the transport energy demand based on PLSR method in China. Energy, vol. 34, no. 9. pp. 1396–1400. ◆ VOLUME 115
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NATEGHI, R. 2011. Prediction of ground vibration level induced by blasting at
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http://dx.doi.org/10.17159/2411-9717/2015/v115n7a11
Large-scale deformation in underground hard-rock mines by E. Karampinos*, J. Hadjigeorgiou*, P. Turcotte†, and F. Mercier-Langevin†
In some underground hard-rock mines, squeezing compressive ground conditions are influenced by the presence of rock foliation and high stress. In these cases, the orientation of the foliation with respect to the drift direction has a considerable impact on the magnitude of the resulting deformation. Irrespective of the reinforcement and support strategy, keeping drives developed sub-parallel to the rock foliation operational is difficult, and often requires excessive rehabilitation during the lifetime of the excavation. This study uses field observations and convergence measurements at the LaRonde and Lapa mines of Agnico Eagle Mines Ltd to provide guidelines of the anticipated squeezing levels at these operations. Keywords Squeezing ground conditions, large deformations, foliation, angle of drift interception, design guidelines.
Introduction Squeezing ground conditions are encountered in several underground hard-rock mines and can result in large-scale deformation and ground instability. The International Task Force on Squeezing Rock in Australian and Canadian Mines reported that ‘in a mining environment squeezing ground conditions were identified as closure larger than ten centimeters over the life expectancy of a supported drive’ (Potvin and Hadjigeorgiou, 2008). Mine drives are usually in operation from 18 months to two years. Squeezing ground conditions in mines are associated with considerable failure of ground support and require significant rehabilitation work. This may cause a slowdown in development and can have severe economic repercussions. In deep and high-stress mines, squeezing ground conditions are driven by the presence of inherent foliation and the orientation of the drift walls with respect to the foliation. Failure in bedded rock masses has been studied though physical modelling by Lin et al. (1984), and analytical methods by Kazakidis (2002). Potvin and Hadjigeorgiou (2008) reviewed ground support strategies used to control large-scale rock mass deformation under squeezing conditions in mines. Despite certain differences in the support philosophies The Journal of The Southern African Institute of Mining and Metallurgy
Case studies in underground hard-rock mines While many hard-rock mines around the world face problems associated with squeezing ground conditions, there are only a few case studies documented (Struthers et al., 2000; Beck and Sandy, 2003; Potvin and Slade, 2007; Sandy et al., 2010; Mercier-Langevin
* University of Toronto, Ontario, Canada. † Agnico Eagle Mines, Toronto, Ontario, Canada. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Nov. 2014 and revised paper received Apr. 2015. VOLUME 115
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Synopsis
between Australian and Canadian mines, it was evident that an effective support system makes use of both reinforcement elements and surface support. A successful system reinforces the rock mass around the excavation and mitigates the rate of deformation. Experience has shown that ductile surface support is an essential part of a successful ground support system. In a mining environment, squeezing ground conditions are defined as those that exhibit strain higher than 2%. Potvin and Hadjigeorgiou (2008) observed that large deformations are generally associated with the presence of a prominent structural feature such as intense foliation, a dominant structural feature or a shear zone, and high stress in weak rock. The presence of joint alteration and mineralogy further increases the severity of squeezing. Mercier-Langevin and Hadjigeorgiou (2011) presented a ‘hard rock squeezing index’ for underground hard-rock mines based on several mining case studies in Australia and Canada and calibrated against in-situ observations at the LaRonde mine in Quebec. The authors proposed the use of the index as a preliminary indicator of the squeezing potential in hard-rock mines with similar ground conditions.
Large-scale deformation in underground hard-rock mines and Hadjigeorgiou, 2011; Mercier-Langevin and Wilson, 2013; Hadjigeorgiou et al., 2013). Karampinos et al. (2014) presented a methodology for modelling the behaviour of foliated rock masses under high stress conditions using a 3D distinct element code. The resulting models addressed explicitly the effect of foliation and reproduced the observed buckling mechanism. Vakili et al. (2012) used a 3D distinct element code as a prelude to a 2D analysis using a finite difference code. Table I summarizes the reported level of deformation from several hard-rock mines. The LaRonde and Lapa mines report some of the highest deformations in hard-rock mines. This necessitates excessive rehabilitation of affected drifts. This study focuses on the LaRonde and Lapa mines as they also experience a large spectrum of squeezing ground conditions. Both mines are situated in the Abitibi region of northwest Quebec, within 11 km from each other, and are operated by Agnico Eagle Mines Ltd. LaRonde exploits a world-class Au-Ag-Cu-Zn massive sulphide lens complex. The ore reserves extend from surface to 3110 m and are still open at depth. The mine, which has been in operation since 1988, uses two mining methods – longitudinal retreat with cemented backfill, and transverse open stoping with cemented and unconsolidated backfill. The deepest production horizon is currently at 2930 m, established after the construction of an 832 m internal shaft. The mine is operating in a variety of ground conditions. At different levels of the mine the observed rock mass behaviour can be hard and brittle or squeezing. The mine reports that the total wall convergence in certain areas can be in excess of 1 m, with fracturing extending up to 6 m into the rock mass. Lapa is a high-grade gold mine and has been in operation since 2009. Access to the mine is provided by a 1369 m deep shaft and production is by two mining methods – longitudinal retreat with cemented backfill, and locally transverse open stoping with cemented backfill. The mine operates under
challenging squeezing ground conditions. Hadjigeorgiou et al. (2013). Mercier-Langevin and Wilson (2013) provided an interpretation of the squeezing mechanisms at Lapa and the support strategies aimed at controlling large deformations.
