Saimm 201902 feb

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VOLUME 119

NO. 2

FEBRUARY 2019

Realising possibilities from mine to market.

Resource Evaluation

Mine Planning

Mining & Mine Development

Materials Handling

Mineral Processing

Tailings & Waste Management

Smelting & Refining

Transport to Market

Environment & Approvals

Non-Process Infrastructure

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VOLUME 119 NO. 2 FEBRUARY 2019

Contents Journal Comment: Diamonds: Source-to-Use, 2018 by T.R. Marshall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . President’s Corner: Diamonds are forever by A.S. Macfarlane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

!"('%*($ + %#& '$#' D. Tudor

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DIAMONDS — SOURCE TO USE 2018 CONFERENCE

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

Kimberlite-country rock contact delineation at Finsch Diamond Mine by T. Khati and M. Matabane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Accurate delineation of the kimberlite-dolomite contact enabled the slot (end) position of each sub-level cave tunnel to be optimized, resulting in easier blasting of longhole rings, and avoidance of contact overbreak and premature waste ingress. This enabled the mine to successfully reduce development costs, with increased confidence in the pipe contact for 3D geological modelling, improved blast design, and mitigation of early waste ingress while maintaining the contact’s integrity.

*(#')"+ + Camera Press, Johannesburg

" )*'(&(# + ) *)&)#'$'( ) Barbara Spence Avenue Advertising Telephone (011) 463-7940 E-mail: barbara@avenue.co.za

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk and increase productivity as we advance deeper into the Atlantic Ocean by S. Kirkpatrick and J. Mukendwa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Theory of Constraints (ToC) has been applied to focus efforts on improving overall business profitability. Through analysis of the mining processes, opportunities were identified, solutions developed, and initiatives implemented with impressive results. This paper outlines the solutions that were adopted and the results of the ToC analysis.

ISSN 2225-6253 (print) ISSN 2411-9717 (online)

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THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS. CopyrightŠ 2018 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 , 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. 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.

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The new Cullinan AG milling circuit – a narrative of progress by L. Musenwa, T. Khumalo, M. Kgaphola, S. Masemola, and G. van Wyk . . . . . . . . . . . . . . . . . . . A review of the design of the modern, fit-for-purpose diamond processing plant at Cullinan Diamond Mine, which was commissioned in 2017, was carried out to provide guidance on what can be expected from the milling circuit. The assessment provided an understanding of the new milling circuit regarding diamond liberation, energy consumption, and the future of diamond processing as a whole.

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Diamond exploration and mining in southern Africa: some thoughts on past, current, and possible future trends by W.F. McKechnie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Entrepreneurial exploration for diamonds in southern Africa will be tempered by the potential value equation and security of investment. Recent developments in diamond recovery technologies provide opportunities to reinvigorate current mines and old prospects previously considered too difficult or costly to exploit. Position on the cost curve will remain a key factor for survival in an increasingly competitive environment.

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Monitoring the performance of DMS circuits using RhoVol technology by M. Fofana and T. Steyn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The densimetric profiles of dense medium cyclone product streams have traditionally been produced by using heavy liquid sink-float analysis, which has certain disadvantages, such as the associated safety and health risks. The RhoVol technology can generate the density distribution of a batch of particles in a rapid, accurate, repeatable, and safe manner. This technology can be applied across all commodities that use dense medium cyclones.

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#')*#$'(%#$ + " (&%* + %$*" 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 D. Vogt, University of Exeter, United Kingdom

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VOLUME 119 NO. 2 FEBRUARY 2019

Financing diamond projects by J.A.H. Campbell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The lack of new significant discoveries in recent years has eroded investor’s confidence, yet no new discoveries are possible without investment in exploration. Alternatives to traditional funding mechanisms for diamond projects are discussed in this paper. The application of XRT in the De Beers Group of companies by A. Voigt, G. Morrison, G. Hill, G. Dellas, and R. Mangera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applications of XRT are found across kimberlite, alluvial, and marine operations as a result of intensive R&D conducted over the years, which has led to the development of a suite of machines that are capable of sorting and auditing diamonds across all size ranges. However, the technical challenge remains in the finer size fractions, for highcapacity-machines as a direct alternative to conventional diamond recovery technologies. Jwaneng – the untold story of the discovery of the world’s richest diamond mine by N. Lock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Despite the pre-eminence of Jwaneng as the world’s richest diamond mine, the discovery story has long been clouded in mystery. This is the 45-year old untold story of the Jwaneng discovery and contemporaneous Bechuanaland/ Botswana political and socioeconomic history. A strategy for improving water recovery in kimberlitic diamond mines by A.J. Vietti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scarcity of a consistent and reliable water supply is a constant reminder to Southern African diamond miners of the need to minimize raw (new) water input while maintaining or expanding mine throughput. This paper presents a first-step strategy that kimberlite diamond mine operators should consider and apply to meet and exceed their water-saving targets.

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PAPERS OF GENERAL INTEREST Investigation of flow regime transition in a column flotation cell using CFD by I. Mwandawande, G. Akdogan, S.M. Bradshaw, M. Karimi†and N. Snyders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two alternative methods are presented in this study in which the flow regime in the column is distinguished by means of radial gas holdup profiles and gas holdup versus time graphs obtained from CFD simulations. Gas holdup versus time graphs were used to define flow regimes with a constant gas holdup indicating bubbly flow while wide gas holdup variations indicate churn-turbulent flow. Development of a blast-induced vibration prediction model using an artificial neural network by A. Das, S. Sinha, and S. Ganguly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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An artificial neural network model was developed for prediction of blast-induced vibration using 248 data records collected from three coal mines with diverse geo-mining conditions. A good correlation between the measured peak particle velocity and the model output was established. The model performance was further improved by using sitespecific structural discontinuities as input. Changes in subsidence-field surface movement in shallow-seam coal mining by X-J. Liu and Z-B. Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Surface subsidence is a complex dynamic spatiotemporal process that constantly changes as mining advances. With Liangshuijing coal mine as a case study, 3DEC numerical software was used to simulate the development of the moving subsidence field from open-off cut to full subsidence on the working face. The results demonstrate that the formation of a subsidence field is directly related to coal seam depth and the extent of the mine workings. Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process by S.P. du Preez, J.P. Beukes, D. Paktunc, P.G. van Zyl, and A. Jordaan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The results of this investigation suggest that recycling of chromite-containing dust collected from pellet sintering offgas and fines screened out from the sintered pellets (collectively referred to as pre-oxidized chromite fines) up to a limit of 32 wt% of the total pellet composition may improve cured pellet compressive and abrasions strengths. Additional benefits may lead to improved metallurgical efficiency and reduced specific electricity consumption during smelting.

nal Jour t men Com

Diamonds: Source-to-Use, 2018

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his edition of the Journal is dedicated to the Diamonds: Source-to-Use conference held during June 2018 at the Birchwood Conference Centre in Johannesburg. It was attended by 107 delegates, some of whom came from as far away as Botswana, Namibia, Canada, the UK, and Belgium. It was the seventh conference in the series, which targets the full spectrum of the diamond pipeline from exploration through to sales and marketing. 2018 saw a renewed enthusiasm in the diamond mining sector in Southern Africa, even if it was tempered by tough economic conditions and political uncertainties. The theme of the 2018 conference, Thriving in Changing Times, bears testimony to how large and small companies are adapting to these circumstances, addressing risk and applying advanced technologies to well-tested exploration, mining, and processing methods.

Ernie Blom (Blom Diamonds), one of the keynote speakers, on the state of the diamond market, Thulo Khati, (Petra Diamonds) discussing aspects of kimberlite/country-rock contact delineation at Finsch Diamond Mine, and Andy MacDonald (SRK Consulting) presenting details of the valuation of diamond projects at the Diamond Reporting Workshop

There were 26 presentations of the highest scientific quality, eight of which were delivered by international delegates. In addition to the geological, metallurgical, and mining papers that formed the backbone of the conference, various presentations also featured ideas on the fundamental aspects of financing diamond projects, as well as a fascinating glimpse into the world of laboratory-grown (synthetic) diamonds. One of the highlights of the conference was the presentation by Norman Lock and Stuart Vercoe of the fascinating history of the discovery of Jwaneng Diamond Mine – the flagship operation of Debswana.

Jwaneng in the 1970s (photo courtesy of Stuart Vercoe) and more recently in April 2018 (photographer unknown) showing the progress in nearly 50 years of mining

The conference also featured a workshop (20 registrants) dealing with public reporting compliance issues around diamond projects and three technical site visits. A full-day visit was made to Cullinan Diamond Mine (courtesy of Petra Diamonds), where 13 delegates were able to see both the underground workings and the upgraded processing plant. Half-day site visits were made to Debtech and Ruzow Diamond Cutting Works. The Symposium proceedings were provided electronically to delegates and are available to the industry at large on https://www.saimm.co.za. The success of the conference was enhanced by the generous sponsorship of Imexsar (Pty) Ltd, the MSA Group, Geo-Explore Store and De Beers Technologies – South Africa. T.R. Marshall

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VOLUME 119

The Journal of the Southern African Institute of Mining and Metallurgy

t’s iden s e r P er Corn

Diamonds are forever

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The Journal of the Southern African Institute of Mining and Metallurgy

VOLUME 119

february 2019

s

his edition of the Journal is dedicated to papers covering many aspects of the exploration, mining, processing, and beneficiation of diamonds: a commodity of great significance to the economy of South Africa for more than a hundred years. Diamonds provide a unique source of interesting history in South Africa, and learnings from the early days of diamond mining are still relevant to the formulation of policy issues today, especially amongst artisanal, emerging, and junior miners. The first known diamond discoveries occurred along the banks of the Vaal River, near Barkly West, in alluvial deposits, after the discovery of the Hopetown diamond in 1807. These alluvial deposits were worked on claims belonging to both black and white owners. Diamonds were discovered in Kimberley after Erasmus Jacobs found a transparent rock on his father’s farm in the Northern Cape, in 1867, on the Colesberg Koppie. This 21.19 carat diamond became known as the Eureka diamond. After it became known that the alluvial tracts contained diamonds, and that the ’blue ground’ of Kimberley was diamondiferous, the first major strike occurred in 1868. Once word was out, prospectors and entrepreneurs descended upon the area, one of whom was Cecil John Rhodes in 1870, who invested three thousand pounds in the Kimberley diggings: a princely sum in those days. The real rush began in 1871, when Johannes and Diederik de Beer bought the Vooruitsig farm, in what was then the Orange Free State, and discovered diamonds on the land. The site of the farm became what is now the Big Hole, and the De Beers Mine. In 1876 Barney Barnato bought four claims in the Kimberley Mine, and this became the basis for the formation of the Barnato Diamond Mining Company. It is estimated that by 1873, some 50 000 miners were working approximately 1 600 claims over the area. These 10 m2 claims were serviced by a criss-crossing network of ropes and pipes as the mining, through pick and shovel, saw the original koppie transform into a 240 m deep pit. Not unlike today, the potential of wealth from minerals in the ground created tensions around land ownership, and rights to the minerals and claims. According to ‘The rock on which the future will be built’, by Emelia Potenza (https://www.sahistory.org.za/archive/allglitters-rock-which-future-will-be-built-emilia-potenza): ‘After diamonds were discovered in the Northern Cape, a battle began about the ownership of the land. The Griquas and Tlhaping who occupied the area claimed exclusive rights to the area. In addition, both Boer Republics (the Transvaal and the Orange Free State) claimed rights to the area.’ A long argument between these parties followed until Sir Henry Barkly, the Governor of the Cape, was asked to mediate. He set up a committee headed by the Lieutenant-Governor of Natal, Robert Keate, to decide on borders. For seven months this committee heard evidence from all four groups who were making a claim. On the basis of this evidence, the committee decided who had rights to the land in an agreement known as the Keate award. The Keate award favoured the Griquas’ claim. This meant that the land which eventually contained Kimberley and the richest diamond fields in the world was given to the Griquas. In the end this agreement helped the Griquas very little. Their leader, Nicholas Waterboer didn't have the power to control the diggers. In the early 1870s the population of Kimberley already numbered 30 000. There was intense rivalry between diggers as they fought over claims. This rivalry often led to racial conflict. Waterboer asked for British help. Barkly took over the area in Britain's name in 1872. A few years later Waterboer was arrested and imprisoned for attempting to free some of his subjects whom he believed had been wrongfully imprisoned and badly treated by the British. Growing unhappiness over ownership of the land and the right to make claims on the diamond fields led to a rebellion in 1878. The Griqua and Tlhaping rose up against British rule and were crushed.’ Small-scale miners, who held one claim each, were made up of people from Europe, Griqualand, India, Malaya, and China, and they in turn used migrant labour from many tribal backgrounds, including San, Khoikhoi, Griquas, Batlhaping, Damaras, Barolong, Barutse, Bapedi, Baganana, Basutu, Maswazi, Matonga, Matabele, Mabaca, Mampondo, Mamfengu, Batembu, and Maxosa. These people constantly moved in and out of the diamond fields, and became easy targets for robbery, either on their way to take up work or on their way back home. Dissent between the white claim-holders from Europe, America, and Australia, and the Griquas, Tlhaping, Malay, Indian, and Chinese holders, created racial tension that culminated in riots in 1875, based on a belief that African claim-holders were involved in illegal diamond buying. The British authorities responded by dispossessing and cancelling all claims owned by black people.

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President’s Corner (continued) Between 1867 and 1877, world diamond production had increased by more than tenfold, with the majority of it coming from Kimberley. At this time, the pit had reached its economic limit, and mining had to progress to more expensive underground methods. As a result, by 1880, the diamond price collapsed, and the number of claims had reduced from 1600 to 400. This was also a time of war, with the Anglo-Zulu War in 1879, and, in 1880, the first Anglo-Boer War. The diamond rush had clearly attracted many people from Europe, Australia, and America as prospectors and claimholders. According to historian Charles von Onselen, it also attracted many colourful characters who saw the opportunity for a new life in a far-off land. Some of these were outcasts from Ireland and England, who came to the Transvaal and Free State republics to either avoid criminal prosecution at home and/or to feast on the riches of the diamond and new-found goldfields through highway robbery, safe-blowing, and fraud, stretching from Kimberley to Barberton. People such as Jack Mcloughlin and the McKeone brothers became legendary as folk heroes who, in Robin Hood fashion, supposedly stole from the rich and gave to the poor. Unfortunately, in reality they often stole from the poor, especially the migrant labourers, and rarely redistributed their new-found wealth, except to themselves. The prevailing circumstances of the drop in prices (which created poverty among people who had to give up their claims), the consolidation of claims, and the aftermath of the wars created a perfect environment for these people, who became known as ’social bandits’, until the advent of the railway curbed their horseback escapades, and forced them into exile or prison. After these turbulent days, consolidation and expansion of the diamond industry continued, and apart from stoppages during the second Anglo-Boer War and the siege of Kimberley, and the first and second world wars, production increased to the record level of 9.7 million carats in 2017, with the discovery of new deposits such as Venetia and Finsch. So what has happened to the small-scale miners and prospectors? While the consolidation of the industry has been the foundation for its growth since 1880, small-scale operators have found it increasingly difficult to survive. The initial challenge faced by a budding small-scale diamond miner is that of gaining access to mineral resources, and being able to assess the quality of those resources for licencing purposes. Further challenges faced by the small-scale and/or emerging miners include the need to comply with regulations and codes (such as the Kimberley Process) which, while absolutely necessary, does impact severely on the small operators in terms of cost. In this regard, safety and health and environmental considerations must be met. Sometimes the small-scale operator may find that, without having internal capacity for technical compliance, they have to pay consultancy fees that are excessive for the company’s bank balance. This leads to another challenge, being one of funding. The small-scale alluvial diamond miner may find it extremely difficult, if not impossible, to fund a start-up venture, because of the requirement of large-scale bulk sampling. The author was, at one stage, involved in helping to establish a small-scale cutting and polishing business plan. While this was unfamiliar territory, a steep learning curve on the complexities of diamond pricing, marketing, licencing, and supply chain management quickly illustrated the challenges faced by a small-scale, but good, business, stuck in a supply chain reliant on intermittent supply from small-scale miners, and the ability to sell into high-paying markets. It feels like we have come full circle, from the small-scale claim days, through consolidation into major companies, and now back to the need to find ways to stimulate the small-scale and emerging mining sector and its value chain. Of course, nowadays this has to be done in a safe and responsible manner, taking into account social, economic, environmental, and legal perspectives. Much very good work has been done through both the public and the private sectors in developing an environment in which this sector can flourish. This has been done through the DTI and the IDC, the Emerging Miners Desk of the Minerals Council South Africa, as well as in the private sector, for example Anglo American’s Zimele Fund, the Tshikululu Social Investment Fund, and the Khula Enterprise Fund. The Institute has provided conferences and seminars on the development of artisanal and small-scale mining in the past, and will continue to do so. Forthcoming events include a breakfast event on 18 February, and this area will be developed into a theme for the future, so that integration of much of the good work now being done can result in real and responsible developments in this area. It is always good to learn from history, whether it be good or bad, and in this case we must remember the words of Karl Marx, who said ‘History repeats itself, first as tragedy, second as farce’. Martin Luther King said ‘We are not makers of history. We are made by history’.

A.S. Macfarlane President, SAIMM

s

vi

february 2019

VOLUME 119

The Journal of the Southern African Institute of Mining and Metallurgy

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a1

Kimberlite-country rock contact delineation at Finsch Diamond Mine by T. Khati* and M. Matabane*

Accurate delineation of the contact between a kimberlite pipe and country rock at production level depths is a challenge due to limited geological data. Geological information is obtained from widely spaced diamond core boreholes which are drilled either from surface or from higher mining levels within the pipe. Kimberlite pipe/country rock contacts are notoriously irregular and variable, further reducing the confidence in contact positions defined by the drill-holes. At Finsch Diamond Mine (FDM), the opportunity arose to further improve the confidence in the contact positions relative to the planned slot (end) positions of each sublevel cave tunnel during the development stage of these tunnels. As a result, the accuracy of the 3D geological model has improved. The use of diamond drill core for this purpose is expensive due to site establishment requirements. The lengthy time taken during site establishment also delays the development of tunnels and support cycles, thereby extending the completion dates. FDM has reduced delays during development by adopting percussion drilling, in conjunction with gamma ray logging. The S36 drill rig is mounted on a moveable platform and does not require a costly and lengthy site establishment. The holes are generally drilled (0°/flat) on grade elevation, and these holes could also be drilled from the rim tunnels (developed in waste) into the kimberlite pipe. A single-boom production drill rig is normally used to drill holes about 20 m in length. On completion of the contact delineation drilling, gamma logging of the holes is conducted using the GeoVista geophysical sonde (or probe) to log the natural gamma signature of the dolomite/ kimberlite contact. The advantage of this tool is that the readings are continuous within centimetre intervals, and due to contrasting characteristics between kimberlite (rich in clay minerals) and dolomite, the contact position can be determined accurately. The better definition of contact positions also adds value to tunnel stopping distance in terms of developing the tunnel’s slot at the optimum distance from the contact (easier blasting of longhole rings, avoidance of contact overbreak and premature waste ingress, and other matters relating to extraction of ore from these tunnels). This method is highly successful and has reduced development costs (on-time completion), improved definition of the pipe’s contact position for geological modelling, improved blast design, and mitigated early waste ingress by maintaining the contact’s integrity. = :748 natural gamma logging, contact delineation, kimberlite-dolomite contact, middling, waste ingress.

;97:4109<:;> The objective of the project is to establish the kimberlite-dolomite contact position with the intention of ensuring proper planning designs and management of waste during development. Contact delineation is performed

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=:5:3<065>8=99<;3> Finsch mine is located 160 km west-northwest of the city of Kimberley, South Africa. The kimberlite main pipe is a near-vertical intrusion with a surface area of 17.9 ha and is elliptical in outline. The main orebody occurs on a northeast-striking dyke called the Smuts Dyke and forms part of the Finsch kimberlite cluster, comprising the Finsch, Shone, and Bowden pipes (Ekkerd, 2005). It has been classified as a Group II kimberlite, with an age of 118 Ma. One of the characteristics of Group II kimberlites is a high potassium content.

* Petra Diamonds, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

97

;:.8<8

with the intention of improving confidence in the resource model. The holes are drilled flat using a percussion drill and logged using a GeoVista geophysics tool. Geological information is available as soon logging is complete and the updated geological model is communicated to the production team. The main aims of the project are to reduce the time delay during contact delineation, and increase confidence in the position of the kimberlitedolomite contact in an endeavour to fast-track the delivery of the development ends to caving. Kimberlite-dolomite country rock delineation is done from the rim tunnels or from within the kimberlite pipe in each tunnel. The aim of planning the mining tunnel and slot at the right location is to ensure stability of the tunnels and safe extraction of ore with no waste being accounted for. Secondary to this, delineation has enabled Finsch Diamond Mine (FDM) to locate the mudstone lenses found at the slot positions of the tunnels, thereby ensuring that slot positions are not established within an incompetent lithology.

Kimberlite-country rock contact delineation at Finsch Diamond Mine

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The Finsch kimberlite consists of eight main kimberlite facies, denoted F1 to F8 (Figure 2). The F5/F6 and F3 facies constitute the precursor bodies on the outer perimeter of the main pipe. These precursor bodies are mainly coherent kimberlite types. F1, F7, and F8 form part of the main pipe, with F1 and F8 being volumetrically the most significant units. F1, F7, and F8 are volcanoclastic kimberlite types. F2 and F4 occur as a hypabyssal plug and dykes within the main pipe and are postulated to represent the last stage of intrusion of the kimberlite (Ekkerd et al., 2003). Kimberlite magma, upon eruption, sampled Karoo and Transvaal Supergroup sediments, and these are now preserved in the main pipe as large xench’ths of basalt, mudstone, dolerite, and sandstone.

*<9=76917=>7= <= In sublevel block caving the middling between the rim tunnels and the kimberlite-dolomite contact should not be more than 15 m to ensure tunnel stability in the SLC environment (Tukker et al., 2016). Moreover, the exact spatial location of the pipe contact is as important as that of the internal kimberlite phase contacts for ensuring the stability and integrity of the rim tunnels. The designs of rings were done based on trial and error or on the application of ‘rule of thumb’ (Onederra, 2004). Moreover, rings must function as designed, and it is important that engineers, geologists, and mine and plant managers have access to accurate modelling and simulation processes and costeffective design parameters at the outset. According to Onederra and Chitombo (2007), the objectives of ring design include (but are not limited to) reduction of dilution and efficient orepass performance, therefore FDM decided to delineate the contact. The difference in blasting domains is a significant factor in ensuring proper ring designs and should be ascertained jointly by the geologist, geotechnical engineer, and drill and blast engineers.

<80188<:;> At FDM, the middling between the slot position and the

98

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,<317=> &,<;80-> <6/:;4>(<;=> </+=75<9=>$60<=8> ) =74> 5 > %%

Table I

=;8<9<=8>$:7>=60->5<9-:$60<=8 *<9-:5:3 F1 F8 Dolerite-basalt breccia (DBB) F3 F4 dyke F2 Sandstone Mudstone Dolomite breccia Dolomite

=;8<9 > 3 0/ 2.53 2.61 2.6 2.54 2.74 2.8 2.24 2.41 2.59 2.81

kimberlite-dolomite contact is 7.5 m to 8 m. The reason for this middling is to achieve the best extraction of ore without damaging the contact. Rings are to be designed in such manner that the interaction with the contact is minimal, i.e. no damage to the contact and yielding more ore before waste ingression. Incompetent lithofacies such as mudstone compromise the stability of tunnels (Tukker et al., 2016), therefore the slot must be within competent lithofacies.

Kimberlite-country rock contact delineation at Finsch Diamond Mine in the drawpoints which, coupled with delineation, effectively controls excessive waste mining and loading. Mathebula (2004) estimates 70% waste ingression towards the end of the block 4. The dolomite sidewalls ravel back to a stable angle, and large volumes of waste will report to the bottom, which will result in low grade due the mixing of dolomite and kimberlite. Bull and Page (2000) characterize sublevel caving as a compromise between ideal layouts and what the orebody allows. For this reason, the mine engineers tend to focus on immediate development costs and conclude that less development is cheaper, thereby overlooking the negative effects on head grade (due to planned and unplanned dilution) and loss of production. The mine plan should consider dilution and production as the most significant factors in mine design layout. During production, the rings must be inclined (10–20º) to prevent waste from being drawn in early before most of the ore material is loaded (Figure 5). Kimberlite-country rock delineation was adopted at Finsch with the intention of reducing development costs such as that due to breached contact support. Figure 6 shows tunnels where the contact was breached which resulted in an increase in mining cycle time, costs, and reinforcement

SWPC Mudstone DBB F1 F4 dyke

F3 F8

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99

A different approach was taken when designing slot positions within the South West Precursor (SWPC). A 2 m middling was opted for as the SWPC is extremely competent hypabyssal kimberlite that provides easy caving. As a consequence, mining too close the country rock poses the risk of introducing large amounts of waste early (Butcher, 2000). Waste or dilution is better prevented than controlled as it threatens the viability of the mine, especially in SLC where up to 40% waste is encountered. To illustrate early introduction of waste, Figure 3 shows a slot ring reporting 70% waste rock upon the first blast. The irregular shape of the SWPC, poor mining practices, and failure to define the contact are the reasons for breaching of the kimberlite-dolomite contact on the 61 level. In contrast to the 61 level, the 63 level orebody boundary was redefined, with few tunnels breaching the contact (see Figure 8). The SWPC (F5/F6) is coherent/hypabyssal, competent kimberlite. Figure 4 shows the kimberlite facies at Finsch mine relative to the country rock (dolomite); there is dolomite situated between the main pipe and SWPC. Finsch mine utilizes vertical and inclined dump ring designs. In addition to raiseboring, vertical dump is used to initiate free fall of ore flow. Experience has shown that a correctly designed middling will enable proper ore caving with less damage to the contact, which is already brecciated/broken, thus ensuring minimal unplanned waste ingress and ring designs to within Âą1–2 m. In addition, the correct middling will enable a ring dump not less than 45°. A flatter angle will make charging and ore extraction difficult because of the flat repose angle (Bullock and Hustrulid, 2001). The steeper the angle of repose, the better the extraction due to the flow of ore under gravity. Proactive waste management practices during the development of tunnels are crucial to ensure that the rings continue to hold back excessive waste ingression. Dilution and ore losses are drawbacks for sublevel caving. Scientific investigations have been conducted to determine the flow of ore in a cave and to identify means of reducing ore losses and minimizing dilution. Dilution varies between 15% and 40% and ore losses can be from 15% to 25%, depending on the geological conditions. Dilution is of less importance for orebodies with diffuse boundaries where the host rock contains low-grade mineralization, or for magnetite ores, which are upgraded by simple magnetic separators (Hamrin, 1986). The geology department at FDM conducts visual estimations to determine waste percentages

Kimberlite-country rock contact delineation at Finsch Diamond Mine

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requirements (class 7 support) (Onederra, 2004). As a result of breaching contacts, there will be high waste ingress in comparison with what the waste ingress model of Page and Bull (2001) predicts (70–80%) at the end of caving. As an illustration, Figure 3 shows 70% waste vs 80% ore recovery. The need to delineate the kimberlite-country rock contact was shown by the irregularity of the SWP, and poor drilling and blasting practices which resulted in contact positions not

being at the anticipated position (see Figure 8). Delineating the contact improves geological confidence in the model and compensates for the core losses encountered during drilling as a result of core handling errors and bad ground conditions (Figure 6). Four out of five tunnels intersected the kimberlite-dolomite contact unexpectedly. If the contact is not delineated accurately, tunnels are blasted through the contact, thereby damaging about 2 m of the baked and brecciated contact margin or zone (Figure 7). Specifically, the visual estimation performed in 63L SW KT6 slot resulted in 70% waste vs kimberlite. Contact delineation has increased confidence in the resources even though there has been a volumetric decline, as shown by the previous contact position (grey vs black outline in Figure 8). The irregular shape of the SWPC and core loss during diamond drilling is compensated with the use of contact delineation (Figure 8). FDM experienced poor mining practices on 61 level, as shown by Figure 8, as four out of five tunnels breached the kimberlite-dolomite contact. In order to implement correct principles in controlling waste, there is a need to define waste, i.e. top, side, or internal. The

,<317=> & 6//6>76 >5:3>:$>+:7=-:5=>) '2 2 >8-: <;3>0:7=>5:88> (696+6;=>6;4> -69< > %2'

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100

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Kimberlite-country rock contact delineation at Finsch Diamond Mine

Geotechnically, it is important to know the lithology being mined, especially as regards density and other properties. A 2 m offset or standoff is considered when designing rings within tunnels next to the kimberlite-dolomite contact. This is important in ensuring that minimal waste is allowed into the cave and there is no damage to the kimberlite-dolomite contact. The inclination of rings is 10 to 20Âş forward to assist in feeding the ore prefentially into the drift by providing a small overhang to diluting material above (Darling, 2011). The inclination is to ensure that holes are drilled on a similar plane for improved blasting results and additional protection of the brow. Inclined brows improve stability and access for charging of the holes. Safety is another reason for inclined ring designs (Kosowan, 1999). In addition, the gradient depends on the relative size and densities of ore and waste rocks. When ore material is coarser than waste material, the dump must be tilted backwards to avoid fine ore material being lost in the void of blasted waste rock (Kosowan, 1999). A forward inclined ring is best for ensuring good breakage of rock blocks and would avoid crossing discontinuities that could potentilly result in cut-offs and poor fragmentation. The purpose of the slot position is to provide a free face when blasting (Table II and Hoek, 2016). The Karoo sediments found within the main pipe and in the SWPC make drilling ahead of the development tunnels a necessity. Mine planning and design flexibility is needed to accommodate changes in the modelled lithological units and their effects on the geotechnical and resource model (Tukker et al., 2016). The middling between rim tunnels and actual contacts should not be less than 15 m to ensure stability of rim tunnels. Geotechnically, sublevel caving requires specific rock mass conditions and stoping geometry; ring drilling may vary and involve or include upholes, downholes, and even inclined holes (Villaescusa, 2014). Figure 8 shows 61L which intersected the dolomite contact prematurely due to a sharp inward turn of the contact. The 61L SW KT6 is situated between tunnels 8 and 9; contact holes were not drilled beforehand and ‘blind’ mining was undertaken in this case. This sharp turn or curve is believed to be the reason for the 70% waste ingress when the first ring was blasted. Contact drilling could have mitigated this issue. In order to implement working waste

,<317=>!&#=79<065> <;05<;=4>41/.>:$>7<;3>4=8<3; # *"()>22!

101

reduction is by definition, design, and draw principles. Unsupported mining in massive orebodies tends to lead to high waste or dilution (40%) (Kosowan, 1999), and dilution increases with more muck being extracted. Mine design is critical as it impacts on development costs, drill and blast, recovery, and geotechnical stability, i.e. where the stress field orientation and rock mass quality influence the mine design. FDM experiences both internal and side dilution, hence contact delineation is aimed at preventing or reducing early ingress of waste. As identified by Butcher (2000), the define, design, and draw principle is aimed at preventing dilution rather than controlling it. However, if prevention fails, controls must be put in place. The FDM geology department conducts visual inspections on a daily basis. According to Butcher (2000), the define principle is geologically significant in delineation of the orebody and surrounding country rock. Geology must determine the boundaries of country rock and orebody. Following the design principle, FDM set the boundary at 7.5–9 m between the orebody contact and stope boundary. This measure will reduce the side and internal dilution through better ring designs and better blasting practices. In addition to prevention principles, FDM conducts daily visual estimations as a control measure in the drawpoints and scanline logging during tunnel mapping. Figure 9 shows ring designs used at FDM and their benefits, i.e. a vertical front is used to initiate the rings and inclined rings have the potential to delay introduction of waste from above, and shield brows from break-back. When designing rings in an SLC mining method, it is important to consider lithology, density, and grade (Mathebula, 2004). Development should take place in competent rocks to accommodate mining-induced stresses. Mudstone is incompetent, hence FDM avoids the development of slots within the mudstone facies. Slot position is dependent on the rock mass conditions, stope access, and extraction sequence. The slot should also be designed to reduce failure within the production rings (Villaescusa, 2014). Support should be increased when mining through incompetent ground (Mzimela, 2013). Finsch kimberlite facies are hard, but upon interaction with water they become as weak as soil; thus the strength is compromised upon exposure to moisture. The emplacement mechanism influences the size and geometry, rock mass competency, and character of the pipe contact zones. These factors affect the mining method and dilution. Characterizing the clay provides important clues to the susceptibility of the various kimberlite facies to weathering. Knowledge of country rock and pipe geology is paramount. Delineation programmes should aim to ensure that the holes penetrate deep enough into the waste rock to ensure it is not a country rock xenolith. One of the geotechnical issues to consider in selecting the mining method and design is the strength and deformation characteristics of the rock mass. Some kimberlite facies are weak to moderately strong. Designing in a strong rock mass is not an issue; however, when kimberlite is exposed to moisture, swelling of smectite clays present in the matrix weakens the rock. The weathering susceptibility of kimberlite must be known in order to build this factor into the mine design and avoid surprises during development, such as a tunnel collapses due to water.

Kimberlite-country rock contact delineation at Finsch Diamond Mine Table II

1+5= =5>06 <;3>4=8<3;>.676/=9=78> := > %2' 676/=9=7

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";<9

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2.4–6.0 m

Extraction heading Width (w)

Maximize draw throat – maintain stability

4.2 m

Spacing (w+W)

Minimize development

14.2 m

Minize to reduce ore loss

3.5 m

Sublevel interval (S)

Height (H)

Related to blast-hole drilling accuracy

15 m

2.5–4.3 m 8–25 m

Height of draw (H)

Less than twice sublevel interval

29 m

12–30 m

Pillar width (W)

Optimize ore recovery

10 m

2–3.5(w+W) Ring Angled forward- minimize dilution and maintain brow stability

+80°

Âą70–90°

Burden (V)

gradient

Balance between ore recovery and waste dilution from previous ring

1.8 m

1.5–3.7 m

Support

Maintain brow and pillar stability

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control measures, the type of waste must be known – namely; top, side, and internal waste. This will lead to waste reduction by defining, designing, and drawing (Butcher, 2000). The middling between the slot position and the contact should be 7.5–8 m. Therefore, owing to the four tunnels intersecting the contact, early waste ingress was experienced when the rings were blasted. This should be avoided at all costs as failure to drill in advance results in mining through the contact. In addition to the sharp inward turn of the contact from previous position, waste ingress was compounded by the type of ring design. An inclined front reduces ingress of waste as it prolongs ore extraction. Moreover, contact delineation helps in ensuring that pockets near contacts are found and properly supported. Pockets of water reduce the stability of the kimberlite-dolomite contact, rendering it weak. The cost of blasting and supporting was high when contact delineation tunnels were developed blindly into the country rock, resulting in unnecessary expenditure.

102

# *"()>22!

Table III

:/.67<8:;>+=9 ==;>4<$$=7=;9>81..:79>05688=8 18=4>69>,<;80->/<;=>6;4>9-=<7>7=8.=09< =>0:898 5688>81..:79

.=>:$>81..:79

Class 6

Cable anchors Roofbolts Transverse straps Longitudinal straps

Class 3b

Anchors Longitudinal straps Transverse straps Roofbolts

Class 3a

Roofbolts Transverse straps Longitudinal straps

Class 1

Transverse straps Roofbolts

16;9<9 >.=7><9=/ :89>.=7>/ 70 84 14 12

Highest

Lowest

7 70

Kimberlite-country rock contact delineation at Finsch Diamond Mine This wastage is in the form support equipment, where for example class 6 is used instead of class 1 (class 6 support is three times the cost of class 1 support). Historically, drilling to confirm inner geological contacts has been an expensive, challenging, and time-consuming exercise. However, at FDM short percussion holes about 30 m in length have been drilled to delineate the contact, with an information turnaround time of less than 24 hours. Contact delineation has afforded FDM with information regarding lithological units and enabled quick decisions such as whether to extend a tunnel or keep to the original design. In addition, where mudstone was intersected slot positions have been moved to a more competent kimberlite facies

EKKERD, J. 2005. The geology of the block 5 resource. Internal Report. Finsch

:;0518<:;8

HUSTRULID, W. and KVAPIL, R. 2008. Sublevel caving – past and future.

Finsch Diamond Mine experienced challenges relating to SLC development tunnels where the kimberlite-country rock contact was breached, resulting in increased development and support costs which in turn led to delays in turnaround times (mining cycle). Drilling a single contact delineation hole per tunnel is found to be workable; however, two to three holes are ideal to ensure covering all sides of the slot area. In order to avoid delays in drilling for the contact, monthly planning should incorporate the delineation holes and the planning and survey departments need to include these holes when issuing survey notes. In the case of tunnels breaching the contact, as shown by Figure 8, penalties should be implemented. Therefore, two to three percussion holes approximately 7.5–8 m in length should be drilled per tunnel. Contact delineation has resulted in cost savings, especially regarding waste reduction. Furthermore, early waste ingress was experienced in certain areas of the mine as a result of breached kimberlite-dolomite contacts. Knowledge of the position of the contacts increased the confidence in resources, as shown by Figure 8 and Figure 10, and geological boundaries were adjusted accordingly. At Finsch Diamond Mine, waste ingress is the result of poor mining practices and incompetent ground conditions. The geology department, through contact delineation, has prevented dilution early on rather than controlling waste, with cost savings estimated to be in the millions. As part of enforcement, financial penalties should be put in place to ensure that mining contractors do not breach the kimberlite-dolomite contact. In addition, monthly planning must take the drilling of these contact holes into consideration.

?=$=7=;0=8 BULL, G. and PAGE, C.H. 2000. Sub-level caving – Today's dependable low cost "ore factory". Proceedings of Massmin 2000, Brisbane, Australia.

Diamond Mine. EKKERD, J., STIEFENHOFER, J., FIELD, M., and LAWLESS, P. 2003. The geology of Finsch Mine, Northern Cape, South Africa. Proceedings of the 8th International Kimberlite Conference, Victoria, BC, 22–27 June 2003. Elsevier, Amsterdam. HAMRIN, H. 1986. Guide to Underground Mining Methods and Applications. Chapter 1. Atlas Copco, Sweden. HOEK, E. 2016. Surface and underground project case histories: Comprehensive Rock Engineering: Principles, Practices, and Projects. vol. 5. Elsevier.

Proceedings of the 5th International Conference and Exhibition on Mass Mining, Luleü, Sweden, 9–11 June 2008. Lulea University of Technology. pp. 124–132. KOSOWAN, M.I. 1999. Design and operational issues for increasing sublevel cave intervals at Stobie Mine. MASc thesis, Laurentian University, Sudbury, Ontario, Canada. MATABANE, M.R. and KHATI, T. 2016. Application of gamma ray logging for kimberlite contact delineation at Finsch Diamond Mine. Proceedings of Diamonds Still Sparkling, Gaborone, Botwsana, 15–16 March. Southern African Institute of Mining and Metallurgy, Johannesburg. MATHEBULA, V. 2004. The design and implementation of the Toop of Block 4(TOB 4)/ BOP resource at Finsch Mine (a division of central mines). Journal of South African Institute of Mining and Metallurgy, vol. 104. pp. 163–170. MZIMELA, B. 2013. Code of Practice: The design, development/construction, safe operation and maintance of draw points, tipping points, rock passes and box. Lime Acres. ONEDERRA, I. 2004. Breakage and fragmentation modelling for underground production blasting application. Proceedings of the IRR Drilling and Blasting 2004, Conference. Perth, WA. ONEDERRA, I. and CHITOMBO, G. 2007. Design methodology for underground ring blasting. Mining Technology, vol. 116, no. 4. pp. 180–195. PAGE, C.H. and BULL, G. 2001. Sub level caving: A fresh look at this bulk mining method. Underground Mining Methods: Fundamentals and International Case Studies. Hustrulid, W.A. and Bullock, R.L. (eds). Society for Mining, Metallurgy, and Exploration, Littleton, CO. p. 718.

Australasian Institute of Mining and Metallurgy, Melbourne. pp. 537–556. TUKKER, H., MARSDEN, H., HOLDER, A., SWARTS, B., VAN STRIJP, T., GROBLER, E., and BUTCHER, R.J. 2000. Dilution control in Southern African mines. Proceedings of

ENGELBRECHT, F. 2016. Koffiefontein Diamond Mine sublevel cave design.

Massmin 2000, Brisbane, Australia. Australasian Institute of Mining and

Proceedings of Diamonds Still Sparkling, Gaborone, Botwsana, 15–16

Metallurgy, Melbourne. pp. 1133–118.

March. Southern African Institute of Mining and Metallurgy, Johannesburg.

DUSTAN, G. and POWER, G. 2011. Sub level caving. SME Mining Engineering

Littleton, CO. pp. 1417–1452.

VILLAESCUSA, E. 2014. Geotechnical Design for Sublevel Open Stoping. CRC Press.

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Handbook. Darling, P. (ed.). Society for Mining, Metallurgy & Exploration,

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http://dx.doi.org/10.17159/2411-9717/2019/v119n2a2

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk and increase productivity as we advance deeper into the Atlantic Ocean by S. Kirkpatrick* and J. Mukendwaâ€

The mining operation at Namdeb’s Southern Coastal Mine (SCM) is unique. It targets gravel layers up to 30 m below sea level, which continue to dip deeper, further west, under the Atlantic Ocean. On this storm-dominated coastline, severe water seepage into mining areas, rugged orebody footwall characteristics, and highly variable resource grades all contribute to a challenging operational environment. Namdeb has a proud history of innovation, and as the mine progresses further westwards and associated technical and economic challenges increase, this innovative culture has become essential to the future of the mine. The Theory of Constraints (ToC) has been widely used at SCM, and across the mining discipline, to focus efforts on improving overall business profitability. Through analysis of the mining processes, opportunities were identified, solutions developed, and initiatives implemented with staggering results across all three mining disciplines, i.e. stripping, load and haul, and bedrock bulking and cleaning. This paper outlines the solutions adopted and the results of the ToC analysis. mining, Namibia, theory of constraints, improvement, beach nourishment.

Southern Coastal Mine (SCM) is one of three Namdeb operations located in the southwestern corner of Namibia, between the Orange River in the south, the Namib Desert to the east and the Atlantic Ocean to the west (Figure 1). These three geographical features set the backdrop for the world’s greatest marine diamond mining placer (Badenhorst, 2003). SCM is situated along the coastline extending from the mouth of the Orange River to Chameis Bay approximately 120 km to the north. The active mining area, called Mining Area 1 (MA1) is, however, limited to very narrow strip, 30 km in length, in the south of the licence area, close to the town of Oranjemund. Namdeb, a wholly owned subsidiary of Namdeb Holdings, is a 50/50 joint venture between De Beers and the Government of Namibia. Diamonds were first discovered along the Namibian coastline in April 1908, close to the town of Luderitz, but mining operations started in the SCM mining area only in February 1929 (Schneider, 2009). Initially all

The Orange River system (Figure 2) has transported coarse gravel material, including diamonds, from the southern African subcontinent to the west coast of South Africa and Namibia since Cretaceous times (Jacob, 2005; Bluck, Ward, and de Witt, 2005). At least two phases of uplift of the interior resulted in renewed river energy, increased erosive power, and associated coarse river load, manifesting as deeply bedrock-incised fluvial channels with remnant abandoned terraces and associated gravel deposits along

* Mining Optimisation, Southern Coastal Mine, Namibia. †Southern Coastal Mine, Namibia. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

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Namdeb mining operations exploited massive land-based deposits, producing nearly 40 million carats from MA1 between 1920 and 1977 (Schneider, 2009) when mining targeted onshore raised beaches west of the high water line. Since 1977 however, SCM and Namdeb have developed a unique mining method to economically extract the resource which extends westward under the Atlantic Ocean. Both the location and mining method unsurprisingly present a unique set of challenges, making economic extraction increasingly difficult as the orebody dips deeper and deeper to the west beneath the sea. Innovation and operational improvement remain crucial to extending the life-of-mine (LoM) of SCM. A number of fundamental changes have been implemented at SCM over the last 3 years which have reduced the operational risk and increased productivity of the core mining functions. This paper describes these changes and their direct results.

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk

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capable of moving cobble-sized gravel at depths of 15 m (de Decker, 1988). Consequently, the sand component of the outfall of the Orange River is transported offshore and north by the longshore drift system, and at certain points it is washed back onshore to feed the Namib Sand Sea. The gravel (and diamond) component of the outfall is retained along the coast for about 300 km where it has been reworked into a series of coarse beaches underlain by the gullied and potholed Late Proterozoic metasedimentary basement. This rugged basement forms fixed trapsites which further upgrade the diamondiferous gravel on wave-cut platforms (Jacob et al., 2006). Four types of gravel beaches have been identified in the Pleistocene-Holocene gravels of the Namibian coastal strip north of the Orange River (Spaggiari et al., 2006; Schneider, 2008). Near the river mouth, gravel accumulates as spits with associated back-barrier lagoons; northwards, with increasing distance from the point source of sediment supply, spits give way to barrier beaches, linear beaches, and finally pocket beaches where the coastline forms natural bays. The active SCM mining areas are situated within the zone of barrier beaches and linear beaches. The diamondiferous orebody comprises a coarse gravel facies that directly overlies and infills a rugged, gullied, and potholed predominantly schistose bedrock footwall. Clay footwall areas form a lesser proportion of the mine plan and are generally less rugged. The ore horizon is overlain by a sandy overburden comprising both marine and accreted sand with minor gravel terrace components.

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the river course. The river ultimately deposited its load of diamondiferous material into the Atlantic Ocean, where marine reworking processes subsequently further upgraded the diamondiferous gravel during repeated sea-level transgressions and regressions (Bluck, Ward, and de Witt, 2005), resulting in a placer deposit comprising 95% gem quality stones. The wave-dominated west coast of Namibia is one of the most vigorous coastlines in the world; 90% of the waves have a height of 0.75–3.25 m, with an average of 1.75 m for winter and 1.5 m for summer (Rossouw, 1981; de Decker, 1988). The coastline is also exposed to storm events associated with passing frontal systems (Smith, 2003) and wave heights above 9 m. This, in combination with the longperiod swell and a strong prevailing SW wind regime (Rogers, 1977) produces a strong northerly longshore drift,

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The mining operation at SCM has transitioned from an extensive above sea-level, volume-driven mining operation, through westward expansion of the mining faces, to an operation largely below sea level. To enable movement of the mining areas westwards a process of deliberate coastal accretion is used to transport and deposit sand onto the beaches at specific areas and at defined rates. Various accretion systems are employed; these include conveyors, treatment plant tailings disposal, and the dumping of overburden material, which is part of the overburden stripping process. The sand deposition profiles are fed into an accretion model, which has been developed and calibrated over a number of years. This model is used to predict the annual +2 m contour positions used in the mine planning process. The +2 m contour is the contour that runs along the beach at 2 m above mean sea level (AMSL) and defines which areas will be available for future mining. The mine plan and accretion model are interdependent; changes in the mine plan alters the deposition points and volumes, resulting in changes to the accretion line predictions. Figure 3 shows the five-year waste deposition volumes and associated annual accretion prediction lines. The sub-sea-level mining operations at SCM, directly adjacent to the Atlantic Ocean, make operations vulnerable to frequent storm events. Workings are protected from storm events by seawalls constructed along the western side of the mining area. Prior to opening a new mining cut, seawalls are constructed along the western side of the cut to a height of between 7 m and 10 m above sea level, with a minimum width of 20 m. To further mitigate risk during storm events, the seawalls are set back a minimum of 25 m east of the

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk

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+2 m contour and are often widened to 30 m. Figure 4 outlines a simplified version of the cycle of accretion, seawall construction, and mining. On completion of the seawall, the stripping fleet, consisting of 80 t excavators and 40 t articulated dump trucks (ADTs), then focuses on loading overburden material out of the mining cut and dumping it on the beach to the west of the seawall (Figure 5). This material dumped is thus made available for beach accretion, creating space for future mining. Material close to surface is generally dry but once the phreatic line has been intersected, seepage from the sea ingresses into the mining cut resulting in much wetter overburden material and, consequently, the need for water management. Water channels are constructed along the eastern toe of the seawall to channel water to a sump where pumps remove water from the cut. Channels and sumps are maintained and deepened as mining progresses down to bedrock. Once excavators ‘hit bedrock’, i.e. once the excavator bucket starts to scrape against the highest bedrock points, the stripping operation has reached completion and bedrock teams can move in to start bulking operations. Bedrock teams consist of both a bulking and a cleaning crew (Figure 6) who, for security reasons, work only during day shift. The bulking process is used to clean out most of the material from bedrock gullies and potholes using hydraulic equipment to break open narrow areas, load out material, and finally scrape the bedrock clean with bullnose buckets. The bulking fleet includes a bulldozer, a 60 t excavator, a number of smaller 20 t hydraulic excavators, and 20 t hydraulic hammers. When cemented material is encountered, hydraulic hammers are used to break this harder material out, after which dozers and the 60 t excavator are used to transfer material to loading areas, where stockpiles can be loaded and hauled to the plant for treatment. Bedrock cleaning crews complete the final vacuuming of the bedrock, removing all fine gravel particles lodged in cracks, potholes, and gullies. Cleaning crews use industrial

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk vacuum units which suck up material into a collection bin that is then trammed to the plant. As many as 12 vacuum units can be used on a single site with two operators manning each suction pipe. Profiles of the bedrock horizon are undulating with variable ruggedness; some areas having a relatively flat profile while others have gullies and potholes more than 5 m deep. Load and haul (L&H) teams are used to transport bulked material from the bedrock sites to the plant and ensure the plant feed rates are maintained. The L&H fleet comprises 320 kW front-end loaders and 40 t rigid frame trucks which tram all diamondiferous material from the mining sites to the plant. Plant feed material from both L&H and bedrock collection bins are treated through a simple process comprising primary and secondary crusher circuits, a series of scrubbers, and finally a dense medium separation (DMS) plant. Plant concentrate is taken to the Red Area Complex (RAC) for final diamond recovery. Plant tailings are conveyed to the sea, thus contributing to beach accretion.

!9 86;9.<9:7+ Namdeb has a valid environmental clearance certificate, issued under the Environmental Management Act (2007) and Regulations (2012) of Namibia, for all relevant mining licence areas, including ML43 which covers Southern Coastal Mine. To ensure continued compliance, Namdeb conducts a number of monitoring programmes, audits, and stakeholder engagement sessions that include: Annual coastal marine monitoring of its beaches Annual ISO14001 environmental management system audit Third-party legal audit conducted every two years Annual Namdeb Environmental Stakeholders Forum Quarterly meetings of the De Beers Marine Risk platform Namdeb Marine Scientific Advisory Committee.

?.-6; <.<9:5 As the SCM operation has advanced westwards beneath sea level, the operation has been exposed to ever-increasing risk; greater possibility of a seawall breach, increased overburden stripping volumes and costs due to deeper mining cuts, increasing water seepage into the workings, which increases pumping volumes and costs, as well as lower confidence in the resource estimation due to practical sampling constraints. These factors all impact on an already marginal operation. Before progressing with improvement initiatives, a solid mine plan and effective execution were deemed to be paramount to the success of any improvement projects. Following the stabilization of the operation, the Theory of Constraints (ToC) has been widely used at SCM, and especially across the mining discipline, to focus efforts on improving overall business profitability. The ToC is a management philosophy introduced by Eliyahu M. Goldratt in 1984, that states that a system output is limited by one or more constraints and that an optimum system runs the constraint or bottleneck at maximum capacity (Goldratt,

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1992). This also requires that all other processes are focused on ensuring the throughput at the constraint is maximized. Defining the mining processes and identifying bottlenecks focused the team on where opportunities exist for value-adding improvement. Following the initial ToC analysis, availability of material to feed the plant was highlighted as the key bottleneck within SCM. On further analysis, across the other mining processes, it was also noted that insufficient buffers existed between the various processes and that bottlenecks would therefore quickly move to the other processes if not addressed simultaneously. The mining team therefore considered the opportunities to create buffers and remove bottlenecks across all three mining disciplines (stripping, L&H, and bedrock cleaning), identified the key focus areas, and then detailed plans around the implementation of the changes. Localized accretion is believed to be dependent on the stripping tons deposited on the beach and as such. stripping and accretion were considered jointly when defining the improvement plan. The improvement plan roll-out therefore began with stripping and accretion, followed by bedrock processes, and finally the L&H operations. However, in practice, many of the improvement projects were implemented simultaneously.

Before any of the improvement initiatives were introduced, it was critical that a ‘solid’ mine plan be developed; i.e. one with sufficient detail and realistic targets. Although the nature of the SCM mining operation requires flexibility that enables changes in the mining sequence, in reality this is time-consuming and disruptive to the process. The development of the plan therefore went through numerous reviews and revisions, considering various options and addressing possible risks. To mitigate risk budget planning included all key stakeholders, especially the high tension electrical team (HT) and the dewatering team. The timing of seawall moves, especially during winter months, was also changed to reduce budget plan risk profiles. With a more ‘rigid’ plan in place, execution became critical. A weekly drive-through planning session was therefore initiated that included all relevant stakeholders, in order to align the whole team around the plan. All tasks and priorities were defined for each area with tracking of compliance to plan. This forum also improved communication across departments, enabling proactive engagement, operational input, and assistance when required. The drivethrough also became a key forum for ensuring the progression of improvement projects.

When the ToC was applied within stripping, the key bottlenecks and improvement opportunities identified included: Long circuit distances that resulted in longer cycle times and reduced productivity Under-loaded trucks that resulted in reduced payloads and lower productivities Poor underfoot conditions that increased rolling resistance, reduced tramming speeds, and consequently lowered productivity rates Lack of redundancy of equipment that resulted in reduced fleet capacity when equipment was unavailable

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk Poor water management in the cut that led to samebench loading and reduced productivity rates Dozing on the groynes that increased ADT queuing times and delayed tipping operations.

To reduce circuit distances, the mining philosophy and layouts had to be completely redesigned. Previously ADTs had exited the mining area on the eastern side of the cut, trammed along the crosswalls, and dumped the overburden material on 20–30 m wide groynes which pushed westwards into the sea. The most obvious solution was to build a ramp straight out of the mining cut, to the top of the seawall and then directly out onto the beach. This layout, however, caused significant challenges with water seepage into the mining cut through the seawall. The solution was to divide the mining cut into a number of 200–300 m wide, north-south ‘paddocks’ and focus stripping on one paddock at time as illustrated in Figure 7. Stripping operations now commence at the paddock with the deepest bedrock elevation, Paddock 1, with a temporary ramp just within Paddock 2. Mining continues in Paddock 1 until the phreatic line is intersected and water starts to seep into the mining cut. The water is then channelled to the deepest side of the paddock where a sump and pump remove the water, pumping it back out to sea. Mining then continues in this paddock until bedrock elevations have been reached and permanent dewatering channels, a sump, and pumping infrastructure can be installed. Stripping teams then move to the second paddock where the previous temporary ramp is mined out and a new temporary ramp positioned just inside Paddock 3. By removing the first temporary ramp, water is then able to flow back into Paddock 1 where pumping infrastructure is now in place. This mining sequence is followed until the entire mining block has been completed.

This philosophy has a number of advantages in addition to the obvious benefit of reduced cycle distances and cycle times. For example, with the initial paddock on bedrock and with pumping infrastructure in place, water management is far more effective, i.e. water flows away from the stripping cut. This allows for mining of bigger benches with increased bucket fill factors, and in addition, drier footwall conditions enabled more material to be loaded using a more productive, top loading configuration; i.e. excavator on the bench above the ADT. Both the bucket fill factor and loading set-up have added benefits by way of reducing loading times and improving productivity.

The payload of the ADTs was noted to be well below loading capacity. Two options for improving payload were therefore considered; namely, installing greedy boards and/or tailgates. Greedy boards are fixed extensions to the side of the ADT bucket (Figure 8), while a tailgate is a mechanism installed at the back of a truck to allow more material to be loaded. Tailgates were considered more effective for loading wet material, but the higher cost and potential mechanical breakdowns of the tailgate mechanism was considered a major disadvantage. Greedy boards were successfully trialled on two trucks and then rolled out to the entire fleet. The greedy boards have increased the average payload capacity by 6 t or 20%.

Underfoot conditions as a result of the ‘soft’, wet nature of the overburden beach sand were not conducive to creating hard, compact roads and as a result, the road conditions deteriorated quickly once stripping commenced. The poor road conditions resulted in high rolling resistance and reduced tramming speeds. To improve road conditions additional support equipment was purchased; two wheel dozers and an additional grader. The additional support equipment has subsequently become an integral part of the stripping process. Improved road conditions in the mining areas in conjunction with more compacted temporary ramps have resulted in higher average tramming speeds into and out of the mining cut.

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The ToC indicated that excavators remained a key bottleneck within the stripping process. As the prime mover, ADTs were reliant on the excavator availability to achieve production targets. With no redundancy within the system, whenever an

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk excavator was taken out of production for scheduled maintenance, repairs, or auxiliary work such as digging sumps and channels, the stripping fleet was unable to achieve the required production targets. To mitigate against these scenarios, a fourth 80 t excavator, referred to as a ‘swing unit’, was added to the fleet, increasing the utilization of the entire stripping fleet and improving production capacity.

While redesigning the stripping layouts and implementing the new mining philosophy, it was suggested that a new accretion methodology be considered for SCM. The new methodology, called beach nourishment, required a change from tipping on the groynes, or point deposition, to building elevated pads along the length of the beach. Although the SCM application of beach nourishment is unique in its aim to provide access to future offshore resources, this methodology is widely recognized and used around the world to protect onshore structures and infrastructure. The beach nourishment methodology was initially implemented as a trial in one of the mining areas. Temporary ramps were extended from the top of the seawall onto the beach. Material was then tipped along the length of the beach, maintaining a floor elevation of 2–3 m AMSL. The beach nourishment pad created a bigger dumping area than was previously available when tipping on the crosswall groynes, with the added benefit of eliminating the delays experienced previously. The proximity of the dumping area was also generally closer to the temporary ramps, reducing tramming distances further. The beach nourishment methodology showed improved localized advance rates of the beach westwards and exceeded predicted accretion lines in all active areas. An added significant benefit has been the protection that the beach nourishment pads have afforded the active mining areas. These pads have moved the erosive surf zone westwards, away from the seawall, and provided a barrier to dissipate wave energy. During a 1:20 year storm event which hit the west coast of Namibia on 7 June 2017, bringing with it +8 m waves, many of the seawalls remained unaffected by the with the beach nourishment pads taking the full force of the storm and protecting the seawalls.

The ToC analysis at the bedrock operation identified the following bottlenecks: Low bedrock bulking rates, resulted in no bulking reserve for the bedrock cleaning process Areas with cracks and narrow gullies experiencing reduced cleaning rates Cementation reducing bulking rates.

Historically, bedrock business improvement projects have focused on bedrock cleaning and generally resulted in improved cleaning rates. However, the ToC analysis identified bedrock bulking as the bottleneck. The area that has been bulked by the hydraulic equipment but not yet cleaned with the vacuum units is referred to as ‘bulk reserve’, and it was highlighted that not only was there limited buffer between the two processes but

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the advance rates of the bulking crew were lower than that of the cleaning crew. Analysis of equipment hours reinforced this observation. Reasons for slower bulking rates included deeper gullies, cemented material, and machine downtime. The obvious solution was to increase the amount of machine hours of the bulking fleet. This could be achieved by either increasing the size of the bulking fleet or increasing the operating hours. Bedrock operations, both bulking and cleaning, were originally limited to day shift due to security concerns, despite the fact that the security risk generally only applies to bedrock cleaning. Therefore an additional afternoon bulking shift was implemented, increasing overall bulking advancement, creating bulking reserves, and enabling higher productivity of the bedrock cleaning crews.

With additional bulking capacity following the implementation of the backshift bulking team, it was now possible for the bulking teams to increase the amount of bedrock broken out, especially when narrow gullies and highly fractured areas were encountered. Traditionally the bulking teams would not break out these areas, cleaning out only the loose material and leaving the cleaning teams to clean all the cracks, fractures, and narrow gullies. This reduced cleaning rates but previously, as bulking remained the bottleneck, the cleaning teams would soon catch up. Additional bulking capacity enabled the bulking teams to also break out these areas, removing the cracks and gullies that previously slowed down the cleaning teams. Consequently, what had originally been a bottleneck for the cleaning teams is now removed by the bulking team with a knock-on overall productivity improvement of the bedrock teams.

In the past, when cemented material was encountered the bulking and cleaning rates were significantly reduced. Breaking out cemented material was most effectively done using the hydraulic hammer, but often only one hydraulic hammer was available per bedrock site. The hydraulic hammer therefore became the bottleneck within the operation as soon as cementation was encountered. To address this problem, bedrock teams would move all hydraulic hammers to the site encountering the cementation, ultimately impacting on bedrock cleaning rates in other areas. Two changes were therefore adopted. Firstly, all excavators were fitted with hydraulic systems suitable for a hammer installation, and secondly, a larger hammer that could be fitted to the 80 t ‘swing unit’ excavator was purchased. The fitting of the hydraulic hammer kits has enabled all the spare hammers to be fitted to excavators when cemented material is encountered, reducing the bottleneck. In extreme cases where cementation either covers vast areas or is very thick, the bigger hammer is fitted to the 80 t swing unit excavator and the higher energy and greater breakout force increases the bulking rates. Although cementation remains a challenge to achieving bedrock production rates, improved tools have reduced the bottleneck.

ToC analysis at the plant feed identified the following bottlenecks where capacity was not optimally used: L&H fleet capacity Mining delays impacting plant feed capacity.

Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk The L&H fleet originally comprised three FELs and seven trucks. In contrast to stripping, where the bottleneck was excavators, the L&H bottleneck was the trucks. With high tramming distances and long cycle times the FELs had significantly more capacity than the trucks and any delays on the trucks therefore had a marked impact on productivity. A ‘spare’ truck was therefore added to the L&H fleet as a redundant unit, used when one or more of the other trucks were unavailable.

The fundamental bottleneck impacting plant feed capacity was a mismatch between the L&H fleet capacity and the plant feed requirements, especially for mining areas far from the plant. The L&H fleet, however, works continuous operations (CONTOPS) and therefore runs more hours than the plant, providing additional capacity while the plant is not running. The rehandling cost, however, had historically resulted in a reluctance to build stockpiles at the plant tipping bin and stockpiles were therefore minimal. The ToC analysis of L&H highlighted a lower hourly production rate of the L&H fleet than the required plant feed rates and, furthermore, minimizing stockpile levels left no buffer available to maintain the required feed capacity. This often translated into plant feed delays due to material unavailability while the plant was running. The L&H teams therefore changed their focus to building stockpiles close to the plant tipping bin while the plant was not operational. They also positioned a FEL permanently at the tipping bin while the plant was running, enabling plant feed to be supplemented from the stockpiles and maximizing plant feed capacity. Allowing the L&H fleet to grow the tipping bin stockpile also ensured that L&H productivity was maintained at all times.

<50+:5 The stripping operation has consistently improved quarteron-quarter since Q2 2016 when the new mining philosophy and revised layout were introduced at SCM. In Q3 2017 a pipeline used during a previous dredge mining project was intersected in the stripping cut. The pipe was open to the sea and required that a concrete plug be pumped into it before it

could be removed. Consequently, mining was relocated to another area, with longer tramming distances and lower efficiencies during this period. Once the pipeline was removed, the SCM stripping team reached a record high production rate in Q4 2017 of 35 414 t/d, up 49% from the productivity achieved in Q1 2016 (Figure 9). Complementing the new mining philosophy, the introduction of beach nourishment, greedy boards, excavator redundancy, and support equipment has assisted in achieving continued productivity improvement.

An afternoon bulking shift, known as the back shift bulking team, was successfully trialled from July 2016, with a 55% increase in hydraulic excavator hours and a 77% increase in vacuum hours. This project has translated into a 32% improvement, increasing monthly bedrock production from 30 798 m2/month to 40 612 m2/month (Figure 10). The additional bulking capacity has enabled bulking to create and sustain a bedrock buffer between the bulking and cleaning teams, as well as improve cleaning rates by removing cracks and gullies that are difficult to clean. Cemented material will remain a challenge for bedrock using the current cleaning techniques, but the introduction of additional hammer capacity has increased bulking rates in these conditions.

A buffer stockpile ahead of the plant feed has had a number of positive impacts on the operation. Consistent plant feed has increased the plant feed rates from 755 t/h at the beginning of 2015 to an average of 936 t/h in the last half of 2017 (Figure 11), an increase of 24%. This has resulted from a 50% reduction of mining-related delays and a reduced impact of unscheduled delays. Although it is acknowledged that the philosophy of stockpiling material comes with an additional cost of rehandling, the benefits have shown that having a buffer ahead of the tipping bin has added significant value to the business. Work is currently underway to improve the current stockpile layouts and reduce loader cycle times.

;91+058;9 Through the ToC process, the mining discipline was identified as the bottleneck at SCM. On further analysis of the mining processes, numerous local bottlenecks were identified that prevented optimum productivity. Buffers, which if managed

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Operational changes enable Namdeb’s Southern Coastal Mining team to reduce risk

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better could build capacity, were identified. Through a focused effort to address these bottlenecks and create buffers, solutions were developed and implemented that have greatly improved productivity, and in the case of beach nourishment reduced the risk of a storm breaching the seawall defence at Namdeb. SCM will continue mining operations up and down the coastline and, more importantly, westward to exploit resources further into, and deeper under, the Atlantic Ocean. The associated challenges for the Namdeb team will continue to grow but we have embarked on a process of innovative and continuous improvement using the ToC method. The bottlenecks are expected to shift from one process to another as we find solutions that make previous processes more efficient and productive. The ultimate challenge is to apply the ToC continuously to define new bottlenecks and then focus on innovative solutions to remove these bottlenecks. Improved productivity and associated unit cost reduction will have significant benefit not only on the profitability of the mine in the short term, but also on the LoM potential of SCM going into the future.

1 9;*+<23<.<9:5 The authors are grateful to Namdeb for the opportunity to compile this paper, with special acknowledgement to Paul Lombard, the SCM Mine Manager, for guidance and more importantly support. To the entire SCM Mining Team for openly embracing the changes and provided continual feedback and suggestions. To the SCM Management team, for the teamwork and support. Finally, Lynette Kirkpatrick for your support and hours of editing.

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<,<6<91<5 BADENHORST, S.H. 2003. The unique Namdeb trilogy – Our past, present and future mining applications in this unique deposit. Journal of the South African Institute of Mining and Metallurgy, vol. 103, November. pp. 539–349. BLUCK, B.J., WARD, J.D., and DE WITT, M.C.J. 2005. Diamond mega-placers: southern Africa and the Kaapvaal craton in a global context. Mineral Deposits and Earth Evolution. McDonald, I., Boyce, A.J., Butler, I.B., Herrington, R.J., and Polya, D.A. (eds). Special Publication 248. Geological Society, London. pp. 213–245. DE DECKER, R.H. 1988. The wave regime of the inner shelf south of the Orange River and its implications for sediment transport. South African Journal of Geology, vol. 91. pp. 358–371. GOLDRATT, E.M. and COX, J. 2004. The Goal - A Process of Ongoing Improvement. 3rd edn. North River Press, Great Barrington, MA. JACOB, J., WARD, J.D., BLUCK, B.J., SCHOLZ, R.A., and FRIMMEL, H.E. 2006. Some observations on diamondiferous bedrock gully trapsites on Late Cainozoic, marine-cut platforms of the Sperrgebiet, Namibia. Ore Geology Reviews, vol. 28, no. 4. pp. 493–506. JACOB, R.J. 2005. The erosional and Cainozoic depositional history of the lower Orange River, Southwestern Africa. PhD thesis, University of Glasgow, UK. Rogers, J. 1977. Sedimentation on the continental margin off the Orange River and the Namib Desert. Bulletin no. 7. Geological Survey/University of Cape Town Marine Geoscience Unit, South Africa. 212 pp. ROSSOUW, J. 1981. Wave conditions at Oranjemund: Summary of wave rider data. March 1976-April 1980. Report no. T/SEA 8106. CSIR, Stellenbosch, South Africa. pp. 1–4. SCHNEIDER, G. 2008. The Treasure of the Diamond Coast – A Century of Diamond Mining in Namibia. 1st edn. Macmillian Education Namibia, Windhoek. SPAGGIARI, R.I., BLUCK, B.J., and WARD, J.D. 2006. Characteristics of diamondiferous Plio-Pleistocene littoral deposits within the palaeo-Orange River mouth. Ore Geology Reviews, vol. 28. pp. 475–492. SMITH, G.G. and SOLTAU, C. 2003. Shoreline modelling insights from projects in Southern Africa. Proceedings of Coastal Sediments ’03. World Scientific Publishing. https://doi.org/10.1142/5315

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a3

The new Cullinan AG milling circuit – a narrative of progress by L. Musenwa*, T. Khumalo*, M. Kgaphola*, S. Masemola*, and G. van Wykâ€

In 2017, Petra Diamonds completed the construction and commissioning of a modern, fit-for-purpose diamond processing plant at Cullinan Diamond Mine (CDM). The design of CDM’s milling circuit is unconventional in that it comprises an autogenous (AG) mill with a grate discharge with large ports, low-revolution jaw crushers, and high-pressure grinding roll crushers with large operating gaps. In this paper we review the design to provide guidance on what is expected from the milling circuit and to demonstrate how the design aims to address challenges experienced in the old plant, which was based on staged crushing technology. We assessed the performance of the CDM AG milling circuit from commissioning and early production stages to examine its impact along multiple dimensions. In the assessment we sought to understand the lessons from our milling circuit regarding diamond liberation, energy consumption, and the future of diamond processing as a whole. kimberlite processing, autogenous milling, high-pressure grinding roll, diamond liberation, diamond damage.

Cullinan Diamond Mine (CDM), previously known as Premier Mine, is an underground diamond mine owned by Petra Diamonds. Petra Diamonds is a leading independent diamond mining group and a growing supplier of rough diamonds to the international market. Petra Diamonds is listed on the Main Market of the London Stock Exchange under the ticker ‘PDL’. Petra’s total resource of 305 million carats ranks third by size after De Beers and ALROSA (Petra Diamonds, 2018). CDM was established in 1902. The mine rose to prominence in 1905, when the 3106 carat Cullinan Diamond — the largest rough diamond of gem quality ever found — was discovered there. The mine has produced over 750 stones that are larger than 100 carats and more than a quarter of all the world's diamonds that are larger than 400 carats. Cullinan is renowned as a source of large, high-quality gem diamonds, including Type II stones, as well as being the world’s most important source of very rare blue diamonds (Petra Diamonds, 2018). In September 2017, MDM Engineering completed the construction and commissioning

* Petra Cullinan Diamond Mine, South Africa. †Thyssenkrupp, Johannesburg, South Africa.

Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, Jet Park, Johannesburg, South Africa.

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of a modern, fit-for-purpose processing plant at CDM, with a throughput capacity of 6 Mt/a. This has replaced the previous plant at CDM, which was originally commissioned in 1947 and which had undergone numerous refurbishments over the years. Due to its age and operational complexity, it had become expensive to maintain, requiring significant stay-in-business capex, and costly to operate, particularly given its large 26 ha footprint. The plant was also mostly based on old crushing technology, which is known to be inefficient for the recovery of large stones. The design of the new plant was therefore aimed at improving recoveries at a lower cost and reducing diamond breakage. The new plant was also designed to use more environmentally friendly processes, which is vital for the long-term sustainable future of the mine. The new plant utilizes gentler processing methods – comminution by attrition and abrasion instead of extensive staged crushing. The new comminution methods used are AG milling and high-pressure grinding rolls (HPGRs), which are expected to reduce diamond breakage and improve recoveries across the full spectrum of diamonds, including the larger/exceptional stones for which the mine is renowned. The top cut size of 75 mm will cater for diamonds of more than 3000 carats, such as the Cullinan Diamond. The plant has a much smaller footprint of just 4 ha, and the engineering infrastructure has been significantly reduced.

The new Cullinan AG milling circuit — a narrative of progress " D6B??B=5DB=D9B@6>=9D8<>7C;;B=5 Traditionally, Southern African diamond processing plants are designed to include various stages of crushing to liberate diamonds from the host rock. These types of plants have been operated to process kimberlite deposits in Southern Africa over a number of years, and include the old CDM plant which was commissioned in 1947. The crushers in a conventional plant use compression and impact forces between two steel surfaces to break the feed material. Although the crushers are optimized to operate at the maximum possible crushing gap, there is still the potential for breaking diamonds between the steel surfaces. Furthermore, cone crusher products, having steep product distributions, are associated with poor liberation of diamonds from the host rock (Daniel, 2010). In recent years AG milling has been slowly introduced in diamond processing in Southern Africa. AG milling is an established comminution process (van Niekerk, 2013), and has been used in Russia over the past 40 years. The Russian experience cannot, however, be translated directly to the processing requirements in Southern Africa. Russian methods have historically focused on maximizing diamond liberation (Janse, 2007). The Russian government regulations require very strict auditing of operational performance to ensure that the orebody is being completely ’exhausted’, typically down to 1 mm. Catoca mine in Angola was the first significant Southern African diamond operation to install AG mills. ALROSA, the Russia-based diamond producer, is a major shareholder in the Catoca mine. The first diamonds were recovered in 1997 and the plant was expanded in 2005, raising the combined processing capacity to over 10 Mt of ore per annum. All of the Catoca plants are equipped with AG mills as the main comminution devices. The Catoca mine’s main orebody or pipe consists of volcanogenic-sedimentary rocks, with the inner ring of the vertical pipe made up of porphyric kimberlite, while the central part is filled with kimberlitic breccias. This type of material can be classified as relatively soft and is therefore ideally suited for AG mill processing (van Niekerk, 2013). In 2012 Karowe mine was successfully commissioned by Lucara in Botswana. The Karowe plant is equipped with an AG mill as the major comminution device. The Karowe operation has been modified and optimized over a number of years to process various material fractions through the plant. At Karowe the material varies from soft and friable to very competent. The operation has been very successful, especially as the world’s second-largest gem diamond, at 1111 carats, was recovered in November 2015 (Lucara, 2018).

This is an essential step in mineral processing as proper liberation allows for maximum recovery of minerals in the downstream processes. Undergrinding leads to inefficient recoveries (Steyn, 2011) and overgrinding, especially in the diamond processing context, leads to major value loss due to breakage of the final product. Knowledge of the ideal product size is essential in the design of any comminution circuit. The new plant has been designed to improve the efficiency of material flow, thereby significantly lowering operating costs. The new plant’s footprint is significantly less than that of the old CDM processing plant. Some of the comparisons to the old plant are summarized in Table I.

& ' %"#$' &!!' % The new CDM plant process flow is illustrated in Figure 1. The new mill plant is designed to handle run-of-mine (ROM) ore as well as reclaimed tailings. The ROM ore is conveyed from a 5000 t capacity ROM silo to the mills. The reclaimed and recirculated ore is conveyed from a 3000 t capacity silo to the mills. The ROM ore from the silos is blended with reclaimed tailings in the desired proportion for optimum grinding. The plant is designed for 6 Mt/a, and will initially process 4 Mt/a ROM plus 2 Mt/a reclaimed ore. The mill is designed for an overall fresh feed rate of 750 t/h feeding two mills in parallel. This is blended as follows: 500 t/h ROM ore 250 t/h reclaimed ore. Each mill is designed for a fresh feed rate of 375 t/h and a 152% circulating load. The mill discharge slurry (–150mm) flows onto a dedicated scalping vibrating grizzly installed for each mill. The grizzly oversize is conveyed to the jaw crushers. The jaw crusher product is conveyed to the recycle ore silo. The jaw crushers were designed with a bypass option to return excess load to the mills or bypass the crushers during maintenance.

Table I

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Throughput per annum

Total footprint

*?9D8?@=A

C0D" D6B??B=5D8?@=A

2.8 Mt ROM 2.5 Mt tailings

6.0 Mt ROM capacity Initial feed: 4.0 Mt ROM 2.0 Mt tailings

Approx. 27 ha

Approx. 4 ha

Major installed equipment 151 belts (15 km)

22 belts (3 km)

Conveyor transfer points

179

32

Screens

88

22

Pumps

121

7

:CD=C0D'+1D6B??B=5D8?@=AD9C;B5=

Crushers

18

4 (excluding mills)

The CDM comminution circuit was designed to reduce diamond breakage and improve recoveries across the full spectrum of diamonds; including the larger/exceptional stones for which the mine is renowned. When applied correctly, AG milling offers a diamond-friendly comminution environment (Daniel, Bellingan, and Rauscher, 2016). The purpose of any comminution process is to liberate or expose locked-up valuable minerals within the host rock or gangue.

Feeders

21

14

Substations

17

2

Electrical motors

589

84

114

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Conveyors

Improved electricity efficiency Power consumption Power consumption per ton

22.5 MW

25.0 MW

4.7

4.2

Improved water consumption mÂł per ton

3.5

1.2

The new Cullinan AG milling circuit — a narrative of progress

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section reports to the recycle silo. The HPGR section is also equipped with a bypass option to bypass excess material past the HPGR or bypass material when the crusher is offline. The DMS treats the –6 +1 mm size fraction. The –12 +6 mm size fraction is separated on the DMS preparation screen and treated through the mids diamond recovery plant, which consists of four BV sorters. The –6 +1 mm) size fraction is treated though four 510 mm DMS cyclones. The concentrate from the DMS cyclones and the mids diamond recovery is conveyed to the secondary recovery section. The mids diamond recovery tailings are conveyed to the HPGR section. The DMS tailings are discarded. In the final recovery plant at CDM, concentrates from the XRL large and coarse recovery, mids diamond recovery, and the DMS are treated using BV sorters for further concentration of diamonds.

' &( $'$&!$' ' &! %$!' The JKTech drop weight test (JKDW) is used to identify rockspecific parameters that are then used for simulating and predicting equipment performance. The drop weight tests are used in conjunction with SAG mill comminution (SMC) test results to model comminution equipment (Weier, 2013). The output from this work can be used when modelling mill throughput, energy consumption, and product size distribution, relative to ore hardness. Effects of changes in the in feed size distribution to the mill as well as operating variables such as mill filling and critical speed can also be examined. Material testing was conducted on the most representative ore fractions from the CDM orebody, using just two samples representing grey kimberlite and hypabassal kimberlite. The results of these tests are displayed in Table II. #*)$1,D22-

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The grizzly screens undersize (–75 mm) gravitates to dedicated mill product sizing screens for each mill stream. The sizing screen oversize (–75 +12 mm) is conveyed to the primary X-ray luminescence (XRL) sorting plant for recovery of large diamonds. The sizing screen undersize gravitates to the de-sliming section for the removal of –1mm slimes through a series of trommels and dewatering vibrating screens. The –12 +1 mm product from the de-sliming section is conveyed to the dense medium separation (DMS) and mids diamond recovery sections. The main purpose of the XRL section is to sort the –75 +12 mm diamonds in the mill product. The feed to XRL is concentrated on the basis of luminescence by five large (–75 +25 mm) and five coarse (–25 +12 mm) diamond XRL sorters supplied by Bourevestnik (BV). The coarse diamond XRL sorter tailings (–25 +12 mm) are conveyed to the HPGR crushing section. This coarse fraction can be bypassed to the recycle silo if so desired. The large diamond XRL sorter tailings (–75 +25 mm) are conveyed back to the recycle silo or, if required, are directed to the HPGR crushers. Concentrates from the large diamond XRL sorters and the coarse diamond XRL sorters are combined and conveyed to the final recovery section for further processing. The HPGR crushing section is designed to incorporate two crushers to assist with the rock breakage before the material is returned to the mill. Initially, only one HPGR unit was installed. The second unit will be added as a future installation. Coarse XRL tailings (–25 +12 mm) and the mids diamond recovery tailings (–12 +6 mm) feed this section. The option exists to route the large XRL tailings (–75 +25 mm) to the HPGR section if this size fraction builds up in the circuit and requires further crushing. The crushed product from this

The new Cullinan AG milling circuit — a narrative of progress Table II

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Grey kimberlite Hypabassal kimberlite Blend

C?@AB CD9C=;BA. "/<@;B>=

"

/

" /

@

88.80

0.45

39.96

0.45

2.68

71.90 78.60

0.55 0.51

39.55 39.71

0.55 0.51

2.80 2.75

The AG mills were designed to incorporate a discharge grate with very large ports with openings ranging from 170 mm to 220 mm in size. Convectional port sizes are less than 45 mm for diamond processing plants and can be as large as 100 mm for other applications (Napier-Munn et al., 1999). The difference in the discharge port of the CDM mill is illustrated in Figure 3. The open area of the discharge grate is 8.36%. With no previous experience of such large port sizes,

Table III

'+1D6B??D;8C7B4B7@AB>=; The lower the Ta value, the more competent the ore, i.e. the higher the resistance to abrasive comminution. Figure 2 shows that the CDM blend resides in the most competent region of the current kimberlite database. The shaded areas represent the domains of the CDM and Jwaneng kimberlites for comparative purposes. This is illustrative of the order-ofmagnitude difference in throughput and power consumption of CDM compared with Jwaneng ores. As a further reference point, Karowe mine has Ta values ranging from 0.26 to 1.56 (van Niekerk, 2013) and Williamson diamond mine is off the scale with Ta values of 1.6 plus.

1B??D8@<@6CAC< Type

8C7B4B7@AB>= Wet AG mill, grate discharge type

Design

Shell supporting with slide shoe bearings

Make/supplier Diameter (inside shell) Effective grinding length

Polysius/ThyssenKrupp 9.20 m 4.88 m

Percentage of critical speed

60–90 %

Required motor power

6 400 kW

Motor speed

995 r/min

Max. operating ball charge Feed size

0% –450 mm

' (%%'! & ( ( "$( #!

Circulating load

130–200 %

The two Polysius mills installed at CDM were supplied by ThyssenKrupp. Table III shows the specification for both mills. Material properties were used by CITIC-SMCC Process Technology to specify and design the grinding circuit.

Mill liner material

Steel liners

Mill shell liner thickness (incl. lifter)

278.4 mm

Discharge grate

Full ported, 170 mm ports

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116

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The new Cullinan AG milling circuit — a narrative of progress the CDM mill circuit was expected to deliver higher throughputs and high recirculation loads of 130–200%. The reason for large port sizes is the presence of large stones, i.e. value protection. The mill liner design was optimized to limit impact collisions inside the grinding chamber and enhance the attrition and abrasion forces, as shown in Figure 4. Particular care was taken to satisfy all of the special requirements specified in the equipment data-sheet. The liner design was enhanced in the pulp chamber to accommodate the flow of large particles.

'! & ( ( "$( #! The CDM HPGR specifications are displayed in Table IV. The HPGR is one of the notable features from the old plant design that was in incorporated in the new design. The first HPGR in a kimberlitic operation was installed at CDM (then called the Premier Mine) in 1987. The HPGR was included in the new mill circuit because of its ability to minimize diamond damage in comparison to impact crushing. Comminution takes place primarily through interparticle crushing due to large operating gaps, which are usually bigger than the feed particles (Ntsele and Sauermann, 2007). The CDM HPGR was designed to operate at a gap of 55 mm to ensure optimal value protection.

'>66B;;B>=B=5 Commissioning of the new plant was started in the third quarter of 2017. The old plant was shut down before the construction of the new plant was completed. Some of the old sections were utilized while construction of the new plant continued. The new processing plant incorporated the fines DMS section (–6 +1 mm) which was commissioned in 2012 as part of the dump treatment plant. Because this DMS section was already operational, the plant could commission some of the major comminution equipment even before the large diamond recovery section was completed. This meant that large particles in the circuit could either be trucked back to the mill or stockpiled to be processed later. Start-up of the AG mills was relatively simple without any significant issues. The mills were ramped up to full

operating speed and achieved maximum power draw levels. This meant that despite the large pebble openings and open area the mill could still be operated at a significant load level. Initially, the circuit was not operated with the full recycle load due to the delay in the completion of the large diamond recovery section. At the time it was therefore impossible to determine the final operating conditions in the circuit. Once the large diamond recovery section was completed the plant was able to be operated as per the intended design. The commissioning team did not immediately achieve the design capacity and some fine tuning and adjustment were required to improve performance. The performance test was, however, successfully completed in November 2017. The production team continues to optimize the process to find the ideal recipe for all conditions. The following sections highlight some of the challenges and successes achieved during commissioning of the plant.

'" "(%" (%($ ' ' %"!!( ( "$( #'"# ' & "$& (# & ( &#$' The most significant issues during plant start-up were classification and dewatering equipment breakdowns. The grizzly screens and trommel screens in the circuit suffered frequent breakdowns during the commissioning process. The grizzly screens, which are located immediately after the two mills, posed the most significant challenge in the comminution circuit. The grizzly screen panels experienced a number of failures due to the impact and load of large particles of up to 170 mm discharged from the mills. A newly developed deck and new panel design were supplied to improve the reliability of the process. The screening apertures were also reduced from 75 mm to 55 mm because some particle shapes in the mill product resulted in very large particles reporting to the XRL section. The mine team, in conjunction with the panel and screen suppliers, continues to develop the screening panels and structure of the grizzly screens to improve the reliability of the process. The plant is designed to utilize motorized trommel screens to dewater the mill discharge pulp before the final fines screening stage. The trommel screens are very efficient at removing the water from the processing stream, but the units were initially not mechanically reliable. Modifications were made to the support wheels to improve the availability of these units.

( ( %$(&!'(#' &!!(# ' & %"( & '$"(%(# !' "$& ("% The plant was designed to treat a significant portion of reclaimed tailings material to supplement ROM ore. The introducing of this fraction of material was very challenging for the commissioning team. Table IV

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Make/supplier Roll diameter Roll width Peripheral roll speed Operating gap Circuit capacity

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8C7B4B7@AB>= Polysius/ThyssenKrupp 2.8 m 0.5 m 2.8 m/s 55 mm 750 t/h

117

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The new Cullinan AG milling circuit — a narrative of progress The first challenge was the handling and conveying of the material, which is sometimes fine, sticky, and wet, and causes blockages in transfer chutes. The second challenge was that a significant portion of this material is finer than 2.5 mm. Material in this size fraction is heavily influenced by the flow of water in the mill and is discharged quite quickly without effective grinding (Steyn, 2011). This has at times caused feed rates higher than 350 t/h to DMS, resulting in the DMS being unable to treat all the mill product and subsequent stockpiling of DMS feed material. The third significant challenge in treating tailings has been the presence of dense, old recovery tailings which resulted in high yield to the final recovery. Solutions to these challenges have been implemented with a large degree of success. The tailings feed grizzly and conveyor transfer points were changed to cater for the fine, wet, and sticky material. The sizing screen panels were changed to 8 mm square apertures to redirect the –12 +8 mm mids diamond recovery feed to the XRL section, which has excess processing capacity. Lastly, the dense, old recovery tailings are carefully blended with old DMS tailings to reduce the recovery yields to manageable levels.

rate of the finer feed to this mill. The installed plugs reduced the total open area of mill no. 2 from 8.36% to 7.38%. Plugging the discharge had no significant effect of the performance, and the mill is still operating with these plugged holes.

(%%' $'"#"% !(! The mill plant design does not cater very well for physical sampling of the mill feed and product streams. Due to the plant layout and physical constraints it is extremely difficult to take samples of the mill feed and discharge product. This constraint necessitated the installation of four Split-Online cameras, one on the feed conveyor and one on each of the three mill product streams, shortly after the commissioning of the plant, to track important size fractions within the process. Figure 7 illustrates the data that can be generated using the Split-Online system. With this system, the plant team is able to keep accurate track of the particles reporting to each process. The system has been subsequently developed to quickly track any deviations from the expected size fractions in each product stream and initiate appropriate corrective

# &%(" (%($ ' ' (%%'% " ' &"! & &#$! The load in the AG mill is determined by load cells installed under the supporting bearings on the non-drive end of the mill. This load level signal is used to run the mill under an automated control loop to optimize the feed rate to the mill. The load cells only give the relative mass of the load in the mill and not an absolutely mass. Measured load values tend to be unreliable due to the scaling of the control signal and/or load reading variations caused by changes in the density of the feed material, i.e. variations in bulk density of the finer recycled material relative to the coarser ROM ore. Figure 5 illustrates the difference in the mill loading for the same load reading. The mill feed rate control is dependent on this load value, and for this reason it is essential to have the most accurate measurement possible. This load information is not only essential for optimizing the throughput of the mill, but can also be used to ensure the mill is operated in the most diamond-friendly way.

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(%%' && '!& & "$( # Segregation occurs in both the ROM silo and recycle silo, due to the orientation and layout of the ROM and recycle feed belts. The segregation results in the coarse fractions from the ROM and recycle silo reporting to mill no. 1, and a high percentage of the fine fractions reporting to mill no. 2, as can be seen in Figure 6. This is a design oversight which can have significant production and financial implications, for two reasons. The first is that segregation impacts the performances of the two mills, due to mill no. 1 receiving excess grinding medium in the form of large particles and mill no. 2 being deprived of sufficient grinding medium. The second reason, which is financial, is that completely eliminating the segregation in the silos would be difficult and costly. To address this challenge the mine team has developed different operating recipes for each mill. In addition, the commissioning team closed some of the ports on the grate discharge of mill no. 2 with rubber plugs to enable the mill to reach operating load by reducing the discharge

118

#*)$1,D22-

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CAAB=5DA>D3=9C<;A@=9DA:CD'+1D" D6B?? Post-commissioning phase, the challenge remains to continue to get the best out the CDM mill circuit. One of the biggest challenges in optimization of an AG mill is the fact that it uses the feed ore as the grinding medium. The loading of grinding medium therefore depends on the feed rate, size, and hardness. When these parameter are highly variable it becomes quite difficult to run the mill under stable conditions (Napier-Munn et al., 1999). In the case of the CDM mill circuit, the feed rate is well controlled and the hardness is not highly variable, as supported by the narrow range of Ta values in Table II. The feed size is, however, highly variable, as illustrated in Figure 7, which makes it difficult to run the mill under stable conditions.