Mitigating the degree of squeezing Ground support Significant differences in the ground support strategies followed by Australian and Canadian hard rock mines in squeezing ground conditions were reported by Potvin and Hadjigeorgiou (2008). Australian mines often use soft reinforcement elements such as split sets, complemented with fibre-reinforced shotcrete. The shotcrete increases the stiffness of the support system and can initially delay the rock mass degradation. However, as shotcrete can accommodate only limited rock mass deformation and can crack, it is often necessary to install screen over shotcrete. This results in a stiff liner early in the squeezing process, followed by a ductile surface support after the installation of the screen. Canadian mines, on the other hand, use a high density of bolts with yielding capability (such as Swellex or hybrid bolts) and weldmesh, often accompanied with mesh straps. In the past, frictions bolts were used at LaRonde with partial success. The split sets ‘lock up’ between the foliation planes when buckling occurs and fail at the contact between the bolt and the plate when deformation surpasses a certain level. Mercier-Langevin and Turcotte (2007b) reported limited success in the use of cemented grouted cable bolts, yielding cable bolts, and modified cone bolts in squeezing conditions, as they either did not yield sufficiently or lost their ability to yield early in the squeezing process. The mine has been more successful using the hybrid bolt (MercierLangevin and Turcotte, 2007b) as part of its ground support strategy. The advantages of the hybrid bolt were presented by
Table I
Squeezing ground conditions reported in hard-rock mines Mine site
Strain range % (defined as total wall deformation over the drift width)
LaRonde (Hadjigeorgiou et al., 2013) Lapa (volcanics and ultramafics) (Mercier-Langevin and Wilson, 2013) Wattle Dam (Marlow and Mikula, 2013) Westwood (Armatys, 2012) Perseverance (Gabreau, 2007) (Potvin and Slade, 2007) (Struthers et al., 2000) Yilgarn Star (Potvin and Slade, 2007) Black Swan (Potvin and Slade, 2007) Maggie Hayes (Mercier-Langevin and Hadjigeorgiou, 2011) Casa Berardi (Mercier-Langevin and Hadjigeorgiou, 2011) Waroonga (Mercier-Langevin and Hadjigeorgiou, 2011) Bousquet (Mercier-Langevin and Hadjigeorgiou, 2011) Doyon altered zone (Mercier-Langevin and Hadjigeorgiou, 2011)
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Lower bound
Upper bound
2.5% 1%
41% >40%
1%
5% Up to 8.5%
1%
2.5%
1%
5%
1%
5%
1%
10%
2.5%
10%
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Magnitude of deformation
2.1 m (total wall convergence)
0.34 m (total wall convergence) 2.5 m total wall convergence Wall convergence > 2 m 3 m total sidewall closure, over 1 m of floor heave Up to 2 m in the hangingwall Up to 1.5 m in one wall
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Large-scale deformation in underground hard-rock mines Turcotte (2010) and include a setup that prevents the resin from escaping into the fractured rock. Furthermore, the hybrid bolt has a high resistance to shear and frictional resistance as compared to split sets. The in-situ behaviour of the hybrid bolt is characterized by a stiff early reaction at low displacements and almost perfectly plastic behaviour when subjected to high load (approximately 15 t). The current standard employed at the LaRonde mine was presented by Hadjigeorgiou et al. (2013) and consists of friction sets and screen in the sidewalls and rebars and screen in the back, complemented by hybrid bolts in the sidewalls and meshed straps installed 12 m away from the face. The ground support system employed at Lapa was inspired by the successful performance of the hybrid bolt at LaRonde (Mercier-Langevin and Wilson, 2013). The support strategy recognizes and accounts for the presence of weak schist zones (ultramafic) prone to squeezing. In areas where squeezing is anticipated, hybrid bolts are installed instead of split set bolts. When the sidewalls are subject to excessive deformation, the drives can become narrow and inadequate for the mining equipment. Under these circumstances, the walls are ‘purged’ using a scoop to remove excess material. This is a costly and time-consuming process, and is conducted only when necessary and under the close supervision of ground control personnel. Following purging of the excavation, additional support such as cable bolts supplemented with screen and straps is installed to further stabilize the walls. Turcotte (2010) reported a considerable reduction of purging since the introduction of the hybrid bolt in LaRonde. Mining under extreme squeezing ground conditions has demonstrated that it is not a realistic aim to completely arrest ground deformation. This can result in early failure of the support and necessitate frequent rehabilitation. Current practice aims to control the resulting deformation under squeezing conditions. Management of drive closure can be improved through an understanding of the factors that define and control the squeezing phenomenon. Mercier-Langevin and Turcotte (2007a) demonstrated that modifying the mining layout by driving drifts in a more favourable direction with respect to the foliation resulted in less purging and rehabilitation. Although this necessitated longer development drives per level, it significantly reduced rehabilitation and production delays. This practice reduced the likelihood of major ground instability in the drive.
23 field observations from drifts at the LaRonde mine. The original database reported the observed squeezing level, the damage to the support, the difference between the orientation of the drift and the foliation, and the influence of stress on the resulting deformation. For the cases where the drifts were developed sub-parallel to the foliation, regardless of the support system employed, it was difficult to keep the drifts in operation unless they were subjected to regular rehabilitation work (Mercier Langevin and Hadjigeorgiou, 2011). The degree of squeezing varied for drifts driven at different angles with respect to the foliation. This orientation phenomenon is supported by mechanistic analysis. Auto-confinement of foliation planes is greater when the angle between the normal to the free face and the normal to the foliation increases (Hadjigeorgiou et al., 2013). It has been shown analytically that even a small confining pressure is sufficient to prevent buckling failure (Kazakidis, 2002).
Updated database for investigating the influence of drift orientation In this work, the original database was extended using quantitative data for drift closure from 57 new case studies at LaRonde and 87 from Lapa. For every case study the following parameters were recorded: the dip and dip direction of foliation; the orientation of the drift; the observed damage; the development date; the stress effect due to mining activity; the support system used; the additional support installed; and the presence of water. Any intervention such as rehabilitation or purging was also recorded. The cavity monitoring survey (CMS) instrument CMS V400 (Optech Incorporated, 2010) was used to capture the wall profile. Figure 3 shows an example of multiple CMS readings in a drift at Lapa. 3D surveys, showing the initial drift dimensions after the development of every drift, are also available for both mines. These surveys are made by surveying one point at the back, the floor, and each sidewall immediately after the development of a drift. The distance between the two sidewalls was extracted from the CMS, and recorded at heights of 1.5 and 2.5 m from the drift floor. The back–to-floor distance was estimated at the centre of each drift and at 1 m on each side from the centre. In cases where a CMS profile was not available, measurements were made using a laser measuring device. It
Influence of drift orientation The influence of drift orientation on the observed degree of squeezing is evident in several places at both Lapa and LaRonde. This was quantified from the angle of interception, defined as the angle between the normal to the foliation planes and the normal to the sidewall (Figure 1). This is illustrated by three drifts that were driven at 2150 m depth at LaRonde (Figure 2). There is no evidence of squeezing for a drift developed perpendicular to the foliation, only minor squeezing when driven at 45 degrees, and severe squeezing for a drift oriented parallel to the foliation. The angle of interception had a direct impact on the performance of the ground support systems used at the LaRonde mine. An investigation of the effect of the orientation of the drift on the degree of squeezing was initially made by Mercier-Langevin (2005). It was based on The Journal of The Southern African Institute of Mining and Metallurgy
Figure 1 – Definition of angle of interception (ψ) (after Mercier-Langevin and Hadjigeorgiou, 2011) VOLUME 115
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Figure 2 – Variations in squeezing severity in three locations less than 100 m apart at 2150 m depth, LaRonde mine. (a) Perpendicular – no squeezing; (b) 45 degrees – minor or no squeezing; (c) parallel – severe squeezing (after Mercier-Langevin and Hadjigeorgiou, 2011)
Figure 3 – Example of multiple CMS in a longitudinal drift at Lapa, 540 m depth
was thus possible to determine the total sidewall and total back–to-floor convergence for each case study. The greatest convergence was derived from CMS readings for 35 case studies at LaRonde and 73 case studies at Lapa. The convergence was determined by comparing the initial 3D survey results during development and the CMS profile after a time interval. Data collection was complicated by the frequent presence of muck on the side of each drift. Figure 4 shows the various methods used to identify the convergence in each case study and the reference points along the drift.