Understanding the impact of the variability of the feed particle size distribution (PSD) and other mill operating parameters on the immediate mill product was one of the main aims in the early operational stages of the new plant. Below are some of the main lessons learnt about the mill circuit to date

( & &#$' (%%(# ' & ( &!'"# ' & "$( #"%' %& ( (%($ The results obtained from the various tests done on the mill have indicated that the mill is capable of producing a wide range of product PSDs. An analysis of the mill product showed that the mills can be operated under different milling regimes (Petersen, 2018). The CDM mill circuit can be operated under three different milling regimes, depending on process requirements. These regimes range from gentle milling to aggressive milling, with a transition regime inbetween. The gentle and aggressive regimes established are illustrated in Figure 8. Aggressive milling generates a large percentage of fines (–1 mm), i.e. ≼40% passing 1 mm on a #*)$1,D22-

119

action to prevent any adverse effect on diamond recovery. The performance of the system is continually verified with physical samples to ensure that the plant can continue to rely on the system.

The new Cullinan AG milling circuit — a narrative of progress

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first-pass basis and ≼80% passing 1 mm on the overall process mass balance. Gentle milling, on the other hand, generates between 20% and 30% passing 1 mm on a firstpass basis. Aggressive milling was achieved, as illustrated in Figure 8, on 16–28 February 2018, while operating the mills at a high percentage of critical speed (+85%) and low water-toore ratio set-points (0.2–0.4). Gentle milling, as illustrated in the second diagram, labelled ‘March’, was achieved at a lower percentage of critical speed (70–85%) and high water-to-ore ratio set-points (≼0.7) The variability of the mill product PSD generated by the different milling regimes directly impacts the mill recirculation load and mill throughput. The average recirculation loads for the gentle and aggressive milling regimes in Figure 8 were calculated to be 180% and 127% respectively, which is within the design capabilities of the circuit. The mill circuit was designed to handle recirculation loads of up to 200%. The mill can therefore be operated in the different regimes to cater for the desired product without operating below the design throughput of 750 t/h. The next step for the CDM production team is to understand the impact of the aggressiveness of the milling regime on other aspects such as diamond liberation, diamond damage, and energy efficiency, in addition to throughput. Efforts to identify the perfect recipe for both mills must take into account the fact that aggressive milling is obtained at high rotational speeds, which may results in more impact damage, while on the other hand gentle milling involves a high recirculation load, which is also not favourable for diamond protection. The scatter plots in Figure 9 and Figure 10 illustrate some of the insight gained during normal production when the mills were operated under steady-state conditions. Steady

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state for the purpose of this analysis is defined as instances when the mill was operated at constant feed with the same settings for at least 2 hours. The illustration in Figure 9 shows that the water-to-ore ratio has a strong correlation to the production of –1 mm mill product. Lower water-to-ore ratios generally result in the generation of more fines, which is favourable because it allows for higher throughput and reduces recirculation loads and the load on downstream processes. Figure 10 illustrates that the operating mill load and speed do not have as strong a relationship to grindability as the water-ore ratio, and this is suggested by the linear relationship between speed and power. The operational flexibility of the new mill plant presents a wide range of options for the operational team compared to the old plant. CDM’s goal is to optimize liberation and reduce diamond damage. The insight gained thus far only takes into consideration the ability of the mill circuit to grind the feed to achieve the required throughput and recirculation loads. Future work is required determine the impact of the mill circuit on the final product.

(3A3<CD0>< Napier-Munn et al. (1999) stress that optimization of an AG mill should never be regarded as complete. It should rather be viewed as an ongoing process which is aimed at ensuring that the circuit is operating at maximum efficiency regardless of other conditions. With this important consideration in mind, current and future work includes the following.

( $&#(# ' (%%' #$ %! The CDM production team is currently looking at different options for automated mill control. Some of the options under

The new Cullinan AG milling circuit — a narrative of progress

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(B53<CD2 & C?@AB>=;:B8D/CA0CC=D6B??D?>@9D@=9D8>0C<D@AD9B44C<C=AD0@AC< A> ><CD<@AB>;

$( ( "$( #' ($ '$ &'&# ' $'(#' (# The main aim of the CDM mill circuit is to liberate diamonds from kimberlite. The circuit is also specially designed to preserve the value of large diamonds. It is therefore important to optimize the circuit with these goals in mind. Current work involves test work with ceramic simulants and valuation of large diamonds recovered under different milling conditions.

'>=7?3;B>= CDM has successful commissioned their AG milling #*)$1,D22-

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consideration include controls based on DMS feed yield or possible incorporation of the online feed and product PSD monitoring system. The production team is also considering the addition of a field instrument that uses vibration monitoring on the mill bearing housings and mill shell to generate accurate and instantaneous signals of the mill filling levels and the toe angle of the load in the mill (Gugel, Rodriguez, and Gutierrez, 2015) as illustrated in Figure 11. This information is not only essential for optimizomg the throughput of the mills, but can also be used to ensure that the mills are operated in the most diamond comminutionfriendly way.

The new Cullinan AG milling circuit — a narrative of progress

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comminution circuit treating a relatively competent kimberlitic ore. The commissioning team has thus far managed to achieve the design throughput. The mill circuit is capable of producing a wide range of product PSDs to cater for different production requirements in comparison with a conventional stage-crushing circuit. The challenge now remains to fully understanding the impact of the mill circuit on liberation and diamond damage. Future work will focus on completing the optimization cycle of the circuit. We plan to present this work at subsequent conferences, with the hope of increasing the benchmark of the largest diamond ever recovered.

v4.pdf JANSE, A.J.A. 2007. Global rough diamond production since 1870. Gems and Gemology, vol. 43, no. 2. pp. 98-119. LUCARA DIAMOND COMPANY. 2018. http://www.lucaradiamond.com [accessed 10 February 2018]. ME ELECMETAL. 2016. DEM simulation of particle trajectory and discharge behavior. Client presentation. NAPIER-MUNN, T., MORELL, S., MORRISON, R., and KOJOVIC, T. 1999. Mineral Comminution Circuits - Their Operation and Optimization. 2nd edn. Julius Kruttschnitt Mineral Research Centre, University of Queensland. NTSELE, C. and SAUERMANN, G. 2007. The HPGR technology – the heart and future of the diamond liberation process? Proceedings of Diamonds –

C4C<C=7C;

Source to Use 2007. Southern African Institute of Mining and Metallurgy,

CLAASSEN, I.C. 2006. Cullinan Diamond Mine – autogeneous milling evaluation. GME Metallurgy Projects Reports. De Beers. DANIEL, M.J. anD MORLEY, C. 2010. Can diamonds go all the way with HPGRs? Proceedings of the Diamonds – Source to Use 2010 Conference, 1–3 March 2010. Southern African Institute of Mining and Metallurgy, Johannesburg. http://www.saimm.co.za/Conferences/DiamondsSourceToUse2010/201Daniel.pdf

Johannesburg. http://www.saimm.co.za/Conferences/Diamonds SourceToUse2007/010-Sauermann.pdf PETERSEN, K. 2018. Cullinan milling project: milling testwork results report. MetalDog Minerals. PETRA DIAMONDS. 2015. The new Cullinan plant. Company presentation. https://www.petradiamonds.com/wp-content/uploads/Cullinan-PlantPresentation-27-July-2015-FINAL.pdf [accessed 15 March 2018].

DANIEL, M.J., BELLINGAN, P., anD RAUSCHER, M. 2016. The modelling of scrubbers and AG mills in the diamond industry and when to use them. Proceedings of Diamonds Still Sparkling, 14–17 March 2016. Southern African Institute of Mining and Metallurgy, Johannesburg. DIGITAL CONTROL LAB. Not dated. MillSlicer. http://www.digitalcontrollab.com/ millslicer.shtml [accessed 15 May 2018]. GUGEL, K., RODRIGUEZ, J.C., and L.J. GUTIERREZ, L.J. 2015. Automated SAG mill speed control using on-contact vibration sensors. Proceedings of the International Conference on Semi-Autogenous and High Pressure Grinding Technology, Vancouver, 20–24 September.

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#*)$1,D22-

SMCC PROCESS TECHNOLOGY. 2014. Review of the proposed twin line AG mill circuit for Petra Diamonds Cullinan underground mine @ 6Mt/a. Indooroopilly QLD, Australia. STEYN, C.W. 2011. Optimisation of a fully autogenous comminution circuit. MEng thesis, University of Pretoria, South Africa. VAN NIEKERK, L.M., NDLOVU, G.N., and SIKWA, N.A. 2013. Commissioning and operating an autogeneous mill at Karowe Diamond Mine. Proceedings of Diamonds—Source to Use 2013. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 175–192. WEIER, M. 2013. JKDW & SMC test report Cullinan Mine. JK Tech, Brisbane.

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a4

Diamond exploration and mining in southern Africa: some thoughts on past, current, and possible future trends by W.F. McKechnie

(7<*8;8 Southern Africa is generally thought to be well explored, with only limited potential for major new diamond discoveries. However, Chiadzwa in Zimbabwe and reports of a significant new kimberlite find in Angola are testimony to the dangers attached to an attitude that ‘there is nothing left to find’. Since the major discoveries in the central interior of South Africa in the 1870s, diamond exploration in the region has been led by market and political factors that influence the key exploration drivers of opportunity and value proposition. Unexpected new discoveries by new players always impact on existing producers and, from time to time, denial of opportunity through political or protectionist policies has inhibited investment in exploration. Entrepreneurial exploration appetite in southern Africa will be tempered by the potential value equation and security of investment. Overlaid on this, developments in diamond recovery technologies provide opportunity to reinvigorate current mines and old prospects previously considered too difficult or costly to exploit. Position on the cost curve will remain a key factor for survival in an increasingly competitive environment.

>( <=68 diamond exploration, production, trends, outlook.

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The diamond industry in southern Africa boasts a history of 150 years of discovery and mining, forming the cornerstone of development of the core, modern economies at the southern tip of Africa. Since the discovery, in the 1860s and 1870s, of the secondary diamond placers and primary diamond-bearing kimberlite deposits in the central interior of the then Cape Colony and Orange Free State, southern Africa has endured as the pre-eminent diamondproducing region in the world. Notwithstanding periodic competition from significant new discoveries in other parts of the globe and the paucity of recent, new discoveries in the region, its ranking is unlikely to change in the short to medium term. In a geological context the region contains the Kaapvaal-Zimbabwe and Angola-Kasai cratons, identified in terms of Clifford’s rule as $ # ?33+

The first wave of diamond exploration in southern Africa was initially driven by opportunistic, entrepreneurial spirit and endeavour but inevitably, and quite quickly, operational and market factors led to consolidation of the operating companies and lateral and vertical integration of the different industry components. From 1870 until shortly before the start of the First World War the diamond mines of South Africa accounted for almost 100% of

* Snowden Group, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

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areas inherently prospective for diamonds, being underlain by portions of the Earth’s crust that are tectonically stable and older than 1.5 billion years (Clifford, 1992). Although the Democratic Republic of Congo (DRC) would, in a geographic context, be considered to be part of Central Africa, for the purposes of this review it is included in southern Africa since northeast Angola and the adjacent parts of the DRC form part of the same geological terrane and diamond production from both countries needs to be considered together. Historical and current diamond production from the northeastern parts of the DRC around Kisangani is not considered to be significant and its inclusion in the overall numbers for the DRC does not materially affect the overall picture presented here. Hence, in this review, the countries considered are South Africa, Zimbabwe, Namibia, Angola, the DRC, Lesotho, Botswana, and eSwatini (previously Swaziland), the first five of which each has a history of diamond production stretching back more than 100 years, and in the case of South Africa, 150 years (Table I).

Diamond exploration and mining in southern Africa: Table I

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South Africa

1867

Vaal River placers and Kimberley and OFS kimberlites

Zimbabwe

1903

Somabula placer and kimberlites

DRC

1907

Tshikapa placers

Namibia

1908

Luderitz placers

Angola

1912

Lunda placers

Lesotho

1958

Kao kimberlite

Botswana

1966

Orapa kimberlites

eSwatini

1973

Ehlane placer and Dokolwayo kimberlite

global diamond production. During the latter part of this period the main diamond-producing company of the time, De Beers, enjoyed a virtual monopoly in diamond supply from its kimberlite mines. This situation started to change shortly after the turn of the 19th century when the Premier Mine at Cullinan, east of Pretoria, came into production. Kimberlites had been discovered in 1897 at Rayton to the south, just before the South African War (Draper, 1898), but lay fallow until hostilities ended and economic conditions normalized. Independent production from the Premier Mine led to increased market competition and volatility of diamond prices, but the situation worsened considerably in the following decade for the established producers (Innes, 1975). In the period between 1907 and 1912, massive secondary diamond deposits were discovered in German South West Africa (now Namibia), at Tshikapa in the Belgian Congo (now the DRC), and in the Lunda Provinces of Portuguese West Africa (now Angola). Immediately following the end of the First World War, further new major diamond placers were found at Mbujimayi in the DRC, in the Gold Coast (now Ghana), in Namaqualand, and around Lichtenburg in South Africa, and other countries

in West Africa, most notably Sierra Leone, (Levinson, Gurney, and Kirkley, 1992). The exceptional quality of the West African production had an enormous impact, with the result that, through to the 1960s, world diamond production was dominated by these major secondary diamond fields to the virtual exclusion, for a time, of the South African kimberlite mines, most of which either closed or drastically cut production in the inter-war period (Janse, 2007). Following the Second World War De Beers, launched its ‘A Diamond is Forever’ marketing campaign to exploit an anticipated post-war economic boom in the USA and Europe. The company embarked on an ambitious investment programme to reopen and modernize its mines in South Africa and South West Africa, and also purchased the Williamson Mine in East Africa. New diamond production from major discoveries in Russia in the late 1950s threatened De Beers’ market leader status, and this encouraged it to increase investment in diamond exploration throughout Africa, commencing in East Africa, extending southwards into Bechuanaland (now Botswana) and into Angola and Zaire (now the DRC). Newly independent Botswana was host to the fabulous discoveries at Orapa and Jwaneng. Smaller finds in Southern Rhodesia (now Zimbabwe) and Swaziland (now eSwatini) spurred renewed interest in South Africa, leading to the discovery of Venetia and other smaller mines in South Africa and Botswana. Figure 1 shows a chart of diamond discovery and mining in southern Africa for all of the countries listed in Table I covering the period 1867 to present. Although the trend has the appearance of being fairly continuous, three definite discontinuities are noticeable, shown as A, B, and C. ‘A’ in Figure 1 represents the initial period of industry consolidation in Kimberley, which led to the formation of De Beers Consolidated Mines and its initial monopolization of the industry. Additional factors that diminished interest in diamond prospecting during that period were the discovery of the Witwatersrand goldfields in 1886 and political events leading up to the South African War of 1899 to 1903. Period ‘B’ followed the discovery of the huge placer deposits in Namibia, Angola, the DRC, West Africa, and Namaqualand and Lichtenburg in South Africa. These had a

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Diamond exploration and mining in southern Africa:

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from re-examination of older mines and prospects. The two exceptions are the Luaxe kimberlite in Angola (Alrosa, 2017) and the Chiadzwa placer in Zimbabwe, which has had a significant influence on the industry for approximately 10 years (Kimberley Process Statistics, 2004–2016; Manenji, 2017).

;:.<76?*=<6259;<7 Figure 2 summarizes annual world diamond production in carats (ct) from 1870 through to 2016, showing the phenomenal growth since the 1950s. Three main production periods are shown. The period 1867 until 1910 was dominated by South African kimberlite mine production. Production from 1910 to about the mid-1960s was dominated by the large African secondary deposits which, for a considerable period, produced more than 50% of global production in value terms. The period from the 1960s through to the present has again been dominated by large kimberlite and lamproite mines, with the result that, by the turn of the millennium, more than 75% of world production by volume and value came from primary deposits, a reversal of the situation that prevailed from 1920 through to 1960 (Janse, 2007). During the last 30 years, new kimberlite and lamproite primary deposits brought into production in Australia and Canada have further reduced southern Africa’s market share in volume and value to an average of approximately 50% of total world diamond production (Kimberley Process statistics, 2004–2016). Annual production data reported by the Kimberley Process Certification Scheme since 2004 provides valuable insight into southern African diamond production. Records from 2004 to 2016 show that world diamond production peaked at almost 177 million carats in 2005, but reduced to an average of 125 million carats between 2009 and 2015, increasing again to almost 145 million carats in 2016, mainly on the back of significantly increased production from Russia. On average, for that period, southern Africa produced 51% of the world’s diamonds in carats, representing 56% of global dollar revenue (Figure 3 and Figure 4). $ # ?33+

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devastating effect on the South African diamond mining industry. Investment in existing mines and exploration for, and development of, new kimberlite operations ceased in South Africa and exploration for primary deposits elsewhere was also deterred. The Great Depression of the 1930s undoubtedly played a part, but the greatest influence came from the low-cost production of high-quality, gem diamonds from West Africa, Angola, and other independent producers. In South Africa, the industry situation was so dire that in 1927, the Union government passed the Precious Stones Bill forbidding prospecting and digging for diamonds on stateowned land in the Cape Province. This restriction was only lifted in 1960 following the discovery of the diamond-bearing dykes at Bellsbank, followed shortly afterwards by the Finsch Mine discovery (Janse, 1995). Similar restrictions were in place elsewhere in the region. In Southern Rhodesia (now Zimbabwe) De Beers held exclusive rights to prospect for diamonds through the British South African Company (BSAC), founded by Cecil Rhodes, which were ceded to the government only in the 1940s. In Angola and the Congo, exclusive diamond rights were held by Diamang and Forminière respectively. The main influence in period ’C’ was the Angolan civil war, which affected the whole subcontinent from its commencement in 1975 to 2002. Although diamond mining continued in Angola throughout most of this period, exploration was not possible until the early 2000s and there were spillover effects into neighbouring countries. Zimbabwe was also not fully accessible between 1975 and the late 1980s. The effect of all of this was that, for an extended period in southern Africa, diamond exploration was restricted to South Africa and Botswana, both relatively mature exploration terranes by that time, and post-Venetia, the only significant new deposits found were small and short-lived operations. An additional factor was the significant exploration successes in in other parts of the world, such as Australia and Canada, which increased competition for exploration spend. In recent years new diamond discoveries in southern Africa have been scarce, with most new production coming

Diamond exploration and mining in southern Africa:

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Botswana is the dominant producer of the region in terms of volume and value, followed by South Africa, Angola, and Namibia. Lesotho is a relatively minor producer, notwithstanding the very high-value gems produced by its Letseng Mine. Since 2006, Zimbabwe has produced a significant volume of generally low-value diamonds, increasing the region’s share of world production at times to almost 60% between 2010 and 2013. Zimbabwe’s production is currently in decline and Botswana’s mines have lowered production by approximately one-third since 2008 in response to challenging market conditions, causing southern Africa’s share of global production to dip below 50% for the first time in 10 years. eSwatini’s only diamond mine in closed in 1996 and is not included in Figure 2 or Figure 3.

Figure 5 compares average dollar per carat values since 2004 for total world diamond production, as reported by the Kimberley Process, to the average values for southern African producing countries. Average values for southern Africa are slightly higher than the global average, which is considered to be due to the

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$ # ?33+

low average values reported for Russia as a result of the low bottom size cut-offs used in Russian operations, often as low as 0.5 mm compared to 1.25 mm or 1.5 mm for other producers. Another feature of this chart is the general longterm upward dollars per carat trend in average diamond values since 2004. The strong initial post-GFC price recovery was short-lived and average diamond values have been variable but effectively flat since 2011, and negative the in last two years. Average diamond values for the different countries in southern Africa are compared in Figure 6 on a logarithmic scale, which allows visual separation of the different country production profiles. Average values for South Africa, Botswana, and Angola cluster together around the $100 per carat line. Diamond values for Lesotho and Namibia are considerably higher, being influenced by the unusually high-value production from the Letseng kimberlite in the case of Lesotho, and the gem-quality diamonds typical of the Namibian west coast placer deposits. Lower value production separates out towards the lower end of the value scale, representing the Zimbabwe secondary Chiadzwa operations and DRC

Diamond exploration and mining in southern Africa:

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;,2=>?% &>=:,>?6;:.<76?&:42>8?"6<44:=8?*>=?5:=:9!?0<=?9'>?6;:.<76 *=<625;7,?5<279=;>8?<0?8<29'>=7? 0=;5:?" ;.)>=4>(? =<5>88?89:9;89;58-?/11 ?9<?/13%!

292=>?*=<6259;<7?:76?> *4<=:9;<7?9=>768 In the medium term (5 to 10 years), given the time needed to develop any new discoveries, the diamond production profile of southern Africa is not expected to change significantly. However, Botswana, having reduced output in recent years, is expected to be able to ramp up supply again should market circumstances change. In the longer term South Africa’s and Botswana’s share of global and regional production will eventually trend downwards as open pit mines go deeper or move underground. The new Luaxe discovery in Angola, announced recently by the Endiama/Alrosa JV, will increase that country’s share of production significantly in due course (Alrosa, 2017; Rough Polished, 2017). Diamond production in the other countries in the region is not expected to change much from current levels and, for the foreseeable future, southern Africa is expected to comfortably maintain its approximately 50% production share in carat and value terms.

The diamond discovery and production history of southern Africa bears testimony to the inherent prospectivity of the region, which justifies continued interest in exploration for new diamond deposits. Previously remote parts of the region and areas inaccessible due to conflict or other reasons are now more accessible. The region’s geology is now better understood compared to when the first kimberlite discoveries were made in Angola and Botswana in the middle of the last century. Cover sequences younger than the last phase of kimberlite intrusion in the region are also better mapped and understood, although these continue to present challenges to kimberlite exploration that are not yet fully overcome. Other parts of the region, such as South Africa, Zimbabwe, Lesotho, and eSwatini, where young cover is generally not considered a problem, have been more intensively explored. This lowers the residual prospectivity and leads some to believe that there remains only limited potential for discovery of new large, long-life deposits in these areas.

It is generally accepted that the potential for the discovery of $ # ?33+

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production, the latter mainly influenced by the diamond population typical of the Mbujimayi area.

Diamond exploration and mining in southern Africa: new mega-placer deposits of the type exploited in southern Africa for the last 100 years is very low. A mega-placer is a diamond deposit that holds at least 50 million carats with 95% or higher of gem quality (Bluck, Ward, and de Wit, 2005), acknowledged examples being the west coast diamond deposits of Namibia and South Africa. The combined inland diamond fields of Angola and the adjoining portion of the DRC would also meet this definition. The diamond-bearing conglomerates of the Umkondo Series sediments in Zimbabwe might narrowly fit the definition in carat terms, but not in terms of gem diamond content. The potential for significant new placer discoveries seems therefore to be limited to extensions of known occurrences in Namibia, Angola, and elsewhere in the region. The feasibility of exploiting the in-situ conglomerates at Chiadzwa in Zimbabwe is yet to be proven, but extensions of this field may still be found. Other potential placer targets might be surficial deposits proximal to new kimberlite discoveries, such as those being exploited at the Krone-Endora deposit adjacent to Venetia. Such small deposits are unlikely to be of interest to major mining companies but would be suitable for exploitation by juniors.

It is evident therefore that most future diamond exploration in southern Africa must be directed towards the discovery of as yet undiscovered kimberlite deposits. The parts of Angola and Botswana covered by younger strata appear to hold promise for new opportunity, with Angola, as result of having been inaccessible during its civil war years, holding pole position in terms of potential. In Angola, Diamang, the original state-owned diamond mining company, discovered and developed the Angolan diamond deposits over a period of 60 years. Until 1971, Diamang was the only company authorized to explore for and mine diamonds in Angola. It exploited alluvial deposits with great success and, starting in the 1950s, its exploration work led to the discovery of kimberlite deposits such as Catoca, Camatchia, and CamĂştuè (Chambel, Caetano, and Reis, 2013). More than 40 years after Diamang‘s replacement by the new state company Endiama, the recent discovery at Luaxe by the Endiama/Alrosa joint venture appears to be the only kimberlite deposit capable of being exploited in Angola so far not found by Diamang. Study of any map of the distribution of kimberlites in northeast Angola leads to the inescapable conclusion that most of the known kimberlites occur in, or proximal to, river valleys, suggesting that these have been found as a result of having been exposed by erosion and that others lie undiscovered under younger cover (Chambel, Caetano, and Reis, 2013). A number of companies have undertaken airborne geophysical exploration on the uneroded interfluves and some new kimberlites have been found, notably the Mulepe cluster by De Beers, but until the Luaxe discovery nothing of significance had emerged. In 2015 the government of Angola embarked on an ambitious geophysical survey of large parts of the country, the results of which were expected by the end of 2017 (Ponce de LeĂŁo, 2015). It is not clear how the Angolan government intends dealing with the results from these surveys but, once available, new exploration targets are likely to be generated.

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In Botswana, some greenfield work continues but most recent diamond exploration interest is being generated around existing mines and reinvestigation of old prospects, mainly as result of the Karowe experience and the idea that the application of new diamond recovery technologies and different processing flow sheets may revitalize some of these (Sasman, Deetlefs, and van der Westhuyzen, 2018). Despite improved geophysical technologies, the Kalahari cover in Botswana continues to hamper exploration, making the discovery process difficult; however, potential for new finds remains. South Africa was considered a mature area for diamond exploration potential even in the 1950s, but this did not prevent two major new discoveries 100 years after the original Kimberley and Free State finds; at Finsch, only 150 km from Kimberley, and 20 years later at Venetia in the far north of the country. Since then, other smaller, but nevertheless economic discoveries have also been made, with small mines developed at The Oaks, Klipspringer, and Marsfontein. Many new kimberlites were found in the Northern Cape Province from airborne geophysical surveys, but to date the only deposit being exploited as a result of that work is the Kareevlei kimberlite, owned by BlueRock Diamonds. Similarly, in Zimbabwe, also considered a relatively well explored part of the region, the Murowa kimberlites and the Chiadzwa placer deposits lay hidden for almost a hundred years after the first alluvial diamonds and kimberlites were found. Factors discouraging diamond exploration historically were the same economic factors applicable in South Africa, combined with exclusive rights to prospect and mine for diamonds being restricted for various reasons and, to some extent, perceptions that there was nothing new to be found Current diamond exploration activity in Lesotho is mainly restricted to investigation of old prospects, also using new diamond recovery technologies to improve efficiencies and reduce costs. A countrywide diamond exploration programme was reported to have been carried out in eSwatini in the early 2000s but nothing new appears to have come from this work. Diamond-bearing kimberlite deposits are not easy to find and, generally having a limited surface expression, require painstaking and diligent work to ensure discovery. Traditional methods of exploration based on soil and drainage sampling for indicator minerals have proven very effective in the past and will continue to be the most important exploration tool. In areas where the effects of unusual geomorphology or younger cover further inhibit direct discovery, it will be necessary to supplement sampling programmes using geophysical technologies. Airborne magnetics has generally been the method of choice, sometimes combined with electromagnetic systems, but the results from airborne gravity systems have been unfulfilling to date.

*4<=:9;<7?> *>76;92=>?9=>768 Estimates of annual exploration spend are prepared each year by S&P Global Market Intelligence (S&P GMI) based on surveys of a large number of mining and exploration companies throughout the world. Data retrieved from S&P GMI in April 2018 has been used in the analysis of diamond exploration data presented here.

Diamond exploration and mining in southern Africa:

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exploration budgets was reported for 2017 (S&P GMI, 2018b) but did not appear to carry through to diamond exploration. S&P GMI (2018b) estimated the total global, non-ferrous metal exploration budget at $8.4 billion for 2017, but only $208 million (2.5%) was allocated to diamond exploration (S&P GMI, 2018a). Current estimates indicate that the total diamond exploration budget for southern Africa in 2017 was approximately $56 million, with 80% split fairly evenly between Angola and Botswana (Figure 8) (S&P GMI, 2018a). The immediate reasons for the post-GFC trends were most likely the inherent risks associated with exploration and the need for companies to conserve cash, especially smaller companies. As the recovery in diamond prices has been slower than hoped for and the long-promised upward price drive has not been forthcoming, the level of annual budgets allocated to diamond exploration has also diminished. Based on analysis of S&P GMI data, supported by a review of company annual reports, more than half of the global diamond exploration budget is spent by just two companies, Alrosa and De Beers. Alrosa in particular has become increasingly active outside Russia and in southern /##

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Figure 7 shows that almost $1 billion was spent cumulatively in 2007 and 2008 on diamond exploration throughout the world, with almost half of that being spent in Africa. For the three-year period from 2006 to 2008, over $400 million was spent annually on African diamond exploration. The global financial crisis (GFC) of 2008–2009 caused significant disruption to the diamond industry and this translated through to significantly reduced expenditure on exploration, which has flowed through to the present (S&P GMI, 2018a). Post-GFC, world diamond exploration budgets more than halved in 2009 compared to the previous year, and similar or greater reductions carried through to African exploration budgets. Budgets reduced further in 2010 and recovered in 2011 and 2012; but this was short-lived, and a steady and continuous decline in global annual budgets is noted, from $520 million in 2012 to $208 million in 2017 (S&P GMI, 2018a). This downward trend in budgets is not confined to diamonds, but has been general across all mineral commodities since 2012. A slight up-kick in overall

Diamond exploration and mining in southern Africa: Africa is undertaking exploration in Angola, Botswana, Namibia, and Zimbabwe. Alrosa’s JV with Endiama in the Catoca mine appears to be successful and is a strong pillar of the Angolan diamond mining industry. The new Luaxe discovery will add to that success, and according to recent reports will double Angola’s annual diamond production once mine development is completed (Rough Polished, 2017).

294<< ?0<=?6;:.<76?> *4<=:9;<7?;7? <29'>=7? 0=;5: Key factors to be considered in undertaking diamond exploration are the inherent prospectivity of the target area, especially if there is a proven track record of production, ease of access, security of tenure and investment, political stability, and an open and transparent operating and fiscal regime. The foregoing has shown that southern Africa scores very highly on the first factor but, as reported in the Fraser Institute Annual Survey of Mining Companies 2017 (Fraser Institute, 2018) and evidenced by prevailing legislation applicable to diamond exploration and mining, the region is a mixed bag as regards the other factors. However, this is not always a deterrent for explorers and investors operating in the African diamond exploration and mining field, some of whom are prepared to operate under high levels of uncertainty.

An important consideration for investors is the potential value of the prize that might be obtained as a result of the exploration investment. All things being equal, areas with potential for higher value diamonds will usually attract higher levels of interest than others, as mines that produce better quality diamonds, especially if the diamond grade is also high, tend to be more robust in market downturns than those that produce poorer quality diamonds. All diamonds are not equal, and a number of factors influence the desirability and value of individual rough stones. These are the size or weight of the diamond in carats, morphology (or shape), colour (shades of white through to yellow and other colour varieties, described as ‘fancies’), and clarity (the impact of inclusions, cracks, or other impurities). Levinson, Gurney, and Kirkley (1992) commented that rough diamonds that yield good-quality polished gems, especially 0.5 ct or larger, are not common, and that stones over 1.5 ct are particularly rare. The reduced output of secondary diamond sources over time, which tend by their nature to contain larger and better quality stones than kimberlite sources, also means that relatively fewer of these better quality stones are being produced than in the past. Also, although 50% of the world's rough diamonds were classified as cuttable at that time, only about 2% to 2.5% were expected to yield good-quality stones of 1 ct or larger. In the author’s experience, these estimates are considered reasonable on average, although diamond qualities vary according to the source area. For instance Siberian and Canadian kimberlite pipes generally contain a higher proportion of better quality diamonds than southern African sources, and variations exist within each region. The DRC is therefore unlikely to attract explorers except for small-scale alluvial operations in the Kasai. Botswana will continue to attract exploration investment due its discovery

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track history and the ease of doing business there. Zimbabwe has recently announced changes to its mining investment regime, but the picture for diamonds is not yet clear, with the Zimbabwe Consolidated Diamond Company enjoying what appear to be exclusive diamond rights, at least in the Chiadzwa area. The situation for South Africa is also unclear, given current uncertainty concerning amended mining legislation (which is still to be put in place) and the time taken to process prospecting right applications. In Angola all rights to explore for diamond deposits are vested in the state company Endiama, and private companies may obtain prospecting rights only by virtue of a concession contract approved by the Angolan Council of Ministers. This is often a lengthy and expensive undertaking, which generally means that only well-equipped and well-financed exploration companies can go through this process. As shown earlier in this paper, in southern Africa the diamond exploration playing field has never been level, with various countries effectively closed off to exploration at times due to legislation or exclusive rights awarded to preferred companies, or conflict. One reason for this is certainly because governments often regarded diamond mining as being of strategic interest, requiring special oversight and protection. This situation still prevails in parts of southern Africa, and indeed other parts of the world, limiting areas easily accessible to private or listed exploration entities. In southern Africa, with the exception of Botswana and Namibia, the right to undertake diamond exploration and mining is not an open door for prospective explorers and investors.

Price stability of the product is a key factor to be considered in developing a diamond mine, and from the very early days, producers and marketers recognized this need. The rationale for restrictive practices in the past was provision of market support to ensure price stability and surety of mining investments already made, as well as revenues going forward (Innes, 1975). In the post-Second World War years, De Beers’ dominance brought a semblance of stability to the diamond market, interrupted at times by new independent discoveries such as in Russia, Australia, and later in Canada. This situation prevailed for some decades but increasing antitrust and competition law pressures, initially from the USA and later from the European Community, forced a policy rethink. Consequently, De Beers relinquished its policy of market dominance, with the result that its market share has fallen from more than 90% in the 1980s to less than 40% today, and it might be argued that the industry has paid for this in terms of increased price volatility. Most market commentators report a stable and positive outlook for the diamond industry going forward and predict that for the period through to 2030, rough diamond demand will grow at average rates of 1% to 4% (Bain and Company, 2017). Industry fundamentals also suggest a supply-demand shortfall going forward, which is expected to influence prices positively. However, this is predicated on normal business continuing as it has in the recent past. In the past, new discoveries, especially large finds, have always impacted on existing producers. The impact of the new discovery in

Diamond exploration and mining in southern Africa: Angola is still to be seen but, if it is as important as implied in recent press articles, then the overall balance of the industry may be affected, and more so if further exploration success is achieved. A further external factor confronting the natural diamond industry is competition from laboratory-grown gem diamonds. A report by Frost and Sullivan (2014) postulated that the predicted developing supply-demand gap for natural gem diamonds could, at least partly, be taken up by synthetic gem diamonds. A key constraint for such a scenario is the cost of building production facilities and consumer resistance, but the report argues that, as natural gems become scarcer and more expensive, the barriers to market entry for grown diamonds may reduce. In a 2017 report ABN AMRO (2017) suggested that, in the coming decades, laboratory capacity for production of laboratory-grown gem diamonds could exceed the mined production of gem-quality diamonds. However, it is considered that laboratory-grown diamonds are more likely to impact on the market for diamonds below 0.5 ct in size, as described earlier, than the larger and rarer categories of gem diamonds. The recent foray by De Beers into this field is an interesting development that will be closely watched (De Beers, 2018).

<75428;<78 Southern Africa is inherently prospective for diamonds and has the potential to continue as the pre-eminent producer of natural diamonds for the foreseeable future. This has been confirmed by recent discoveries in Angola and Zimbabwe, but some parts of the subcontinent have greater exploration potential than others. Entrepreneurial exploration appetite in southern Africa will be tempered by perceptions of the potential value equation and security of investment in diamond exploration. The uneven investment climate that prevails in the region will also affect levels of exploration investment, but recent changes in the political environment may provide potential for new exploration opportunities.

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DAMARUPARSHAD, A. 2007. Historical diamond production (South Africa). Report R61/2007. Department of Minerals and Energy, Pretoria. DE BEERS GROUP OF COMPANIES. 2018. De Beers to launch new fashion jewelry brand with laboratory grown diamonds. Media release, 29 May. https://www.debeersgroup.com/en/news/company-news/companynews/de-beers-group-to-launch-new-fashion-jewelry-brand-withlaborato.html DE WIT, M.C.J. 2010. Identification of global diamond metallogenic clusters to assist exploration. Proceedings of Diamonds Source to Use, Gaborone, Botswana, 1-3 March 2010. Southern African Institute of Mining and Metallurgy, Johannesburg. http://www.saimm.co.za/Conferences/DiamondsSourceToUse2010/015deWit.pdf DE WIT, M.C.J., BHEBHE, Z., DAVIDSON, J., HAGGERTY, S.E., HUNDT, P., JACOB, J., LYNN, M., MARSHALL, T., SKINNER, C., SMITHSON, K., STIEFENHOFER, J., ROBERT, M., REVITT, A., SPAGGIARRI, R., and WARD, J. 2016. Overview of diamond resources in Africa. Episodes, vol. 39, no. 2. pp. 199–237. DRAPER, D. 1898. On the diamond pipes of the South African Republic. Transactions of the Geological Society of South Africa, vol. IV. p. 5. FRASER INSTITUTE. 2018. Annual Survey of Mining Companies 2017. https://www.fraserinstitute.org/studies/annual-survey-of-miningcompanies-201 FROST AND SULLIVAN. 2014. Grown diamonds: Unlocking future of diamond Industry by 2050. https://ww2.frost.com/news/press-releases/growndiamonds-key-unlocking-future-diamond-industry-finds-frost-sullivan/ INNES, D. 1975. The exercise of control in the diamond industry of South Africa: Some preliminary remarks. African Studies Institute, University of the Witwatersrand. African Studies Seminar Paper, March 1975. JANSE, A.J.A. 1995. A history of diamond sources in Africa: Part I. Gems and Gemology, vol. 34, no. 4. pp. 228–255. JANSE, A.J.A. 1996. A history of diamond sources in Africa: Part II. Gems and Gemology, vol. 35, no.1. pp. 2–30. JANSE, A.J.A. 2007. Global diamond production since 1870. Gems and Gemology, vol. 43, no. 2. pp. 98–119. KIMBERLEY PROCESS ROUGH DIAMOND STATISTICS, 2004 to 2016. https://kimberleyprocessstatistics.org/public_statistics LEVINSON A.A., GURNEY J.J., and KIRKLEY M.B. 1992. Diamond sources and productivity: Past, present and future. Gems and Gemology, vol. 28, no. 4. pp. 234–254. MANENJI, T. 2017. The trajectory of Zimbabwean Marange diamond revenue remittances from 2016 to 2013. Scholedge International Journal of Business Policy and Governance, vol. 4, no. 6. doi: 10.19085/journal.sijbpg040601

ABN AMRO. 2016. Diamond sector outlook, nothing is forever ‌ 19 January 2016. https://insights.abnamro.nl/en/2016/01/diamond-sector-outlooknothing-is-forever/

PONCE DE LEĂƒO, T. 2015. Geological mapping of Angola; An example of knowledge share between GeoSurveys of Europe and Africa. Proceedings of the EGS-ASGMI Director’s Joint Workshop, October 2015. EuroGeoSurveys, Brussels, Belgium.

ABN AMRO. 2017. Diamond sector outlook, nothing is forever – part 2‌ 8 December 2017. https://insights.abnamro.nl/en/.../diamond-sectoroutlook-nothing-is-forever-part-2/

ROUGH POLISHED. 2017. Luaxe kimberlite to help Angola double output by 2022. 10 February 2017. http://www.rough-polished.com/en/news/105846.html

ALROSA. 2017. ALROSA to participate in the development of the largest deposit in Angola. http://eng.alrosa.ru/alrosa-to-participate-in-the-developmentof-the-largest-deposit-in-angola/ BAIN AND COMPANY INC. 2017. The global diamond industry 2017. The enduring story in a changing world. 33 pp. http://www.bain.com/publications/articles/global-diamond-industryreport-2017.aspx

S&P GLOBAL MARKET INTELLIGENCE. 2018a. User platform. S&P GLOBAL MARKET INTELLIGENCE. 2018b. World Exploration Trends. A Special Report from S&P Global Market Intelligence for the PDAC International Convention. March 2018 STOCKLMAYER, V. AND STOCKLMAYER, S. 2015. A review of diamonds in Zimbabwe – a century on – part 1, Geological Society of Zimbabwe Newsletter, October 2015. pp. 4–11.

BLUCK, B.J., WARD, J.D., and DE WIT, M.C.J. 2005. Diamond mega-placers: southern Africa and the Kaapvaal craton in a global context. Special Publication 248. Geological Society, London. pp. 213–245. https://doi.org/10.1144/GSL.SP.2005.248.01.12

SASMAN F., DEETLEFS B., and VAN DER WESTHUYZEN, P. 2018. Application of diamond size frequency distribution and XRT technology at a large diamond producer. Journal of the Southern African Institute of Mining and Metallurgy, vol. 118. pp. 1–6.

CHAMBEL, L., CAETANO, L., and REIS, M. 2013. One century of Angolan diamonds. Report prepared by Sinese – Consultoria Lda and Eaglestone Advisory Limited.

WILSON, A.N. 1982. Diamonds: From Birth to Eternity. Gemmological Society of America, Santa Monica, CA.

ZIMNISKY, P. 2014. The state of the global rough supply 2014. Paul Zimnisky Diamond Analytics. 8 pp. http://www.paulzimnisky.com/the-state-ofglobal-rough-diamond-supply-2014 $ # ?33+

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http://dx.doi.org/10.17159/2411-9717/2019/v119n2a5

Monitoring the performance of DMS circuits using RhoVol technology by M. Fofana* and T. Steyn*

One of the most important performance indicators of a dense medium cyclone (DMC) circuit is the Tromp curve, and by extension the separation density and Ecart Probable (Ep) values. The densimetric profiles of DMC product streams have been traditionally acquired using heavy liquid sinkfloat analysis, which has certain disadvantages, such as the associated safety and health risks. More recently, non-toxic media such as lithium hetero-polytungstates (LST) have been used, with the desired densities being achieved by maintaining the solutions at specific temperatures. However, the high costs of these liquids can be prohibitive. The long turnaround time of the sink-float analysis is a further disadvantage for timeous interventions to the operating set-points of the DMC process. The RhoVol technology can generate the density distribution of a batch of particles in a rapid, accurate, repeatable, and safe manner. Additional data of interest, such as particle size and shape, are also measured and reported on a per-particle basis. Furthermore, samples can be sorted into discrete sorting bins based on any of the measured parameters of the particle, making further analyses of the material possible. This technology has applications across all commodities that use the DMC, particularly in the size fractions –25 +8 mm and –8 +3 mm. To date, laboratory results have proved very encouraging – separation densities are within 5% of traditional sink-float results, and the technology is being introduced to diamond DMC plants. ?: 75-4 dense medium cyclone, RhoVol, particle size, particle shape, density.

>3857-0,8973 Dense medium separation is a commonly applied beneficiation technique for the density separation of valuable products from crushed ores. The industrial applications of dense medium separation have been summarized and reviewed by various authors (Scott and Napier-Munn, 2000; Bosman, 2014; NapierMunn, 2018). Dense medium cyclones (DMCs) are widely used in the coal and diamond industries to produce clean coal or to preconcentrate kimberlitic ores, respectively. Cyclone performance (separation density monitoring) techniques are varied. In the mineral industry. Density tracer tests (NapierMunn, 1985, 2014; Davis, Wood, and Lyman, 1985) without mineral material, are widely used, although their accuracy in predicting operating cut-points becomes limited once mineral material is introduced. More recently,

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* De Beers Technologies, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

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radio frequency tracers have been also used for the determination of cyclone efficiency (Partition Enterprises, 2017). Density fractionation (the sink-and-float process) is a traditional practice used throughout mineral processing laboratories to ascertain the efficiency of density-based separators. It has also been used as a yield predictor in the coal and iron ore industries, where the density profile of the crushed runof-mine (ROM) ore is correlated with the plant yield. Densimetric evaluation methods have historically used heavy liquids, tetrabromoethane (TBE) of various densities. but these liquids carry health and environmental risks (Wills, 1987). More recently, non-toxic lithium heteropolytungstates (LST) have been used, where the desired densities are achieved by maintaining the solutions at specific temperatures (Koroznikova et al., 2007). Although sink-float densimetric analyses give a useful indication of cyclone performance, their practicalities in providing information to effect timeous plant-control and optimization are often limited. Long delays (up to 12 weeks) between sample collection and availability of results are not uncommon (Ndlovu, 2015). Furthermore, the repeatability of data can often be poor due to the inconsistent nature of its operation, since sinkfloat analysis is a manual process and very dependent on the dexterity and experience of the operator. The RhoVol process begins by accurately measuring the mass of each particle, followed by a 3D reconstruction of its volume using images from multiple cameras (Forbes, Voigt, and Bodika, 2003; Forbes et al., 2006). The

Monitoring the performance of DMS circuits using RhoVol technology particle density is then simply the ratio of the mass to volume. Additional particle features such as size and shape (compactness, flatness, or elongation) are readily available. The process is rapid, repeatable, accurate, and safe. In addition, sample size is much smaller compared to requirements in conventional sink-float techniques, which amount to tens of kilograms. This is because it has been proven statistically that only a thousand representative particles are required to generate repeatable and consistent data (Mangera, Morrison, and Voigt, 2016). The objective of this paper is to present RhoVol as a competitive alternative to sink-float techniques. Ultimately, a comparative case study will be conducted by analysing data from both conventional sink-float and RhoVol methods, using similar samples. This study will include the generation of partition curves, and the determinations of operating cutpoints and Ecart Probable (Ep) values.

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RhoVol is a densimetric measurement device developed and manufactured by De Beers Technologies SA. The machine operates in an automated batch process configuration. Particles in a particular batch are individually weighed and their volumes are estimated by 3D reconstructions (Forbes, Voigt, and Bodika, 2003; Forbes et al., 2006). The device discretely generates mass, volume, density, size, and particle shape descriptors such as compactness, flatness, and elongation. It also has the capability of re-processing samples multiple times (in multi-pass mode) to increase measurement accuracy. The multi-pass algorithm can re-identify any individual particle in a sample and merge the data when it is reintroduced subsequently, and ultimately reconstruct a higher fidelity 3D model. Furthermore, the sample can be sorted into discrete bins based on any of the measured parameters of the particles, making further analyses of the material possible. The sample processing time is specified at 1000 particles per hour, resulting in statistically valid sample data being obtained in just over an hour in single-pass mode. Currently, the machine is available in two configurations, a fines machine for particles in the 3–8 mm fraction (including sorting as an option) and a coarse machine for particles in the 8–25 mm fraction (where the sorting option will be available soon). The requirements for the samples are that they should be dry, free of dust, and properly screened to the specified size fraction. Figure 1 illustrates the multi-stage internal functions of the RhoVol device. It entails a feed presentation stage which uses two feeders. The weighing station makes use of a high-speed weighing cell to ensure accurate measurement of particle mass. The 3D volumetric model of the particle is constructed by analysis of multiple images provided simultaneously by several cameras, offset at various angles. Sorting is done by an algorithm that can classify particles based on any of the obtained particle features, such as density, mass, size, or shape parameters.

overestimate the particle volume (Wang et al., 2009; Okonta and Magagula, 2011; Mangera and Morrison, 2016). The volume correction factor is primarily shape-dependent, but it is also material-dependent, since ultimately the nature of the material defines its final shape after comminution. Figure 2 presents an example of a 3D model of the volume of a particle generated by the RhoVol. Because particle shape is one of the most critical parameters affecting volume accuracy, a relationship between shape and volume correction factor was established. Hence, all samples were sorted on the RhoVol based on three shape factors, namely compactness, elongation, and flatness. The compactness factor was ultimately used in all subsequent studies, because preliminary tests found better correlation with the volume correction factor. The definitions of the shape factors are deduced from measurements made, using the 3D model of the particle as inputs (Voigt and Twala, 2012): a, b, c—Caliper diameters along each of the particle’s principal directions, resulting in three orthogonal measurements where ‘a’ is the longest caliper, ‘b’ the intermediate, and ‘c’ the shortest; all measured in millimetres Flatness—A value larger than unity related to the flatness of the particle and defined by the ratio of b to c. A sphere has flatness equal to unity

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(7)72;-686;,629 568973; Prior to generating any accurate density profiles by the RhoVol, an appropriate function for the volume correction factor for the 3D reconstructed volumes of particles needs to be established. Previous studies have proven that multi-view silhouette-based volume reconstruction methods will always

134

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Monitoring the performance of DMS circuits using RhoVol technology Elongation—A value larger than unity related to the elongation of the particle and defined by the ratio a:b. A sphere has elongation equal to unity Compactness—A value smaller than unity related to the compactness of the particle and is defined by bc 1/3. A sphere’s compactness measures unity a2

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Two kimberlite samples were utilized in this test work; namely, DMC float-and-sink audit products (2016) from Venetia Mine in the size fraction –8+2.8 mm. The kimberlite material types were identified as KO1 and KO2 MVK. Compactness values ranging from 0.45 to 0.85 in steps of 0.04 were set on the RhoVol and the float-and-sink audit products were run separately. This generated for each product twelve sub-samples (or shape classes of material) for the shape factor compactness. The first ten were realized from the initial compactness values plus two additional shape classes of material, one less than 0.45 and another greater than 0.85. A calibrated gas pycnometer was used for the volume measurements. Gas pycnometer evaluation consisted of determining the mean volume of each of the twelve subsamples. Finally, a volume correction factor for each shape class was established as the ratio of corresponding volumes from the gas pycnometer and the RhoVol. Lastly, the volume correction factor function (VCFF), relating volume correction factors to shape classes (for the compactness shape factor), was established. The photographs in Figure 3 illustrate compactness for kimberlite after classification in the RhoVol. The material was sourced from the DMC float. These pictures are a typical example of the sorting capability of the RhoVol, from flatter shapes (in the 0.45 to 0.49 range) to rounder shapes (in the 0.81 to 0.85 range). A similar compactness sorting was also found when the DMC sink material was studied, although not presented here. This shape classification can be ascribed to the nature of the material being analysed, in this instance, kimberlite.

Figure 4 shows the compactness distributions for the DMC float and sink products. Similarities can be observed between the two compactness distributions. Namely, the shapes of the bulk of the material are shifted toward higher values of compactness, hence the particles are expected to be predominantly rounder. This could have positive implications for separation in the DMC, since it is well known that the behaviour of flat particles has always been challenging to predict accurately. Furthermore, easy access to shape distribution of any given material, provided by the RhoVol device, would be welcome since it can give some indications of the nature of the separation to be expected in the DMC. Figure 5 illustrates the correction factors obtained using DMC sink and float products. The results indicate similarities between the two trends; which was expected because of good parallels between the two shape distributions seen in Figure 4. Hence, a single correction factor function was adopted in all subsequent studies. This was done by using mean values of correction factors for each compactness category from the DMC sink and float samples. Figure 6 shows such an example. The data demonstrates a good linear correlation between correction factor and shape (compactness) class; R2 was found to be 0.99.

64:;480- <;-:349/:859,;6362 4:4;71;):3:896;-/, 60-984;46/ 2:4 The kimberlite material used was identical to that described in the previous section. Representative samples were generated by riffling; one set for RhoVol analysis and another for sink-float analysis. The sink-float samples were sized into –8 +6.7 mm, –6.7 +4.75 mm, –4.75 +3.35 mm, –3.35 +2.36 mm, –2.36 +1.18 mm, and –1.18 mm fractions. However, due to RhoVol’s bottom size limitation for fines, of

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(b) Kimberlitic material sorted based on compactness in the range of 0.81 to 0.85; –8 +2.8 mm material

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Monitoring the performance of DMS circuits using RhoVol technology +2.8 mm, the samples were only sized as –8 +6.7 mm, –6.7 +4.75 mm, –4.75 +3.35 mm, and –3.35 +2.8 mm. For particle sizes finer than 3 mm, the resolution of the current weighing scale is insufficient to register adequate particle mass, especially for material such as coal. In addition, material handling for such fine sizes with the current feeder system will also be challenging.

In the sink-float evaluation process, tetrabromoethane (TBE) liquids covering a range of densities from 2.5 to 3.6, in incremental steps of 0.1, were used. Then, representative samples (as described in the section Sample preparation) of the various size fractions were separately added to the liquid of lowest density first. After stirring and allowing the mixture to settle, each float product was removed, washed, and put aside; while each sink product was transferred to the next higher density, and so forth. The final sink and float products were drained, washed, and dried, along with all the previous float products. Finally, all the products were weighed to give the overall density distribution of each sample, by mass (Figure 7).

After establishing the VCFF (volume correction factor function) all the RhoVol samples, as described in the section Sample preparation, were processed. Hence, information on mass, volume, size, and shape factors of individual particles was collected; from which sample density, size, or shape distributions could be created (Figure 8). The measured volume of each particle in each size fraction sample was adjusted by first determining its appropriate correction factor. This was achieved by using particle compactness and the fitted curve of the plot in Figure 6. Lastly, the corrected volume was just the product of volume measured by the RhoVol and the correction factor.

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Previous studies (Mangera, Morrison, and Voigt, 2016) have demonstrated the repeatability of results by RhoVol if data for at least 1000 particles is captured from a given sample. It was also found that the standard deviation of the median density ( 50) of the sample, after multiple analyses, was negligible. Figure 9 shows an illustration of the overlapping of the density distributions after multiple analyses, as a function of sample size, for a kimberlite sample. The results indicate a general improvement in the repeatability of the density distribution as the sample size increased, culminating in optimum data when 1000 particles (or more) were analysed. This is an important finding as it can, in future, help reduce the size of samples to be treated for densimetric analyses. Moreover, the finding also suggests that RhoVol can deliver consistent and repeatable results. This is unlike sink-float operations, for which repeatability of data is a challenge due to the fact that sink-float is a manual process and prone to human error.

The separation density and probable error (Ep) of the fines DMC (Module 1, DF1, Venetia Mine) were determined using sink and float products. Subsequently, density fractionation was performed (as described in the section Sink-float test work) followed by tests in the RhoVol device (explained in the section RhoVol test work) to establish the relevant sizeby-size partition curves.

Figure 10a presents the individual partition curves of all the sink-float size fractions (described in the section Sample preparation) alongside the curve for the overall fraction of – 8+1 mm. Table Ia summarizes the corresponding separation densities and Ep values. Figure 10b illustrates the partition curves of the RhoVol size fractions; as well as the curve for the combined fraction of –8 +2.8 mm. Table Ib, similarly, shows all the separation densities and Ep values. The separation densities and Ep values were all derived from experimental data fitted using the partition equation developed by Scott and Napier-Munn (1990). The partition curves and Ep results in Figure 10 and Table I show a general increase in separation density and Ep with decreasing particle size; a phenomenon that is a commonly observed in DMC separations. It is a physically related phenomenon of size-by-size cyclone partition curves (Scott, Davis, and Manlapig, 1986; Napier-Munn, 1990, 2018; Dunglison, 1999). RhoVol gives smoother partition curves, including the clear presence of a ‘pivot point’; which

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tends to occur in some DMC separations and has been used in the past for modelling purposes (Scott and Napier-Munn, 1992). The deteriorations in separation efficiency for the finest size fractions, particularly below 4 mm, appear more pronounced in the sink-float results. Noise in these finer fractions, which previously may have been attributed to short-circuiting, may be measurement artefacts. The Ep values generated as a result of tests on the RhoVol are for the most part lower, except for one outlier for the +3 –4 mm fraction. Figure 11 and Table II present the comparative separation data (where available) generated by the sink-float and RhoVol methods. The RhoVol densities are consistently higher than the sink-float densities, albeit within 5%. The

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reason for these differences between the two densimetric methods is still being studied. Nonetheless, it is common knowledge that the sink-float method has its drawbacks for near-density particles. That is because those particles may not have enough time to reach the sink or float product and could be misplaced into the wrong product during collection. Conversely, the RhoVol device accurately measures particle mass with excellent volume estimates, hence with fairly accurate particle density calculations.

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laboratories to ascertain the efficiency of density-based separators. Although the method does provide useful indications of cyclone performance, its practicality in supplying information to effect timeous plant control and optimization is often limited. The RhoVol machine is capable of rapidly and safely providing repeatable and accurate data, with significantly smaller sample size requirements, i.e., only thousands of particles as compared to tens of kilograms in sink-float analyses. Additional particle features such as size, volume, mass, and shape are readily available, and sorting on any of these properties is an option. This latter feature of RhoVol sets it apart from many other types of laboratory equipment. Ultimately, particle density versus size could be used to study mineral liberation, for instance. Comparative densimetric analyses were conducted on sub-samples riffled from a main common kimberlite sample that originated from Venetia Mine fines DMC float and sink products. The size fractions were varied between 8 and 1 mm in the sink-float method, while fractions between 8 and 2.8 mm were used for the RhoVol tests due to the bottom size limitations of +2.8 mm material. The partition curves and Ep results, using both methods, show a general increase in separation density and Ep with decreasing particle size; a phenomenon that is commonly observed in DMC separations. However, the RhoVol gives smoother partition curves from which conclusions are more easily drawn. In addition, the density difference between the two methods has consistently been within 5%, with reference to the sink-float technique. This finding is significant because it indicates convergence of results within a small margin of error. Ultimately, RhoVol data shows that it can be a very competitive alternative to the sink-float method, which is still very manual and labourintensive and suffers from accuracy problems.

138

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BOSMAN, J. 2014. The art and science of dense medium selection. Journal of the Southern African Institute of Mining and Metallurgy, vol. 114. pp. 529–536. DAVIS, J.J., WOOD, C.J., and LYMAN, G.J. 1985. The use of density tracers for the determination of dense medium cyclone partition characteristics. Coal Preparation, vol. 2, no. 2. pp. 107–125. DUNGLISON, M.E. 1999. A general model of the dense medium cyclone. PhD thesis, JKMRC, University of Queensland, Australia. FORBES, K., VOIGT, A., and BODIKA, N. 2003. Using silhouette consistency constraints to build 3D models. Proceedings of the Fourteenth Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2003). pp. 33–38. FORBES, K., NICOLLS, F., DE JAGER, G., and VOIGT, A. 2006. Shape-from-silhouette with two mirrors and an uncalibrated camera. Proceedings of the 9th European Conference on Computer Vision (ECCV 2006), Graz, Austria, May 7-13, 2006. Springer. pp. 165–178 KOROZNIKOVA, L., KLUTKE, C., MCKNIGHT, S., and HALL, S. 2007. The use of lowtoxic heavy suspensions in mineral sands evaluation and zircon fractionation. Proceedings of the 6th International Heavy Minerals Conference ‘Back to Basics’. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 21–29. MANGERA, R., MORRISON, G., and VOIGT, A. 2016. Particle volume correction using shape features. Proceedings of the Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), Stellembosch, South Africa, 30 November - 2 December, 2016. IEEE, New York. NAPIER-MUNN, T.J. 1990. The effect of dense medium viscosity on separation efficiency. Coal Preparation, vol. 8. pp. 145–165. NAPIER-MUNN, T.J. 2014. Statistics for Mineral Engineers; How to Design Experiments and Analyse Data. JKMRC, University of Queensland. 628 pp. Section 6.9.1. NAPIER-MUNN, T.J. 2018. The dense medium cyclone – past, present and future. Minerals Engineering, vol. 116. pp. 107–113. NDLOVU, S. 2015. Venetia Mine DMS evaluation report. Ven-R00545-718-001, 10-11-2015. DebTech. OKONTA, F. and MAGAGULA, S. 2011. Railway foundation properties of some South African quarry stones. Electronic Journal of Geotechnical Engineering, vol. 16 B. pp. 179–197. PARTITION ENTERPRISES. 2017. http://www.partitionenterprises.com.au/ [accessed 20 March 2017]. SCOTT, I.A., DAVIS, J.J., and MANLAPIG, E.V. 1986. A methodology for modelling dense medium cyclones. Proceedings of the 13th Congress of the Council of Mining and Metallurgy Institutions, Singapore, May. Australasian Institute of Mining and Metallurgy, Melbourne. pp. 67–76. SCOTT, I.A. and NAPIER-MUNN, T.J. 1990. A dense medium cyclone model for simulation. Proceedings of the Fourth Samancor Symposium on Dense Media Separation, Dense Media 90, Cairns, Australia. Samancor Ltd. SCOTT, I.A. AND NAPIER-MUNN, T.J. 1992. A dense medium cyclone model based on the pivot phenomenon. Transactions of the Institution of Mining and Metallurgy, vol. 101. pp. C61–C76. VOIGT, A. and TWALA, C. 2012. Novel size and shape measurements applied to jig plant performance analysis. Journal of the Southern African Institute of Mining and Metallurgy, vol. 112, no. 3. http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S222562532012000300007 WANG, L., LU, Y., LANE, D., and DRUTA, C. 2009. Portable image analysis system for characterizing aggregate morphology. Transportation Research Record: Journal of the Transportation Research Board, vol. 2104. pp. 3–11. WILLS, B. 1987. Mineral Processing Technology. 4th edn. Pergamon Press. pp. 420–456.

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a6

Financing diamond projects by J.A.H. Campbell*

Investment in diamond exploration has been declining over the past decade, in spite of positive long-term industry fundamentals and a growing interest in diamonds as an investment category. The lack of new significant discoveries in recent years has eroded investor confidence, yet no new discoveries are possible without investment in exploration. Junior ‘mine finders’ have been the hardest hit. Their agility, tenacity, and appetite for risk are not sufficient to attract the funding required, even at the greenfield stage. Developing new discoveries into mineral resources can be crippling without solid financial support. Junior incubators could play a crucial role, especially at the project evaluation stage – but where are they? Alternatives to traditional funding mechanisms have become available, many still untested in the junior diamond exploration space. Valuable lessons can be drawn from the past and used to inform emerging new strategies. B @?7; diamond exploration, project development, financing, revenue streaming, crowdfunding, incubators.

=<?@78:<A@= Diamonds are the ultimate expression of love. Their ancient and timeless allure continues to influence consumer emotions and behaviours across continents, cultures and age groups. Analysts are predicting a sustained supplydemand shortfall in the years ahead which will trend diamond prices generally upwards. With positive long-term industry fundamentals, one cannot help but wonder why funding for diamond exploration and development projects is so scarce. Investment in exploration across commodities – including diamonds – has been declining for a number of years, reaching a historic eleven-year low early in 2017. The lack of major diamond discoveries in recent years has done little to strengthen the case for investing in diamond projects, yet no new discoveries can be made without investment in exploration. Global estimates of known carats in the ground stand currently at 2.5 billion carats, which equates to around 18 years of production at current levels of 140 million carats (mct) per annum. Sustainable replenishment of depleting diamond resources

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A>9@=7CA=78;<? C28=7>9B=<>4; Diamonds are traditionally less prone to volatility and uncertainty than mainstream mineral commodities. With almost no exposure to speculative capital (roughly 1%), diamonds have proven significantly more stable than other tangible assets like gold and silver. Gold, on the other hand, has a speculative investment share of approximately 40%, and as a result is highly volatile (Figure 1) (Duffield and Shellhas, 2013). Diamond prices over the long term depend on consumer-driven supply-demand dynamics and economic growth. Favourable supply and demand fundamentals have led many industry experts to believe that diamonds will be among the top performing tangible assets for years. In fact, it is anticipated that diamond prices will outperform gold for the better part of the next decade, hitting annual record highs through 2020. Short-term speculative bubbles are to be expected within the rough diamond trade, but the polished market is generally more stable.

* Botswana Diamonds plc. Botswana. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

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is possible only through the development of new discoveries. This is what junior explorers are best at – but they need investors to fund their evaluation activities from early in the project development continuum. Junior explorers usually rely on equity financing to fund their early stage exploration and resource development until such time as an Indicated Resource is declared, along with completion of a bankable Feasibility Study (BFS) and debt funding becomes potentially accessible. With availability of traditional forms of financing becoming increasingly constrained, miners are considering alternative financing options.

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New investment mechanisms have been introduced in recent years to stimulate investment in diamonds. In August 2017, the Indian Commodity Exchange (ICEX) became the world's first exchange to start trading in diamond futures contracts; shortly thereafter, India’s Multi Commodity Exchange (MCX) also applied for the launch of futures trading in diamonds. Although it is still early days to assess the success of some of these mechanisms, there are clear indications that diamonds are beginning to attract interest as an investment category. The supply-demand outlook is modestly optimistic (Figure 3); however, the diamond industry faces three key challenges – namely the slowdown in demand for diamond jewellery, the impact of laboratory-grown diamonds, and the financial stability of the midstream segment of the value chain (Bain & Company, 2017). In an effort to address such key challenges, rough diamond producers are investing significantly to promote diamond sales; an estimated $150 million was invested in generic and branded marketing in 2017, representing an increase of about 50% from recent years. This was in addition to retailers’ own marketing spend (Bain & Company, 2017). The diamond industry has recently mobilized to fill the gap in generic consumer advertising left by De Beers,

who stopped promoting diamonds for the whole industry in the early 2000s. A new trade body called the DPA (Diamond Producers Association) has a $60 million annual budget to attempt to reinvigorate the category and drive future consumer demand, particularly targeting millennials. Three new diamond mines have come on stream during 2017: Stornoway’s Renard and Mountain Province’s Gahcho KuÊ in Canada, and Firestone’s Liqhobong in Lesotho. New supply from two of these mines may offset the loss of production from Alrosa’s Mir mine – which remains closed indefinitely – with limited impact on the long-term supply forecast (The Diamond Loupe, 2017; Ziminsky, 2017b). Despite the challenges that the diamond industry is facing on many fronts, the diamond market’s long-term outlook remains positive with rough diamond demand expected to grow 1–4% per annum and rough diamond supply remaining stable until 2030. The ’base case’ forecast for a sustained supply-demand shortfall in the years ahead will trend diamond prices generally upwards (Bain & Company, 2017).

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Financing diamond projects + 64@?A=3C2@?C7A>9@=7; C?A; C>=7C8=:B?<>A=< Exploring for diamonds is a business with high uncertainty and risk and little, if any, guarantee of reward. The primary risk of exploration lies with the mineralization in the ground; this is inherent in any form of mineral exploration, but diamond mineralization has a higher degree of complexity than other types. Such primary risk is partly mitigated by increasingly refining the understanding of the mineralization through a process of systematic mineral resource development which leverages new and past geological data. International codes such as SAMREC, JORC, or CIM govern such processes and stipulate the reporting parameters for the different categories of mineralization and Mineral Resource (Figure 4). On top of the geological and resource risk are country and political risks: explorers must go where the right geology and diamonds can be found, and neither of these follow humanimposed country boundaries (Figure 5). Risk appetite differs from one exploration company to the next, and more markedly between majors and juniors. Some juniors are prepared to trade some political risk for the advantage of ‘being at the right address’, i.e. where the geological risk is lower (Teeling, 2017). Risk mitigation in this respect requires a careful and ongoing assessment of prospectivity versus country and political risk, and ease of doing business. While geology remains unchanged (although understanding of it might improve), the investment attractiveness of a particular country (Figure 6) may change over time, depending on political, social, and economic circumstances. The fiscal regime of a prospective diamond country is a key influencing factor in investment decisions. Even more important are government policies on local ownership and indigenization and any legislated free-carry government interest, particularly when these are introduced suddenly. A stable and predictable regulatory regime is conducive to a viable investment climate, while sudden legislative changes in areas affecting the economics of an investment risk – such as state ownership and royalties – can have a chilling effect on investment (Leon, 2018).

The collapse of the commodities boom post-2011 has led to an upswing in resource nationalism and increasing indigenization requirements in some sub-Saharan African countries (Table I) (Davenport, 2016). A recent change to the Tanzanian mining regulations requires that government have

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a 16% free carry interest in any future mining project (this was accompanied by a blanket ban on the export of unprocessed minerals). Namibia appears to be following the South African economic empowerment approach, while the new Zimbabwe government has retained the 51% indigenization requirement for platinum and diamonds, which it had initially considered repealing. The Angolan government is entitled to hold a 10% equity in mining production through a State-owned company. Botswana is the only diamond-producing country in Africa to require no local ownership in mining companies.

&BC 8?7B=C@2C?B;@8?:BC7B(B4@69B=< Explorers operate at the tough end of the diamond funding pipeline, where the odds are stacked against them due to prevalent risk aversion and scepticism. As confidence in the mineralization grows, so does investors’ appetite (Figure 7). However, the resource risk mitigation process attracts additional costs which increase exponentially as a project advances through the development pipeline. While the announcement of a new diamond discovery generates positive sentiment in the market – often with a favourable impact on share price – this important milestone for any explorer is only the beginning of the demanding and capital-intensive process of proving up a diamond resource. This is a journey that can span decades, and which requires the ability to maximize optionality by adopting a phased approach to resource development, with targets set by phase and clearly identified triggers and decision points, in order to extract value or cut losses early (Still, 2016).

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Explorers with a track record of commercializing discoveries know that technology and deep expertise are key enablers when it comes to mitigating risk and extracting early value. To quote Dr. John Teeling, ’Exploration begins anew every 20 years with improvement in technology’ (Teeling, 2017). Technology plays a fundamental role throughout the diamond pipeline, from early exploration through evaluation, mine design, and diamond recovery, and further downstream where technology is used to identify, grade, and mark natural diamonds. Considerable improvements have been made in diamond recovery technology, especially in the identification of diamonds within kimberlite prior to crushing using X-ray transmission (XRT) technology, such as the XRT sorters installed at Karowe (Tassell, 2017). Nevertheless, breakage of diamonds during processing remains one of the key risk factors that producers have to mitigate in order to maximize value, or even determine viability. Unlike other commodities, diamonds are valued according to nonlinear models; diamond size is the main driver of diamond price, along with colour, quality, and shape. For established producers, especially mines that are known to yield large stones, the public acknowledgement of diamond breakage (most recently by Gem Diamonds and Stornoway Diamonds) can be a downgrading factor in terms of investor confidence. On the other hand, the recovery of a large, valuable stone constitutes a ‘material event’ that can influence the company’s share price and the market’s perception of the company’s value. Such events are more material the smaller the size of the company and the higher the value of the diamonds recovered (Ziminsky, 2017c). Material events require prompt and full disclosure to markets and regulators; their newsworthiness, however, depends on the company’s appetite for exposure and publicity. Privately owned diamond mining companies have seldom publicized the recovery of individual diamonds, although this is changing for many of them. For small diamond miners, on the other hand, the recovery of a single high-value diamond is a newsworthy event and its announcement can have a significant impact on share price. The journey from discovery through evaluation to production can differ depending on the geological settings and jurisdictions, as well as the companies involved. The recent history of Karowe mine is a particularly telling example.

Financing diamond projects &BC7B(B4@69B=<C@2C >?@ BC9A=B Karowe mine, located in the Orapa region of Botswana, is an open pit kimberlite operation and one of the world’s top diamond producers by value. The mine began production in 2012 and has an anticipated life of mine of 15 years. Estimates for 2018 are 270 000–290 000 carats for a revenue of $170–200 million. Since 2010, Karowe is fully owned by Lucara Diamond Corporation, a TSX and Stockholm Stock Exchange listed company with a market capitalisation in excess of C$800 million. A highly profitable producer, Karowe has generated revenue in excess of $1.02 billion to date, at an average price of $566 per carat. The mine is a proven large stone producer, famous for its magnificent Type II diamonds. The second largest diamond ever found, the 1 109 ct Lesedi La Rona, was unearthed at Karowe in 2015. AK6 (now Karowe) was discovered by De Beers in 1969. The potential of AK6 was not apparent at the time of its discovery and early assessment in the 1970s and 1980s. One ought to consider that this was a time of economic stagflation, when high inflation combined with slow growth and high unemployment crippled the global economy. Worldwide, diamond sales were declining sharply and diamond prices collapsing. A radically transformed market and new strategic direction in the 2000s, coupled with a long-term outlook of declining diamond supply and increasing demand, prompted De Beers to reassess many of the uneconomic kimberlites discovered in the 1960s and 1970s using second-generation exploration technology and analytical techniques. AK6 was among these kimberlites. The evaluation of AK6 took place in the ambit of the Boteti JV (51% De Beers, 49% African Diamonds). The process was fast-tracked to deliver the first mineral resource statement in 2007. Techno-economic studies were initiated in parallel to Phase 2, thereby further accelerating project development. Contrasting strategic, corporate, and financial agendas, risk appetite, approach to mine development, and diamond pricing methodologies were pivotal to the strategic decisions that shaped the AK6 project. The valuation of the project by the different players was vastly different, with economics ranging from robust to marginal. When De Beers became unable to finance the project in the aftermath of the Global Financial Crisis, African Diamonds proposed to buy out De Beers’ share, realizing that the project had robust economics; however, they were unable to raise the necessary funds. The mining boom had ground to a halt in 2009 and investment activity in the mining sector had dropped dramatically. Not surprisingly, juniors were the hardest hit.

In 2009, Lucara Diamonds, facilitated by African Diamonds, was able to buy 70% of a $1 billion business for $49 million, proving once again that ‘cash is king’, and especially so at the bottom of the economic cycle. Lucara’s market capitalisation prior to this acquisition was just C$30 million. In 2010, Lucara purchased African Diamonds’ share at a 30% premium. From an economic perspective, the AK6 story demonstrates that the right set of technical and corporate skills, coupled with the right opportunity and timing, can generate attractive returns for shareholders through increased share price (Figure 8). African Diamonds raised cash at 2 p, listed at 7 p in 2004, and sold AK6 for an equivalent 52 p. African Diamonds’ exploration assets were spun off into Botswana Diamonds plc. The original shareholders of African Diamonds made forty times their money through shares in Botswana Diamonds and dividends and capital growth in Lucara Diamonds (Figure 9). Much of the experience that was hard won through the AK6-Karowe journey from a submarginal project to one of world’s greatest diamond mines is today retained within the Botswana Diamonds team.

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Throughout 2016, the largest mining companies were responsible for the majority of exploration spending. For the third consecutive year, companies allocated more to minesite work than to grassroots exploration, as the former is perceived as a less expensive and less risky means of replenishing resources. With early-stage exploration being driven by the majors, overall spend on greenfield exploration fell to new lows, while proportionally more was spent on moving late-stage projects towards production or making them attractive for acquisition (mining.com, 2017) (Figure 11). Against this background of restricted exploration funding, investors’ appetite for diamond exploration has been further curbed by the lack of new significant discoveries in the last decade (with the exception of Alrosa’s Luele kimberlite at Luaxe in Angola and Lucara’s Karowe mine in Botswana); as a result, funding for diamond exploration remains extremely tight. The paradox is that the lower the exploration spend, the fewer the chances of new discoveries.

E8=A@?CB 64@?><A@=C28=7A=3 The discoverers and developers of new economic deposits tend to be agile, fast, and innovative small-cap companies

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with a healthy appetite for risk. Exploration spend is the lifeblood, yet these juniors typically have no production cash flow to fund their exploration activities. Funding of early stage exploration and resource development activities usually comes from ‘family and friends’ (Teeling, 2017) with some appetite for risk; only once an Indicated Resource is declared, along with completion of a BFS, does debt funding potentially become accessible. This is largely because institutional investors look for positive cash flows, low or mitigated risk, high returns and, preferably, some form of tax incentive. As a company de-risks its projects, more value is unlocked for shareholders. The Lassonde curve (Figure 12) illustrates the value that the market attributes to each stage of the life-cycle of a mining company from exploration to production. The discovery phase is high-risk, high-potential, and early investors can see substantial gains in a short timeframe. Once the mineralization is delineated, the project enters the development phase. This period can be accompanied by a lack of news as companies work on economic studies, which leads to sell-off by many impatient investors. When a company finally begins construction, the reality of cash flow becomes apparent, and institutional

Financing diamond projects

investors begin to enter the scene. From there onwards, it is up to the company to maintain a healthy and active pipeline to ensure that value creation outpaces depletion (Palisade Research, 2017). The intervening phase between discovery and development is the most critical for junior explorers. As speculators exit the scene, the company enters an ‘orphan period’ from which it may not recover unless funding is obtained. It is precisely at this stage that junior incubators have a crucial role to play. Yet, these are few and far between, and particularly so in the diamond exploration space. Internationally, companies such as the 162 Group, HDI (Hunter Dickinson Inc.), and the Lundin Group have operated effectively in the resources space by applying a mix of entrepreneurial skills, business experience, and risk appetite to identify viable opportunities for new ventures. HDI has raised more than C$1.9 billion in equity financing since 1985 and established a large network of institutional and retail investors in leading financial centres; together with their affiliated companies, they have successfully acquired and developed mineral properties with high potential for value growth.

Stock exchange

/A=>=:A=3C@2C7A>9@=7C6?@ B:<;C C &><C>?BC<&B >4<B?=><A(B;D The availability of financing through ‘traditional’ equity and debt markets has been constrained by falling commodity prices, a legacy of cost inflation during the supercycle, and continued uncertainty in the international economy. Miners, however, need to generate funding for operations, including new developments and expansions. Mining companies with substantial debt burdens need to reduce the existing debt on their balance sheets (Eastman, Graves, and Mariage, 2017). According to a recent study of private capital in the resources sector, two-thirds of institutional investors have shown diminished appetite for the mining and metals sector, primarily due to volatility and uncertainty. In contrast, private capital investment in the mining and metals sector has been increasing through a range of investment vehicles

Mining-specific reporting requirements (post-listing)

Mineral reporting standard Diamond companies (selected)

ASX

Annual and half-year financial report Quarterly report by CP on production and development activities (incl. expenditure); exploration activities; mineral results and ore results

JORC

Merlin Lucapa

JSE

Annual and quarterly financial report. Description of exploration and mining activities by CP; Mineral Resource and Reserve statement

SAMREC

Trans-Hex

TSX/TSX-V

Annual and quarterly financial report.

CIM (NI 43-101)

Diamcor DFI Kennady Lucara Mountain Province Peregrine Rockwell Stornoway Tsodilo Dundee Minerals

LSE/AIM

Annual and half-year financial report. Interim management statement. Resource updates by CP (AIM).

JORC, SAMREC, CIM, other selected codes

Botswana Diamonds Firestone Petra Blue Rock

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The remarkably successful Lundin Group has recently been shifting its focus towards the consolidation of the midsize mining space (Business News Network, 2017). The 162 Group focuses on high-potential natural resource start-ups, providing seed capital and initial management and taking the venture to market normally within three years. Over time the 162 Group has established 15 listed resources companies with interests across the globe, including AIMlisted Botswana Diamonds which is actively exploring in South Africa and Botswana. The perception that Canadian and Australian institutional investors may be less risk-averse than their London-based counterparts has prompted certain London-listed junior miners to seek secondary listings in Toronto in the hope of higher valuations (Denina and Lewis, 2017). From 2009 to 2016, equity financings for mining companies listed on the Toronto Stock Exchange or TSX Venture Exchange fell by 58% (Eastman, Graves, and Mariage, 2017). However, the growing number of mergers and acquisitions and the reemergence of IPOs on the TSX-V during 2017 are signs that some confidence is returning to the junior mining sector (PwC, 2017).

Financing diamond projects

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and strategies, including private equity and debt funds (Els, 2016). As the global mining industry slowly recovers, many mining companies are turning to emerging or alternative financing options, chiefly mineral royalties and revenue streaming (Figure 13). Both options are seen as better suited to junior and mid-tier miners, as they attract investors with higher risk appetite and faster decision-making processes than traditional banks; moreover, these options involve sequences of payments rather than large upfront risk capital (Grieve, 2018). Such alternative financing options, however, are often inaccessible to early stage ‘greenfield’ project developers (Eastman, Graves, and Mariage, 2017). While royalties have been in use in the mining industry for a long time but have only recently become mainstream options for generating funds, stream arrangements were only introduced in 2004 for precious metals, and have recently extended to other commodities such as base metals and diamonds. Both options share some common features and each has its advantages and disadvantages, as illustrated below. Streaming finance is particularly applicable to countries in legislative flux like South Africa, as streamers are not concerned with equity and are thus less deterred (Grieve, 2018). In 2014 Stornoway Diamonds made history by securing C$944 million to develop the Renard project through a combination of debt, equity, and revenue streaming. It was the biggest project financing package for a publicly listed diamond company. The deal was designed to fully fund the mine construction through to completion and, although dilutive to the operation’s NPV, it allowed the project to move forward without risk of delays due to capital constraints (van Praet, 2014). An emerging alternative funding model is crowdfunding, or CSEF (crowdsourced equity funding). Crowdfunding enables project owners to raise funds through secured online platforms from a large number of small investors who can

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now own equity in an emerging mining company for as little as $500. Unlike ‘crowdselling’, a common strategy practiced by institutional investors which left many mineral exploration companies on the brink of insolvency (Lasley, 2016), crowdfunding has the ability to reach a vast number of potential investors through social networks, thereby expanding the pool of potential investors far beyond the confines of banks, venture capitalists, and other mining companies. However, it is imperative that communications with this audience be frequent, direct, and transparent; and the information clear and current. This will require a major shift in the marketing strategy of most mining companies. Dedicated crowdfunding platforms for mining companies have been launched in Canada and Australia and others are in development, such as ResourceFunding in the UK. Mining companies using such online investment platforms commit to transparency of information to investors and undergo rigorous due diligence processes which are designed to protect the crowd (Breytenbach 2016). Regulators in Canada and the USA have already adopted rules allowing the sale and purchase of securities via crowdfunding portals. Lobbying is ongoing in Australia to change the legislation in support of crowdfunding (Lasley, 2016; Banks, 2016) Crowdfunding is changing the investment landscape by removing barriers for investors and connecting investors and mining companies.

A;:8;;A@= With solid industry fundamentals, indications of growing interest in diamonds as an investment category, and alternative financing mechanisms available, why is it that junior diamond explorers struggle to attract investment early in the project development continuum? Where and how can juniors source sufficient funding to navigate the ’orphan period’ between the ’friends and family’ and the Inferred Resource stage? This is where mining project incubators could play a crucial role. The South African government has identified small business incubators as a priority, yet it remains unclear how these incubators would get access to finance when they don’t have their own, government funds are limited, and banks are averse to funding start-ups. The recent introduction of Section 12J of the Income Tax Act incentive structure allows companies and individuals to receive a tax deduction of 28% on their investments. This may entice more individuals to invest in small businesses, particularly high-risk, high-growth start-ups. Moreover, investments into a Section 12J venture capital company can attract enterprise development or sustainable development points, where the underlying investments qualify. Will this incentive be enough to stimulate the establishment of mining project incubators in South Africa? The portfolio of projects of the JSS Empowerment Mining Fund, a Section 12J company launched in 2017, is primarily in the coal mining sector (Odendaal, 2017). Coal is the only South African resource sector to have been leading the conversation on mining industry incubators (SRK, 2017), which is not surprising when one considers that over 90% of South Africa’s energy is generated from coal. Coal mining is, however, a relatively low-risk proposition, and particularly so compared to diamonds.

Financing diamond projects

@=:48;A@=; Diamond exploration is a complex business with a high failure rate. It requires trusted teams of ‘mine finders’ with proven skills, deep commitment, and healthy doses of tenacity and optimism. It requires the careful, ongoing assessment of the interplay between geological and country risks and the agility to respond promptly to any changes in the latter – be they improvements in fiscal regime or sudden indigenization legislative changes. It also requires financing: right from the early stages, when public listing is neither viable nor acceptable by the main stock exchanges, and increasingly so through the evaluation stage, when confidence in the mineralization is not yet sufficient to entice the more risk-averse investors. Entrepreneurs with a following and isolated ’junior incubators’ have had a crucial role to play in the growth of certain diamond exploration companies; however, these are few and far between. New and alternative financing mechanisms have appeared in the recent years, including crowdfunding. Their viability in the diamond exploration space, however, remains to be ascertained.

B2B?B=:B; BAIN & COMPANY. 2017. The global diamond industry 2017. http://www.bain.com/publications/articles/global-diamond-industryreport-2017.aspx [accessed 11 February 2018]. BANKS, J. 2016. Crowdfunding - a credible new source of investment for mining? http://www.stratum-international.com/blog/crowdfundinginvestment-in-mining/ [accessed 10 March 2018]. BREYTENBACH, M. 2016. Alternative fundraising model seen flourishing but regulatory issues could stifle uptake in South Africa. http://www.miningweekly.com/print-version/despite-game-changingexpectations-regulatory-challenges-remain-key-concern-ofcrowdfunding-for-mining-sector-2016-05-20 [accessed 10 March 2018]. BUSINESS NEWS NETWORK. 2017. Rick Rule interviews Lukas Lundin on what sets the Lundin Group apart from competitors. https://www.bnn.ca/investment-trends/video/rick-rule-interviews-lukaslundin-on-what-sets-the-lundin-group-apart-from-competitors~1264442 [accessed 18 February 2018]. CAMPBELL, J.A.H. 2005. The continuing transformation of De Beers – a case study. Presentation to the Executive MBA Study Tour of South Africa. Tanaka Business School, Imperial College, London, UK CAMPBELL, J.A.H. 2017. Country models that are working and why. Panel discussion at the Africa Mining Summit, Gaborone, Botswana. DAVEPORT, J. 2016. Time for African countries to rethink mining policies. https://www.howwemadeitinafrica.com/time-african-countries-rethink-miningpolicies/55089/ [Accessed 9 February 2018]. DENINA, C. and LEWIS, B. 2017. Small mining companies shun London market after IPO flops. https://www.reuters.com/article/us-mining-ipo/smallmining-companies-shun-london-market-after-ipo-flops-idUSKBN1CU26A [accessed 10 March 2018].

DUFFIELD, C. AND SHELLHAS, K. 2013. Could diamonds outperform gold this decade?http://www.traderplanet.com/articles/view/163898-coulddiamonds-outperform-gold-this-decade-gld/ [accessed 1 March 2018]. EASTMAN, N., GRAVES, B., and MARIAGE, F. 2017. Global financing alternatives: a primer on royalty and stream financing. https://gettingthedeal through.com/area/22/article/29109/mining-2017-global-financingalternatives-primer-royalty-stream-financing/ [Accessed 11 February 2018]. ELS, F. 2016. Private capital is ready to invest $7 billion in mining. http://www.mining.com/private-capital-is-ready-to-invest-7-billion-inmining/ [Accessed 10 March 2018]. ELS, F. 2017. Mining exploration spending drops to 11-year low. http://www.mining.com/greenfields-share-exploration-spending-dropsrecord-low/ [accessed 4 February 2018]. EULER HERMEs. 2017. Country risk map Q1 2017. http://www.eulerhermes.com/ economic-research/publications/Pages/country-risk-map-q12017.aspx?postID=1033 [accessed 11 February 2018]. EXPLORATION INSIGHTS. 2016. Fatal flaws in the junior mining sector. https://www.explorationinsights.com/articles/fatal-flaws-in-the-juniormining-sector/ [accessed 10 March 2018]. GRIEVE, N. 2018. Juniors asked to consider royalty, streaming project finance deals. http://www.miningmx.com/news/energy/31512-junior-askedconsider-royalty-streaming-project-finance-deals/ [accessed 9 March 2018]. LASLEY, S. 2016. Crowdfunding awakens. https://www.miningnewsnorth.com/ story/2016/01/10/news/crowdfunding-awakens/3551.html [accessed 10 March 2018]. LEON, P. 2018. Mining sector needs a strong dose of Ramaphosa’s Eskom treatment. https://www.businesslive.co.za/bd/opinion/2018-02-05mining-sector-needs-a-strong-dose-of-ramaphosas-eskom-treatment/ [accessed 2 March 2018]. ODENDAAL, N. 2017. JSS eyes R1bn capital raise to fund junior miners. http://www.engineeringnews.co.za/article/jss-eyes-r1bn-capital-raise-tofund-junior-miners-2017-01-27 [accessed 10 March 2018]. PALISADE RESEARCH. 2017. Junior mining’s infamous guide to investing. https://palisade-research.com/junior-minings-infamous-guide-toinvesting/ [accessed 8 March 2018]. PWC. 2013. 3 things you need to know about alternative financing in the mining industry. https://www.pwc.co.uk/assets/pdf/alternative-financingin-the-mining-industry.pdf [accessed 22 February 2018]. PwC. 2017. Junior mine 2017: Confidence rekindled. https://www.pwc.com/ca/en/industries/mining/publications/junior-mine2017.html [accessed 9 March 2018]. SAMREC. 2016. South African Mineral Resource Committee. The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (the SAMREC Code). 2016 Edition. http://www.samcode.co.za/codes/category/8-reportingcodes?download=120:samrec SRK. 2017. Could coal mining be the SA mineral sectors business incubator? https://www.srk.co.za/en/za-could-coal-mining-be-sa-mineral-sectorsbusiness-incubator [accessed 10 March 2018]. STILL, R. 2016. Lessons from the exploration business. Presentation at the Junior Indaba, Johannesburg, South Africa. TASSELL, A. 2017. XRT technology ushers a new era in diamond recovery. Modern Mining, May 2017. TEELING, J. 2017. Have I a deal for you? Presentation at the Junior Indaba, Johannesburg, South Africa. http://www.juniorindaba.com/presentations [accessed 1 March 2018]. THE DIAMONDLOUPE.COM. 2017. ALROSA rough Sales $258M in August, to close Mir mine indefinitely.https://www.thediamondloupe.com/articles/201709-07/alrosa-rough-sales-258m-august-close-mir-mine-indefinitely [Accessed 25 February 2018]. VAN PRAET, N. 2014. Stornoway financing deal pushes Quebec's first diamond mine closer to reality. http://business.financialpost.com/commodities/ mining/stornoway-financing-deal-pushes-quebecs-first-diamond-minecloser-to-reality [accessed 8 March 2018]. ZIMINSKY, P. 2017a. Quarterly review of rough diamond prices in the 10 years since the Global Financial Crisis. http://www.paulzimnisky.com/10-yearquarter-by-quarter-recap-of-rough-diamond-prices-since-global-financialcrisis [accessed 4 February 2018]. ZIMINSKY, P. 2017b. 2018 Global diamond industry primer. http://www.paulzimnisky.com/2018-global-diamond-industry-primer [accessed 4 February 2018]. ZIMINSKY, P. 2017c. The discovery of newsworthy diamonds is increasing. http://www.paulzimnisky.com/the-discovery-of-newsworthy-diamonds-isrising [accessed 4 February 2018]. #!*",+C55)

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The JSE, South Africa’s long-established stock exchange, prides itself in having the world’s highest migration rate from the small-cap secondary board (the AltX) to the main listing platform. Why then are so many junior explorers active in South Africa not listed on AltX? Perhaps the value proposition of some of the firms operating as JSE/AltX sponsors or designated advisers could be enhanced by offering business incubator services to potential new entrants. The rise of crowdfunding in mining presents an attractive option for juniors seeking exploration seed funding. Could it become a viable alternative at a time when traditional methods for raising exploration capital are failing junior explorers? Could it become the future of fundraising?