Quantifying observed convergence Previous analysis of the data at the time it was collected focused on a qualitative interpretation. As more quantitative data was collected and more case studies were documented, it became possible to provide a preliminary quantitative interpretation (Hadjigeorgiou et al., 2013). The work presented in this paper includes more case studies and further information that reports on back-to-floor
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convergence, wall-to-wall convergence, and sidewall deformation. The recorded convergence in the case studies presented in the past was also updated. For the purposes of this investigation the total wall-towall convergence (δtotal) was estimated from the difference between the surveyed width (L) and the lowest sidewall distance measured. Similarly, the total back-to-floor convergence was derived from the lowest back-to-floor distance and the height of the drift. The total wall-to-wall and back-to-floor convergences were expressed as percentages of the total strain (εtotal): [1] It is recognized that operational restrictions can influence the data collection process. In particular, when the wall-towall closure is close to 3.5 m, the drift becomes nonoperational for equipment and therefore it is purged. Consequently, a value of 3.5 m was used as the lowest wallThe Journal of The Southern African Institute of Mining and Metallurgy
Large-scale deformation in underground hard-rock mines
Figure 4 – Estimation of drift convergence
[2] The influence of the angle of interception (ψ) on the The Journal of The Southern African Institute of Mining and Metallurgy
resulting strain for each wall at the LaRonde and Lapa mines is presented in Figure 7 for the sidewalls and in Figure 8 for the back and the floor. These diagrams capture only a part of the behaviour of a rock mass under squeezing ground conditions. There are further factors that can influence the resulting strain, such as the time of measurement, the foliation spacing, the stresses, the strength of the rock, and the condition of the joints. In addition, operational constraints do not allow for a drive with a wall-to-wall distance less than 3.5 m. Figures 6 to 8 include a threshold of the highest expected strain for a given angle of interception. It is noted, however, that there is limited data for an angle of interception less than 10 degrees in extreme squeezing conditions. The observed trend is supported by recent numerical modelling work by the authors. Nevertheless, the field data clearly indicates that an increase in the angle of interception between the drift and the inherent foliation will invariably reduce the resulting level of squeezing. The south walls demonstrated the highest strain, exceeding 50% in certain case studies. These values are also higher than the total sidewall strain recorded. The difference between the convergence on each sidewall (north walls and south walls) at Lapa was identified by Mercier-Langevin and Wilson (2013). Higher strain, resulting in frequent rehabilitation, was linked with the presence of ultramafics, whereas lower strain, easily managed, was associated with relatively VOLUME 115
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to-wall distance measured in these cases. A revised classification scheme for quantifying squeezing in hard-rock mines is proposed: ➤ No or low squeezing (0% < ε < 5%) ➤ Moderate squeezing (5% < ε < 10%) ➤ Pronounced squeezing or rehabilitated drifts (10% < ε < 35%) ➤ Extreme squeezing (35% < ε). Published work in civil engineering tunnelling applications summarized by Potvin and Hadjigeorgiou (2008) reported considerably lower ranges than those proposed here. This is a result of the higher tolerance of rock mass failure in a mining environment. Typical squeezing examples observed in LaRonde and Lapa mines are presented in Figure 5. The influence of the angle of interception (ψ) on the resulting total wall-to-wall and back-to-floor strain is shown in Figure 6. For comparison purposes, the convergence was also examined for each wall separately. This was defined as the ratio of the highest recorded convergence (δ) for each wall to half of the surveyed width (L) for the sidewalls or to half the surveyed height for the back and the floor. The convergence for each wall was expressed as percentage strain (ε):
Large-scale deformation in underground hard-rock mines
Figure 5 – Examples of drifts subjected to squeezing at the LaRonde and Lapa mines (Hadjigeorgiou et al., 2013)
Figure 6 – Influence of angle of interception (ψ) on resulting total strain at the LaRonde and Lapa mines. (a) Total wall-to-wall strain, (b) total back-to-floor strain
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Figure 7 – Influence of angle of interception (ψ) on resulting sidewall strain at the LaRonde and Lapa mines. (a) South wall strain, (b) north wall strain The Journal of The Southern African Institute of Mining and Metallurgy
Large-scale deformation in underground hard-rock mines Currently there is a lack of a ground support system that can fully control pronounced and extreme squeezing ground conditions. Consequently, exploring changes in the orientation of development can be an effective strategy in mining under such conditions. While this has been an opportunity in LaRonde, it is more difficult at Lapa, due to the lower flexibility allowed by the mining method. Hoek and Marinos (2000) used the ratio of the uniaxial compressive strength (σcm) of the rock mass to the overburden stress (po) to predict the extent of squeezing in tunnelling, using a similar method to estimate the wall strain. This approach does not consider the anisotropic behaviour of the rock mass and the presence of any dominant structure. A successful classification system for the prediction of the level of squeezing at LaRonde and Lapa should take into account the influence of foliation and the angle of interception (ψ). This study has demonstrated the need to better define and capture the transition between the various squeezing zones. This can potentially be attained by combining field observations with numerical studies.
Conclusions
competent sediments and volcanics. The ultramafics have typically much smaller foliation spacing and talc-chlorite alteration is usually present. The examined south walls at Lapa were comprised of ultramafic formations while the north walls were mostly driven in sediments. Visual observations suggest that the strain in the north walls at Lapa is lower than that recorded in many cases. The strain may be overestimated as sometimes the walls in sediments follow the dip of the foliation (approximately 85°), which was not considered in the 3D survey. Higher strain can also be a result of errors in the positioning of the CMS or any shape irregularities on the wall, as the 3D survey considers the wall as a plane surface. The reported total sidewall and total back-to-floor strain has significant practical implications for the functionality of the drifts and the need for rehabilitation to maintain them in operation. The determination of the strain for each individual wall can allow for a more representative consideration of the geological and mineralogical conditions that can influence the squeezing level. Consequently, the influence of other factors controlling the degree of squeezing, such as the foliation spacing, the alteration, intact rock strength, and the stress can be explored in greater detail. The Journal of The Southern African Institute of Mining and Metallurgy
Acknowledgements The support of Agnico Eagle Mines Ltd, Division LaRonde and Lapa, and the Natural Science and Engineering Research Council of Canada is gratefully acknowledged. VOLUME 115
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Figure 8 – Influence of angle of interception (ψ) on resulting back and floor strain at the LaRonde and Lapa mines. (a) Back strain, (b) floor strain
The LaRonde and Lapa mines exhibit large-scale deformation in a range of ground conditions. The estimation of the total sidewall and the total back-to-floor strain indicated the problems encountered in the functionality of the drifts and the need for rehabilitation work when pronounced and extreme squeezing conditions are faced. An analysis of the reported strain for each drive wall revealed a strong correlation between the squeezing level and the geology. Variations in the geology at each side of a drift can result in significantly more pronounced squeezing conditions. Under these circumstances it is optimal to implement a different ground support standard for each wall. Acceptable squeezing levels in a mining environment are considerably higher than in civil engineering tunnelling operations. Although this allows more flexibility, mining operators have to work under greater economic constraints in terms of support. Squeezing ground conditions in mining applications often involve considerable failure of ground support and necessitate significant rehabilitation work. The LaRonde and Lapa mines follow similar ground support strategies for developing excavations in squeezing ground conditions. The support systems aim to control the extreme deformation rather than prevent it, which is not a realistic objective in a mining context. This paper has presented the results of extensive field work in the quantification of the influence of the angle of interception between the drift and the foliation. The choice of a favourable angle of interception can result in a more manageable squeezing level and increase the performance of an appropriate support system for squeezing ground conditions. The results from this study are in agreement with the squeezing index proposed by Mercier-Langevin and Hadjigeorgiou (2011) and contribute towards its validation and extension.
Large-scale deformation in underground hard-rock mines References ARMATYS, M. 2012. Modification des classifications géomécaniques pour les massifs rocheux schisteux. Master's thesis, École Polytechnique de Montréal, Canada. BECK, D.A. and SANDY, M.P. 2003. Mine sequencing for high recovery in Western Australian mines. Proceedings of the Twelfth International Symposium on Mine Planning and Equipment Selection, Kalgoorlie, 23–25 April 2003. [CDROM]. GAUDREAU, D. 2007. Current ground support practices at Perseverance mine. Challenges in Deep and High Stress Mining. Potvin, Y., Hadjigeorgiou, J., and Stacey, D. (eds). Australian Centre for Geomechanics, Perth. pp. 363–369. HADJIGEORGIOU, J., KARAMPINOS, E., TURCOTTE, P., and MERCIER-LANGEVIN, F. 2013. Assessment of the influence of drift orientation on observed levels of squeezing in hard rock mines. Proceedings of the Seventh International Symposium on Ground Support in Mining and Underground Construction, Perth, Australia, 13–15 May 2013. Brady, B. and Potvin, Y. (eds). Australian Centre for Geomechanics, Perth. pp. 109–118. HOEK, E. and MARINOS, P. 2000. Predicting tunnel squeezing problems in weak heterogeneous rock masses. Tunnels and Tunnelling International, vol. 32, no. 11. pp. 45–51. KARAMPINOS, E., HADJIGEORGIOU, J., TURCOTTE, P., DROLET, M-M., and MERCIERLANGEVIN, F. 2014. Empirical and numerical investigation on the behaviour of foliated rock masses under high stress conditions. Proceedings of the Seventh International Conference on Deep and High Stress Mining, Sudbury, Canada, 16-18 September 2014. Australian Centre for Geomechanics, Perth. KAZAKIDIS, V.N. 2002. Confinement effects and energy balance analyses for buckling failure under eccentric loading conditions. Rock Mechanics and Rock Engineering, vol. 35, no 2. pp. 115–126. LIN, Y., HONG, Y., ZHENG, S., and ZHANG, Y. 1984. Failure modes of openings in a steeply bedded rock mass. Rock Mechanics and Rock Engineering, vol. 17. pp. 113–119. MARLOW, P. and MIKULA, P.A. 2013. Shotcrete ribs and cemented rock fill ground control methods for stoping in weak squeezing rock at Wattle Dam Gold Mine. Proceedings of the Seventh International Symposium on Ground Support in Mining and Underground Construction, Perth, Australia, 13–15 May 2013. Brady, B. and Potvin, Y. (eds). Australian Centre for Geomechanics, Perth. pp. 133–148. MARLOW, P. and MIKULA, P.A. 2013. Shotcrete ribs and cemented rock fill ground control methods for stoping in weak squeezing rock at Wattle Dam Gold Mine. Proceedings of the Seventh International Symposium on Ground Support in Mining and Underground Construction, Perth, Australia, 13–15 May 2013. Brady, B. and Potvin, Y. (eds). Australian Centre for Geomechanics, Perth. pp. 133–148. MERCIER-LANGEVIN, F. 2005. Projet de convergence des galeries - Phase 1: Consolidation de l'information disponible à la mine. Internal memo, Agnico Eagle Mines Ltd - LaRonde Division, Cadillac, Canada. 13 pp. MERCIER-LANGEVIN, F. and HADJIGEORGIOU, J. 2011. Towards a better understanding of squeezing potential in hard rock mines. AusIMM, Mining Technology, vol. 120, no. 1. pp. 36–44.