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http://dx.doi.org/10.17159/2411-9717/2019/v119n2a7

The application of XRT in the De Beers Group of companies by A. Voigt*, G. Morrison*, G. Hill*, G. Dellas*, and R. Mangera*

" !#! X-ray transmission (XRT) sorting has become the preferred recovery technology option in several parts of the diamond-winning flow sheet. In the De Beers Group, applications of XRT are found across kimberlite, alluvial, and marine operations. This is the result of intensive R&D conducted over the years to arrive at a suite of machine embodiments capable of sorting and auditing diamonds across all size ranges. The first applications in the marine environment used the technology in an auditing mode, and served as a useful early predictor of diamond content weeks ahead of sorthouse returns. The same machines are now available with ejection capability to produce high final product grades. The next application was tests on coarse alluvial gravels as an alternative to dense medium separation. The results were very encouraging, and tests are planned for both green- and brownfield kimberlite environments, as well as to explore an alternative to conventional techniques in final diamond recovery. At the Jwaneng mine Large Diamond Pilot Plant (LDPP), the objective is to recover diamonds in the size fraction –45 +25 mm. The technical challenge remains in the finer sizes, for high-capacitymachines as direct alternatives to conventional diamond recovery technologies. This is an area of ongoing R&D and it is only a matter of time before the breakthrough emerges.

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XRT makes use of X-ray imaging techniques to analyse objects and materials, and has a wide range of applications from baggage scanning for security purposes (Martz et al., 2016), to recycling of waste material (Owada, 2014). During the last decade XRT has been applied to an increasing extent in the minerals processing industry (von Ketelhodt and Bergmann, 2010; Sasman, Deetlefs, and van der Westhuyzen, 2018). Dual-energy XRT (DE-XRT), in which images of the target material are obtained at both high and low Xray energies, allows for elemental analysis and therefore can be used to discriminate between various minerals. A DE-XRT system makes use of a dualenergy X-ray line scan sensor to generate images of transmitted X-rays (Figure 1). Dualenergy refers to the camera, which contains

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two sensors, one responding to low-energy Xrays and one to high-energy X-rays. Feed material can be imaged either while on the belt, or while in flight. Examples of raw X-ray images of diamondiferous feed material are given in Figure 2. Particles appear as shadows in the

* De Beers Technologies (SA), Johannesburg, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

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The application of XRT in the De Beers Group of companies

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image, as X-rays are absorbed by the particles and fewer Xrays arrive at the detector. The shadows appear darker in the low-energy image than the high energy-image, since lowenergy X-rays are more readily absorbed than high-energy X-rays. The extent to which materials absorb X-rays depends on the elements present, density and thickness of the material, as well as the characteristics of the X-rays. Each mineral absorbs high- and low-energy X-rays in different ratios. Figure 3 shows the theoretically predicted absorption of highand low-energy X-rays for various minerals of varying thickness, derived from the material properties (XCOM, 2018). It is evident that diamond can be distinguished from other minerals. Note that Figure 3 represents the ideal case. In practice, detector noise and other variations result in imperfect discrimination. This is especially true for small particles. The high- and low-energy images can be combined, and each pixel coloured according to its classification as shown in Figure 4.

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Image processing techniques are used to identify particles in the classified image. Various tests are carried out on each particle before it is identified as a target or non-target. In a sorting machine embodiment, the target particles are typically ejected from the material stream using an air ejection system. In addition to identifying particles, the XRT absorption images contain information from which the size and mass of particles can be inferred. This allows for diamond auditing applications, which are discussed further below.

>6/1197,>8.>%")><;>:5=> => ==76>A78/The first XRT system in De Beers was developed by the De Beers Diamond Trading Company in 1990. This is a laboratory system used for detecting and analysing liberated diamonds in the Kimberley Microdiamond Laboratories, and a version is still in use today. Following this, in the early 1990s, the De Beers Mineral Processing Division in Johannesburg evaluated XRT as one of the technologies for detecting unliberated diamonds within kimberlite. At that time, fast neutron radiography was identified as the preferred method for detecting these unliberated stones. During this work, XRT again showed great potential for the detection of liberated diamonds, although the commercially available excitation and detection technology was not yet mature enough for application to kimberlite processing or diamond recovery. The major advantages that XRT offered were: All diamond types would be recovered with no concerns regarding low-luminescing stones Ultra-low yields would be possible, making new flow sheet designs feasible Lower sensitivity to dust and masking effects than conventional optical techniques.

The application of XRT in the De Beers Group of companies However, challenges remained for XRT on multiple fronts (as described later by Buxton and Crosby, 2008): Predicted throughputs were low at the targeted size ranges The robustness of the enabling technology was poor The cost of developing a viable sorter to compete with existing equipment was prohibitive Reliable performance benchmarks were nonexistent, and thus trust was low. Nevertheless, during the next decade the underlying excitation, detection, and data processing technologies made steady progress and the development of viable sorting machines became a possibility in the mid-2000s. The following application areas for XRT were identified and pursued: To provide online diamond auditing information. The first XRT auditing trials were run in 2013 on offshore marine vessels. To complement X-ray luminescence (XRL) sorting machines in the recovery plants to produce concentrates containing a high weight percentage of diamonds. XRT reconcentration machines have been operating on offshore marine vessels since 2016. As a low-OPEX alternative to coarse DMS that would be insensitive to feed density. The first XRT pilot plant was operational in 2014 at Namdeb. To recover large diamonds early in the flow sheet. The Jwaneng mine LDPP will operate from 2018 to 2019. Going forward, challenges remain as regards fine diamond recovery, particularly with wet fine material. In addition, applications in the re-deployable and ultra-coarse domains are being watched with interest and may be able to unlock further value for De Beers.

!97<;=>9/2<:<;0>9;2>687:<;0>9--4<39:<8;6 XRT can provide real-time estimates of diamond size frequency distributions (DSFD). This is because high-

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resolution images of the feed material are captured and the absolute X-ray absorption of a particle depends on its size and mineralogy. The carat mass of individual diamonds can therefore be estimated as they are processed. Real-time DSFD information provides particular benefit in the marine mining environment. In a typical marine operation, the sea floor is systematically mined in welldefined sections. The final concentrates, usually the product of X-ray reconcentration unit processes, are canned on board. Consignments are exported to land on a regular basis, where they are sorted and sized in a hand-sorting facility. It is only at the end of this process (which can take several days) that the recovery statistics for a particular mining area become known. Plant metallurgists rely on proxy measures, such as the number of ejections recorded by the diamond recovery machines. These measures vary significantly depending on the machine set-up parameters and the mineralogy of gangue material being fed to the circuit. De Beers Marine (DBM), De Beers Marine Namibia (DBMN), and De Beers Technologies (SA) began a collaborative XRT test programme in 2013. A mine test unit

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The application of XRT in the De Beers Group of companies (MTU) was installed on the Debmarine Namibia DebMar Atlantic Mining Vessel. The final concentrate underwent XRT analysis before being canned for export. Over a one-year test period, the XRT estimate of DSFD and total carat content was compared with the sorthouse results. The tests were successful, showing that XRT provides an accurate and timeous estimate of the DSFD and carat content of the final concentrate. The XRT unit process (BDT1122) has since been commercialized and installed on five DBMN mining vessels as shown in Figure 5. The machines are available in either an auditing configuration (providing carat and DSFD information), or a reconcentration configuration (where sorting functionality is included). Figure 6 shows a typical correlation between carats recovered in the sorthouse versus the total carat content estimated by XRT, for +3ds material. XRT generally provides an accurate estimate of total carats recovered, and has replaced other proxy methods as a real-time measure of recoveries on many marine operations. While the application of XRT in the marine mining environment has proved useful so far, a few challenges remain. These are primarily associated with the detection of fine (–3ds or –1.3 mm square mesh sieve) material. Such material is at the limit of the resolution capability and noise characteristics of dual-energy X-ray detectors. In order to ensure that fine diamonds are detected, and recovered, the algorithm parameters are such that a significant number of false detections occur, increasing the yield on the finer material. Indeed, the outlier cases seen in Figure 6 can be associated with a period that had a high incidence of dusty or gritty material in the feed. In such cases, XRT overestimates the carat content. The reconcentration version of the machine (BDT1122) includes a multi-channel ejection system. Its purpose is to provide a high diamond by weight (DBW) product, and it is being piloted at various marine and land-based plants. The same challenge exists with fine material, as discussed in the previous paragraph. Various initiatives are underway to address the challenges presented above. These include improved dust and grit removal systems, as well as higher resolution X-ray camera technology.

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Table I

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86:><:=1 Electricity Labour Maintenance and consumables Downstream costs

14% 11% 7% 2%

36% 8% 32% 23%

Total

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cost alternative to DMS. These machines are predicted to run at lower operating costs and to produce lower and more consistent yields that will result in downstream treatment cost benefits and simplifications. A comparison is shown in Table I. During 2014 to 2016, Namdeb and De Beers Technologies SA collaborated to run a joint pilot test programme with the goal of evaluating high-throughput XRT in the alluvial application as an alternative to DMS plants. Figure 7 shows the installation in Oranjemund. A number of novel operational features were evaluated, including feed rate control loops, and generation of DSFD information. The machine used in the tests was the De Beers Technologies SA XRT Coarse Concentrator (XRT-CC) BWT1186. This containerized sorting machine has a 900 mm wide belt and is designed to process material from 6 mm to 75 mm. The programme tested the technology with material in size ranges between 6 mm and 32 mm. Dry and wet materials were specifically tested separately to determine the effect of moisture on sorting performance. In addition, as this was an alluvial operation, the impact of marine shell in the feed was also investigated. The test programme set out to determine the following critical parameters:

The optimal feed rate per size fraction Diamond recovery efficiency at these throughputs The availability and reliability of the XRT technology The operating costs of the XRT technology.

The results indicated that the XRT-CC machine is well suited as an alternative to coarse DMS. The tests also showed that feed preparation is important as increased undersize material in the feed reduces the throughput to less than the optimum for 100% diamond recovery. Wet fine material required special consideration due to the material sticking to the belt, leading to either higher-than-expected yield (for topdown ejection systems) or more frequent maintenance (for bottom-up ejection systems). It is worth noting that these findings are not XRT-specific concerns but would apply to any belt-fed sensor sorting system.

The application of XRT in the De Beers Group of companies 970=>2<918;2>7=38 =7, The high throughput and low yielding performance of XRT sorting machines enable them to be applied upstream of existing concentration circuits to target the large liberated diamonds above the conventional top particle cut-off size. This configuration allows this revenue to be recovered as early as possible and reduces the probability of damage to the large stones (Sasman, Deetlefs, and van der Westhuyzen, 2018). The LDPP at Jwaneng mine in Botswana (Figure 8) will undergo a 12-month test programme during 2018 and 2019 to quantify the occurrence rate of large diamonds at that operation. The plant will treat the 25 mm to 45 mm scrubber oversize size fraction with two De Beers Technologies (SA) BWT11162 machines. This containerized sorting machine has an 1800 mm wide belt and can process material from 6 mm to 75 mm.

4:=7;9:< =6>.87>38976=>2<918;2>7=38 =7,>-86:+ ! As much as the application of high-capacity XRT machines is seen as an alternative to DMS and upfront large diamond recovery, it also enables alternative flow sheet configurations (Valbom and Dellas 2010). Venetia mine is installing a De Beers Technologies (SA) XRT-CC machine after DMS, targeting the coarse (–25 +8 mm) size fraction, as shown in Figure 9. As feed to the plant becomes harder and denser, and with varying PSDs, the limitations of the current flow sheet configuration have become evident. High-throughput XRT technology will alleviate both the capacity and PSD variation challenges by a simple modification of the flow sheet as shown in Figure 9. The main advantage of the new configuration is the separation of the coarse circuit from the final recovery plant into a standalone high-capacity circuit. The recovery plant will then be reconfigured for the fines and middles size fractions at improved capacity.

#<;=>2<918;2>7=38 =7,>9:>5<05>:578/05-/: XRT machines that treat fine material could find application in the recovery plant and as a replacement for fines DMS.

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However, the throughput requirements in the DMS application make any sensor-based sorting system (including XRT) commercially unviable due to the current CAPEX and OPEX estimations for such projects. Apart from the commercial considerations, the treatment of fine material at high throughput presents various technical challenges to sensor-based sorting machines. While the demand placed on the detection and classification system is highest, the feed presentation system and the ejection system are also strained.

Systems dealing with large particles are fairly robust to dust and grit that is entrained with the material. However, as the size fraction being treated becomes smaller the detection system is less able to operate effectively. Added to this is the challenge of feeding fine, wet material which sticks to the belt and tends to clump. This adversely affects the recovery efficiency and yield.

The biggest challenge presented by finer material relates to requirements placed on the detection system. Difficulties arise because finer particle sizes require smaller pixels on the Xray sensor. To ensure that a reasonable signal is achieved on these smaller pixels, a number of engineering solutions are possible.

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Increasing the signal integration time, resulting in reduced throughput due to the associated reduction in the speed of the material passing the sensor Bringing the X-ray source closer to the sensor, resulting in reduced throughput due to reduced belt width Increasing the X-ray power, placing greater pressure on the engineering of X-ray safety and resulting in increased failure rates of the X-ray generation and detection components.

The application of XRT in the De Beers Group of companies

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To achieve high throughput when treating fine material, a much higher data processing rate is required. This is due to the finer detector resolution, which requires a high number of small pixels. Another challenge associated with small pixels is that the signal-to-noise level is decreased. The resulting data is noisier, which makes the task of the classification system more difficult.

For handling fine material, the ejection system needs to be more precise. Ejectors can be thought of as having a footprint – the bigger this footprint, the greater the number of ‘passenger’ particles that will be recovered with each ejection of a target particle. As the material becomes finer, the ejection footprint needs to be reduced, otherwise the grade of the recovered material will be too low.

)=6:>.93<4<:,><;> 859;;=6*/70 A permanent and secure XRT test facility is located at the De Beers Technologies (SA) campus in Johannesburg (Figure 10) and forms part of the Mineral Sample Treatment Plant (MSTP). Several performance parameters, including recovery efficiency, yield, throughput, and size range, as well as the interaction between these parameters, can be studied under controlled conditions.

8;34/6<8;6 XRT technology has had a positive, expanding impact across the De Beers Group. As we have discussed in this paper, this covers a wide range of applications from auditing in the marine environment to large diamond recovery at Jwaneng mine. The remaining challenges are associated with the fine diamond processing applications, particularly with wet fine

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material. Potential applications in the re-deployable and ultra-coarse domains are being considered, and may be able to unlock further value for De Beers.

3?;8(4=20=1=;:6 Sincere gratitude goes to the following: De Beers Consolidated Mines Venetia mine, Debmarine Namibia, Debmarine South Africa, Debswana Corporate Centre, Debswana Jwaneng mine, and Namdeb Diamond Corporation.

"=.=7=;3=6 BUXTON, M. and CROSBY, R. 2008. Ore sorting – An Anglo American perspective. Proceedings of Sensor Based Sorting 2008, Aachen, Germany. GDMB, Clausthal-Zellerfeld, Germany. MARTZ, H.E., LOGAN, C.M., SCHNEBERK, D.J., and SHULL, P.J. 2016. X-Ray Imaging: Fundamentals, Industrial Techniques and Applications. CRC Press. OWADA, S. 2014. SBS technology for creating environment friendly process of various metals recycling – Advances and problems to be solved. Proceedings of Sensor Based Sorting 2014, Aachen, Germany. GDMB Clausthal-Zellerfeld, Germany. pp. 11–23. SASMAN, F., DEETLEFS, B., and VAN DER WESTHUYZEN, P. (2018). Application of diamond size frequency distribution and XRT technology at a large diamond producer. Journal of the Southern African Institute of Mining and Metallurgy, vol. 118, no. 1. pp. 1–6. VALBOM, D.M.C. and DELLAS, G. 2010. State of the art recovery plant design. Proceedings of Diamonds – Source to Use 2010. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 243–252. VON KETELHODT, L. and BERGMANN, C. 2010. Dual energy X-ray transmission sorting of coal. Journal of the Southern African Institute of Mining and Metallurgy, vol. 110, no. 7. pp. 371–378. XCOM. 2018. National Institute of Standards and Technology Physical Measurement Laboratory X-ray cross-sections. https://physics.nist.gov/PhysRefData/Xcom/html/xcom1.htm [accessed 3 March 2018].

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a8

Jwaneng — the untold story of the discovery of the world’s richest diamond mine by N. Lock*

$5:-494 Despite the pre-eminence of the Jwaneng Diamond Mine as the world’s richest diamond mine, the discovery story has long been clouded in mystery. This is the 45-year old untold story of the Jwaneng discovery and contemporaneous Bechuanaland/Botswana political and socioeconomic history. Diamond exploration by De Beers in Bechuanaland began in 1955, with the first true kimberlite discovery near Letlhakane in 1967, before Clifford’s Rule was applied and before the use of indicator mineral chemistry. Surface deflation soil sampling and bioturbation by termites was the foundation for exploration into the Kalahari. The 1962 reconnaissance programme south of Jwaneng had failed to make any discoveries. Improved sampling methods were applied to the Kalaharicovered areas in 1969 and positive results were received by year-end. The Whateley’s Wish percussion drill-hole in December 1972 was confirmed as Jwaneng 2424D/K2 with drill core in early 1973. Jumper drills were required to penetrate the Kalahari sand cover. Diamond resources were demonstrated despite multiple technical problems. ;$ :814 Botswana, Jwaneng, kimberlite, diamond, discovery.

praise and interviews. In contrast, it was not until many years after its discovery that Jwaneng was the subject of praise from Harry Oppenheimer. The discovery, which spanned some five years from 1969 to 1973, has never received the same publicity as Orapa. This paper will trace the background events and the Botswana political and socioeconomic history leading to the untold story of the drilling of the Whateley’s Wish discovery percussion drill-hole in December 1972, with drill core confirmation of Jwaneng 2424D/K2 in early 1973. The discovery presented another conundrum because of the 45 m cover of Kalahari sediments that made thenconventional evaluation techniques ineffective. Evaluation proceeded by large-diameter drilling (LDD). The first ever threedimensional diamond resource demonstrated economic viability for mine development, despite the multiple technical problems and complex parameters that required integration to achieve a reasonable level of confidence.

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The Jwaneng Diamond Mine is, by value, the world’s richest diamond mine. Harry Oppenheimer is quoted as saying that Jwaneng is ‘the most important primary deposit found anywhere in the world since the discovery at Kimberley more than a century ago.’ (Taylor III, 1982) The mine opened in 1982 after some nine years of evaluation and construction since discovery in February 1973. Debswana (2017) states that 2015 production was 7.87 Mt of ore from which 10.408 million carats were recovered at a grade of 132 carats per 100 t (cpht). Although the value is not reported, it was noted that Jwaneng contributes 60–70% of Debswana’s total revenue from all mines, including Orapa, Damtshaa, and Letlhakane (Debswana, 2017). Despite the value and economic preeminence of Jwaneng, the discovery story has long been clouded in mystery. In 1967, Orapa was front-page news in the South African press shortly after the discovery, with the discovery geological team receiving personal

The Bechuanaland Protectorate has a central place in African colonial history, not least because the territory straddled the route of Cecil Rhodes’ projected Cape to Cairo railroad. He said ‘I look upon this Bechuanaland territory as the Suez Canal of the trade of this country, the key of its road to the interior ‌’ (Keppler-Jones, 1983). The so-called ‘Missionaries Road’ crossed the Protectorate

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* Retired, Private Consulting Geologist, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

Jwaneng — the untold story of the discovery of the world’s richest diamond mine from south to north, and the Batswana Dikgosi (chiefs) strengthened their relationship with the missionaries as a defense against the expansionist machinations of Rhodes’ British South Africa Charter Company. The representations of the revered three Dikgosi, when they visited London in 1895 to defend Tswana independence, and the disastrous Jameson Raid into the South African Republic in the same year, paved the way for what eventually became independent Botswana. The failure of the Charter Company to consolidate control of the Protectorate at the end of the nineteenth century resulted in a long period of disinterest in development in the territory while attention was concentrated to the north and south. At this time Tshekedi Khama (the uncle of and regent for the future first Botswana President, Sir Seretse Khama) and the tribal elders believed ‘mineral development should only come when the people could welcome these developments on their own terms’ (Prain, 1981). Traditional cattle ranching dominated tribal life and the country remained one of the poorest in the world. The Bechuanaland Protectorate government was very much of a laissez faire nature until British Prime Minister MacMillan’s ‘Winds of Change’ speech that heralded the post-Second World War world of the 1960s. Even in the early 1960s, some members of the Bechuanaland European Advisory Council advocated the incorporation of the Protectorate into either Rhodesia (now Zimbabwe) or the Union of South Africa. Fortunately, the winds of change were blowing strongly and Bechuanaland moved towards independence with the establishment of political parties and progressed to self-government and parliamentary rule in 1965. Full independence followed as the Republic of Botswana with Sir Seretse Khama as the first President on 30 September 1966. Although Botswana was expected to require British government grant-in-aid for the foreseeable future, fiscal prudence achieved a surplus before mineral revenue began to accrue in the early 1970s. The opening of the Orapa diamond mine in 1971 was the first step on the road to the transformation of the Botswana economy and the rapid expansion of social services and national infrastructure. Investment has been made in schools and hospitals, roads and power, piped clean water to urban areas, and telecommunications benefiting from new technologies that leapfrog older landline links. Janse (2007, compiled from sources listed in Table I ) illustrated global diamond production by volume (millions of carats) since 1869, with the Orapa and Jwaneng production startup dates. It is notable that Botswana’s contribution made a more marked global impact from 1982 when production from Jwaneng began. This fact is further demonstrated if production value is considered (Janse, 2007). Grateful as the country must have been for the new Orapa mine, the big step forward came from the Jwaneng mine. The much higher grade and value ensured the success of consecutive National Development Plans for socioeconomic investment.

@.;<;%:3+69:5<:)<,95;873<37 <751<0:50;449:54< Tshekedi Khama was Regent for the Bamangwato from 1926 to 1949, and a strong political voice until his death in 1959. It was his struggle, during the period 1929 to 1932 with the British South Africa Charter Company and the British

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Protectorate Administration before the first Mines and Minerals Proclamation of 1932, which ensured that Botswana’s mineral wealth was preserved for the people of Botswana (Crowder, 1985). Tshekedi Khama and Sir Ronald Prain, Chairman of Rhodesian Selection Trust, met for lunch in London in the mid-1950s (Prain, 1981). After lunch they discussed the outline for what became the last mineral concession in Bechuanaland under pre-independence mineral law, and the road to the evaluation and development of the Selebi-Phikwe Ni-Cu mine. This relationship also included consideration for diamond exploration and the Consolidated African Selection Trust Ltd (CAST) Crown Grant to explore for diamonds across the eastern part of the Bamangwato Tribal Territory. It was this CAST concession that recovered the first diamonds from the Motloutse River near Foley in northeast Bechuanaland in 1959 (Willis, 1960). The agreement was only finally signed when Tshekedi Khama lay on his deathbed in London. His goal to preserve and protect mineral development for the Batswana was secured. The machinations of Europeans, Cecil Rhodes and his British South Africa Charter Company, and elements of the British Protectorate Administration were largely defeated, but that is another story (see Crowder, 1985; Lock, 1988).

@.;<)9846< 9,?;8396;<1940:%;8$<76<';63.7 75; Serious diamond exploration by De Beers began in 1955, but it was another decade before kimberlite discoveries were made in the Letlhakane village area. Along the way it is clear that corporate patience was tested, the targeting strategy evolved, and sampling and processing methods were transformed. De Beers followed up the CAST work in 1962 and was able to duplicate the results. It is possible that De Beers’ exploration effort could have died through corporate lethargy but for an idea first expressed by CAST (Willis, 1960). While the official CAST conclusion, a basal Karoo conglomerate origin for the diamonds, may have been directed by corporate pressure, Willis also recognized the possible effects of crustal warping on the Motloutse drainage as envisaged by Lester King in his classic book, South African Scenery: A Textbook of Geomorphology. published in 1943. Dr Gavin Lamont, who was the Country Manager for Kimberlitic Searches, subsequently De Beers Botswana Prospecting, from 1955 until his retirement in 1979, independently came to the same conclusion (Lamont, 2011) through consideration of Alex du Toit’s Kalahari-Rhodesia Axis (du Toit, 1933), but he also read the CAST report (Lamont, 2001; Lock, 2012). De Beers’ Consulting Geologist Arnold Waters was persuaded by Dr Lamont in 1964 to test this theory. A road reconnaissance sampling programme over the watershed to the west into the Letlhakane area was eventually undertaken in 1966. This was a seminal moment in the Botswana diamond story as Waters’ patience had been tried and almost exhausted. The immediate recovery of kimberlitic garnets and ilmenite quickly led to the discovery of kimberlites 2125B/K1 north of Letlhakane village and 2125A/K1 west of Letlhakane village near a cattle post named Orapa in March and April 1967 respectively.

Jwaneng — the untold story of the discovery of the world’s richest diamond mine & -3:8769:5<-.93:4:-.$<751<47,-395/<6;0.59=+;4 Tom Clifford was the senior lecturer in metamorphic petrology at Leeds University in the 1960s and early 1970s at the time of the Research Institute of African Geology, partfunded by Anglo American Corporation, and during the author’s time as an undergraduate there. Figure 4 in Clifford (1966) empirically supports the association of diamondiferous kimberlites with cratons stable since 1 500 Ma. Although Clifford had nothing to do with the naming of this observation, it has been adopted as Clifford’s Rule in the diamond industry. Clifford’s Rule still stands the test of time, but was never explicitly applied in the Bechuanaland and early Botswana exploration. The largest portion of Botswana is Kalahari Desert (Carney, Aldiss, and Lock, 1994). Only in the east is traditional geological mapping possible. The Kalahari Group in Botswana is preserved as a sequence of early fluvial sediments and later aeolian deposits. It contains remnants of landforms from both wetter and drier periods. The current sand surface is variously vegetated with grass and scrubland, and sporadic trees. Although only limited geological mapping had been undertaken in Botswana pre-independence, the occurrence of ancient Archaean basement rocks in the countries surrounding Botswana was well documented and the extension of most of these rocks into Botswana was also known. A new geological map of Botswana was published in 1973 (Jones, 1973; Figure 1). Geophysical studies (e.g. Reeves and Hutchins, 1976; Terra Surveys Limited, 1978) have provided much knowledge and detail as to how these geological terrains come together under the Kalahari and

Karoo sequences of central Botswana. With hindsight, it is thus possible to conclude that much of the early De Beers diamond exploration fortuitously targeted potentially diamondiferous cratonic terrain. Also, detailed mineral chemistry analysis in diamond exploration had not yet begun in the mid-1960s; the now famous G10 garnet was only defined in 1975 (Dawson and Stephens, 1975). Observation of garnet colour usually allowed discrimination of metamorphic from magmatic/mantle garnets. The metallic lustre on conchoidal fracture surfaces is a distinguishing feature of picro-ilmenite. Part of De Beers’ success was the consequence of evolving sampling techniques. Early stream sediment sampling in eastern Bechuanaland was inappropriate in the Kalahari regions of the central and western parts of the country. Even in eastern Botswana stream development was poor and did not provide a uniform areal coverage. Lamont (2001) wrote ‘It is of interest to mention that in the Tuli Block in 1955 I devised the continuous scooping method of soil sampling to cover the interfluve areas between the drainage lines. When I demonstrated this new sampling method to Arnold Waters he was not particularly impressed and he called it "the lame duck method" because every ten to fifteen paces the man taking the samples stoops down to scoop up some soil. Anyway it was thanks to this soilsampling method that we eventually found Orapa and Letlhakane and then Jwaneng.’ We can now appreciate that this was far from being a lame duck method! Lamont (2001) critiqued the sample processing method thus: ‘In 1962 Jim Gibson and Jim Platt were still using the gold pan to concentrate their soil-samples and that is

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Jwaneng — the untold story of the discovery of the world’s richest diamond mine certainly why they missed the 1 millimetre and smaller garnets from the Jwaneng pipes halo.’ Gibson and Platt undertook this reconnaissance sampling along the Kanye to Ghanzi trans-Kalahari road; gold panning was discarded in favour of gravitating subsequent to this failed road reconnaissance and prior to the Letlhakane road reconnaissance in 1966. De Wit (in press) has provided some detail on the change from gold pan concentrating to hand gravitating by shaking and rotating a screen under water, and the continuous scoop sampling technique developed by Lamont in Bechuanaland during the early 1960s. Lamont (2001) correctly hypothesised that bioturbation by white ants (termites) would transport kimberlite indicator minerals (KIMs) to the surface in areas covered by Kalahari sediments. Bioturbation is the disturbance of loose soil or sedimentary deposits by living organisms and is not limited to activity by termites. However, it is termites’ thirst for moisture that motivates their excavations down to the water table, with ‘waste’ including KIMs deposited at surface. Lamont (2001) wrote ‘Some years after I retired I paid a visit to Jwaneng and by that time the open pit was well developed. Around the sides of the pit the interface between the kimberlite and the overlying fifty metres of Kalahari sand and other sediments were (sic) exposed, and there we were able to see a few small fossil tunnels that had been made hundreds of thousands of years ago when the first miners at Jwaneng, the termites, burrowed down to get moist clay with which to build their large mounds at the surface; in bringing up moist clay from the kimberlite, they also brought up the small kimberlitic garnets and ilmenites, and it was these mineral grains that our soil-sampling programmes found and that led us to the Jwaneng pipe.’ In the early 1980s, similar termite tunnels in the underground sampling development drives at depths up to 150 m were observed by both the author and the project geologist, Stuart Vercoe. The KIMs brought to surface are subject to concentration by rain and wind and natural deflation into a weak heavy mineral anomaly, especially around roots of grasses and shrubs that may provide windbreaks similar to trap-sites in stream sediment sampling. The deflation anomaly may well become weaker with increasing thickness of sand cover. It is probably important to comment that bioturbation is never a one-step process; it is incremental and cumulative over geological time. After the Jwaneng discovery it was possible to develop a profile through the Kalahari sediments. This demonstrated to Vercoe that there were at least three deflation surfaces; the oldest immediately on the eroded kimberlite surface, interpreted as the African Surface at about 60 Ma; and two further younger surfaces. Reconnaissance soil sampling (RSS) was conducted on traverses at one mile (1.6 km) intervals (Figure 2), with subsamples along these lines allowing a preliminary graphic plot. Detailed soil sampling (DSS) was conducted over a reduced grid scale (quarter mile or 500 m) collecting spot samples, with similar sample treatment and plotting as for RSS. Finally, detailed grid loaming (DGL) was undertaken over selected priority anomalous areas of limited extent at a grid spacing of 100 m or 50 m. Grain count contour plots from

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DGL were usually adequate for drill-hole targeting in the Orapa area. Imperial grid dimensions (miles and yards) were changed to metric (kilometres and metres) in the Jwaneng area at the time of the duplicate sampling in late 1972, described below.

@.;<8:71<6:< 75;5/ The Trans-Kalahari road in the 1960s and early 1970s was a broad double sand track maintained to enable the transport of cattle from the Ghanzi farms in the west to the Botswana Meat Commission export abattoir in Lobatse. During the summer rains it often became almost impassable for days at a time because the tracks were graded deeper and deeper during maintenance, with the rain converting these into pools or small lakes. Lamont wrote ‘I decided to try some soil-sampling on either side of the road that goes out from Lobatse to Ghanzi where the sand-cover is relatively thin. Jim Gibson and Jim Platt carried out this work but nothing of interest was found in their samples although they worked to within some twenty kilometres south of today’s Jwaneng mine, and they certainly covered ground where there are small kimberlitic garnets in the "halo" of indicator minerals that surrounds the Jwaneng group of kimberlites. This was the first time that we had moved into the Kalahari environment and our sampling methods in those days could not cope. That is how we missed Jwaneng in 1962 ‌’ (Lamont, 2001). De Beers had surmised that areas of the Kalahari with thin sand cover surround isolated bedrock outcrops. This observation and the changed sampling methods provided a locus for renewed exploration. Only after the Orapa discovery were the improved sampling and treatment techniques applied, moving west into the Kalahari in 1969. As with the 1966 Letlhakane area road reconnaissance, the first indications of the discovery to come were quickly demonstrated by laboratory results.

940:%;8$<95< ;0;,?;8<2* <751<0:5)98,769:5<95 ;783$<2* A comprehensive Kalahari sampling programme commenced in early 1969. The author was involved in this programme as a field assistant/officer in the vicinity of Molepolole, before

9/+8;< 0.;,7690<197/87,<:)<8;0:557944750;<4:93<47,-395/

Jwaneng — the untold story of the discovery of the world’s richest diamond mine leaving in July 1969 to study geology at Leeds University in the UK. A number of geologists were involved in this programme but it was the RSS work of Mike Whateley that produced the first signs of potential discovery. Table I shows the discovery timeline together with the individual geologists involved at each stage. Lamont (2001) reported ‘By the end of the third quarter of 1969 the Anglo American Research Laboratories reported the first definite and probable ilmenites from reconnaissance soil-samples taken over 150 square miles (390 km2) across the Kweneng/Ngwaketse boundary, and these ilmenites are therefore considered the first indication of the Jwaneng kimberlite province’. RSS work continued over the whole licence area before anomalous areas were covered with DSS, and priority targets were sampled with DGL. The details of the phased programme results up to the end of 1971 (as with all the results discussed in this paper) are no longer available to the author but are illustrated in Figure 3, from Lock (1985), annotated with the eventual kimberlite numbers. The geologists also named the KIM targets, with 2424D/K1 and 2424D/K3 named Malan 1 and 2, 2424D/K2 named Whateley’s Wish, and 2424D/K4 named Lynn’s Luck. Whateley left the company in the first half of 1971 and thus missed the first kimberlite discovery in 1972. 1972 marked a seminal moment in the Jwaneng story. Peter Bickerstaff led a stepped-up programme that moved quickly from ground magnetics and gravity to drilling. The magnetic survey was undertaken using the cumbersome Scania vertical-field magnetometer that required levelling for each station reading. It was thus very slow but provided a good outline on which to plan a drilling campaign. A gravity survey displayed a similar-shaped anomaly. A Holman tractor-mounted, down-the-hole hammer VOLE drill, first used at Orapa, was relocated to the southern Kalahari in early 1972. It was limited to 120 feet (37 m) depth, and it was thus not suitable for thick overburden areas. However the sand thickness was not specifically known until the first hole was drilled, although there was rock outcrop at Jwana Hill nearby and another rocky hill was visible on the skyline. Fortunately, 2424D/K1 did not have a

thick sand cover and drilling intersected kimberlite within the drill’s capability. A headgear used at Orapa was brought to site and two pits were sunk to 35 m depth to further prove the presence of kimberlite and recover small samples to test for diamond content. Botswana Mining Law limited the depth of surface pitting/trenches to 120 feet (37 m). 2424D/K1 was shallow enough for this sampling method, which had been used at Orapa, but representative samples sent to the Diamond Research Laboratory in Johannesburg returned negative results. Whateley’s Wish (2424D/K2) was also drilled by Bickerstaff in 1972, but the drill limitation prevented further kimberlite discovery at this time; the hole or holes did not reach the base of the Kalahari. It was suggested that the holes may have been mislocated to the south of the anomaly. These disappointments resulted in Bickerstaff being relocated to other projects while Lamont and the Stuart Vercoe reviewed all the results to date and developed a new

9/+8;< 3:6<:)<8;/9:573< <93,;596;</8795<0:+564< ':0 <2*

Table I

940:%;8$<69,;395; ;78

069%96$

;:3:/946

940:%;8$

1962

Road reconnaissance

Jim Gibson/Jim Platt

Barren samples

1963–1968

No activity

1969

Reconnaissance soil sampling

Mike Whateley/Keith Huxham and others

First KIM recoveries confirmed by DRL*

1970

Detailed soil sampling

Whateley/Bruce Lynn

Progressive KIM spatial disbution results Progressive KIM spatial distribution results

1971

Detailed grid loaming

Whateley/Lynn

1972

Ground magnetics and gravity/drilling

Peter Bickerstaff

2424D/K1 - March 1972

DSS/DGL/ground magnetics/drilling

Stuart Vercoe/Norman Lock

2424D/K2 - December 1972 and into early 1973

1974

DGL/ground magnetics/drilling/airborne magnetics

Vercoe and others

2424D/K4 & 2424D/K3

1975

DGL/ground magnetics/drilling

Vercoe and others

2424D/K5 & 2424DK6 - Q3

1976

DGL/ground magnetics/drilling

Vercoe and others

2424D/K7 - Q3

1977

DGL/ground magnetics/drilling

Vercoe and others

2424D/K8 - Q2

1972–1973

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*DRL: Diamond Research Laboratory, De Beers Campus, Crown Mines, Johannesburg)

Jwaneng — the untold story of the discovery of the world’s richest diamond mine work plan for implementation from September 1972. This work plan required repeat DSS and DGL sampling on a new metric grid over Whateley’s Wish (2424D/K2) and a repeat ground magnetic survey using the newly introduced Geometrics proton precession magnetometer. Vercoe supervised the soil sampling, assisted by Moses Rapoo, and submitted sample concentrates to an onsite heavy liquid separation laboratory with KIM grain picking under a binocular microscope. The mineral recoveries were dominated by ilmenite, with a subordinate pyrope garnet component. Despite these favourable results, grid plotting did not result in a coherent contoured plot. This may have been due to surface disturbance by cattle from the local cattle post, and/or by the thousands of annually migrating hartebeest and wildebeest, as was feared by Lamont. This author devised

simple moving average and root mean square algorithms to smooth of the erratic sample counts that sometimes jumped from low single digits to hundreds, and back again in adjacent samples (Lamont, 2001). This approach, entirely manual being before the advent of field computers, proved most efficacious with a fairly robust low domal feature being defined (Figure 4). The proton magnetometer survey was carried out by this author, assisted by Tabona Machinye. This early Geometrics model was entirely manual with readings recorded on paper and all levelling carried out back in camp in the evening. A 50 m Ă— 50 m grid provided corrected readings for contouring at 5- and 10-gamma intervals. The result was a weak domal anomaly (Figure 5) that coincided well with the smoothed ilmenite plot.

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160

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Jwaneng — the untold story of the discovery of the world’s richest diamond mine Target selection for a new drilling campaign was now possible with reasonable confidence of success. It is worthy of note that when an aeromagnetic survey was undertaken in 1974, 2424D/K1 showed a weak but well defined magnetic high whereas 2424D/K2 displayed only a broad weak domal high that may not have been selected as a priority without the supporting sample results. The first new drill target, N44/12, returned suspected kimberlite at 138 feet depth at 11 am on 16 December 1972 (Lock, 1972, Figure 6). Although the drill chips were very fine pink clayey material with no recognizable texture, the presence of abundant KIMs was sufficient evidence for Murray (1973a) to report ‘One hole went into what is thought to be kimberlite ...’ The drill chip samples were concentrated using a simple hand-operated Gerryts jig that had been designed by Bert Gerryts in the 1950s as a substitute for other methods of concentrating loam samples. A further four holes logged by this author intersected kimberlite over the next seven weeks. Core drilling in early 1973 (Murray, 1973b, 1973c) provided rock samples from below the Kalahari-kimberlite interface, although official confirmation awaited a 1974 petrology report by Clement (1974). The discovery was officially recognized as 2424D/K2 in the second quarter of 1973 (Murray, 1973c), in line with the De Beers scheme of that time, but the name ‘Whateley’s Wish’ persists informally with those involved. By the third quarter of 1973 enough drilling had been undertaken for the Jwaneng 2424D/K2

kimberlite to be reported (Murray, 1973d) as â€˜â€Ś an oval shape approximately 850 m long by 350 m to 400 m wide.’ Clement (1974) reported that â€˜â€Ś preliminary examination indicates quite clearly that the pipe is, at least in part, overlain by kimberlitic sediments and/or kimberlitic tuffs.’ This description is broadly consistent with modern terminology in Webb, Stiefenhofer, and Field (2003) who note â€˜â€Ś two contrasting textural types of kimberlite, which have been interpreted as (a) resedimented volcaniclastic kimberlite and (b) dark pyroclastic kimberlite.’ The challenge of using this underpowered drill was underlined when, a few holes after the discovery, the whole drill string was lost, at great expense.

&%73+769:5<0.733;5/;4<751<;0:5:,90<%97?9396$ The Naledi valley is a broad very shallow grass-covered paleo-valley. A small rural community was domiciled around the low Jwana Hill, where a single water borehole sustained the cattle post and helped De Beers in the early days of exploration. Lamont (2001) wrote ‘Stuart then took over the drilling programme around Norman's kimberlite intersection, and as the pipe got bigger and bigger we knew we were really onto something significant.’ (N.B. this refers to Norman Lock’s intersection of the 2424D/K2 kimberlite in drill-hole N44/12, Figure 6). Lamont continued ‘It was clear that we needed much heavier drilling equipment to cope with the Kalahari sand-cover, and I thought about the percussion "jumper"

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161

9/+8;< ;-8:1+069:5<:)<-786<:)<6.;< <1940:%;8$<18933<3:/

Jwaneng — the untold story of the discovery of the world’s richest diamond mine drills that Jim Gibson and I had used in the early days at Orapa to drill the first water-supply boreholes.’ The standard jumper drill bit for water boreholes was 4½ inches (114 mm), but specialized bits for this and future sampling programmes were progressively increased in size to 24 inch (610 mm). Initial outline drilling of the 2424D/K2 kimberlite was supervised by Vercoe using the VOLE drill. A series of holes was drilled along the assumed northeast-trending long axis of the kimberlite at 100 m intervals, controlled by KIM and magnetic data. The dimensions of the South and Central Lobes of the tri-lobate Jwaneng pipe were of the order of 900 m by 500 m. A great deal of difficulty was experienced using this depth- and power-limited drill in penetrating the Kalahari overburden. Six water bores to replace the village supply were sunk around the margins of the pipe using a jumper drill (cable tool drilling rig) with the first hole providing 1 200 gallons per hour or 546 L/h containing 1 200 ppm total dissolved solids (TDS). The rig was then used, with other jumper drills, drilling 6-inch (152 mm) diameter holes to further delineate the kimberlite. The jumper drills used had the advantage of: The low cost per metre A simple construction, low cost, and ease of maintenance and repair A larger sample from the 6-inch (152 mm) or 8-inch (203 mm) hole size compared to the VOLE drill. The disadvantage was the slow penetration rate of approximately10 m per 8-hour shift compared to the VOLE drill. The holes were cleaned of drill cuttings and water by a dart or flap valve bailer into half-drums in one-metre sections. Dried kimberlite samples were shipped to the Anglo American Research Laboratory/Diamond Research Laboratory in Johannesburg.

It was not possible to sink shallow prospecting shafts, as had been done at Orapa. Preliminary evaluation therefore used the jumper drill with strengthened 15-inch (381 mm) chisel bits. The initial thirty holes of 15 inch (381 mm) diameter were drilled at 100 m line spacing and 200 m hole spacing to 150 m depth. Potentially significant economic grades were demonstrated despite the spatial inhomogeneity of grade. Underground bulk sampling was rejected by the Botswana Government, which favoured continued large-diameter jumper drilling despite a number of data integrity issues. Diamond recoveries showed that the Central Lobe was the richest area and the South Lobe less so. There was very poor grade correlation between sampling drill-holes. No sampling data for the North East Lobe was available from this initial programme. Closer spaced sampling data was required with holes drilled on a 100 m sampling grid to 200 m depth and finally at 50 m centres, also encompassing the North East Lobe. Drill sampling by jumper drills was completed in 1982. Poor horizontal and vertical grade correlations and other issues listed below persisted.