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MERCIER-LANGEVIN, F. and TURCOTTE, P. 2007a. Expansion at depth at Agnico Eagle’s LaRonde Division – meeting geotechnical challenges without compromising production objectives. Challenges in Deep and High Stress Mining. Potvin, Y., Hadjigeorgiou, J., and Stacey, D. (eds). Australian Centre for Geomechanics, Perth. pp. 189–195. MERCIER-LANGEVIN, F. and TURCOTTE, P. 2007b. Evolution of ground support practices at Agnico Eagle’s LaRonde Division-innovative solutions to high stress yielding ground. Rock Mechanics – Meeting Society’s Challenges and Demands. Proceedings of the 1st Canada–US Rock Mechanics. Symposium. Eberhardt, E., Stead, D., and Morrison, T. (eds). Taylor & Francis, Vancouver. pp. 1497–1504. MERCIER-LANGEVIN, F. and WILSON, D. 2013. Lapa mine - ground control practices in extreme squeezing ground. Proceedings of the Seventh International Symposium on Ground Support in Mining and Underground Construction, Perth, Australia, 13-15 May 2013. Brady, B. and Potvin, Y. (eds). Australian Centre for Geomechanics, Perth. pp. 119–132 . OPTECH INCORPORATED. 2010. CMS V400 Operation Manual, Cavity Monitoring System. Ontario, Canada. 126 pp. POTVIN, Y. and HADJIGEORGIOU, J. 2008. Ground support strategies to control large deformations in mining excavations. Journal of the Southern African Institute of Mining and Metallurgy, vol. 108, no. 7. pp. 393–400. POTVIN, Y. and SLADE, N. 2007. Controlling extreme ground deformation; Learning from four Australian case studies. Challenges in Deep and High Stress Mining. Potvin, Y., Hadjigeorgiou, J., and Stacey, D. (eds). Australian Centre for Geomechanics, Perth. pp. 355–361. SANDY, M., SHARROCK, G., and VAKILI, A. 2010. Managing the transition from low stress to high stress conditions. Second Australasian Ground Control in Mining Conference, Sydney, New South Wales, 23-24 November 2010. Hagan, P. and Saydam, S. (eds). Australasian Institute of Mining and Metallurgy, Carlton, Victoria. STRUTHERS, M.A., TURNER, M.H., MCNABB, K., and JENKINS, P.A. 2000. rock mechanics design and practice for squeezing ground and high stress conditions at Perseverance Mine. Proceedings of MassMin 2000, Brisbane, Australia, 29 October – 2 November 2000. Chitombo, G. (ed). Australasian Institute of Mining and Metallurgy, Melbourne. pp. 755–764. TURCOTTE, P. 2010. Field behaviour of hybrid bolt at LaRonde Mine. Proceedings of the Fifth International Seminar on Deep and High Stress Mining (Deep Mining 2010), Santiago, Chile, 6–8 October 2010. Van Sint Jan, M. and Potvin, Y. (eds). Australian Centre for Geomechanics, Perth. pp. 309–319. VAKILI, A., SANDY, M., and ALERCHT, J. 2012. Interpretation of non-linear numerical models in geomechanics - a case study in the application of numerical modelling for raise bored shaft design in a highly stressed and foliated rock mass. MassMin 2012: Proceedings of the 6th International Conference and Exhibition on Mass Mining, Sudbury, Canada, June 2012. Canadian Institute of Mining, Metallurgy and Petroleum, Sudbury, Canada. [CD-ROM]. ◆
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Visions for challenging assets in the South African coal sector by Z. van Zyl*
The southern African coal industry is facing the reality that coal reserves are becoming deeper and harder to mine than before. Alternative visions need to be considered to offset the impact of these challenges on productivity, cost, and profitability. One of the main challenges is to maintain current production levels under more difficult conditions, and improve productivity where conditions allow. There is a need to move from the reactive event-based management system towards the more adaptable and flexible process-based management system. This paper focuses on underground coal mining, but the principles are also applicable to other commodities and mining methods. This paper is based on research undertaken by the personnel from Mining Consultancy Services Pty Ltd (MCS) on trends in the underground coal mining sector, as well as practical experience over a period of 16 years in the field of electronic monitoring of mining machinery and productivity optimization. Keywords productivity, optimization, Prodmate, reporting, handheld device, monitoring, improvement, utilization, key performance indicators, coal mining.
More challenging conditions facing the coal mining industry There are a number of challenges facing the coal mining industry. These range from physical conditions through to socioeconomic challenges. This paper will deal mostly with physical challenges, although the approach discussed would apply equally to any challenge. An analysis of the changes in physical mining conditions over the past seven to eight years found that the following elements have had an impact on productivity: ➤ The introduction of the 12 m cutting rule in the first part of the previous decade. This rule limits the distance that a continuous miner can cut from the last row of roof support to 12 m, instead of longer distances (up to 24 m and more) that were used previously. The rule was introduced as a measure to prevent explosions by limiting dust levels, as well as preventing roof falls ➤ Reduction in seam thickness and mining height ➤ Shortening of panels and more frequent section moves, which have an adverse influence on production The Journal of The Southern African Institute of Mining and Metallurgy
Case study: seam thickness and mining height reduction MCS has recently undertaken an analysis of the change in the practical mining height of approximately 28 continuous miner (CM) sections in the Witbank and Highveld coalfields over a period of eight years, from 2005 to 2012. The results are shown in Figure 1. As can be seen, the average practical mining height decreased from above 4 m to just under 2.9 m over the period analysed, and the trend seems to be continuing. This has partly come about due to the depletion of 2 Seam reserves with high seam heights, and the subsequent migration to 4 Seam areas with lower seam heights. The 2 Seam reserves were targeted first due to their higher yield. The other major reason is that, where given the choice, mines elected to mine the higher 4 Seam areas in preference to the lower areas, and as the higher seam areas become mined out the average seam height decreases. Thus
* Mining Consultancy Service (Pty) Ltd. © The Southern African Institute of Mining and Metallurgy, 2015. ISSN 2225-6253. Paper received Mar. 2015 and revised paper received June 2015. VOLUME 115
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Synopsis
➤ Greater frequency of geological problems: faults, dolerite intrusions, floor rolls etc. Each problem poses a challenge to both productivity and safety, and these are more frequent as the more easily accessed resources on aging mines have already been depleted ➤ Increased support density requirements to prevent roof falls. Most coal mines are now on systematic support rules and ribside support is becoming more prevalent. This is as a result of the impact of aging mines’ strategies (to mine more difficult areas, as described above) as well as restrictions imposed internally by companies and through the Department of Mineral Resources (DMR) to combat roof and rib-side related accidents.
Visions for challenging assets in the South African coal sector more linear metres have to be mined for every ton of coal that is produced, putting pressure on productivity.