162

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Five-metre drill samples averaged 1.5 t, computed from caliper logging and density. Caliper logs showed break-back and contamination of lower levels by the less competent upper tuffaceous pyroclastic/debris flow horizons. The 2424D/K2 15-inch (381 mm) jumper drill sample sludge was classified into three fractions (0.5–1.5 mm; 1.5– 4 mm; >4 mm) using Sweco screens. Samples were dried and weighed. Density by water displacement was only a first approximation of the true bulk density. The two smaller size fractions were concentrated by Pleitz jigging and then combined for milling with neoprene-lined mills using ceramic balls. The >4 mm fraction was transferred directly for milling separately. Milling was required for pre-conditioning prior to diamond recovery over grease. Hand sorting was conducted on site. Over 200 holes were drilled, producing 5 000 t of kimberlite. The work resulted in the delineation of about 90 Mm3 of kimberlite for the 54 ha surface area. The sampling data at Jwaneng was down to 0.5 mm diamond size and thus overlapped with the typical microdiamond sample size range. However, factorization to larger bottom cut-offs was undertaken for practical production purposes.

From the beginning of 1978, samples were obtained from shafts, three in the Central Lobe, two in the South Lobe, and one in the North East Lobe. This bulk sampling provided material for: Metallurgical test work Geology and emplacement model concepts Comparisons of diamond release from shaft and drive samples with that of highly comminuted drill slurry. For this latter purpose three 15-inch holes to 200 m were drilled around each shaft Collection of a larger diamond parcel for valuation. Initially, the material was treated in rotary pans (for shafts 1–4) with final recovery by jigging, milling, and grease recovery. Once the bulk sample plant was completed and commissioned, material from shafts 5 and 6 was treated by conventional DMS and X-ray recovery. From selected horizons, 20-t samples were sent to DRL for metallurgical test work. Mineral resource estimation using ordinary kriging was undertaken in Kimberley. This was the first time that De Beers had cumulated a sample database to support a full three-dimensional resource estimate. The estimation challenges at Jwaneng, which required several adjustments, included:

Sample size Downhole break-back contamination Shafts not representative of the whole orebody Differences in treatment methods and efficiencies between jigging/grease recovery and rotary pans/DMS/X-ray Possible diamond breakage by drilling Density variations with depth Main Treatment Plant crushing to 25 mm rather than 12 mm used to feed rotary pans, and intense comminution of drill samples

Jwaneng — the untold story of the discovery of the world’s richest diamond mine

940+449:5<751<0:503+49:54 There are a number of little-known or unknown facts and opinions surrounding the Jwaneng story from the 1960s and 1970s, and beyond, that deserve mention. Lamont commented on how different history might have been if Jwaneng had been found in 1962, before Orapa. There is no indication what thoughts may have been in Lamont’s mind, but the following two points would surely have had significant possible consequences if the Jwaneng kimberlite had been found in 1962: Pre-independence Bechuanaland was a very different place to the Independent country we know today. One of the first post-independence Acts of Parliament was the Mineral Rights in Tribal Territories Act, under which the various tribes voluntarily ceded their ownership of mineral rights to the new state of Botswana. This simple act of cession has allowed Botswana to avoid the challenges of many African and other countries worldwide in the post-independence era. The implications of different decisions with regard to these points are left to the opinion of the reader, with perhaps the thought that any consequences could well have been highly detrimental for the future of the country. De Beers and Debswana record the Jwaneng discovery as being in 1972, whereas the Jwaneng 2424D/K2 kimberlite was arguably properly confirmed with drill core only in early 1973. 2424D/K1 had been found the previous year, 1972, but there was a hiatus in exploration while the challenge of the thickness of the Kalahari sand cover was considered. In contrast with Orapa, there is a diversity of reporting of the Jwaneng discovery; the following examples are provided: In his discussion of the Debswana Joint Venture, Mr Blackie Marole (1988) stated ‘Shortly after the establishment of the Letlhakane Mine, a further diamond bearing pipe was discovered at Jwaneng.’ In reality, the Letlhakane Mine was established in 1975, some two years after the Jwaneng discovery. An important worldwide catalogue of kimberlite occurrences (Janse and Sheahan, 1995) strangely reports 1975 as the discovery year (as did Marole, see above). At least two well-known books about diamonds provide the historically accurate discovery date of 1973 (Bruton, 1979; Wannenburg and Johnson, 1990). Three books (Brook, 2012, 2016a, 2016b) report discovery dates of 1971 and 1973, 1971, and only the date of mine opening in 1982, respectively. Lock (1985) published the distinction between the 2424D/K1 discovery in 1972 and the 2424D/K2 discovery in 1973, although in hindsight both may reasonably be

recorded for the year 1972. Subsequently, Botswana Geological Survey Bulletin 37 (Carney, Aldiss, and Lock, 1994, p. 94) contained a similar statement of the Jwaneng discovery history. This publication should have authority as the definitive historic statement, but any new edition should amend the dates consistent with this paper. It is fair to say that market pressures were developing during the 1970s with new and increased production from Orapa, and the Argyle project development, after its 1979 discovery, moving ahead in parallel with the Jwaneng evaluation (see Figure 2 in Janse, 2007). Notwithstanding these pressures, the Jwaneng Mine opened at the start of a dramatic upturn in the market through the 1980s and was sustained, even with new Canadian and additional Russian production, until the global financial crisis in 2008. Whatever impact these pressures may have exerted on the market and De Beers, the Botswana government held rather different development objectives. The very high Jwaneng profit margin would always have sheltered Botswana from the impact of a market squeeze. Indeed, during the 1980s Debswana accumulated its own stockpile to protect local employment. This stockpile was used in 1987 to purchase a 5% interest in De Beers Consolidated Mines (Marole, 1988, Masire, 2006). Perhaps history will link the parallelism of events following the Orapa Mine opening in 1971. With the timeline to the opening of the Letlhakane Mine, ongoing negotiations reviewing the Debswana Joint Venture concluded in 1975 with a revised 50/50 equity relationship (Marole, 1988; Masire 2006). President Masire commented in his autobiography (Masire, 2006) that ‘In 1976, only a year after we had finalised the new agreement for the 50/50 partnership, De Beers came to tell us that they had found another payable deposit. It was Jwaneng, 80 kilometres west of Kanye in Ngwaketse.’ There were clear differences and difficulties in the negotiations between the joint venture partners expressed by President Masire that can perhaps be summed up in his conclusion (Masire, 2006) that â€˜â€Ś De Beers knew more about the potential than we did, so we were at a disadvantage in discussing the terms of an agreement that would be fair to both sides.’ As noted in the introduction, Jwaneng is Debswana’s and the world’s richest diamond mine. Published future mine planning for Jwaneng Cuts 8 and 9 will extend the open pit to some 850 m below surface over the next 20 years. It is not inconceivable that Debswana will still be mining Jwaneng well after 2050 and it is reasonable to predict a future underground mine to perhaps 1 000 m depth or more, with the life of mine exceeding a century since discovery or even mine opening. Stuart Vercoe and I have always emphasised the team effort that, in the words of Isaac Newton, allowed us to ‘see farther by standing on the shoulders of giants.’ Dr Lamont’s memoirs make clear the contributions of several geologists over a number of years, from the early work of Mike Whateley in 1969 to the discovery drill-holes in 1972/3 and beyond (see discovery timeline in Table I). Fairly apportioning credit among the many geologists, and others who worked on the project, would have been problematic, though the discovery drill-hole marks a point in time. This paper can hopefully stand as a definitive factual historic record. "#'!(&<22*

163

Main Treatment Plant lower cut-off of 1.65 mm vs 0.5 mm for drill samples and rotary pans Poor understanding of the orebody geology and emplacement mechanism Diamond security could also have been better. The results supported a very positive outcome and the decision in 1978 to build a mine.

Jwaneng — the untold story of the discovery of the world’s richest diamond mine

0 5: 3;1/;,;564 Stuart Vercoe was the project geologist at the time of the discovery that was shared with the author here. His contribution in the form of personal communication, documents, and photographs has been very important to the completeness of this story; full credit is given here to his input even though he left preparation of the manuscript to this author. I wish to thank the De Beers Group for permission to access original maps and reports relating to the Jwaneng discovery in their archives, and to present the information in this publication. In particular I am very grateful to the peer reviewer who in 2018 first drew attention to my deficient memory of the timing of events some 45 years ago, and facilitated this permission and access.

KEPPLER-JONES, A. 1983. Rhodes and Rhodesia. The White Conquest of Zimbabwe. 1884-1902. McGill-Queens University Press, Kingston and Montreal. 427 p. LAMONT, G.T. 2001. My Memoirs. Unpublished. LAMONT, G.T. 2011. The quest for diamonds in the Bechuanaland Protectorate and Botswana. Prospecting in Africa. De Wit, M.C.J., KÜstlin, E.O., and Liddle, R.S. (eds). De Beers Consolidated Mines Limited. pp. 171–202. LOCK, N.P. 1972. De Beers Prospecting – Botswana. air drill log N44/12. LOCK, N.P. 1985. Kimberlite exploration in the Kalahari region of southern Botswana with emphasis on the Jwaneng kimberlite province. Proceedings of Prospecting in Areas of Desert Terrain, Rabat, Morocco 1985. Institution of Mining and Metallurgy, London. pp. 183–190. LOCK, N.P. 1988. A history of mineral rights, ownership and law in Botswana. Proceedings of a Seminar on Mineral Policy in Botswana. Special Publication no. 1. Botswana Geoscientists. Association. pp. 5–15.

;);8;50;4

LOCK, N.P. 2012. Orapa Mine: the role played by CAST geologists in its

BROOK, M.C. 2012. Botswana’s Diamonds – Prospecting to Jewellery. Selfpublished. 286 p. BROOK, M.C. 2016a. Botswana Diamond Jewellery. Self-published. 136 p. BROOK, M.C. 2016b. Botswana. 50 Years of Growth. Self-published. 81 p. CARNEY, J.N., ALDISS, D.T., anD LOCK, N.P. 1994. The geology of Botswana. Bulletin 37. Geological Survey of Botswana. 113 p. CLEMENT, C.R. 1974. Preliminary notes on core specimens from Jwaneng,

discovery. Letter to Geological Society of South Africa. Geobulletin, vol. 55, no. 3. MASIRE, Q.K.T. 2006. Very Brave or Very Foolish. Memoirs of an African Democrat. Lewis Jr, S.J. (ed.). Macmillan Botswana. 358 p. MAROLE, B. 1988. Debswana Joint Venture; a case study. Proceedings of a Seminar on Mineral Policy in Botswana. Special Publication no. 1. Botswana Geoscientists. Association. pp. 26–29. MURRAY, L.G. 1973a. Quarterly report, State Grant 7/72, Ngwaketsi District.

Botswana. De Beers Consolidated Mines Limited, Geology Department.

Report on prospecting activities for the period 1st October to 31st

7 p.

December 1972. De Beers Prospecting Botswana (Pty) Limited. p. 1.

CLIFFORD, T.N. 1966. Tectono-metallogenic units and metallogenic provinces of Africa. Earth and Planetary Science. Letters, vol. 1. pp. 421–434. CROWDER, M. 1985. Tshekedi Khama and mining in Botswana. 1929-1959. African Studies Seminar Paper. African Studies Institute, University of the Witwatersrand. 23 p. DAWSON, J.B. and STEPHENS, W.E. 1975. Statistical classification of garnets from kimberlite and associated xenoliths. Journal of Geology, vol. 83, no. 5.

MURRAY, L.G. 1973b. Quarterly report, State Grant 7/72, Ngwaketsi District. Report on prospecting activities for the period 1st January to 31st March 1973. De Beers Prospecting Botswana (Pty) Limited. p. 2. MURRAY, L.G. 1973c. Quarterly report, State Grant 7/72, Ngwaketsi District. Report on prospecting activities for the period 1st April to 30th June 1973. De Beers Prospecting Botswana (Pty) Limited. p. 1. MURRAY, L.G. 1973d. Quarterly report, State Grant 7/72, Ngwaketsi District. Report on prospecting activities for the period 1st July to 30th June 1973.

pp. 589–607. DE BEERS GROUP. 2017. Jwaneng Mine. http://www.debeersgroup.com/en/ourstory/our-history.html#1960 DEBSWANA. 2017. Jwaneng Mine. http://www.debswana.com/Operations/Pages/ Jwaneng-Mine.aspx DE WIT, M.C.J. 2018. Prospecting history leading to the discovery of Botswana’s diamond mines: from artefacts to Lesedi La Rona. Proceedings of the 11th International Kimberlite Conference, Gaborone, Botswana (in press). DU TOIT, A.L. 1933. Crustal movement as a factor in the geographical evolution of South Africa. South African Geographical Journal, vol. XVI. pp. 3–19. GURNEY, J.J and SWITZER, G.S. 1973. The discovery of garnets closely related to diamonds in the Finsch Pipe, South Africa. Contributions to Mineralogy and Petrology, vol. 39, no. 2. pp. 103–116. JANSE, A.J.A. 2007. Global rough diamond production since 1870. Gems & Gemology, vol. 43, no. 2. pp. 98–119. JANSE, A.J.A. and SHEAHAN, P.A. 1995. Catalogue of world wide diamond and

De Beers Prospecting Botswana (Pty) Limited. p. 1. PRAIN, R. 1981. Reflections on an Era. Metal Bulletin Books, London. 262 p. PRETORIUS, D.A. 1966. Conceptual geological models in the exploration for gold mineralisation in the Witwatersrand Basin. Information Circular no. 33. Economic Geology Research Unit, University of the Witwatersrand. REEVES, C.V. anD HUTCHINS, D.G. 1976. The national gravity survey of Botswana, 1972-3. Bulletin no. 5, Geological Survey of Botswana, Lobatse. 36 p. TAYLOR III, A.L. 1982. A gem that lost its luster. Time Magazine. 30 August 1982. http://content.time.com/time/magazine/article/ 0,9171,921271,00.html TERRA SURVEYS LIMITED. 1978. Reconnaissance aeromagnetic survey of Botswana, 1975-1977. Botswana Geological Survey Department and Canadian International Development Agency. 199 p. WEBB, K., STIEFENHOFER, J., and FIELD, M. 2003. Overview of the geology and emplacement of the Jwaneng DK2 Kimberlite, Southern Botswana. [Long abstract]. Proceedings of the 8th International Kimberlite Conference.

kimberlite occurrences: a selective and annotative approach. Journal of

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WILLIS, J.H.A. 1960. Final report on the Bechuanaland investigation. Central African Selection Trust Ltd.

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a9

A strategy for improving water recovery in kimberlitic diamond mines by A.J. Vietti*

Drought conditions and the possible lack of sufficient raw water supply are a constant reminder to the Southern African diamond miner of the need to minimize raw (new) water input while maintaining or expanding mine throughput. Since water input volume is directly related to water output (loss) volume, mainly to the tailings, it is obviously beneficial to dewater the tailings as much as possible. However, this may not always be as easily achieved as first thought. Owing to the unique mineralogy of each kimberlite deposit and the unique chemical profile of each raw water source, dewatering of the slurry is not a standard process that can be applied equally across the entire diamond industry. This paper presents a first-step strategy that kimberlite diamond mine operators should consider and apply to meet and exceed their watersaving targets. >9) 35,2 clays, colloids, thickening, water recovery, water conditioning.

7653,/16837 A common metric for measuring water use efficiency in a metallurgical plant is the amount of raw water used per ton of head feed treated (m3/t). The metric includes all water recycled from the dewatering process and from the tailings storage facility (TSF), and the only inputs are tons of ROM ore and cubic metres of raw water make-up to replace water lost to evaporation, seepage, and lock-up at the TSF (Figure 1). The volume of new raw water available to the process is not unlimited and a maximum monthly allowance is typically set during the mine permitting process. Consequently, if the volume of water lost exceeds the volume of new water available or alternatively, in times of drought where the allowable volume is unavailable, the plant has no other option but to reduce throughput. However, if the plant operator is able reduce the volume of water lost to the TSF, the plant will have additional capacity to either increase tonnage throughput or sustain nameplate capacity during periods of severe drought. The obvious conclusion, therefore, is to reduce the volume of water lost to the tailings by improving the efficiency of the

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* Vietti Slurrytec (Pty) Ltd, South Africa. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. This paper was first presented at the Diamonds — Source to Use 2018 Conference, 11–13 June 2018, Birchwood Hotel and OR Tambo Conference Centre, JetPark, Johannesburg, South Africa.

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dewatering process (typically gravity thickening) simply by increasing the density of the tailings being discharged. In practice, however, the unique mineralogy of each kimberlite deposit and the chemical profile of the raw water source used imply that at some mine sites, solid/liquid separation (or settling) of tailings slurries at the dewatering process is more easily achieved than at others. In these cases, simple practical steps such as firstly improving the flocculant reagent make-up and dosing control, and secondly focusing on thickener discharge control, are the only requirements for enhancing water recovery at the dewatering circuit. At other mine sites however, solid/liquid separation of the tailings may not be achieved at all, in which case efforts to increase water recovery by improving reagent dosage and thickener control would be fruitless unless the colloidal properties of the thickener feed slurry are modified. This paper describes a strategy that can be implemented at most kimberlitic diamond mines to ensure that solid/liquid separation of the tailings slurry occurs efficiently and that the subsequent improvements in water recovery and quality (in terms of clarity) at the dewatering unit process can take place.

A strategy for improving water recovery in kimberlitic diamond mines

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most cases the dominant clays belong to the 2:1 smectite or swelling group of clays and the settling or solid/liquid separation properties of these slurries are determined largely by the chemical quality of the process water. Consequently, controlling the chemical conditions in the process water circuit is the first step in improving plant water recovery. Before describing a strategy for controlling the chemical quality of the process water it is necessary to describe the fundamental mechanisms by which kimberlitic clay slurries are generated and which control their colloidal behaviour.

In a typical kimberlite metallurgical circuit, the first contact between freshly mined ore and the plant process water is at the scrubbing or milling unit process. This step is critical in determining the colloidal characteristics of the resulting slurry, since the clays within the ore absorb water and other polar molecules between their unit layers, which initiates the swelling and ultimately the dispersion process. Two forms of swelling are known, which depend on the moisture content to which the clays are exposed. Firstly, under conditions of low moisture content a limited stepwise expansion of the clay unit layers, known as interlayer (or type I) swelling, begins. This form of swelling occurs once the virgin ore is exposed to atmospheric moisture after blasting or crushing. Water molecules are drawn between the dehydrated clay platelets and up to three layers of water molecules are covalently bonded to the tetrahedral surfaces in a semi-crystalline structure that resembles that of ice. This mode of swelling leads to at most a doubling in the volume of the dry clay and may be responsible for a weakening of the rock matrix (Grimm, 1968). The second form of swelling occurs at saturation (i.e. during the scrubbing or milling process) and can lead to an unlimited or complete separation of individual clay layers. This is known as osmotic (or type II) swelling. Under this condition, the exchangeable cations dissociate from the clay surface and move to the hydrated region between the clay particles; as such, they are regarded as being ’in solution’ and hence lower the activity of the water between the particles. This allows more water from the surroundings to move into the interlayer region by osmotic forces, thereby increasing the interlayer swelling. This form of swelling may continue indefinitely and result in a slurry suspension in which normal electrical double layers separate the individual clay particles from one another (Figure 2).

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The degree of osmotic swelling depends largely on the nature and concentration of the cations in the contacting water and the degree of octahedral substitution of the clay type. Monovalent cations in low concentration tend to cause unlimited swelling, a condition commonly referred to as uncontrolled dispersion, since they are small and can ’dissolve’ more easily in the semi-crystalline water layers that surround the clay particles, thereby drawing more water between the adjacent particles by osmotic action. Divalent cations, on the other hand, tend to cause limited swelling since they have a disruptive effect on the structure of the water layer and can provide links between charged sites on adjacent silicate sheets. This condition is commonly referred to as controlled dispersion (Sequet, de la Calle, and Pezerat, 1975; Mering, 1946). From a practical point of view, kimberlite mines that draw their raw water from surface sources such as rivers, dams, or lakes will tend to introduce good quality water into the process water circuit, which will result in the process water having a relatively low cation content and low conductivity. This quality of process water will promote uncontrolled dispersive conditions and the resulting slurries will tend to respond poorly, or not at all, to anionic polymer flocculant dosing, and even after excessively high flocculant doses the recovered thickener overflow clarity is generally poor (Figure 3). Under this condition it is normally necessary to dose a cationic polymer coagulant before flocculant addition to achieve solid/liquid separation and good overflow clarity. As a result, operating costs are high. On the other hand, kimberlite mines that draw their raw water from deep wellfields or brackish water sources will have process water circuits with elevated cation contents and high conductivities. This quality of process water inhibits clay swelling and dispersion, resulting in slurries that are easily flocculated at low doses without the need for additional coagulant dosing. Recovered thickener overflow will have excellent clarity, and there are further beneficial spin-offs such as better thickener underflow density and lower underflow yield strengths, due to the absence of excessive amounts of polymer reagent (Figure 3).

Soil scientists and agriculturalists have long recognized the effects of water quality on the behaviour of the clay component in a soil (Mitchell and Soga, 2005; Richards,

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A strategy for improving water recovery in kimberlitic diamond mines However, in practice, determining the ESP of the ore is not a simple procedure and is prone to experimental error due to mineral contaminants in the ore. A simpler method is to measure the cation content of the contacting water. The sodium adsorption ratio (SAR) value of the contacting water can then be used instead of the ESP to determine whether a process water is likely to generate slurries which are dispersive or not. The SAR is defined as: [2]

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1969). To understand the mechanisms controlling these clay/water interactions, a diagnostic system using three criteria was developed, which enables the soil scientist to determine the potential deleterious effect of the water on the soil (Richards, 1969). These criteria can equally be applied to kimberlitic ores to indicate the slurry dispersive potential which the raw/process waters may impart to a particular ore type.

The cation exchange capacity (CEC) is the total capacity of a soil (or kimberlite ore) to hold exchangeable cations (irrespective of type or valence), in meq/100 g and is directly related to the specific surface charge and charge density of the clays in the ore (Mitchell and Soga 2005). Once the CEC has been determined, the proportion of sodium ions which are adsorbed onto the surfaces of clays is a useful parameter to know. This has an important influence on the colloidal behaviour of the slurry and can be expressed as the exchangeable sodium percentage (ESP) of the suspension, defined as: [1] This measure provides an indication of slurry sodicity. Slurries exhibiting high ESP values tend to be alkaline and colloidally dispersive, while slurries with low ESP values tend to be naturally coagulated and settling (Figure 4).

Since the SAR is easily derived from a normal chemical analysis of the process water (in meq/l) and since it is related to the ESP, it is more widely used. Process waters with SAR values greater than approximately 10 are likely to generate uncontrolled dispersive slurries, while those less than 10 are likely to generate coagulated or settling slurries (Mitchell and Soga, 2005; Richards, 1969).

Once in an uncontrolled dispersive state, the pH of the slurry is the second factor which can control coagulation and solids/liquid separation. Since clay particles have a plate-like morphology, the charges at the particle face and edge may be of similar or opposite sign, depending on the slurry pH. These changes in charge and charge distribution at the clay surface can also affect the slurry solid/liquid separation behaviour. The dominant surface of a clay particle is the flat face, which has a permanent negative charge due to imperfections in the clay crystal lattice (isomorphous substitution), which results in excess negative charge at the face. This excess negative charge is compensated by the adsorption of cations onto the crystal lattice surface (Figure 5). Once in a slurry state, these compensating cations may be exchanged with cations from the solution, depending on how strongly they are bound to the crystal surface. For this reason, they are known as exchangeable cations and their concentrations can be used as a measure of the amount of lattice charge, or cation exchange capacity (CEC), of the clay (van Olphen, 1977). Clay particle edges, on the other hand, can be positively or negatively charged depending on the pH of the slurry. Under acidic conditions, hydroxyl- (OH-)-bearing groups at the clay crystal edge become protonated to carry an overall positive charge, leading to edge-to-face attraction which may

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A strategy for improving water recovery in kimberlitic diamond mines

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result in particle aggregation. Under alkaline conditions, the OH- groups will become deprotonated until an overall negative edge charge dominates, leading to edge-to-face repulsion and causing the clay particles to become colloidally dispersed (Svarovsky, 1981) (Figure 5). The pH of a kimberlite clay suspension therefore greatly affects the charge associated with the clay particle edges. At low pH, tailings slurries are coagulated naturally and settle under gravity, while at high suspension pH they tend to remain dispersed and non-settling (Figure 6).

concentration is known as the critical coagulation concentration (CCC) (Gregory, 1989). The CCC can equally be termed the critical coagulation conductivity (in mS/cm), which would be the process water conductivity at which coagulation occurs for a particular ore type and at which solid/liquid separation is achieved. This is a measurable variable which can be used in a feedback loop to control the conductivity at which a process water circuit should be maintained, and forms the basis of the strategy for improving water recovery.

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The third and overriding mechanism for controlling the colloidal properties of a slurry is the absolute ionic concentration. This is commonly defined by the conductivity of the slurry and is typically measured in units of mS/cm. The interaction between adjacent particles in dispersed colloidal systems is described by the Derjaguin, Landau, Verwey, and Overbeek (DLVO) theory of colloid stability and is based on the interaction between the van der Waals attractive force and the diffuse double layer repulsion force occurring between approaching particles (Figure 7). At low conductivity, a net potential energy barrier presents an obstacle to aggregation and the particles remain in a dispersed state. At moderate conductivity levels, the height of the energy barrier is reduced, and the particles may remain weakly coagulated in a secondary minimum well. As the ionic content of the dispersion is increased a point is reached where the energy barrier disappears, and the particles are spontaneously coagulated (Figure 8). This ionic

An example of the effectiveness of a process water conditioning strategy for improving water recovery and quality is provided below by way of case studies for two kimberlite mines in Southern Africa. Mine A derives its raw water supply from shallow boreholes located along a major river course. Consequently, the plant process water circuit has a relatively low average conductivity. The mine suffers from periods during the year in which the process water clarity deteriorates and additional polymer coagulant dosing is required ahead of flocculation to clarify the thickener overflow. The process water clarity issues are not related only to the ore types being treated; they are also a seasonal phenomenon. During the summer rainfall months, an increase in the use of polymer coagulant reagent use has been recorded, while during the winter season, the operation of the thickener tends to improve, resulting in better overflow clarity and lower reagent costs (Figure 9).

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A strategy for improving water recovery in kimberlitic diamond mines

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continuous dosing. ClariVie44ÂŽ is dosed into the process water circuit to increase the conductivity; thereafter, smaller top-up doses are required to compensate for the diluting effect of the raw water make-up (Figure 11). To test the principle, a notoriously difficult ore from kimberlite mine A was contacted with plant process water that had been treated with ClariVie44ÂŽ to increasing the conductivity. The dispersion and natural settling behaviours were measured as illustrated in Figure 12. At process water conductivities below 3 mS/cm (plant process water conductivity was measured at 2.9 mS/cm), uncontrolled dispersion was encouraged, resulting in the establishment of colloidally stable suspensions. At higher conductivity, controlled dispersion was favoured, resulting in the formation of colloidally unstable suspensions which achieved better clarity. In the case of mine A, the recommended strategy would be to maintain the process water circuit conductivity at a minimum of 3.3 mS/cm to ensure that effective solid/liquid separation after flocculation occurs at the thickener. A similar strategy has been employed at another kimberlite diamond mine (mine B) since January 2017. In this case, raw water to the plant is sourced only from rainfall or snowmelt. The pristine quality of the raw water is, unfortunately, detrimental to the recovery of clear overflow from the dewatering thickener. ClariVie44ÂŽ has been implemented to condition the process water circuit to a minimum conductivity target of 1.2 mS/cm, with excellent #:--!

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These observations appear to be directly related to the seasonal wetting and drying cycle to which the kimberlitic ore in the pit or in stockpiles is exposed. During the summer season, the clays undergo uncontrolled dispersion when wetted with zero-conductivity rainwater in situ. When introduced to the plant, the process water conductivity is insufficient to overcome the CCC of the clays and often additional coagulant dosing is required before flocculation becomes effective. During the winter season, the ore reporting to the plant is either dry or has not been previously wetted. When this ore is contacted with the plant process water, the clays therein undergo controlled dispersion since the conductivity of the water exceeds the CCC of the clays. Additional coagulant dosing ahead of flocculation is therefore not required (Figure 10). To accommodate the seasonal change in clay dispersion properties and the varying CCC requirement of clays in different ore types, a minimum conductivity threshold for the mine process water circuit needs to be set, at which controlled dispersive slurries will be generated when any ore type is treated. Vietti Slurrytec (VST) has developed the ClariVie44ÂŽ process water conditioner, which is a blend of specific cations designed to increase the conductivity of process water without introducing environmental or corrosive pollutants. Since the process water conditioner is not polymer-based, it is recycled in the process water circuit without the need for

A strategy for improving water recovery in kimberlitic diamond mines

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results showing a clear correlation between increasing process water conductivity and decreasing flocculant consumption (Figure 13).

<821/22837 In general, kimberlite mines can be categorized into two types in terms of tailings slurry colloidal and dispersive properties.

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Non-problematic mines—The tailings slurries at these mines are non-dispersive and easily flocculated at relatively low reagent dose. The thickener underflow densities are generally relatively high (depending on the type of dewatering equipment selected), overflow clarities are generally excellent, and water recoveries are good. The metallurgical plant therefore operates

A strategy for improving water recovery in kimberlitic diamond mines

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3710/2837 For the kimberlite diamond mine operator, increasing water use efficiency in terms of cubic metres per ton of feed processed means treating more tons with the existing water supply volume or treating existing tons with less water. In both cases, achieving this outcome depends on improving the water recovery efficiency at the dewatering unit process by increasing the density of the tailings being discharged. The basis of this strategy depends on the colloidal state of the tailings slurry being such that solid/liquid separation takes place naturally. Some kimberlite mines are in the fortunate position of having saline or brackish raw water sources which impart a high conductivity to the plant process water. Under these conditions, the smectite clays in the kimberlitic ore are unable to swell and disperse once in suspension and are flocculated easily at the plant thickener. The mine operator is then able to concentrate on the real drivers for improving thickener underflow density, such as reagent make-up and dosing control and, more importantly, thickener underflow discharge control. However, at other kimberlite mines where the raw water supply is drawn from low-saline sources, the primary step of solid/liquid separation does not readily occur naturally due to the dispersive slurry properties generated. In these cases, a strategy for increasing the process water conductivity by introducing a water conditioning reagent such as ClariVie44ÂŽ is required before the subsequent drivers for improving water recovery can be implemented.

9*9597192 GREGORY, J. 1989. Fundamentals of flocculation. CRC Critical Reviews in Environmental Control, vol. 19, no. 3. CRC Press. pp. 185–230. GRIMM, R.E. 1968. Clay Mineralogy. 2nd edn. McGraw Hill, New York. Mering, J. 1946. The hydration of montmorillonite. Transactions of the Faraday Society. vol. 42. pp. 205–219. MITCHELL, J.K. and SOGA, K. 2005. Soil-water-chemical interactions. Fundamental of Soil Behaviour. Wiley, Hoboken, NJ. Chapter 6. RICHARDS, L.A. 1969. Diagnosis and improvement of saline and alkali soils. Handbook no. 60. US Department of Agriculture. SEQUET, H., DE LA CALLE, C., and PEZERAT, H. 1975. Swelling and structural organization of saponite. Clays and Clay Minerals, vol. 23. pp. 1–9. SVAROVSKY, L. 1981. Solid-Liquid Separation. 2nd edn. Butterworths, London. VAN OLPHEN, H. 1977. Clay Colloid Chemistry. Wiley, New York. #:--!

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with clean process water. These mines do not need to further condition the process water as the natural conductivity of the raw water supply is sufficient to prevent uncontrolled clay dispersion. Further improvements in water recovery would only require interventions to optimize reagent make-up and dosing control and/or the thickener discharge control. The latter is not straightforward since the new thickener control philosophy of generating higher underflow densities would need to be compatible with the capabilities of the existing hydraulic tailings discharge infrastructure and the existing TSF operating and management methodology. Problematic mines—The tailings slurries at these mines are highly dispersive and difficult to flocculate and settle. High flocculant and/or polymer coagulant doses are required before solid/liquid separation will occur, underflow densities are generally low, and water recoveries are generally poor, even after treatment. At these mines the natural conductivity of the raw water supply is too low, and conditioning of the process water would be required to improve water recovery and for returning clarified process water back to the plant. Once the colloidal properties of the slurry have been brought under control, the subsequent strategies for increasing water recovery as described above can be implemented. There are, however, some mines in this category that operate normally by returning unclarified process water to the process plant. This is possible since the viscosity of highly dispersive process water can remain low even up to relatively high solids loadings (10 to 15% by mass of the –20 Οm fraction). Above this solids loading, however, the ultrafine solids begin to influence the water’s viscous properties and may affect diamond recovery efficiency in the dense media separation (DMS) circuit. There is a danger, however, that by implementing a process water conditioning strategy at this point, the solids in the process water may in fact ’gel’ instead of achieving solid/liquid separation. In this event, the water in the entire process circuit would take on the consistency of yoghurt and would need to be flushed out and replaced with fresh water, thereby dramatically increasing water loss.

Skills for the future – yours and your mine’s Glenhove Conference Centre, Melrose Estate, Johannesburg

BACKGROUND Previous SAIMM Mine Planning forums have clearly highlighted deficiencies in mine planning skills. The 2012, 2014, and 2017 colloquia all illustrated developing skill-sets with a variety of mine planning tools in a context of multiple mining methods. Newer tools and newer skills for the future of mining will feature in 2019.

OBJECTIVES

WHO SHOULD ATTEND

Using a backdrop of a generic description of the multidisciplinary mine planning process, the forum provides a platform for the mine planning fraternity to share mining business-relevant experiences amongst peers. While different mining environments have their specific information requirements, all require the integration of inputs from different technical experts, each with their own toolsets. The forum’s presentations will highlight contributions from a series of technical experts on current best practice, and will be augmented by displays of state-of-the-art mine planning tools in order to create a learning experience for increased planning competencies.

Mine planners and practitioners Mine designers Technical support staff Cost and financial modellers Chief planners Mineral resource managers Mine managers responsible for planning Consultants Independent mine planning consultants

FOR FURTHER INFORMATION CONTACT: Camielah Jardine • Head of Conferencing • SAIMM Tel: +27 11 834-1273/7 • Fax: +27 11 833-8156 E-mail:camielah@saimm.co.za •Website: http://www.saimm.co.za

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a10

Investigation of flow regime transition in a column flotation cell using CFD by I. Mwandawande*, G. Akdogan*, S.M. Bradshaw*, M. Karimi†, and N. Snyders*

Flotation columns are normally operated at optimal superficial gas velocities to maintain bubbly flow conditions. However, with increasing superficial gas velocity, loss of bubbly flow may occur with adverse effects on column performance. It is therefore important to identify the maximum superficial gas velocity above which loss of bubbly flow occurs. The maximum superficial gas velocity is usually obtained from a gas holdup versus superficial gas velocity plot in which the linear portion of the graph represents bubbly flow while deviation from the linear relationship indicates a change from the bubbly flow to the churn-turbulent regime. However, this method is difficult to use when the transition from bubbly flow to churn-turbulent flow is gradual, as happens in the presence of frothers. We present two alternative methods in which the flow regime in the column is distinguished by means of radial gas holdup profiles and gas holdup versus time graphs obtained from CFD simulations. Bubbly flow was characterized by saddle-shaped profiles with three distinct peaks, or saddle-shaped profiles with two near-wall peaks and a central minimum, or flat profiles with intermediate features between saddle and parabolic gas holdup profiles. The transition regime was gradual and characterized by flat to parabolic gas holdup profiles that become steeper with increasing superficial gas velocity. The churn-turbulent flow was distinguished by steep parabolic radial gas holdup profiles. Gas holdup versus time graphs were also used to define flow regimes with a constant gas holdup indicating bubbly flow, while wide gas holdup variations indicate churn-turbulent flow. D@%*;<39 flotation column, maximum superficial velocity, flow regime, bubbly flow, transition, churn-turbulent flow.

C4=<;387=?;4 Two types of flow can be distinguished in gasliquid flows in bubble columns: bubbly flow and churn-turbulent flow. The bubbly flow regime is characterized by uniform flow of bubbles of uniform size. Churn-turbulent flow, on the other hand, is characterized by a wide variation in bubble sizes with large bubbles rising rapidly and causing liquid circulation. Flotation columns are normally operated in the bubbly flow regime, which is the optimal condition for column flotation (Finch and Dobby, 1990; Yianatos and Finch, 1990; Ityokumbul, 1992). However, it has been generally observed that flotation column performance deteriorates when the superficial gas velocity is increased beyond a certain limit where the flow regime changes from bubbly flow to churn-turbulent flow (Xu, Finch, and

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* University of Stellenbosch, South Africa. †Department of Chemistry and Chemical

Engineering, Chalmers University of Technology, Sweden. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Sep. 2017; revised paper received Jun. 2018.

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Uribe-Salas, 1991). The identification of this critical or maximum superficial gas velocity is therefore important for optimal operation of flotation columns. Xu, Finch, and Uribe-Salas (1991) and Xu et al. (1989) investigated three phenomena that can be used to identify the maximum superficial gas velocity in column flotation: loss of interface, loss of positive bias flow, and loss of bubbly flow. Loss of interface occurs when the hydrodynamic conditions in the froth zone of the column become identical to the conditions prevailing in the collection zone. This will result in the loss of the cleaning action associated with the froth zone. In column flotation, wash water, which is continuously added at the top of the column, maintains a net downward flow of water that prevents entrained particles from reaching the concentrate. This net downward flow of water is referred to as positive bias flow. By minimizing entrainment of unwanted particles, a positive bias maximizes the concentrate grade. Loss of positive bias occurs when the superficial gas velocity is high enough to cause a reversal of the net flow of water at the froth/collection zone interface. On the other hand, the loss of bubbly flow occurs when the superficial gas velocity is sufficiently high to bring about a transition from bubbly flow conditions to churnturbulent flow. The increased mixing associated with churn-turbulent flow is unfavourable for mineral recovery in the column. Considering the froth and liquid (pulp) phases as distinct flow regimes with different liquid holdups, Langberg and Jameson (1992) investigated the hydrodynamic conditions under which the froth and pulp phases can

Investigation of flow regime transition in a column flotation cell using CFD coexist in a flotation cell. The effects of superficial gas velocity and bubble size on the limiting conditions for flow regime coexistence and for countercurrent flow across the froth-liquid interface were studied using a one-dimensional two-phase flow model. Their study identified two hydrodynamic limiting conditions relevant to the operation of flotation cells and columns: the limiting condition for the coexistence of the froth and liquid (pulp) phases, and the limiting condition for countercurrent flow. Of the three phenomena used to identify the maximum superficial gas velocity in column flotation, the loss of bubbly flow is the most difficult to determine (Xu, Finch, and UribeSalas, 1991; Xu et al., 1989). The relationship between gas holdup and superficial gas velocity is used to determine the loss of bubbly flow in the column. In this method, a linear gas holdup versus superficial gas velocity relationship represents bubbly flow while deviation from linearity defines loss of bubbly flow. However, the loss of bubbly flow is difficult to identify using this method because of the gradual nature of the transition from bubbly flow to churn-turbulent conditions in the presence of frother (Xu et al., 1989). During the transition from bubbly flow to churn-turbulent flow, the bubble size increases rapidly due to bubble coalescence. However, the presence of frothers suppresses bubble coalescence, causing the transition to churn-turbulent flow to become gradual. In this research, the maximum superficial gas velocity for transition from bubbly flow to churn-turbulent flow was studied using computational fluid dynamics (CFD). Besides the gas holdup versus superficial gas velocity relationship, two alternative methods of flow regime characterization were employed to identify the loss of bubbly flow in a pilot-scale column flotation cell. The first method involves examining the evolution of radial gas holdup profiles as a function of superficial gas velocity. The shape of the radial gas holdup profile has been recognized as a function of the flow pattern in two-phase flows (Kobayasi, Iida, and Kanegae, 1970; Serizawa, Kataoka, and Michiyoshi, 1975). A graph of gas holdup versus time can also be used to identify the prevailing

flow regime in the flotation column (Shen, 1994). Wide variations in the gas holdup versus time graph characterize churn-turbulent flow conditions, while gas holdup is almost constant under bubbly flow conditions.

!@=6;39A0;<A0:;*A<@/?5@A?3@4=?0?7>=?;4 The relationship between gas holdup and superficial gas velocity can be used to define the prevailing flow regime in the flotation column (Finch and Dobby, 1990). In the bubbly flow regime, the gas holdup increases linearly with increasing superficial gas velocity. However, the gas holdup deviates from this linear relationship when the superficial gas velocity is increased above a certain value, as shown in Figure 1. The superficial gas velocity at which deviation from the linear relationship occurs is thus the maximum or critical velocity, above which the flow regime changes from bubbly flow to churn-turbulent flow. In other words, it is the maximum superficial gas velocity for loss of bubbly flow. Xu et al. (1989) applied the gas holdup versus superficial gas velocity relationship to determine the maximum superficial gas velocity for loss of bubbly flow in a pilot-scale flotation column. However, they reported difficulties in the identification of the regime transition point as a result of the gradual nature of this transition, particularly in the presence of frother. The present study therefore applies the following alternative methods to distinguish the different flow regimes and thus identify the maximum velocity above which the transition from bubbly flow to churn-turbulent flow will occur.

& $& )(& ) % ) "% $ ' Two general gas holdup profiles are known to exist, the parabolic profile and the saddle-shaped profile. Kobayasi, Iida, and Kanegae (1970) studied the characteristics of the local void fraction (gas holdup) distribution in air-water twophase flow. They reported a ‘peculiar’ distribution, different from the previously accepted power law distribution in bubbly flow conditions. The distribution associated with bubbly flow had its peaks near the pipe wall. On the other

,?/8<@A2# >9A6;:381A>9A>A0847=?;4A;0A981@<0?7?>:A/>9A$@:;7?=%A)>3>1=@3A0<;5A,?476A>43A ;..% A2&& (

174

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Investigation of flow regime transition in a column flotation cell using CFD hand, the distribution in slug flow had its maximum at the centre of the pipe. Serizawa, Kataoka, and Michiyoshi, (1975) also studied various local parameters and turbulence characteristics of concurrent air-water two-phase bubbly flow. They found that the distribution of void fraction (radial gas holdup) was a strong function of the flow pattern. The void fraction distribution changed from saddle-shaped to parabolic as the gas velocity increased. A saddle-shaped distribution is therefore associated with bubbly flow conditions, while a parabolic one represents slug flow.

& ) % ) '" )!$ ')("& A plot of gas holdup versus time can also be used to identify the existing flow regime in a column The gas holdup versus time will be relatively constant when the column is in the bubbly flow regime. On the other hand, the gas holdup will show wide variations when the column is operating in the churn-turbulent regime (Shen, 1994). A similar method has been used by previous researchers who used variations in conductivity signals to characterize the flow regime in the downcomer of a Jameson flotation cell (Mecklenburg, 1992; Summers, 1995).

@97<?1=?;4A;0A=6@A7;:854 The CFD model developed in the present research was used to simulate the flotation column that was used in the experimental work of Xu, Finch, and Uribe-Salas (1991) and Xu et al. (1989). The column was made of Plexiglas and was 400 cm in height and 10.16 cm in diameter. The column was

operated continuously, with air introduced into the bottom of the column through a cylindrical stainless steel sparger, 3.8 cm in diameter and 7 cm in length. This sparger geometry gives a ratio of column cross-section to sparger surface area of about 1:1. A schematic diagram of the column is presented in Figure 2. A detailed description of the experimental set-up is presented in Xu, Finch, and Uribe-Salas (1991).

, A5@=6;3;:;/% !$ & ') % ' A Eulerian-Eulerian (E-E) multiphase modelling approach was used in this study. The Eulerian-Eulerian model was selected on the basis of its lower computational cost compared to other modelling approaches such as the volume of fluid (VOF) and the Eulerian-Lagrangian methods. A VOF model would entail tracking of the interface between different phases, while the Eulerian-Lagrangian method would involve tracking of the motion of individual bubbles using an equation of motion. Both of these methods are therefore not suitable for systems that involve large numbers of bubbles, such as those in the present research. In the E-E approach, both the continuous phase and the dispersed phase are considered as interpenetrating continua and both are modelled in the Eulerian frame of reference. The equations for conservation of mass and momentum are then solved for each phase separately. In this study, the two-phase flow was modelled considering water as the continuous phase (or primary phase) and air bubbles as the dispersed phase (or secondary phase).

"! A22&

175

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Investigation of flow regime transition in a column flotation cell using CFD Interaction between the phases was accounted for by inclusion of the drag force between phases. The drag force is included in the respective conservation of momentum equations as a source term. The volume-averaged mass and momentum equations are written as follows:

[7] where Re is the relative Reynolds number for the primary phase L and the secondary phase G defined as: [8]

[1]

[2] where k is the phase indicator, k = L for the liquid phase and k = G for the gas phase, k is the volume fraction, k is the phase density, and uk is the velocity of the kth phase, while Sk is a mass source term. MG,L is the interaction force k is between the phases, and g is the gravity force, while = the kth phase stress-strain tensor, given by: [3] The volume fraction (or gas holdup) of the secondary phase was calculated from the mass conservation equations as: [4] where rG is the phase reference density, or volumeaveraged density of the secondary phase in the solution domain. The volume fraction of the primary phase was calculated from the secondary phase one, since the sum of the volume fractions is equal to unity. The multiphase model was implemented and solved using the CFD code ANSYS FLUENT 14.5.

The interaction force between the two phases was modelled through the drag force incorporated into the multiphase model. The drag force per unit volume for bubbles in a swarm is generally presented as: [5] where CD is the drag coefficient, dB is the bubble diameter,

is the slip velocity. There are a number of

–u and u G L empirical correlations that can be used to calculate the drag coefficient, CD. The drag coefficient is normally presented in these correlations as a function of the bubble Reynolds number (Re), defined as: [6] In the present study, the drag coefficient was calculated using the universal drag laws. In this case, the drag coefficient is defined in different ways depending on whether the prevailing regime is in the viscous regime category, the distorted bubble regime, or the strongly deformed capped bubbles regime. The different regimes are defined on the basis of the Reynolds number (Kolev, 2005). The subsequent expressions for the drag coefficient are derived from singlebubble equations, which are then modified to account for bubble swarm effects. In the viscous regime the drag coefficient is presented as:

176

"! A22&

and Îźe is the effective viscosity for the bubble-liquid mixture given by: [9] In the distorted bubble regime the drag coefficient is given as follows: [10] where RT is the Rayleigh-Taylor instability wavelength, defined as: [11] and is the surface tension, g the gravitational acceleration, and GL is the absolute value of the density difference between the phases G and L. For the strongly deformed, capped bubbles regime, the following drag coefficient is used: [12] Under churn-turbulent flow conditions, the drag coefficient is calculated using Equation [12]. Further details about the universal drag laws are available in a recent multiphase flow dynamics book (Kolev, 2005).

" ' ') % ' $ ( Turbulence in the continuous phase was modelled using the k- realizable turbulence model, a RANS (Reynolds-Averaged Navier-Stokes)-based model in which the time-averaged Navier-Stokes (RANS) equations are solved in place of the instantaneous Navier-Stokes equations to produce a timeaveraged flow field. Alternative turbulence modelling approaches such as direct numerical simulation (DNS) or large-eddy simulation (LES) would require very dense computational grids and small time-steps, with subsequent increase in the computational effort required for the simulations. These methods are therefore not available when using the E-E multiphase model in ANSYS FLUENT. The averaging procedure introduces additional unknown terms; the Reynolds stresses, which are subsequently resolved by employing Boussinesq’s eddy viscosity concept where the Reynolds stresses (or turbulent stresses) are related to the velocity gradients according to the following equation: [13] where t is the turbulent or eddy viscosity, kE is the turbulent kinetic energy, and ij is the Kronecker delta. The Kronecker delta is important in order to make the eddy viscosity concept applicable to normal stresses where i = j.

Investigation of flow regime transition in a column flotation cell using CFD The turbulent viscosity is in turn related to a velocity scale and a length scale of the turbulence according to the PrandtlKolmogorov formula, which can be written as: [14] The velocity scale is calculated from the turbulent kinetic energy (kE) equation, while the length scale is obtained from the turbulent dissipation ( ) equation. The k- model is therefore a two-equation model in which two separate transport equations are solved to determine the velocity scale and the length scale of the turbulent motion. Turbulence in the near-wall region was modelled using standard wall

Table I

6@A0?$@A5@96A9? @9A>43A=6@?<A<@91@7=?$@ 76><>7=@<?9=?79!@96

85.@<A ;0A7@::9

85.@<A ;0A4;3@9

8>:?=%

!?4?585A ;<=6;/;4>:A

" =?5@A)6(

Mesh 1 Mesh 2 Mesh 3 Mesh 4 Mesh 5

13 940 34 160 79 755 98 112 114 696

17 510 39 474 88 560 108 433 126 764

0.9318 0.939 0.773 0.831 0.878

2.5 4.5 14 16 17

functions to calculate turbulence quantities in the region near to the walls of the column.

'% '!" )& ) ' For the purposes of the present research, the model considers only the collection zone of the flotation column. Therefore the froth zone was not included in the model. A further simplification was achieved by leaving the sparger out of the model geometry. Instead, the air was introduced from the bottom part of the column over the entire column crosssection. This will not affect the required gas holdup prediction since the ratio of column cross-section to sparger surface area was about 1:1 in the experimental flotation column. The result of these simplifications is that the model geometry is reduced to a cylindrical vessel of height equal to the collection zone height (305 cm in this case) and diameter equal to the diameter of the experimental flotation column. A mesh comprising mainly hexahedral elements was generated over the model geometry using the sweep method in ANSYS Meshing. Five mesh sizes were investigated in order to obtain grid-independent numerical results. The mesh sizes are summarized in Table I, together with their respective attributes. The axial water and bubble velocity profiles obtained for the different mesh sizes are shown in Figure 3 and Figure 4, respectively. Grid independence was achieved with mesh 3 to mesh 4 and 5. Mesh 3 (79 755 elements) was therefore selected for all subsequent simulations in this study.

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177

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Investigation of flow regime transition in a column flotation cell using CFD % &" ) % $!$% Air bubbles were introduced into the column through mass and momentum source terms at the column bottom. The source terms were calculated from the respective superficial gas velocities and were applied over the entire column crosssection at the bottom (source) and at the top (sink) of the collection zone. For the liquid phase, the top of the collection zone was modelled as a velocity inlet boundary where inlet velocity was specified as equal to superficial liquid velocity Jl. Since the computational domain being considered is the collection zone of the column, the superficial liquid velocity must include the feed rate plus the bias water resulting from wash water addition. The superficial liquid velocity is therefore equal to the superficial tailing rate, Jt. The bottom part was also modelled as velocity inlet where exit velocity was set equal to minus superficial liquid velocity. At the column wall, no slip boundary conditions were applied for both the air bubbles and the liquid phase.

'"$ & ) % !$% ) '! % The momentum and volume fraction equations were discretized using the first-order upwind scheme. The firstorder upwind scheme was also employed for turbulence kinetic energy and dissipation rate discretization. A time step size of 0.05 seconds was used in all the simulations. The simulations were run up to a flow time of at least 240 seconds. Time averaging was carried out over the last 120 seconds.

@98:=9A>43A3?97899?;4 If the entire range of bubble sizes encompassing the different flow regimes is known, CFD simulations can be conducted for a range of superficial gas velocities covering the different flow regimes. A plot of gas holdup versus superficial gas velocity can then be used to determine the point of departure from bubbly flow conditions, as described earlier. For a system comprising water and air without frother, an empirical formula derived by Shen (1994) can be used to calculate the Sauter mean bubble size as a function of the superficial gas velocity. However, a similar empirical equation derived for column flotation conditions where frother plays an important role in determining the bubble size is limited to superficial gas velocities ranging from 1 to 3 cm/s. The first set of CFD results presented in this study is therefore obtained using bubble sizes calculated for a system without frother. The gas holdup versus superficial gas velocity relationship obtained is then used to delineate the different flow regimes prevailing in the column. Once the flow regimes are identified, the evolution of radial gas holdup profiles and gas holdup versus time graphs can be examined for the flow regimes determined from the gas holdup versus superficial gas velocity graph. Radial gas holdup profiles and gas holdup versus time graphs are then used to determine the maximum superficial gas velocity for a column operating with an average bubble size of 1.5 mm, which is comparable with typical bubble sizes in industrial flotation columns. The radial gas holdup profiles were obtained at the mid-height position (152.5 cm height) in the column. On the other hand, the gas holdup versus time

178

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graphs were obtained from a surface monitor located at the column mid-height position, from which area-weighted average gas holdup measurements were recorded at 5-second intervals. It is important at this stage to clarify the definition of regime transition and churn-turbulent flow regime as used in the present study. Churn-turbulent flow is normally associated with a wide bubble size distribution, including large bubbles. However, the CFD simulations in the present research were conducted with a single mean bubble size assigned for each superficial gas velocity. Furthermore, in order to investigate the flow regime transition for conditions relevant to column flotation, the other set of CFD simulations was performed with the bubble size held at a constant value of 1.5 mm while increasing the superficial gas velocity to determine the maximum superficial gas velocity for that particular bubble size. The reference to churn-turbulent flow in the present study is therefore not based on a wide bubble size distribution consisting of of large bubbles in the presence of smaller ones. On the other hand, a CFD model coupled with a bubble population balance model can be used to predict bubble size distributions in the column. However, bubble population balance modelling was beyond the scope of the present research. A possible alternative that can be used to simulate bubble size distributions is to apply a EulerianLagrangian model. This approach was not applied in the present study due to its higher computational cost. A Eulerian-Lagrangian model would also require prior knowledge of the largest and smallest bubble sizes. This information was not readily available in the present research. According to Lockett and Kirkpatrick (1975) there are three main reasons for breakaway from ideal bubbly flow: flooding, liquid circulation, and the presence of large bubbles. It should therefore be possible to identify regime transition from changes in the pattern and intensity of the liquid circulation in the column even if changes in bubble size are negligible or absent. However, the absence of large bubbles is likely to have an effect on the prediction of the location of the transition zone as well as on the shape of the radial gas holdup profile. On the other hand, the liquid circulation pattern and its intensity depend on the prevailing radial gas holdup profile in the column (Hills, 1974). Saddle-shaped gas holdup profiles have already been related to bubbly flow conditions in two-phase flows (Kobayasi, Iida, and Kanegae, 1970; Serizawa, Kataoka, and Michiyoshi, 1975). However, the bubbly flow regime is generally characterized by a radially uniform gas holdup distribution (Ruzicka et al., 2001; Vial et al., 2001; Shaikh, and Al-Dahhan, 2007). Both saddle and flat gas holdup profiles can therefore be considered to indicate the existence of the bubbly flow regime in the column. On the other hand, the churn-turbulent flow regime is generally distinguished by a non-uniform radial gas holdup distribution causing bulk liquid circulation. Parabolic gas holdup profiles are therefore interpreted to signify churn-turbulent flow conditions in the column.

&!'")& )&$")% ) $! % !) "%! '" For the water and air only (no frother) system, CFD simulations were carried out for superficial gas velocities ranging from 1.01 to 14 cm/s to encompass both the bubbly

Investigation of flow regime transition in a column flotation cell using CFD flow and churn-turbulent flow regimes. The Sauter mean bubble size for a multi-bubble system without frother (i.e., water and air only) can be calculated as a function of the superficial gas velocities from the following empirical formula (Shen, 1994): [15] This empirical formula was derived for a laboratory flotation column with a porous stainless steel sparger similar to the one that was used in the column modelled in the present work. The equation was therefore used in the present work to calculate the average bubble sizes that were subsequently used in the CFD simulations.

The graph of gas holdup versus superficial gas velocity obtained from CFD simulations is presented in Figure 5. The different flow regimes can be cleared delineated as follows: Bubbly flow regime; Jg 1.01–6.12 cm/s (linear portion of the graph) Transition; 6.12 < Jg 11.71 cm/s (deviation from linear relationship between gas holdup and superficial gas velocity) Churn-turbulent flow; Jg > 11.71 cm/s (third portion of the gas holdup versus superficial gas velocity graph). The maximum superficial gas velocity (Jg max) before loss of bubbly flow is therefore equal to 6.12 cm/s for the

water and air only system without frother. This value compares well with the maximum superficial gas velocity of 5.25 cm/s for loss of bubbly flow predicted from drift flux theory by Xu, Finch, and Uribe-Salas (1991). Having delineated the different flow regimes using the gas holdup versus superficial gas velocity relationship, the evolution of the radial gas holdup profiles was examined as the superficial gas velocity increased from 1.01 cm/s to 14 cm/s.

A number of different types of radial gas holdup profiles were obtained from the CFD simulations, including saddle-shaped, flat, and parabolic profiles. These profiles were then related to the flow regimes defined from the gas holdup/superficial gas velocity relationship (Figure 5). Three types of radial gas holdup profiles were observed in the bubbly flow regime. At the lower superficial gas velocities (Jg 1.01–2.73 cm/s), saddle-shaped profiles with three distinct peaks were observed with one peak at the centre and two other peaks located near the walls of the column, as shown in Figure 6 for Jg = 1.84 cm/s. The profiles then changed to ones with two near-wall peaks and a central minimum point as the superficial gas velocity increased to Jg = 4.44 cm/s, as illustrated in Figure 7. With further increase in the superficial gas velocity the central minimum point disappeared and the radial gas holdup profile became flat. The maximum superficial gas velocity (Jgmax = 6.12 cm/s)

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179

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Investigation of flow regime transition in a column flotation cell using CFD

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before loss of bubbly flow is therefore characterized by a flat radial gas holdup profile with intermediate features between saddle and parabolic profiles, as shown in Figure 8. The transition from bubbly flow to churn-turbulent flow was gradual and characterized by flat (Jg = 7.41 cm/s) to parabolic (Jg 8.70–11.71 cm/s) radial gas holdup profiles. The parabolic profiles became progressively steeper as the superficial gas velocity increased. Eventually the flow regime changes into churn-turbulent flow (Jg > 11.71 cm/s), which is

180

0

characterized by steep parabolic gas holdup profiles as shown in Figure 9.

Another method that was used to distinguish flow regimes in the water–air system using CFD simulations was by means of gas holdup versus time graphs. The two extreme cases, bubbly flow and churn-turbulent flow, are compared in Figure 10. It can be seen that the gas holdup versus time was mostly

Investigation of flow regime transition in a column flotation cell using CFD

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Table II

855><%A;0A , A9?58:>=?;4A;0A=6@A*>=@<A>43A>?<A9%9=@5A)*?=6;8=A0<;=6@<( A#( & +A -2 A75'9 #( )75'9(

>8=@<A5@>4A.8..:@A 9? @ A )75(

>9A6;:381A )0<>7=?;4>:(

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;55@4=9

1.01 1.84 2.73 4.44

0.33 0.38 0.42 0.47

0.045 0.083 0.125 0.209

Constant Constant Almost constant Almost constant

Bubbly flow Bubbly flow Bubbly flow Bubbly flow

6.12

0.51

0.279

Small to moderate variations

Bubbly flow (maximum superficial gas velocity)

7.41 8.70 9.56 10.42 11.71 13.00

0.53 0.55 0.57 0.58 0.60 0.61

0.315 0.333 0.345 0.353 0.348 0.367

Saddle with three peaks Saddle with three peaks Saddle with three peaks Saddle profile with two near-wall peaks Flat profile with intermediate features between parabolic and saddle profiles Flat Flat-like parabolic Parabolic Parabolic Steep parabolic Steep parabolic

Transition Transition Transition Transition Transition (last point) Churn-turbulent flow

14.00

0.62

0.377

Steep parabolic

Moderate variations Moderate variations Significant variations Significant variations Large variations Wide variations (large variations) Wide variations (large variations)

Churn-turbulent flow

Table III

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Bubbly flow

• Characterized by saddle-shaped radial gas holdup profiles accompanied by a constant gas holdup versus time graph • The profiles at lower Jg have three peaks that give way to profiles with two near-wall peaks and a central minimum and eventually flat profiles as Jg increases • Characterized by flat to parabolic gas holdup profiles • The profiles become increasingly steep with increasing Jg while gas holdup versus time varies from moderate to large fluctuations • Characterized by steep parabolic profiles with very wide variations in gas holdup versus time

Churn-turbulent flow

constant in the bubbly flow regime (Jg = 1.84 cm/s), while very wide variations in gas holdup were observed in the churnturbulent flow regime (i.e., Jg = 14 cm/s). The transition from bubbly flow to churn-turbulent flow pattern was gradual and characterized by moderate to large fluctuations in gas holdup, with the gas holdup variations becoming increasingly intense as the superficial gas velocity increased.

The results from the CFD simulations of the water and air only (no frother) system, where bubble sizes are calculated from the superficial gas velocity according to Equation [15], are summarized in Table II. The progression from bubbly flow conditions to churn-turbulent flow can also be clearly appreciated from this table. The distinguishing characteristics of the flow regimes are summarized in Table III. "! A22&

181

Transition

Investigation of flow regime transition in a column flotation cell using CFD

&!'") $! ) "%! '") & )$ ) % ) %!&!$% In flotation columns, the bubble size depends not only on the superficial gas velocity, but also on other physical chemical characteristics of the gas-liquid system. In this case, the bubble size can be calculated as a function of the superficial gas velocity according to the following relationship (Finch and Dobby, 1990; Yianatos and Finch, 1990). [16] where C and n are constants. The constant C is a fitting parameter which depends mainly on frother concentration, the sparger size, and column size. Simulations were carried out for the experimental conditions used by Xu, Finch, and Uribe-Salas (1991) and Xu et al. (1989), particularly the case in which the frother concentration was 10 ppm. The value of C was therefore equal to unity while n was 0.25. The gas holdup obtained from the CFD simulations is plotted as a function of superficial gas velocity in Figure 11. The experimental data from Xu et al. (1989) is included in the figure for comparison. It can be seen that the predicted gas holdup is in good agreement with the experimental data up to superficial gas velocity Jg = 3.60 cm/s. Above Jg = 3.60 cm/s, the relationship in Equation [16] is not applicable because the value of the exponent n (0.25) is valid for Jg 1–3 cm/s (Yianatos and Finch, 1990). Unfortunately, there is no equation relating bubble size and superficial gas velocity for flow conditions above Jg = 3 cm/s. Therefore, the gas holdup versus superficial gas velocity graph obtained from CFD simulations cannot be used to determine the maximum superficial gas velocity since the range of bubble sizes for the different flow regimes cannot be completely determined. Xu et al. (1989) used the gas holdup versus superficial gas velocity relationship to identify the maximum gas velocity for loss of bubbly flow. However, they reported a gradual and unclear transition from bubbly flow to churnturbulent flow conditions. The maximum superficial gas velocity for conditions applicable in flotation columns can be obtained using radial gas holdup profiles and the gas holdup versus time graphs as already described. In this regard, CFD simulations are performed in the present study for a stipulated bubble size of 1.5 mm, which is similar to common bubble sizes used in column flotation. The bubble size is held

constant during the simulations while increasing the superficial gas velocity to encompass both bubbly flow and churn-turbulent flow conditions.

& $ ) '" $ $& )(& ) ' % $! ) %")&) % % '"&!$ () $! ) ) )& '"&(') ') $ ' CFD simulations were performed with a constant bubble size of 1.5 mm for superficial gas velocities ranging from Jg = 1.01 to 6.12 cm/s. The liquid superficial velocity was maintained at Jl = 0.38 cm/s. Radial gas holdup profiles were examined for all the superficial gas velocities together with their corresponding gas holdup versus time plots.

Saddle-shaped radial gas holdup profiles with three peaks were observed for Jg = 1.01 to 1.54 cm/s. The radial gas holdup profile for Jg 1.01 cm/s is presented in Figure 12 for elaboration. The profiles then changed to ones with two distinct peaks near to the column wall as Jg increased (Jg = 1.84 to 2.73 cm/s). On the other hand, a flat profile with intermediate features between saddle-shaped and parabolic profiles was observed for Jg = 3.12 cm/s. The column was therefore operating under bubbly flow conditions from Jg = 1.01 to 3.12 cm/s. In the churn-turbulent regime, steeper parabolic radial gas holdup profiles were observed from Jg = 5.28 cm/s, as shown in Figure 13.

Gas holdup versus time graphs were used to confirm the existing flow regime in the column. A relatively constant gas holdup versus time was observed for Jg = 1.01 to 1.84 cm/s while moderate fluctuations in gas holdup were observed from Jg = 2.28 to 3.12 cm/s, confirming that the column was indeed in the bubbly flow regime in this range of superficial gas velocities. In contrast, very wide variations in gas holdup versus time were observed when the churn-turbulent flow regime prevailed in the column. The two extreme conditions, bubbly flow and churn-turbulent flow, are compared in Figure 14 for Jg = 1.01 cm/s and Jg = 5.28 cm/s.

The maximum (critical) superficial gas velocity for transition into churn-turbulent flow was identified by the first appearance of flat radial gas holdup profiles with

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Investigation of flow regime transition in a column flotation cell using CFD

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Salas (1991) and Xu et al. (1989) with reference to loss of interface. Indeed, these authors did report that loss of interface and loss of bubbly flow occurred at approximately the same superficial gas velocities. The predicted gas holdup at Jg,max is equal to 20.1%, which compares favourably with the maximum gas holdup of 20 to 24% reported by Dobby, Amelunxen, and Finch (1985). "! A22&

183

intermediate features between parabolic and saddle-shaped profiles at Jg = 3.12 cm/s. The maximum superficial gas velocity (Jg,max) before loss of bubbly flow is therefore 3.12 cm/s for a flotation column operating with an average bubble size of 1.5 mm at superficial liquid velocity (Jl) equal to 0.38 cm/s. This compares favourably with the maximum gas velocity of 3.60 cm/s reported by Xu, Finch, and Uribe-

Investigation of flow regime transition in a column flotation cell using CFD The radial gas holdup profile at Jg,max is shown in Figure 15, and its corresponding gas holdup versus time graph in Figure 16. It can be seen that the maximum superficial gas velocity is characterized by a flat gas holdup profile accompanied by moderate gas holdup fluctuations. The results from the CFD simulations for 1.5 mm bubble size are summarized in Table IV. The progression from bubbly flow conditions to churn-turbulent flow can be clearly seen in the table.

$ $ ) ' % $! ) ' !%" Two different flow patterns were observed, depending on superficial gas velocity and bubble size. With increasing superficial gas velocity the well-known ‘Gulf Stream’ circulation pattern, in which the liquid rises in the centre of the column and descends near the column wall, was observed. In contrast, an ‘inverse’ circulation flow pattern, in which the liquid rises near the column wall but descends in

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Constant Very small variations Moderate fluctuations Moderate fluctuations Moderate fluctuations Larger fluctuations Larger fluctuations Large variations Very large variations

Bubbly flow Bubbly flow Bubbly flow Bubbly flow Bubbly flow Bubbly flow (Jgmax) Transition Transition Transition Churn-turbulent flow

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Investigation of flow regime transition in a column flotation cell using CFD the centre and immediately adjacent to the walls, occurred at lower superficial gas velocities. This is the first study to report such an inverse flow pattern in column flotation. However, similar flow reversals have been observed in experimental work on fluidized bed reactors (Lin, Chen, and Chao, 1985). The inverse circulation pattern has been theoretically investigated in bubble columns and is associated with fully developed saddle-shaped radial gas holdup profiles (Clark, Atkinson, C., and Flemmer, 1987; Clark, van Egmond, and Nebiolo E. 1990). In the present study, the inverse circulation pattern was observed at Jg = 1.01 cm/s and Jg = 1.54 cm/s for simulations with bubble size = 1.5 mm and superficial liquid velocity = 0.38 cm/s. Saddle-shaped gas holdup profiles with two distinct peaks near the walls of the column were also present under these conditions. The liquid velocity vector plots obtained from CFD simulations are presented in Figure 17 and Figure 18 for the two circulation patterns. Clark, van Egmond, and Nebiolo (1990) have described the sequence of events that may initiate liquid circulation in bubble columns. In general, liquid circulation in bubble columns is initiated by density differences in the gas-liquid mixture, depending on the prevailing radial gas holdup profile. If the concentration of air bubbles is higher in the central part of the column compared to the outer annular region, the mean density of the mixture will be lower near the centre of the column than in the outer annulus. The hydrostatic pressure head will therefore be higher in the outer annulus, hence a radial pressure difference is set up inside the column. This will cause an inward radial movement of liquid and initiate liquid circulation. The inverse circulation pattern will occur if the concentration of bubbles is higher in the outer annular region, as in the case of saddleshaped gas holdup profiles.

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Two possible flow patterns were revealed in the simulated column in this study: the ‘Gulf Stream’ circulation pattern and an inverse circulation pattern. The latter was observed only in the presence of fully developed saddle-shaped radial gas holdup profiles. "! A22&

185

In this study, the evolution of the shape of the radial gas holdup profile in a pilot-scale flotation column was studied using CFD to delineate the maximum gas velocity for loss of bubbly flow. With increasing superficial gas velocity, the gas holdup profile passes through different stages, which can be used to define the prevailing flow regime in the column. The different flow regimes were also verified by the intensity of the local variations of the gas holdup. In the bubbly flow regime, saddle-shaped and flat radial gas holdup profiles were obtained. These profiles were accompanied by minor to moderate gas holdup variations in the column. The transition regime was gradual and characterized by flat to parabolic gas holdup profiles. The parabolic profiles became progressively steeper as the superficial gas velocity increased. The corresponding gas holdup versus time graphs in the transition regime showed moderate to wide variations in gas holdup. On the other hand, the churn-turbulent flow regime was distinguished by steep parabolic gas holdup profiles with very wide variations in gas holdup versus time. For conditions relevant to column flotation, the maximum superficial gas velocity was determined for a column operating with an average bubble size of 1.5 mm and superficial liquid velocity equal to 0.38 cm/s. The maximum superficial gas velocity was found to be equal to 3.12 cm/s. The corresponding maximum gas holdup value was 20.1%.

Investigation of flow regime transition in a column flotation cell using CFD The CFD simulations in the present study assumed a constant average bubble size for the flotation column. However, for the churn-turbulent regime, a bubble size distribution exists that could have an effect on the hydrodynamics of the column. CFD models coupled with a bubble population balance model should therefore be considered in future studies in order to account for bubble size distributions in the transition and churn-turbulent flow regimes.

@0@<@47@9 CLARK, N., ATKINSON, C., and FLEMMER, R. 1987. Turbulent circulation in bubble columns. AIChE Journal, vol. 33, no. 3. pp. 515–518. CLARK, N., VAN EGMOND, J., and NEBIOLO E. 1990. The drift-flux model applied to bubble columns and low velocity flows. International Journal of Multiphase Flow, vol. 16, no. 2. pp. 261–279. DOBBY, G., AMELUNXEN, R., and FINCH, J. 1985. Column flotation: Some plant experience and model development. Proceedings of the First IFAC Symposium on Automation for Mineral Resource Development, Brisbane, Australia, 9–11 July. IFAC Proceedings, vol. 18, no. 6. pp. 259–263.

B7 4;*:@3/@5@4=9 The authors would like to acknowledge the Centre for International Cooperation at VU University Amsterdam (CISVU) and the Copperbelt University (CBU) for providing the scholarship and subsequent funding for this research through the Nuffic-HEART Project. We would also like to acknowledge the Process Engineering Department at Stellenbosch University for providing the necessary facilities that made this research possible. The CFD simulations in our studies were performed using the University of Stellenbosch's Rhasatsha High Performance Computing (HPC) cluster: http://www.sun.ac.za/hpc

FINCH, J.A. and DOBBY, G.S. 1990. Column Flotation. Pergamon, Oxford, UK. HILLS, J. 1974. Radial non-uniformity of velocity and voidage in a bubble column. Transactions of the Institution of Chemical Engineers, vol. 52, no. 1. pp. 1–9. ITYOKUMBUL, M. 1992. A new modelling approach to flotation column desing . Minerals Engineering, vol. 5, no. 6. pp. 685–693. KOBAYASI, K., IIDA, Y., and KANEGAE, N. 1970. Distribution of local void fraction of air-water two-phase flow in a vertical channel. Bulletin of JSME, vol. 13, no. 62. pp. 1005–1012. KOLEV, N.I. 2005. Multiphase Flow Dynamics 2: Thermal and Mechanical Interactions. 2nd edn. Vol. 2. Springer, Berlin, Germany. LANGBERG, D. and JAMESON, G. 1992. The coexistence of the froth and liquid

;5@47:>=8<@

phases in a flotation column. Chemical Engineering Science, vol. 47,

CD dB dBS

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g Jg Jg,max Jl kE P Re Sk u u'

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Drag coefficient, dimensionless Bubble diameter, mm Sauter mean bubble size Drag force per unit volume, N/m3 Gravitational acceleration, 9.81 m/s2 Superficial gas velocity, cm/s Maximum superficial gas velocity Superficial liquid velocity Turbulence kinetic energy, m2/s2 Pressure, Pa Reynolds number, dimensionless Mass source term for phase k, kg/m3-s Reynolds averaged velocity, m/s Velocity fluctuation, m/s

LIN. J., CHEN, M., and CHAO, B. 1985. A novel radioactive particle tracking facility for measurement of solids motion in gas fluidized beds. AIChE Journal, vol. 31, no. 3. pp. 465–73. LOCKETT, M. and KIRKPATRICK, R. 1975. Ideal bubbly flow and actual flow in bubble columns. Transactions of the Institution of Chemical Engineers, vol. 53. pp. 267–273. MECKLENBURG, M.M. 1992. Hydrodynamic study of a downwards concurrent bubble column. MEng dissertation, McGill University, Montreal, Canada. RUZICKA, M., ZAHRADNIK, J., DRAHOŠ, J., and THOMAS N. 2001. Homogeneous– heterogeneous regime transition in bubble columns. Chemical Engineering Science, vol. 56, no. 15. pp. 4609–4626. SERIZAWA, A., KATAOKA, I., and MICHIYOSHI, I. 1975. Turbulence structure of airwater bubbly flow – II. local properties. International Journal of Multiphase Flow, vol. 2, no. 3. pp. 235–246. SHAIKH, A. and AL-DAHHAN, M.H. 2007. A review on flow regime transition in bubble columns. International Journal of Chemical Reactor Engineering,

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Air volume fraction or gas holdup Volume fraction Turbulence dissipation rate, m2/s3 Rayleigh-Taylor instability wavelength Viscosity, kg/m-s Turbulent viscosity, kg/m-s Density, kg/m3 Surface tension (N/m) Viscous stress tensor, Pa

vol. 5, no. 1. https://doi.org/10.2202/1542-6580.1368 SHEN, G. 1994. Bubble swarm velocities in a flotation column. PhD dissertation, McGill University; Montreal, Canada. SUMMERS, A.J. 1995. A study of the operating variables of the Jameson cell. MEng dissertation, McGill University, Montreal, Canada. VIAL, C., PONCIN, S., WILD, G., and MIDOUX, N. 2001. A simple method for regime identification and flow characterisation in bubble columns and airlift reactors. Chemical Engineering and Processing: Process Intensification, vol. 40, no. 2. pp. 135–151. XU, M., FINCH, J., and URIBE-SALAS, A. 1991. Maximum gas and bubble surface rates in flotation columns. International Journal of Mineral Processing, vol. 32, no. 3. pp. 233–250.

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XU, M., Uribe-Salas, A., Finch, J., and Gomez, C. 1989. Gas rate limitation in column flotation. Processing of Complex Ores: Proceedings of the International Symposium on Processing of Complex Ores, Halifax, Nova Scotia, 20-24 August 1989. Elsevier. pp. 397–407. YIANATOS, J. and FINCH, J. 1990. Gas holdup versus gas rate in the bubbly regime. International Journal of Mineral Processing, vol. 29, no. 1. pp. 141–146.

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a11

Development of a blast-induced vibration prediction model using an artificial neural network by A. Das*, S. Sinha*, and S. Gangulyâ€

In an opencast mine explosives are used for fragmentation of rock. Inefficient use of explosive energy in an opencast operation produces excessive ground vibration, which is measured by peak particle velocity (PPV). To mitigate ground vibration, it is essential to develop a model to predict PPV. At present empirical models are used. These models are based on only a few input variables, hence they fail to take into account the effects of the myriad factors that cause ground vibration. Due to lack of explicit knowledge about the complex mine blasting system the scope of application of mathematical and statistical modeling techniques is limited. The artificial neural network (ANN) technique is a learning algorithm that can remove some of these limitations and can be applied to predict PPV. In this paper an ANN model is developed for prediction of blast vibration using 248 data records collected from three coal mines with diverse geomining conditions. The correlation coefficient between measured PPV and model output was found to be 0.96 and the average error percentage 11.85. The ANN model output was compared with the output of three empirical models that are widely used for prediction of PPV. The correlation coefficient between the PPV predicted by an empirical model and measured PPV data was 0.63 and the relative error percentage 38.47. This result demonstrates the superiority of the ANN model compared to empirical blast models. By using site-specific structural discontinuities as input the model performance can be further improved. Sensitivity analysis and 3D plotting were used to gain further knowledge about blast-induced ground vibration. @ <:97 artificial neural network, peak particle velocity, sensitivity analysis, 3D plot.

>=:<985=?<>A In an opencast coal mine explosives are used for fragmentation of coal and overburden. If the explosive energy is not fully utilized it causes blast-induced ground vibration, which may damage nearby structures. Ground vibration is expressed as peak particle velocity (PPV). During different stages of mine planning and operation, it is necessary to use a ground vibration prediction model for blasthole design. Selection of the modelling technique is crucial. Mathematical and statistical modelling techniques have limited application because of the lack of explicit knowledge about the complex mine blasting system. Vogiatzi (2002) highlighted the problem of multicollinearity in case of statistical modeling techniques. Mutalib et al.

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* Department of Mining Engineering, IIEST, India. †Department of Metallurgy, National Institute of

Technology, Raipur. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Jun. 2018; revised paper received Oct. 2018.

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(2013) stated that mathematical models are unable to capture the nonlinear relationship between several blasting-related parameters due to the complexity of the model input data. However, the difficulty involved in modelling complex blast vibration problems can be removed by adopting an alternative soft computing modelling approach. One of the soft computing techniques is the artificial neural network (ANN). Ragam and Nimaje (2018) developed an ANN model for predicting PPV using six input variables. Kosti et al. (2013) stated that the conventional predictors fail to provide acceptable prediction accuracy. They showed that a neural network model with four mine blast parameters as input could make significantly more accurate on-site predictions. Sayadi et al., (2013), using a database from Teheran Cement Company limestone mines, found that a neural network resulted in maximum accuracy and minimum error. Khandelwal and Singh (2009) developed an ANN model using 150 data records from an Indian coal mine with site-specific rock characteristics and geomining setting. Khandewal and Singh (2007) built a ground vibration prediction model for a magnesite mine using four prediction variables with 20 data records. Kamali and Ataei (2010) predicted PPV in the structure of the Karoun III power plant and dam using an ANN. El Hafiz et al. (2010) evaluated ground vibration predictors using data from a single-station seismograph at a limestone quarry in Egypt. ANN prediction models have been built for one Indian coal mine and one limestone mine. Using the findings of these initial studies, it is essential to enhance the application of ANN in various mines in different Indian coal mining

Development of a blast-induced vibration prediction model using an artificial neural network regions. In this investigation we collected 248 data records with 15 input variables from three mining regions. A combined database was built after randomization of the data. With the help of this database an ANN model was built and the final output obtained. The robustness of the model was tested by using data from other mines and using the model to predict PPV. Site-specific features, including fracture zones due to the presence of underground workings and fault planes, are expressed as a non-quantifiable variable by using an ordinal scale. Tests were carried out to establish whether the model prediction can be improved by using the nonquantifiable variable. Sensitivity analyses were performed by using input and output connection weights of the model. Also, 3D plotting was done to understand the interplay between two input variables, keeping the other variables equal to the mean values.

#,,A2<9@6 Blast-induced ground vibration often damages structures near a mine site. The intensity of mine blast-induced vibration must be predicted prior to blasting operations near any important surface structure. PPV can be considered as the representative indicator of ground vibration. It is a significant factor in control of structural damage (Bureau of Indian Standards, 1973; Kahriman, 2002; Singh and Singh, 2005; Bakhshandeh, Mozdianfard, and Siamaki, 2010; Khandelwal and Singh 2009). Khandelwal, Kumar, and Yellishetty (2011) observed that the empirical equations do not include physicomechanical parameters of rock mass, blast design, and explosive type, which are relevant for the calculation of PPV. The empirical method of PPV estimation is discussed here. The basic relationship between the variables is V=KWa Db, where W is the weight of explosive charge; D is the distance from the blast; V is the magnitude of vibration; and K, a, and b are constants whose values depend on the sitespecific geomining conditions. V is expressed as PPV (mm/s). The prediction equation derived by the US Bureau of Mines is V=K (D/Q0.5)-b. A plot of PPV as a function of scaled distance (D/Q.05) on a log-log scale gives a straight line for mine sites. To derive the values of K, a, and b for a particular site it is necessary to monitor test shots and plot PPV against scaled distance on a log-log scale. Ghasemi, Ataei, and Hashemolhosseini (2013) observed that since only a limited number of variables are considered for deriving empirical equations, the PPV predictions are not accurate enough for demarcation of a safe zone around a mine blast site. (Table I). The nature and intensity of blast-induced ground vibration is dependent on many factors, the most important of which are shown in Table II.

Artificial neural network (ANN) models can overcome some of the drawbacks of empirical models (Girish, 2007). ANN is a nonlinear self-adaptive approach without any prior assumptions about the interrelations between series of input variables. A back-propagation neural network (BPNN) is used as a learning algorithm for training a multilayer feedforward neural network. It provides a computationally efficient method for changing the weights in a feed-forward network, with different activation function units. Dey et al. (2016) designed an ANN model that consists of number of inputs, a single output, and an intermediate hidden layer. Training of the network is the process of learning when the error is calculated as the difference between the predicted output and actual output (target). As the error reaches a user-defined error tolerance limit, the training is stopped; otherwise the weights are readjusted by back-propagation. All inputs to a node are weighted independently, summed with bias, and fed into logistic or other nonlinear functions. The output is then connected to all neurons of the next layer. Sivaprasad et al. (2006) and Hornik, Stinchcombe, and White (1989) stated that an ANN could act as a universal approximation of nonlinear functions. Rahman et al. (2013) noted that an ANN can be trained to identify nonlinear patterns between input and output values of opencast blasting phenomena. Maqsood et al., (2002) affirmed that ANNs do not require any prior knowledge of the system under consideration and are well suited for modellng dynamic systems on a real-time basis. Huang and Foo (2002) and Scardi (2001) observed that an ANN can be used either where no precise theoretical model is available, or when uncertainty in input parameters complicates deterministic modelling. GarcĂ­a, RodrĂ­guez, and Tenorio (2011) observed that the ANN technique can also perform tasks based on training or initial experience, and does not need an algorithm to solve a problem. This is because it can generate its own distribution of the weights of the links through learning.

Table II

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Site constants k = 239.56 & b = 1.166 k = 1207.96 & b = 1.175 k = 2.195 & b = 3.389 k = 2.195 & b = 1.694

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Development of a blast-induced vibration prediction model using an artificial neural network Mohamad (2009) used several ANN models in the Assiut limestone mine in Egypt and concluded that increasing the number of input variables can improve the capability of an ANN to predict PPV. Monjezi, Ghafurikalajahi, and Bahrami (2011) developed an ANN model to predict PPV at the Siahbisheh pumped storage project in Iran, using the maximum charge per delay, the distance from the blasting face to the monitoring point, stemming, and hole depth as input parameters and compared their results with empirical models and multivariate regression analysis. Using artificial intelligence approaches Khandelwal and Singh (2007), Mohamed (2011), Kamali and Ataei (2011), and Singh and Singh (2005) predicted PPV using hole depth and diameter, number of holes, burden, spacing, and the distance from the blast face as inputs. They concluded that the ANN is a more accurate approach compared to regression analysis. Other researchers predicted PPV based on ANN models in different projects. Amnieh, Mozdianfard, and Siamaki (2010), Amnieh, Siamaki, and Soltani (2012), and Alvarez et al. (2012) compared the results of both ANNs and empirical models with multiple linear regression (MLR) analysis to establish the applicability of each method. A discussion on superiority of ANN modeling techniqueas is included in Appendix A. This subsection contains a brief discussion of the conceptual framework in ANN model-building. Based on the discussions so far an ANN model is built by training the network using input data from the study areas. Database development for model input is discussed in the next subsection. The training data constitutes 70% of the database. The network is trained in supervised manner with a back-propagation algorithm training a multilayered feedforward network. Initially, training data is preprocessed by normalizing input and output data. A flow chart of the model-building process is shown in Figure 1.

;=;(;7@A Mine blast-induced ground vibration (PPV) was recorded in three mechanized coal mines. Study area I is located in the Angul district in the state of Odisha. Study area II is located in the Raniganj coalfield, Burdwan District, in the state of West Bengal. Study area III is located in the North Karanpura coalfield, Chatra District in the state of Jharkhand. PPV data was collected from 140 blasts: 50 blasts in study area I, 36 in study area II, and 54 in study area III. The blasting pattern is described in Table III. SME explosive and a Nonel initiation system were used. Ground vibration was recorded using BLASTMATE III, manufactured by Instantel, Canada. Two instruments were stationed at distances of 40 m to 320 m from the blast site. PPV was recorded at different distances for each blast. Only those values above 1 mm/s were included in the database. Two hundred and forty-eight data records were obtained from 140 blasts. Input variables are presented in Table IV. These variables were selected based on the authors’ experience as well as a study of the relevant literature in the section ‘ANN model’. PPV data measured on the mine sites is described in Table V. The ANN technique can detect similarities between these

Table III

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140 248 150–160 3.50–6.50 3.0–4.50 3.5–5.0 15.10–70.10 70.10 7113.50

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input variables. This property gives it excellent interpolation capability, especially when the input data is noisy (not exact). An ANN is capable of calculating arithematic and logical functions, generalizing and transforming independent variables to dependent variables, parallel computations, nonlinear processing, handling of noisy data, function approximation, and pattern recognition (Sayadi et al., 2013). ANN can be applied to combat the problem of multicollinearity in the data (Hermosilla and Carpio, 2005). The correlation coefficient is widely used in statistics but correlation is a measure of the linear association between variables. If two variables are related in a nonlinear manner the correlation coefficient will not be able to do justice to the strength of relationship (Makridakis, Wheelwright, and Hyndman, 2005). The neural network is capable of capturing

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the interactions between the inputs, because of the hidden units are able to handle extreme nonlinearity. The nature of these interactions is implicit in the values of the weights. Therefore multicollinearity in the input data is not an issue for training a neural network. Further discussion of this aspect is presented in the review paper by Bhadesjia (1999).

*$" ($,)'#,#+$ "$$+&' The neural network toolbox of MATLAB 2015 was used to build an ANN blast-induced vibration model. The ANN architecture (Table VIII) has fifteen input variables, one hidden layer, and four nodes. Four nodes were selected since this gives a high R value (Table VII). The network was trained up to maximum epoch of 1000 and the error goal was set at 1e-7. In Figure 2 and Table IX the association between the PPV predicted by the model and the actual PPV measured in the field is 0.968, and the average relative error is 11.85. Therefore, the prediction capability of the model was deemed to be good and the model ready for use. An attempt was made to improve the model prediction by including nonquantifiable variables. Diverse structural features were observed during inspection of different mine sites. Some of the quantifiable rock parameters are included in Table III as input to ANN models. Site-specific structural features are mostly non-quantifiable variables, therefore an ordinal scale of 1 to 3 is used. To cite an example, if the site has minimum structural discontinuities then a value 1 is assigned, while 3 represents highly fractured and faulted strata (Table XI). However, further research on use of non-quantifiable variables as model input is essential. By including the above variable a marginal increase in R value from 0.968 to 0.975 was obtained. The model performance was also compared

Development of a blast-induced vibration prediction model using an artificial neural network Table VII

#,,A2<9@6A4@:3<:2;>5@A( A.;: ?>0A>82(@:AA<3A><9@7A +?99@>A><9@7

:;>73@: A=:;?>?>0A38>5=?<>

1 2 3 4 5

A :;?>?>0

A @7=?>0

A);6?9;=?<>A

A'.@:;66

Tansig/ Trainlm

0.95

0.92

0.92

Tansig/ Trainscg

0.89

0.86

0.85

0.94 0.87

Tansig/ Trainlm

0.97

0.94

0.93

0.95

Tansig/ Trainscg

0.92

0.90

0.89

0.90

Tansig/ Trainlm

0.97

0.94

0.94

0.95

Tansig/ Trainscg

0.94

0.91

0.92

0.92

Tansig/ Trainlm

0.97

0.96

0.97

0.97

Tansig/ Trainscg

0.96

0.94

0.95

0.95

Tansig/ Trainlm

0.97

0.94

0.94

0.95

Tansig/ Trainscg

0.94

0.91

0.92

0.92

Table VIII

,@= <: A;:5/?=@5=8:@ Number of input variables Number of nodes in hidden layer Number of hidden layers Transfer function

15 4 1 Hyperbolic tangent sigmoid transfer function (tansig) for hidden layer and Purelin for output layer

?08:@A :;4/?5;6A:@4:@7@>=;=?<>A<3A;5=8;6A;>9A4:@9?5=@9A.;68@7A <3A )

Table IX

@:3<:2;>5@A<3A#,,A2<9@6A 4@A<3A2<9@6

#,,

-86=?.;:?;=@A6?>@;:A:@0:@77?<>A

No. of observations Correlation coefficient Av. relative error (%)*

248 0.97 11.85

248 0.80 16.01

with the multivariate linear regression model (Table IX) and results show the benefits of an ANN for enhancing accuracy in model prediction. The robustness of the training was tested with different initialized states of the network parameters for the given architecture while holding other training parameters and algorithms constant. Typical results for the 15-4-1 architecture with the sigmodal transfer function and Levenberg-Marquardt training function retrained for five runs are presented in Table VI. The result shows significantly good convergence under repeated training with different initialized states, leading to a close variation in prediction performance. Twenty-eight new data records were used to examine the prediction performance of the ANN model. The output is compared with measured PPV in Table X. Also, the same database was used for predicting PPV by four empirical models (Table I). The reasons for selection of four out of several available empirical models are stated below. Scaled distance (maximum charge weight divided by the cube root or square root of actual distance) is used for deriving empirical formulae for Indian mines. The correlation coefficients between the predicted and measured PPVs in case of the ANN and empirical model are 0.96 and 0.67 respectively (Table X). Other formulae, which are not included in Table II, are based on inelastic effects, which cause energy losses during blast wave propagation. Inelastic attenuation of elastic waves is dependent on the geotechnical properties of the rocks. In case of other formulae, empirical constants are derived for specific geomining features and therefore they were not considered.