Case study: CM section output For underground coal mining, ‘millionaire’ status is an aspirational target, which means that a section produced a million run-of-mine (ROM) tons in a year. From a benchmark database of more than 100 CM sections in South Africa we extracted the results to determine the number of ‘millionaire’ sections over the past few years. The results, shown in Figure 2, clearly illustrate that, during a period when management practices and technology continuously evolved with the aim of improving productivity levels, there had been a steady decline in the number of millionaire sections; largely due to the factors mentioned above.
Attributes of top performers Mining conditions are important, but are not the only driver of productivity. Figure 3 illustrates this by comparing the output of 20 CM sections, with similar mining conditions and with the same equipment, over a period of one year. There is a wide differentiation between the top and bottom performers. Through the involvement of MCS with productivity optimization projects at many of the underground coal mines in South Africa, a model, shown in Figure 4, was developed to explain this phenomenon.
Figure 1 – Change in mining height, 2005–2012
Figure 2 – Decline in the number of ‘millionaire’ sections, 2006–2012
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Figure 4 illustrates that all sections are exposed to the same: ➤ Production events, such as the need to relocate from one roadway to the next after completing the 12 m cut ➤ Equipment events, such as breakdowns and advances in technology ➤ Geological events, such as encountering anomalies i.e. faults and dykes ➤ Market events, such as changes in prices and logistical constraints ➤ HR events, same staff complement ➤ Legislative events, regulating activities of all sections. Despite these events, which are common to all sections, there is a difference in output as illustrated in Figure 3. Why? The reasons are that the top performers: ➤ Know about the events, like all average performers would ➤ Understand the events, like most average performers would ➤ Understand the production process, like some average performers would ➤ Understand the impact of the events on the production process, like few average performers would ➤ Make changes to improve the affected process. For average performers, this rational progression is unlikely. The steps that connect the events to the change in process that would mitigate the event’s adverse effect is what we refer to as the ‘process-based management ladder’, as shown in Figure 4. The second step in the ladder ‘Provide adequate and reliable information’ is often done in underground coal mines through the introduction of electronic machine monitoring systems on the CMs, which are the primary coal-winning equipment. The results from the monitoring systems are then compared to other sections with similar systems to benchmark the sections in question against industry best practice and determine the improvement potential. The results from the machine monitoring systems are expressed in such a way that they measure the fundamental mining processes from the CMs. These process-based metrics allow the underlying constituent processes of the mining method to be managed and improved. By improving the process that deviates the most from industry benchmarks, inherent value is unlocked and the productivity of the mining method is improved.
Figure 3 – Productivity differences, top and bottom performers The Journal of The Southern African Institute of Mining and Metallurgy
Visions for challenging assets in the South African coal sector
Figure 4 – Model illustrating reasons for differences in productivity
Process-based production management was first envisaged and introduced in the late 1980s as a means of redirecting the focus of production optimization to manageable practices that would produce sustainable improvements. This management system encourages the section personnel to build a strong foundation of the process steps, instead of focusing on the result. Through MCS’s experience in more than 30 successful productivity optimization projects on coal mines, the author has found time and again that the change from event-based management to process-based management is the key to unlocking latent potential. This can be done only when the mine has the ability to measure each of the production processes that occur in a section accurately and reliably. It is therefore no surprise that the application and growth in process-based management has gone hand-in-hand with improvements in the monitoring hardware and software, resulting in progressively more accurate and reliable data. The fact that almost all new continuous miners sold into the market now come equipped with advanced monitoring capability, bears testimony to this. As the demands and pressures of reducing costs and maintaining performance have recently increased, new advances and approaches in technology to support processbased management are required, as described further in this paper.
➤ Creating a management system that can be used to manage all aspects of the production operation ➤ Effective time management while fostering simple and less confrontational accountability among employees ➤ Making information transparent so that communication and effective use of skills and experience is improved. This is backed up by effective change management so the new methodologies are less prescriptive and more likely to become habit-forming, thereby sustaining the improvements. Process-based management works. It is an effective means of advancing the value of the asset and output by sustaining the day-to-day improvements gained by revolutionizing the operating methods. The effectiveness of process-based management methodology is substantiated by the results achieved, as illustrated in Figures 5 and 6. By empowering people with relevant information to manage the performance of their section, management
Process-based production management
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Figure 5 – Effectiveness of process-based management, illustrated by increasing production per shift VOLUME 115
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MCS has come to understand exactly what process-based production management is. In its simplest form it is the management of primary productivity drivers. The first step towards managing constraints effectively entails understanding what the constraints are. Radical advances in the use of progressive software and hardware to monitor and examine section data has led to many benefits, such as: ➤ The ability to integrate machine-generated and manually captured data
Visions for challenging assets in the South African coal sector
Figure 6 – Effectiveness of process-based management, illustrated by monthly production increases
➤ Training. Training of relevant personnel to analyse, interpret, and understand the measurement system and what they can do towards improving each of the KPIs that are under their control ➤ Improvement process. A practical improvement process where KPIs are reviewed; action plans are generated, implemented, and tracked; and unquestionable accountability for the KPIs is held across the management structure on the mine. This holistic approach to productivity optimization has consistently delivered the best results. It is based on the interconnectedness of all the aspects; leaving one out will erode the effectiveness and sustainability of the process. Owing to the factors described at the beginning of this paper, it has become increasingly difficult to maintain productivity at constant and acceptable levels, before aspiring for improvements. Over the past seven to eight years, productivity improvements were much more likely to be achieved through improvements in production rates (efficiency) rather than improvements in production time (utilization). This is illustrated in Figures 8, 9, and 10, which show utilization (Figure 8) and efficiency (Figures 9 and 10) KPI trends over a sample of 25–30 bord and pillar CM sections. Figure 8 shows a decline in the average production time per shift, but the research personnel found that the productivity of the sections from which the data was taken did not decline, but rather remained constant. This is due to the
Figure 7 – The three pillars of process-based management and productivity optimization Figure 8 – Decrease in utilization – production time per shift
encourages workers to embrace improvement through technology and change. Experience over the past 26 years has indicated that there are three pillars to effective and sustainable process-based management and productivity optimization (Figure 7). ➤ Monitoring. Accurate and reliable process measurement, using data obtained from production machines. The monitoring is based only on the primary cutting machine, and focus on the following production rate (efficiency) KPIs: • Loading time • Away time • Tram time per metre cut These are primarily machine-related issues. Time management would include the monitoring of the first and last operation of the continuous miner
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Figure 9 – Improvement in efficiency – decreased loading times The Journal of The Southern African Institute of Mining and Metallurgy
Visions for challenging assets in the South African coal sector
Figure 10 – Improvement in efficiency – decreased CM relocation times
Mining process measuring systems Traditional machine monitoring systems have been effective at measuring and managing the efficiency of machines, but inadequate when it comes to measuring and managing utilization. This led to the development of a underground reporting system, ProdMate®, that allows for monitoring of key utilization performance indicators on production machines (CMs, roofbolters etc.), downtime (maintenance), planning (HR, inventory, supplies), and procedures. The reporting system combines manual and electronic data to produce a range of outputs, which can be utilized by generic enterprise reporting programmes (like SAP) to streamline production and improve utilization. Time-related issues include reporting of utilization of the machine as well as the downtime. Non-reported time has become an increased problem in recent years, and the introduction of handheld reporting devices has reduced this phenomenon significantly. The system essentially comprises a suite of software applications that are run on an intrinsically safe handheld computer or personal digital assistant (PDA) as shown in Figure 11. Information is entered by the user (usually the section miner) and serves as a replacement for paper-based reports. The inputs that relate to machines, materials, and other downtimes can then be integrated with the data from the electronic monitoring systems to provide an integrated reporting solution that encompasses close to 100% of the total shift time, as illustrated in Figure 12. Data can be extracted either via Wi-Fi interface or a cradle when docking the device to charge. Where a Wi-Fi data transfer system is The Journal of The Southern African Institute of Mining and Metallurgy
Figure 11 – Personal digital assistant running ProdMate®
used, the information is available in near real-time for use. With the introduction of the unit to coal mines in Australia and South Africa, users have seen potential beyond the wide range of applications originally anticipated. This has led to the expansion of the system’s capabilities (as demonstrated in Figure 13) to include the following: ➤ Maintenance management system ➤ Inventory management system ➤ In-time production status updates ➤ HR control, time and attendance, and licence control ➤ Interactive mine planning and forecasting ➤ Task and work order creation and management ➤ Production reporting ➤ Downtime reporting ➤ Material and supplies management ➤ Document storage and retrieval ➤ Have you done it? (where any ad-hoc or periodic tasks can be loaded and managed).
Conclusions This paper has indicated that the challenges that face the coal mining sector in South Africa are significant and serious. It shows that process-based management has served as an effective tool to improve and maintain productivity in the face of these challenges, but that many of the improvements have been in efficiency rather than utilization, i.e. production rate rather than production time. It indicates that as the challenges are likely to become more severe in the future, the application of process-based management will remain important. With the addition of improved measuring systems on utilization though applications such as the ProdMate® system, process-based management becomes even more effective. ◆ VOLUME 115
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improvement in efficiency of the sections as indicated in Figure 9, which shows the improvement in the time taken to fill a coal hauler, and Figure 10, which shows improvements in the efficiency with which the CMs are relocated from one cutting position to the next. Traditional machine monitoring systems have largely been used to improve efficiency using loading and away time, which offsets the losses caused by decreased production time, as illustrated by the CM loading time trend in Figure 9 and tramming efficiency in Figure 10 for the same data sample. The declining trend in utilization (Figure 8) prompted the research team to investigate better ways of measuring machine utilization, as described in the next section.
Visions for challenging assets in the South African coal sector
Figure 12 – Integrated reporting solution
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Figure 13 – Capabilities of the expanded ProdMate® system
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INTERNATIONAL ACTIVITIES 2015 6–7 August 2015 — MINPROC 2015: Southern African Mineral Beneficiation and Metallurgy Conference Vineyard Hotel, Newlands, Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za 19–20 August 2015 — The Danie Krige Geostatistical Conference: Geostatistical geovalue —rewards and returns for spatial modelling Crown Plaza, Johannesburg Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 25–27 August 2015 — Coal Processing—Unlocking Southern Africa’s Coal Potential Graceland Hotel Casino and Country Club Secunda Contact: Ann Robertson, Tel: +27 11 433-0063 26–28 August 2015 — MINESafe 2015—Sustaining Zero Harm: Technical Conference and Industry day Emperors Palace Hotel Casino, Convention Resort, Johannesburg Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za 28 September-2 October 2015 — WorldGold Conference 2015 Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Gauteng, South Africa Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156, E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za 12–14 October 2015 — Slope Stability 2015: International Symposium on slope stability in open pit mining and civil engineering In association with the Surface Blasting School 15–16 October 2015 Cape Town Convention Centre, Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za 20 October 2015 — 13th Annual Southern African Student Colloquium Mintek, Randburg, Johannesburg Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za
8–13 November 2015 — MPES 2015: Twenty Third International Symposium on Mine Planning & Equipment Selection Sandton Convention Centre, Johannesburg, South Africa Contact: Raj Singhal E-mail: singhal@shaw.ca or E-mail: raymond@saimm.co.za Website: http://www.saimm.co.za
2016 14–17 March 2016 — Diamonds still Sparkle 2016 Conference Botswana Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 13–14 April 2016 — Mine to Market Conference 2016 South Africa Contact: Yolanda Ramokgadi Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: yolanda@saimm.co.za, Website: http://www.saimm.co.za 17–18 May 2016 — The SAMREC/SAMVAL Companion Volume Conference Johannesburg Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za May 2016 — PASTE 2016 International Seminar on Paste and Thickened Tailings Kwa-Zulu Natal, South Africa Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za 9–10 June 2016 — 1st International Conference on Solids Handling and Processing A Mineral Processing Perspective South Africa Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za 1–3 August 2016 — Hydrometallurgy Conference 2016 ‘Sustainability and the Environment’ in collaboration with MinProc and the Western Cape Branch Cape Town Contact: Raymond van der Berg Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: raymond@saimm.co.za, Website: http://www.saimm.co.za
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
16–19 August 2016 — The Tenth International Heavy Minerals Conference ‘Expanding the horizon’ Sun City, South Africa Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za
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21–22 October 2015 — Young Professionals 2015 Conference Making your own way in the minerals industry Mintek, Randburg, Johannesburg Contact: Camielah Jardine Tel: +27 11 834-1273/7, Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za
Company Affiliates The following organizations have been admitted to the Institute as Company Affiliates 3 M South Africa
Fraser Alexander Group
PANalytical (Pty) Ltd
AECOM SA (Pty) Ltd
Glencore
AMIRA International Africa (Pty) Ltd
Goba (Pty) Ltd
Polysius A Division Of Thyssenkrupp Industrial Sol
Anglo Operations Proprietary Limited
Hall Core Drilling (Pty) Ltd
Anglo Platinum Management Services (Pty) Ltd
Hatch (Pty) Ltd
Precious Metals Refiners Rand Refinery Limited Redpath Mining (South Africa) (Pty) Ltd
Herrenknecht AG Anglogold Ashanti Ltd HPE Hydro Power Equipment (Pty) Ltd Arcus Gibb (Pty) Ltd Impala Platinum Holdings Limited
Rosond (Pty) Ltd Royal Bafokeng Platinum
Atlas Copco Holdings South Africa (Pty) Limited
IMS Engineering (Pty) Ltd
Aurecon South Africa (Pty) Ltd
JENNMAR South Africa
RungePincockMinarco Limited
Aveng Moolmans (Pty) Ltd
Joy Global Inc.(Africa)
Salene Mining (Pty) Ltd
Axis House Pty Ltd
Leco Africa (Pty) Limited
Sandvik Mining and Construction Delmas (Pty) Ltd
Barloworld Equipment -Mining
Longyear South Africa (Pty) Ltd
Becker Mining (Pty) Ltd
Lonmin Plc
Sandvik Mining and Construction RSA (Pty) Ltd
BedRock Mining Support Pty Ltd
Ludowici Africa (Pty) Ltd
SANIRE
Bell Equipment Limited
Lull Storm Trading (PTY)Ltd T/A Wekaba Engineering
Sasol Mining(Pty) Ltd
Blue Cube Systems (Pty) Ltd
Roymec Technologies (Pty) Ltd
Scanmin Africa (Pty) Ltd
Magnetech (Pty) Ltd
Bluhm Burton Engineering Pty Ltd(BLU003)
Sebilo Resources (Pty) Ltd
Magotteaux (Pty) Ltd
SENET (Pty) Ltd
CAE Mining (Pty) Limited
MBE Minerals SA Pty Ltd
Caledonia Mining Corporation
MDM Technical Africa (Pty) Ltd
Chamber of Mines
Metalock Engineering RSA (Pty)Ltd
Concor Mining
Metorex Limited
Concor Technicrete
Metso Minerals (Sweden) AB
Department of Water Affairs and Forestry
Minerals Operations Executive (Pty) Ltd
Deutsche Securities (Pty) Ltd
MineRP Holding (Pty) Ltd
Digby Wells and Associates
Mintek
Technology Innovation Agency
Downer EDI Mining
MIP Process Technologies
Time Mining and Processing (Pty) Ltd
DRA Mineral Projects (Pty) Ltd
Modular Mining Systems Africa (Pty) Ltd
Tomra Sorting Solutions Mining (Pty) Ltd
DTP Mining
MSA Group (Pty) Ltd
Ukwazi Mining Solutions (Pty) Ltd
Duraset
Multotec (Pty) Ltd
Umgeni Water
E+PC Engineering and Projects Company Ltd
Murray and Roberts Cementation
VBKOM Consulting Engineers
New Concept Mining (Pty) Limited
Vietti Slurrytec (Pty) Ltd
Northam Platinum Ltd - Zondereinde
Webber Wentzel
Osborn Engineered Products SA (Pty) Ltd
Weir Minerals Africa
Outotec (RSA) (Proprietary) Limited
Worley Parsons RSA (Pty) Ltd
Elbroc Mining Products (Pty) Ltd Exxaro Coal (Pty) Ltd Exxaro Resources Limited
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Senmin International (Pty) Ltd Smec South Africa SMS Siemag SNC Lavalin (Pty) Ltd Sound Mining Solution (Pty) Ltd SRK Consulting SA (Pty) Ltd
The Journal of The Southern African Institute of Mining and Metallurgy
Forthcoming SAIMM events...
IP PONSORSH EXHIBITS/S ng to sponsor ishi Companies w ese t at any of th bi hi ex or and/ e th t ac cont events should rdinator -o co conference ssible as soon as po
SAIMM DIARY or the past 120 years, the Southern African Institute of Mining and Metallurgy has promoted technical excellence in the minerals industry. We strive to continuously stay at the cutting edge of new developments in the mining and metallurgy industry. The SAIMM acts as the corporate voice for the mining and metallurgy industry in the South African economy. We actively encourage contact and networking between members and the strengthening of ties. The SAIMM offers a variety of conferences that are designed to bring you technical knowledge and information of interest for the good of the industry. Here is a glimpse of the events we have lined up for 2015. Visit our website for more information.
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2015 ◆ CONFERENCE MINPROC 2015: Southern African Mineral Beneficiation and Metallurgy Conference 6–7 August 2015, Vineyard Hotel, Newlands, Cape Town ◆ CONFERENCE The Danie Krige Geostatistical Conference 2015 19–20 August 2015, Crown Plaza, Johannesburg ◆ CONFERENCE MINESafe 2015—Sustaining Zero Harm: Technical Conference and Industry day 26–28 August 2015, Emperors Palace Hotel Casino, Convention Resort, Johannesburg ◆ CONFERENCE World Gold Conference 2015 28 September–2 October 2015, Misty Hills Country Hotel and Conference Centre, Cradle of Humankind, Muldersdrift ◆ SYMPOSIUM International Symposium on slope stability in open pit mining and civil engineering 12–14– October 2015 In association with the Surface Blasting School 15–16 October 2015, Cape Town Convention Centre, Cape Town ◆ COLLOQUIUM 13th Annual Southern African Student Colloquim 2015 20 October 2015, Mintek, Randburg, Johannesburg ◆ CONFERENCE Young Professionals 2015 Conference 21–22 October 2015, Mintek, Randburg, Johannesburg
For further information contact: Conferencing, SAIMM P O Box 61127, Marshalltown 2107 Tel: (011) 834-1273/7 Fax: (011) 833-8156 or (011) 838-5923 E-mail: raymond@saimm.co.za
◆ CONFERENCE AMI: Nuclear Materials Development Network Conference 28–30 October 2015, Nelson Mandela Metropolitan University, North Campus Conference Centre, Port Elizabeth ◆ SYMPOSIUM MPES 2015: Twenty Third International Symposium on Mine Planning & Equipment Selection 8–12 November 2015, Sandton Convention Centre, Johannesburg, South Africa
Website: http://www.saimm.co.za
Integrated systems of support
Applying Poka Yokes in the mining industry
Patents Pending
+27 11 494 6000 www.ncm.co.za
Š New Concept Mining 2015