&@>7?=?.?= A;>;6 7?7

*The absolute error is the difference between the measured value and the true value. The relative error is defined as the absolute error relative to the size of the measurement.

Explicit knowledge about the interplay between different variables responsible for blast-induced ground vibration is largely lacking. To extract knowledge from the ANN model,

Table X

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$& -A

#2(:;7@ 7A;>9A+@>9:<>A

";>0@3<:7 ?/67=:<2A

>9?;>A&=;>9;:97A4:@9?5=<:A@ 8;=?<>

No. of observations Correlation coefficient Av. relative error (%)

28 0.96 11.8

28 0.67 37.3

28 0.63 38.47

28 0.68 37.89

28 0.74 35.19

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191

-<9@6A4@:3<:2;>5@A

Development of a blast-induced vibration prediction model using an artificial neural network Table XI

&?=@%74@5?3?5A7=:85=8:;6A3@;=8:@7A<3A=/@A5;7@A7=89 A;:@;7A @;=8:@7 Presence of fractures Faults Structural discontinuities expressed in ordinal scale

;7@A7=89 A;:@;A

;7@A7=89 A;:@;A

;7@A7=89 A;:@;A

Minor fracture planes

Highly fractured due to presence of underground working below opencast

Prominent fractures not found

Minor faults

Three major faults of throw 80–240 m

No major faults

1

3

2

?08:@A #5=8;6A 4:@9?5=@9A.;68@7A<3A )A87?>0A=:;?>?>0 A=@7= A.;6?9;=?<> A;>9A;66A9;=;

output sensitivity analysis was carried out. Sensitivity analysis is the method of studying a model by assessing the significance of each input variable on the model output. By this means it is possible to identify how different input variables influence the model output. A connection weight approach was adopted. Calculation of the product of the raw input-hidden and hidden-output will assign weights between each input neuron and output neuron. Summation of the products across all hidden neurons is done for finding out connection weights. Olden and Jackson, (2002) observed that the sensitivity analysis approach can determine the significance of the variables in the neural network. Negative connection weights represent inhibitory effects on neurons and decrease the value of the predicted response, whereas positive connection weights represent excitatory effects on neurons and increase the value of the predicted response. The sensitivity of the network is given by connection weights used in the network architecture by means of the following calculations. (i) Input-hidden-output connection weights: the product of input-hidden and hidden-output connection weights for each input and hidden neuron(Table XII); For Input I = 1 to j where j = no. of variables, hidden neurons h= 1 to i, where i = no. of hidden neurons

192

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and For Output O = 1 to o, where o = no. of outputs Weight of input variable j and hidden neuron i = Wji Weight of output o and hidden neuron i = Woi Contribution of each input neuron to the output via each hidden neuron is given by (Table XIII):

Cji = |Woi| x |Wji| ii.

iii.

[1]

Overall connection weight: the sum of the inputhidden-output connection weights for each input variable Relative importance (%) for each input variable based on Garson’s algorithm

Garson, 1991 gave the procedure for partitioning the connection weights to determine the relative importance of various inputs. The method essentially involves partitioning the hidden-output connection weights of each hidden neuron into components associated with each input neuron. i.

For each hidden neuron h, multiplying the absolute value of the hidden-output layer connection weight by the absolute value of the hidden input layer connection weight. This is donefor each input variable j. The following product Pji is obtained Table XIV: [2] The product Pji = |Woi| x |Wji

Development of a blast-induced vibration prediction model using an artificial neural network Table XII

Table XIV

-;=:? A5<>=;?>?>0A?>48=%/?99@>A;>9A<8=48=%/?99@> 5<>>@5=?<>A @?0/=7

:<985=A<3A/?99@>A?>48=%<8=48=A5<>>@5=?<>A @?0/=7

+?99@>A1

+?99@>A

+?99@>A?%1

+?99@>A?

W(1,1) W(2,1) W(3,1) W(4,1) W(j-1,1) W(j,1) W(0,1)

W(1,2) W(2,2) W(3,2) W(4,2) W(j-1,2) W(j,2) W(o,2)

W(1,i-1) W(21,i-1) W(3,i-1) W(4,i-1) W(j-1,i-1) W(j-1,i-1) W(0,i-1)

W(1,i) W(2,i) W(3,i) W(4,i) W(j-1,i) W(j,i) W(o,i)

Input 1 Input 2 Input 3 Input 4 Input j-1 Input j Output

IInput 1 Input 2 Input 3 Input 4 Input j-1 Input j

+?99@>A1

+?99@>A

+?99@>A?%1

+?99@>A?

P(1,1) P(2,1) P(3,1) P(4,1) P(j-1,1) P(j,1)

P(1,2) P(2,2) (3,2) P P(4,2) P(j-1,2) P(j,2)

P(1,i-1) P(2,i-1) P(3,i-1) P(4,i-1) P(j-1,i-1) P(j,i-1)

P(1,i) (2,i) P P(3,i) P(4,i) P(j-1,i) P(j,i)

Table XIII

Table XV

<>=:?(8=?<>A<3A@;5/A?>48=A>@8:<>A=<A=/@A<8=48=A @;5/A/?99@>A>@8:<>

@6;=?.@A5<>=:?(8=?<>A<3A@;5/A>@8:<>A=<A=/@ <8=0<?>0A7?0>;6A<3A@;5/A/?99@>A>@8:<>

+?99@>A1

+?99@>A

+?99@>A?%1

+?99@>A?

C(1,1) C(2,1) C(3,1) C(4,1) C(j-1,1) C(j,1)

C(1,2) C(2,2) C(3,2) C(4,2) C(j-1,2) C(j,2)

C(1,i-1) C(2,i-1) C(3,i-1) C(4,i-1) C(j-1,i-1) C(j-1,i-1)

C(1,i) C(2,i) C(3,i) C(4,i) C(j-1,i) C(j,i)

Input 1 Input 2 Input 3 Input 4 Input j-1 Input j

Input 1 Input 2 Input 3 Input 4 Input j-1 Input j

+?99@>A1

+?99@>A

+?99@>A?%1

+?99@>A?

Q(1,1) Q(2,1) Q(3,1) Q(4,1) Q(j-1,1) Q(j,1)

Q(1,2) Q(2,2) Q(3,2) Q(4,2) Q(j-1,2) Q(j,2)

Q(1,i-1) Q(2,i-1) Q(3,i-1) Q(4,i-1) Q(j-1,i-1) Q(j,i-1)

Q(1,i) Q(2,i) Q(3,i) Q(4,i) Q(j-1,i) Q(j,i)

Table XVI

&82A<3A?>48=A>@8:<>A:@6;=?.@A5<>=:?(8=?<>A<3A@;5/A/?99@>A>@8:<>A

Input 1 Input 2 Input 3 Input 4 Input j-1 Input j

ii.

+?99@>A1

+?99@>A

+?99@>A?%1

+?99@>A?

&82A&

Q(1,1) Q(2,1) Q(3,1) Q(4,1) Q(j-1,1) Q(j,1)

Q(1,2) Q(2,2) Q(3,2) Q(4,2) Q(j-1,2) Q(j,2)

Q(1,i-1) Q(2,i-1) Q(3,i-1) Q(4,i-1) Q(j-1,i-1) Q(j,i-1)

Q(1,i) Q(2,i) Q(3,i) Q(4,i) Q(j-1,i) Q(j,i)

S1 = (Q(1,1) + Q(1,2) +..+ Q(1,i) S2 = (Q(2,1) + Q(2,2) +..+ Q(2,i) S3 = (Q(3,1) + Q(3,2) +..+ Q(3,i) S4 = (Q(4,1) + Q(4,2) +..+ Q(4,i) Sj = (Q(j-1,1) + Q(j-1,2) +..+ Q(j-1,i) Sj = (Q(j,1) + Q(j,2) +..+ Q(j,i)

For each hidden neuron,Pjiis divided by sum for all the input variables to obtain Qji (Table XV). For example for Hidden neuron1,

Q(1,1)= P(1,1)/ (P(1,1) + P(2,1)+‌‌‌+ P(j,1)) Pji i.e. Qji = ∑ Pji

[3]

(iii) For each input neuron sum the product Sj formed from the previous computation Qji (Table XVI).

Sj = ∑ Qji iv.

[4]

Sj is divided by the sum for all the input variables and expressed in terms of percentage, which gives the relative importance or distribution of all output weights attributable to the given input variable.

Relative Importance Percentage =

Sj/ ∑Sj

[5] ?08:@A #:5/?=@5=8:@A<3A#,,A>@= <:

>< 6@90@A0;?>A( A7@>7?=?.?= A;>;6 7?7

sensitive to the above variables. A negative sign indicates an inverse relationship with blast-induced ground vibration. Other variables with moderate sensitivity are the blast design parameters like spacing, burden, and depth. It is common )'"$-*A11!

193

Weights are plotted on the y axis (Figure 4), which gives a measure of sensitivity from least to highest sensitivity. The graph shows that the maximum charge per delay and distance from the face have the highest sensitivity. This implies that blast-induced ground vibration (PPV) is highly

Development of a blast-induced vibration prediction model using an artificial neural network knowledge that some of the variables are related to blastinduced ground vibration but the degree of sensitivity of these variables is definitely knowledge gained from the model output. The details of sensitivity analysis are given in Table XVII and graphically represented in Figures 4 and 5.

A46<==?>0AA Three-dimensional shaded graphs were drawn to study the responses of blast-induced ground vibration to the changes in various input variables. Out of the fifteen variables, only values of two variables were changed at a time, and the remaining variables were kept constant at their mean values. In this manner a two-way interaction of variables or sensitivity is created. The purpose is to study how ground vibration is sensitive to the changes of these input variables.

A database was prepared with the input variables varying at regular intervals within the range of values collected from the mines. For one value of a variable there will be ten values of the other variable. Keeping the remaining variables at their mean values, the selected variables were varied at regular intervals. A 3D plot (Figure 7) was constructed using area to represent two input variables, distance of the monitoring station and charge per delay, and showing the model output PPV. The following conclusions can be drawn from the plot. (a) PPV is more sensitive to changes in distance between the blast site and the monitoring station at distances greater than 150 m. (b) PPV is more sensitive to charge per delay when distance between the blast site and the monitoring station is less than 150 m.

Table XVII

<>>@5=?<>A @?0/=7A;>9A:@6;=?.@A?24<:=;>5@A3<:A=/@A>@8:;6A>@= <: A2<9@66?>0A ,;2@A<3A.;:?;(6@7A No. of holes Hole diameter (m) Hole depth (m) Burden (m) Spacing (m) Charge length (m) Stemming length (m) Max. explosive charge/hole (kg) Charge per round (kg) Density of explosive (g/cm2) Young's modulus (GPa) Poisson's ratio P-wave velocity (m/s) Rock density (g/ cm2) Distance of monitoring point from face (m)

<>>@5=?<>A @?0/=7 Nh Hdia Hd B S Cl St Ed Et De E P Vp Rd DB

0.5 -0.5 0.4 0.3 0.5 0 -0.1 2.4 -0.05 0.05 0.4 -0.3 -0.2 0.05 -2.5

@6;=?.@A?24<:=;>5@A 6.08 6.15 4.85 3.45 5.97 0.04 1.4 29.25 0.88 0.45 4.63 3.53 2.33 0.64 30.33

* Variables shown in Figure 4

?08:@A &@>7?=?.?= A;>;6 7?7A<3A9?7=:?(8=?<>7A<3A?>48=%/?99@>%<8=48=A5<>>@5=?<>A @?0/=7

194

)'"$-*A11!

Development of a blast-induced vibration prediction model using an artificial neural network

?08:@A @6;=?.@A?24<:=;>5@A<3A2<9@6A?>48=A4;:;2@=@:7A<>A<8=48=A )

?08:@A A46<= A9?7=;>5@A<3A2<>?=<:?>0A7=;=?<> A5/;:0@A4@:A9@6; A ;>9A )

An ANN model was developed using fifteen variables covering blast design, rock characteristics, and the distance between the blast site and monitoring station as input variables. The ANN model was found to perform better than the conventional models. Thus far, application of the ANN modelling technique to predict blast-induced ground vibration is limited and the number of input variables taken into consideration is small. In this research an attempt was made to train the model at three coal mining sites across India, which are being operated under highly diverse geological and mining conditions. The predictive capability of the model was further tested with a new set of data. An attempt was made to gain knowledge about ground vibration by analysis of the model output. The findings will help engineers to design optimum blasting patterns for their mines. The paper provides a basis for future research on identification of some significant variables that can further enhance the performance of the prediction model. Also, the applicability of the model can be further expanded by extensively training the model using data from different opencast coal mining sites.

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#44@>9? A# @.?@ A<3A;.;?6;(6@A6?=@:;=8:@A<>A=/@A784@:?<:?= A<3A#,,7A<.@:A<=/@:A2<9@66?>0A=@5/>? 8@7A?>A4:@9?5=?>0A2?>@ (6;7=%?>985@9A0:<8>9A.?(:;=?<> The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site-specific and indirectly related to the physical, mechanical, and geological properties of the rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for the blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast-holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. For a blasting engineer it is essential to gain knowledge, which is otherwise very limited, about the interplay between the several blast variables, since a blasting engineer will have to design, by trial and error, an optimum blast-hole pattern to achieve a high powder factor and optimum fragmentation. In commonly used empirical models this knowledge base is not available as it is primarily based on two variables. ANN models are claimed to be better than empirical models. Also, in the case of empirical models it is not possible to predict PPV for new blast sites unless test shot are fired and the results are used for deriving two site constants. Some of the empirical predictors are highlighted in Table A.1

Table A.1

*24?:?5;6A2<9@67 &6A>< 1 2

@3@:@>5@A Duvall and Petkof (1959) Langefors and Kihlstrom (1963)

*24?:?5;6A2<9@6 v = k(R/Q

)

v = k(Q/R

)

Ambraseys and Hendron (1968)

v = k(R/Q

Nicholls et al. (1971)

v = 0.362D

5

IS 6922 (1973)

v = k(Q

6

Siskind et al. (1980)

v = 0.828D

)

−1.63

2/3

1.25

/R)

−1.32 b

1/2

v = k(R/Q

@3@:@>5@A Kahriman et al. (2006) Rai and Singh (2004)

*24?:?5;6A2<9@6 −1.432

v = 0.561D b

âˆ’Îą

v = kR− Qmaxe

−1.52

14

Nicholson (2005)

v = 0.438D

15

Rai et al. (2005)

Qmax = k(vD )

16

Ozer (2008) (sandstone)

v = 0.257D

17

Ozer (2008) (shale)

v = 6.31D

2 b

−1.03

−1.9 −1.69

18

Ozer (2008) (limestone)

v = 3.02D

1/2 −1

)

19

Ak et al. (2009)

v = 1.367D

1/2 −1

20

Badal (2010)

v = 0.29D

21

Mesec et al. (2010)

v = 0.508D

Pal Roy (1991)

v = n + k(R/Q

9

CMRI (1993)

v = n + k(R/Q

10

Kahriman (2002)

v = 1.91D

Kahriman (2004)

ÎąR

)− e−

8

11

13

1/3 −b

4

Ghosh and Daemen (1983)

12

2/3 b/2

3

7

&6A><

1/2 −b

)

−1.13

−1.59

−1.296 −1.37

−1.79

v = 0.34D

v is the PPV (m/s); D is the scaled distance (m/kg1/2), which is defined as the distance R (m) divided by the square root of explosive charge mass Q (kg) net equivalent charge weight, i.e. D = R/Q1/2; k and b are site-specific constants. Generally, site constants k and b are determined by regression analysis of blasting results. Peak particle velocity (PPV) is a function of the borehole pressure, confinement, charge weight, distance from blast area, manner of decay of compressive waves through the rock mass, and the firing sequence of adjacent holes. In the case of empirical predictors there is no uniformity in the predicted result since different predictors give different values of PPV for various amounts of allowable charge per delay in the same operating area. (Dowding, 1985; Khandelwal and Singh, 2007; Monjezi et al., 2011). Moreover, empirical methods are unable to incorporate the numerous factors that affect the PPV and their complex interrelationships, hence the need for other techniques, including artificial neural networks (Khandelwal, 2010). Table A.2 highlights some of the applications of the ANN technique for modelling some of the detrimental effects of blasting.

198

)'"$-*A11!

Development of a blast-induced vibration prediction model using an artificial neural network Table A.2

#,,A2<9@67 @3@:@>5@

@5/>? 8@

>48=

'8=48=

,< A<3A9;=;%7@=

Iphar, Yavuz, and Ak (2008) Bakhshandeh, Mozdianfard, and Siamaki (2010) Monjezi et al. (2011a) Khandelwal, Kumar, and Yellishetty (2011) Mohamed (2011)

ANFIS ANN ANN ANN ANN, FIS

DI, C ST, DI ,C ,N HD, ST, DI, C DI, C DI, C

PPV PPV PPV PPV PPV

44 29 182 130 162

Fisne, Kuju, and Hudaverdi (2011) Li, Yan, and Zhang (2012) Mohamadnejad et al. (2012)

FIS SVM SVM, ANN

DI, C DI, C DI, C

PPV PPV PPV

33 37 37

Ghasemi, Ataei, and Hashemolhosseini (2013) Monjezi et al. (2013a) Armaghani et al. (2013) Hajihassani et al. (2014b) Ghoraba et al. (2015) Khandelwal and Singh (2005) Mohamed (2011)

FIS ANN ANN-PSO ANN-ICA ANN ANN ANN, FIS

B, S, ST, N, C, DI C, DI, TC S, B, ST, PF, C, D, N, RD, SD BS, ST, PF, C, DI, Vp, E BS, DI, C, ST, HL DI, C DI, C

PPV PPV PPV PPV PPV AOp AOp

120 20 44 95 115 56 162

Khandelwal and Kankar (2011) Bakhshandeh, Siamaki, and Mohamadi (2012) Hajihassani et al. (2015) Monjezi et al. (2010) Rezaei et al. (2011) Monjezi et al. (2011) Monjezi et al. (2012) Ghasemi et al. (2012)

SVM ANN ANN-PSO ANN FIS ANN ANN-GA SVM, ANN

DI, C HD, S, B, N, D, ST, PF HD, S, B, ST, PF, N, DI, C, RQD HD, BS, ST, PF, SD, N, C, RD HD, S, B, ST, PF, SD, RD, C HD, BS, ST, PF, D, SD, C, B HD, S, B, ST, PF, SD, D, C, RMR HL, S, B, ST, PF, SD, D

AOp AOp AOp Flyrock Flyrock Flyrock Flyrock Flyrock

75 38 62 250 490 192 195 245

Mohamad et al. (2013) Armaghani et al. (2013) Monjezi et al. (2013b) Khandelwal and Monjezi (2013) Marto et al. (2014) Trivedi, Singh, and Raina, (2014) Ghasemi et al. (2014)

ANN ANN-PSO ANN SVM ANN-ICA ANN ANN, FIS

HD, BS, ST, PF, C, D, N, RD, SD S, B, ST, PF, C, D, N, RD, SD HD, S, B, D, C HL, S, B, ST, PF, SD RD, HD, BS, ST, PF, C, Rn B, ST, qI, q, Ďƒc, RQD HL, S, B, ST, PF, C

Flyrock Flyrock Flyrock Flyrock Flyrock Flyrock Flyrock

39 44 310 187 113 95 230

Armaghani et al. (2015)

ANN, ANFIS

BS, ST, PF, C, DI

PPV, Aop, Flyrock

166

B Blastability index B Burden BS Burden to spacing C Maximum charge per delay D Hole diameter DI Distance from the blasting face E Young’s modulus GA Genetic algorithm HD Hole depth HL Hole length ICA Imperialist competitive algorithm N Number of row PF Powder factor PSO Particle swarm optimization

R2 = 0.98 R2 = 0.99 R2 = 0.95 R2 = 0.92 R2ANN=0.94; R2FIS=0.90 R2 = 0.92 R2 = 0.89 R2SVM=0.89; R2ANN=0.85 R2 = 0.95 R2 = 0.93 R2 = 0.94 R2 = 0.98 R2 = 0.98 R2 = 0.96 R2FIS=0.92; R2ANN=0.86 R2 = 0.85 R2 = 0.93 R2 = 0.86 R2 = 0.98 R2 = 0.98 R2 = 0.97 R2 = 0.89 R2SVM=0.97; R2ANN=0.92 R2 = 0.97 R2 = 0.94 R2 = 0.98 R2 = 0.95 R2 = 0.98 R2 = 0.98 R2FIS=0.94; R2ANN=0.96 R2ANFIS=0.77; R2ANN=0.94 R2ANFIS=0.86; R2ANN=0.95 R2ANFIS=0.83; R2ANN=0.96

q Specific charge qI Linear charge concentration RD Rock density RMR Rock mass rating RQD Rock quality designation S Spacing Sb Subdrilling SD Specific drilling ST Stemming SVM Support vector machine TC Total charge Vp P-wave velocity Ďƒc Unconfined compressive strength

)'"$-*A11!

199

Khandelwal and Singh (2006) applied an ANN for predicting ground vibration by including relevant parameters for rock mass, explosive characteristics, and blast design. The ANN was trained by 150 data-sets with 458 epochs and 20 testing data-sets. The ANN was found to be superior to a conventional statistical relationship. The correlation coefficient determined by ANN for PPV was higher than that determined by statistical analysis. Khandelwal and Singh (2007) investigated the prediction of PPV at a magnesite mine in a tectonically active hilly terrain in the Himalayan region in India. This study established that the feed-forward back-propagation neural network approach seems to be the better option for predicting PPV in order to protect surrounding environment and structures. Khandelwal and Singh (2009) predicted PPV in a coal mine in India based on ten input parameters using an ANN technique. The network architecture was a three-layer, feed-forward back-propagation neural network with 15 hidden neurons. Input parameters were trained using 154 experimental and monitored blast records. Comparison of the results by using correlation and mean absolute error (MAE) for monitored and predicted values of PPV showed that that ANN results for the PPV were very close to the field data-sets compared to the conventional predictors and MVRA predictions. Monjezi et al. (2010) predicted blast-induced ground vibration using various types of neural networks such as multi-layer perceptron neural network (MLPNN), radial-basis function neural network (RBFNN), and general regression neural network (GRNN) at Sarcheshmeh copper mine, Iran. MLPNN gave the best results, with a root mean square error and correlation coefficient of 0.03 and 0.954 respectively. ANN, multivariate regression analysis (MVRA), and empirical, analysis were used by Kamali and Ataei (2010) to predict the blast-induced PPV at the Karoun III power plant and dam.

Development of a blast-induced vibration prediction model using an artificial neural network Table A.2

#,,A2<9@67A 5<>=?>8@9 The best model was the ANN, since its outputs were highly correlated to the measured and observed data. Tang et al. (2007) developed a backpropagation neural network model to predict the peak velocity of blast vibration. They considered the charge hole diameter, distance, depth, column distance between charge holes, line of least resistance, maximum charge of single hole, maximum charge weight per delay, stemming length, total charge, magnitude of relative altitude and explosive distance as input parameters and found the predicted results from the ANN closer to the measured values than those from empirical predictors. Singh and Singh (1995) studied the blast-induced ground vibration at Dharapani magnesite mine, Pitthoragarh Himalaya in India and predicted PPV using neural networks. The artificial intelligence method of simulating and predicting ground vibration due to blasting is accurate, reliable, practical, user-oriented, and easy to operate as well as useful for engineers (Lianjon, Guojian, and Yingxian, 2002). Neural network models provide descriptive and predictive capabilities and, for this reason, have been applied through the range of rock parameter identification and engineering activities (Hudson and Hudson, 1997). A number of conventional statistical, empirical equations and ANN systems have been employed by various researchers to predict rock fragmentation, ground vibration and air blast prior to blasting operations. However, the ANN is preferred over the other predictive techniques due to its ability to incorporate the numerous factors affecting the outcome of a blast among other advantages. The ANN model generated is site specific, the input parameters can be expanded to include mechanical and geotechnical rock parameters such as rock strength, RQD, rock hardness, number of joints etc. to provide the ANN model a wider application. The artificial intelligence method of simulating and predicting blasting is accurate, reliable, practical, user-oriented, and easy to operate as well as useful for engineers (Lianjon, Guojian, and Yingxian, 2002). Cai and Zhao (1997) discussed ANN applications and incorporated the numerous factors that affect the PPV and their complex interrelationships. Chakraborty et al. (2004) underlined the effectiveness of multilayer perceptron (MLP) networks for estimation of blasting vibration and proposed a fusion network that combines several MLPs and on-line feature selection technique to obtain more reliable and accurate estimations compared with the empirical predictors. Mohamed (2009) used three different variations of ANN for prediction of PPV due to blast-induced ground vibrations and observed that the ANN using a large number of inputs parameters gave better prediction of PPV than an ANN using one or two inputs parameters. Also, the ANN model with two input parameters provided better results than the model with one input parameter. That is to say, increasing the number of input variables improves the ability of the ANN to learn and to predict more precisely. Singh (2005) attempted to predict the ground vibration in Indian coal measure rocks using ANN and confirmed that the network provided better results than a conventional multivariate regression method. PPV is generally used to assess potential blast vibration damage to structures on surface, but this parameter cannot fully explain the effect on structures situated far from the point of blasting and which can be damaged even at very low PPVs. (Ramchandar and Singh, 2001; Singh, Singh and Singh, 1994). The longer wavelength and smaller amplitude of these vibrations damage structures more severely than higher amplitude, shorter wavelength vibrations (Perrson, 1997). Maity and Saha (2004) used a neural network to assess damage in structures due to variation of static parameters. Using ANN, P-wave velocity and anisotropic property of rocks were investigated by Singh et al. (2004a). Lu (2005) studied the blastinduced ground shocks using an ANN at underground mines. Singh and Singh (2005) studied the dynamic constants of rock mass with a neural network and neuro-fuzzy system. This literature review provides a broad overview of the wide application of the ANN modelling technique. The observations quoted by several researchers, as discussed above, establish the superiority of ANN as modelling technique in predicting blast-induced ground vibration .

SAMCODES CONFERENCE 2019 Best Practice An Industry Standard for Mining Professionals in South Africa

8–9 October 2019 Birchwood Hotel & OR Tambo Conference Centre, Johannesburg OBJECTIVES The conference provides Competent Persons and Competent Valuators the opportunity to prepare and present details of recognised standards and industry benchmarks in all aspects of the SAMREC and SAMVAL Codes. These contributions will be collated into a Companion Volume to provide a guideline and industry standard for the public reporting of Exploration Results, Mineral Resources and Mineral Reserves and the Valuation of Mineral Projects. The conference will provide a wide range of information pertaining to industry best practice including aspects of a various geological deposit types, commodities, permitting and legal obligations, resource estimation, mining engineering methodologies, metallurgical and process arrangements, engineering/infrastructure design, social and environmental factors etc for SAMREC Code reporting. Other papers will cover the application of the various methods of valuation and where and when they should be applied in accordance with the SAMVAL Code.

For further information contact: Camielah Jardine, Head of Conferencing, SAIMM, E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za

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http://dx.doi.org/10.17159/2411-9717/2019/v119n2a12

Changes in subsidence-field surface movement in shallow-seam coal mining by X-J. Liu* and Z-B. Chengâ€

Intensive mining of shallow coal seams tends to result in severe subsidence of the ground surface. Surface subsidence is a complex dynamic spatiotemporal process that constantly changes as mining advances. With Liangshuijing coal mine as a case study, 3DEC numerical software was used to simulate the development of the moving subsidence field from open-off cut to full subsidence on the working face. The results demonstrate that the formation of a subsidence field is directly related to the coal seam depth H and the extent of the mine workings. Taking the advance distance of the working face as the unit of scale, the process of surface subsidence can be divided into four stages: micro-change (H/π), the development of surface subsidence (2H/π), the formation of a movement subsidence field (3H/π), and dynamic balance (4H/π). The changing movement of a subsidence field can be fully described by the characteristics of surface subsidence value, curve slope, and the inflection position. The results provide important technical support for predicting surface subsidence and the temporal–spatial evolution features of mining subsidence. coal seam, subsidence, surface movement, mining.

China’s coal output was about 3.52 Gt in 2017 (China National Coal Association, 2018), making it the largest coal producer in the world, accounting for almost half of total world coal production. Coal production and distribution play a vital role in the energy industry of China. However, entire overlying strata, from coal seam to surface, are disturbed as a result of long-term, high-intensity and large-scale mining, causing surface subsidence. This results in groundwater loss, surface desertification, vegetation loss, and mud-rock flows. These issues have become topics of common concern throughout society. According to related research results (Liu 2008), the area of surface subsidence caused by coal mining in China has reached about 600 000 ha, some 0.2 ha (and up to 0.42 ha) of surface subsidence for every 10 000 t of coal produced. Surface subsidence disasters not only damage buildings, water conservancy, transportation infrastructure, and farmland, but also cause adverse effects on individual and community lifestyles, environmental

* State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing, China. †Department of Ground Engineering, School of Engineering, University of Warwick, Coventry, United Kingdom. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Nov. 2017; revised paper received Aug. 2018.

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hygiene, and economic development. Deterioration of the ecological environment in mining areas has a long history and is very harmful (Lu, 2015). Surface subsidence is an extremely important control index for underground excavations. Many experts and scholars have devoted resources to the research and remediation of damage caused by coal mining from the surveying, numerical simulation, mathematical, mechanical, geological, and mining engineering points of view. These studies have revealed general laws of mining subsidence (Hu, 2012). The most prominent characteristic of these is that surface subsidence has an obvious time-dependency, and may continue for several months, or even years, from the start to the end of surface movements. Since the 1970s, with the wide application of computer methods, numerical simulation has been increasingly applied in the calculation of mining subsidence and the analysis of subsidence mechanisms, and many experts and scholars have undertaken research in this field, such as Dahl and Choi (1981). The West German scholar Kratzsch (1974) summarized methods of predicting coal mining subsidence in his book ’Mining Damage and Protection’. Ma and Yang (2001) researched the spatial and temporal effects of rock movement using the discrete element method. Cui and Deng (2017) carried out a real-time displacement analysis of surface movement and deformation for the main section of a coal mine, and studied mining subsidence utilizing a rheological model. Surface subsidence continually changes during the exploitation of a mining face;

Changes in subsidence-field surface movement in shallow-seam coal mining

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therefore it is beneficial to understand its spatial and temporal evolution by adopting the combined research models of excavation, strata movement, and surface subsidence. Moreover, studying the development and changing trends of surface movement and the subsidence field during coal mining has great scientific significance with respect to responsible exploitation of underground mineral resources and reduction of surface subsidence and environmental damage.

3.,03,.71 2+ 1,.+407 1,#16)7507 Underground engineers assume different structural models and propose diverse structural hypotheses of surface subsidence based on their own research and practical experience. A surface subsidence basin can take three main forms, as shown in Figure 1 in plan view, depending on the ratio of the length of the working face (B) to the advance distance (L). If the distance of advance of the working face (L) is less than the length, the surface subsidence basin is elliptical. As the distance of advance of working face increases, the shape of the surface subsidence is transformed from elliptical to circular when L is equal to B, and then back to an ellipse when L exceeds B.

The maximum surface subsidence occurs in the middle of the goaf. Therefore, the surface displacement curve, which is shown in Figure 2, can be obtained by taking the distance of advance of the working face as the X axis and the central axis (Y axis) of the surface subsidence basin as a vertical section. Surface subsidence changes as the coal seam is mined, thus the surface subsidence curve differs with the intensity of excavation. The entire process of surface subsidence can be divided into stages from W1 to W12, as shown in Figure 2. According to the surface subsidence value and characteristics of the curve, the evolution of surface subsidence can also be divided into four major stages: the micro-change stage, the development of surface subsidence, formation of a moving subsidence field, and dynamic balance. The first stage (I) represents micro-changes, represented by curves W1, W2, and W3 in Figure 2. If the advance distance of the working face is less than H/Ď€, then surface subsidence is not significant. An advance distance of H/Ď€ can therefore be considered as heralding the onset of surface subsidence. The second stage (II) is the development of surface subsidence, represented by curves W4, W5, and W6. In this stage, the advance distance usually varies from H/Ď€ to 2H/Ď€. The rate of surface subsidence is greater than that in stage I; therefore, surface subsidence increases significantly as excavation progresses. An advance distance of 2H/Ď€ can be considered as the critical point, at which the surface enters into full subsidence. When the advance distance increases from 2H/Ď€ to 4H/Ď€, the moving subsidence field can be regarded as being in the third stage (III) in the overall process of surface subsidence, such as represented by the curves W7, W8, and W9. In this stage, the curve of surface

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Changes in subsidence-field surface movement in shallow-seam coal mining subsidence behind the goaf is no longer affected by mining and is stable on the goaf side. Moreover, if the deviation of the inflection point of curve S1 is equal to the mining height (M), a moving subsidence field in front of the goaf is gradually formed until the advance distance increases to 4H/π, at which time a basin begins to form. If the advance distance exceeds 4H/π, the dynamic balance stage (IV) is established, represented by the curves W10, W11, and W12. The moving subsidence field of the working face continuously moves forward as mining progresses. The area distribution is shown as a red dotted outline in Figure 2. In this stage, surface subsidence reaches full subsidence and the basin begins to sink. The length and width of the basin can be calculated by dt = LT – 4H/π and db = max(B – H, B/2) respectively, in the direction of the working face arrangement. The deviation of the inflection point of the lateral surface subsidence curve, which can be calculated from S3 = πBM/(2H), is stable on the goaf side. The inflection point of the surface subsidence curve ahead of the working face can be calculated from S2 = (5~12)M. The inflection point fluctuates on the goaf side as the working face advances. A magnification of the curve representing the moving subsidence field is shown in Figure 3. This can be regarded as an S-curve distribution and conforms to the DoseResp curve characteristics. The formula of this curve is given by

where A1 represents the maximum value of surface subsidence, A2 is the subsidence value of the surface boundary point, LOGx0 is the deviation of the inflection point, and p is the slope of the curve. Parameters A1, A2, and LOGx0 can therefore be used to characterize the evolution characteristics of surface subsidence.

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:,*7.604( 16*,(43625 *2)7( The geological and mining technology conditions of the 42108 working face of Liangshuijing coal mine, China, were combined with borehole data to simulate the process of overall rock movement from open-off mining to full subsidence. The three-dimensional discrete element simulation software 3DEC was employed. The numerical simulation model is shown in Figure 4. The length and width of the model were 500 m and 400 m, respectively. The burial depth of the coal seam was 120 m. The section is shown in Figure 5, indicating that there are seven lithological types, from top to bottom: aeolian sand (20 m), mild clay (32 m), medium coarse-grained sandstone (20 m), medium-grained sandstone (24 m), finegrained sandstone (7 m), coal seam (3 m), and siltstone (10 m). The strata overlying the coal seam have a total thickness of 106 m. The lithology of the roof is mainly siltstone and fine sandstone, medium-grained sandstone, and mudstone; the immediate roof is mudstone. According to this lithology, the roof can be regard as being of medium stability (Class II) and of Type 2. The lithology of the floor is mainly siltstone, fine-grained sandstone, and mudstone. The physical and mechanical parameters of these rocks were evaluated using laboratory tests, and the values

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Changes in subsidence-field surface movement in shallow-seam coal mining Table I

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10

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18

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24

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19

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Fine-grained sandstones

0.9

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Coal seam 4-2 coal seam

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employed in the numerical simulation model are shown in Table I. The Mohr–Coulomb failure criterion was adopted. According to Peng (2006), there is a critical value of the working face width (B) that is linearly related to mining depth (H) when the ground surface is fully subsided. Therefore: B = 100 + 1.048H = 212 m. Because the length of this model was 500 m and the width of the boundary coal pillars on both sides was 80 m, the advance distance was 340 m, which is approximately π times the mining depth (H). From the above result, it can be seen that the length of the working face was larger than the critical length of full mining.

,.+407 1,#16)7507 %4.4*737. 454( 161 The concave (internal) and convex (external) portions of the surface subsidence curves are separated by inflection points. These are situated where the slope of the surface subsidence curve reaches a maximum and the curvature is zero. The inflection point occurs at the junction of the coal body and goaf under ideal conditions; in fact, the inflection point position varies as the stope advances and finally stabilizes at

the side of the goaf. Therefore, to discuss changes in the inflection point of the subsidence curve in detail, the front side of the advancing goaf was defined as the front inflection point of the surface subsidence curve; similarly, the back and lateral side of the advancing goaf were defined as the back and lateral inflection points, respectively. Changes in the inflection point of the surface subsidence curve as the working face advances are shown in Figure 6. The red curve in Figure 6 indicates the change of the inflection point in front of the goaf with advance of the working face. Overall, the location of the inflection point at the side of the goaf showed an approximately linear relationship with the advance distance of the working face during the periods of micro-change, development, and formation of the movement subsidence field. However, when the advance distance exceeded 4H/Ď€, which represents the dynamic balance stage, the location of the inflection point was in homeostasis and fluctuated between 5M abd 12M on the goaf side due to the influence of the moving abutment pressure in front of the working face. Similarly, the black curve illustrates the change of the inflection point at the back of the goaf with advance of the

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Changes in subsidence-field surface movement in shallow-seam coal mining working face. The starting position of the inflection point was ten times the mining height on the side of the coal body behind the open-off cut. As the working face advanced, the inflection point became offset to the side of the goaf. When movement of the subsidence field remained at the stage of micro-change and development (i.e., the advance distance was less than 2H/Ď€), the location of the inflection point was always stable at the side of the coal body; its location transfers to the side of the goaf during the stage of movement of the stress field. Finally, when the advance distance exceeded 4H/Ď€, the inflection point position stabilized at 3 m on the side of the goaf in the dynamic equilibrium stage. The blue curve shows changes of the inflection point lateral to the goaf with advance of the working face. Because the length of the working face was much larger than the advance distance in the initial stage, the deflection velocity of the lateral region was obviously greater than that of the front and back, thus the inflection point was transferred from the coal body to the goaf in the micro-change stage, and then to deep in the goaf in the development stage. Finally, the location of the inflection point returned some distance and stabilized at 7 m, which can be calculated by the formula (Ď€BM)/(2H).

The process of surface subsidence caused by underground mining is a complex space-time phenomenon. In theory, the amount of subsidence is equal to the increase in underground space; in fact, surface subsidence does not have the same value at different positions of the goaf due to the continuous advance of the working face. The changes in the amount of subsidence of the boundary point and the maximum subsidence of the surface are shown in Figure 7 from the open-off cut to a position at which the advance distance of the working face is equal to its length (L=B). The maximum subsidence of the surface caused by excavation is shown as the green and pink curves in

Figure 7. These curves show that the peak value of subsidence bore little relation to the length of the working face and the amount of subsidence increased with advance of the mining distance. The peak value of surface subsidence no longer increased and began to stabilize when the advance distance reached 4H/Ď€ and movement of the subsidence field occurred. Similarly, the subsidence increased with working face mining in the stages of micro-change and development to the back and front boundary points of the excavation. However, the back boundary point was static, so the subsidence increased to a certain extent and was then no longer influenced by mining when the advance distance exceeded 2H/Ď€, which meant that the initial subsidence field had formed. For the front boundary point, the next value fluctuated and the subsidence value was always lower than that of the rear value because of moving abutment pressure. Regarding the lateral boundary settlement, there was no change when the advance distance reached 160 m, which was less than the empirically calculated critical value of about 240 m, because the length of working face (240 m) was larger than 4H/Ď€. Therefore, if the size of the excavation is less than 2H/Ď€, the ground surface does not form a movement subsidence field; if the excavation size is less than 4H/Ď€, a movement subsidence field can form. However, the working face can reach the full mining extent and the moving stress field can enter the dynamic balance stage without the advance distance reaching the length of the working face once the excavation size exceeds 4H/Ď€.

The distribution and process of change of a surface subsidence curve caused by coal seam mining can be represented by the slope of the subsidence curve. The changes in slope of the surface subsidence boundary curve about the working face are shown in Figure 8 for the open-off cut to an advance distance of the working face equal to its length. The slopes on the front, back, and lateral side of the goaf can indicate changes in the surface subsidence curve. The curve obviously underwent fluctuation, which meant that

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surface subsidence exhibited a non-uniform velocity variation as the working face advanced. When the width of the working face exceeded 4H/Ď€, the slope of the lateral surface subsidence curve always increased until it stabilized. The slope of the subsidence curve is affected by mining and can fluctuate in the micro-variable stage. It then constantly increases in the development stage, finally reaching a peak value before decreasing again during the stage of subsidence field formation. The slope therefore displays significant variation when the advance distance is 2H/Ď€. With continuous advance of the working face, the slope decreased until the advance distance equalled 4H/Ď€, at which point the slope tended to stabilize. The slope of the subsidence curve on the front side of the surface rises sharply from the open-off cut, reaches a peak value when the advance distance is 2H/Ď€, then drops continuously until the advance is 4H/Ď€ where it reaches a local minimum; however, it begins to increase again during the full mining stage. In summary, the slope of the front subsidence curve is in a weak fluctuation and cannot be stabilized due to the influence of the working face excavation. The slope of the surface subsidence curve at the back of the goaf is always greater than that of the lateral side, which indicates that the slope in the advancing direction is always larger than that of inclination.

250(,1625 Surface subsidence is a gradual process of formation and development, the characteristics of which are related to burial depth and the dimensions of the excavation. The distances of influence of the movement subsidence field at the front of the coal body and the side of goaf are H/Ď€ and 2H/Ď€, respectively. The width of the basin can be calculated by reducing the working face advance distance by 4H/Ď€. According to the ratio of burial depth to advance distance and the characteristics of the movement subsidence field, the evolution of the surface subsidence curve can be divided into four stages: the micro-change stage, the development of surface subsidence, formation of a moving subsidence field, and dynamic balance. When the burial depth, width of the

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working face, and advance distance are designated H, B, and L, respectively, the distance of the micro-change stage varies from zero to H/Ď€, that of the development of surface subsidence varies from H/Ď€ to 2H/Ď€, the formation of the moving subsidence field varies from 2H/Ď€ to 4H/Ď€, and the range of the dynamic balance stage exceeds 4H/Ď€. The width of the basin that is formed by surface subsidence is the maximum value of both (B – H) and B/2 or (L − 4H/Ď€). These results can provide scientific guidance for green mining and ecological mitigation and control.

80'52&(7)-*753 This research is funded by the National Key Basic Research Program (2016YFC0501100). The authors are grateful to their State Key Laboratory of Water Resource Protection and Utilization in Coal Mining for their help and Kathryn Sole, PhD, for editing the English.

7+7.75071 China National Coal Association. 2018. Annual report on China's coal industry in 2017. Choi, D.S. and Dahl, H.D. 1981. Measurement and prediction of mine subsidence over room and pillar in three dimensions. Proceedings of the Workshop on Subsidence due to Underground Mining. Peng, S.S. (ed.). West Virginia University, Morgantown, WV. pp. 34–47. Cui, X.M, and Deng, K.Z. 2017. Research review of predicting theory and method for coal mining subsidence. Coal Science and Technology, vol. 45. pp. 160–169. Hu, H.F. 2012. Research on surface subsidence regularity and prediction under composite media with different thickness rations of loose bedrock. PhD thesis, Taiyuan University of Technology. Kratzch, H 1983. Mining Subsidence Engineering. Springer-Verlag, Berlin, Heidelberg. Liu, Z. 2008. Research on the deformation rule of surface movement caused by mining under the conditions of thick alluvium and hard solid rock. Master's thesis, Xi`an University of Science and Technology. Lu, T.T. 2015. Surface change detection using InSAR technology. Master's thesis, Chang'an University. Ma, F.H. and Yang, F. 2001. Research on numerical simulation of stratum subsidence. Journal of Liaoning Technical University Natural Science, vol. 20. pp. 257–261. Peng, S.S. 2011. Longwall Mining, 2nd edn. Science Press, Beijing.

http://dx.doi.org/10.17159/2411-9717/2019/v119n2a13

Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process by S.P. du Preez*, J.P. Beukes*, D. Paktunc†, P.G. van Zyl*, and A. Jordaan*

The chromium (Cr) content of stainless steel originates from recycled scrap and/or ferrochrome (FeCr), which is produced mainly by the carbothermic reduction of chromite ore. The oxidative sintered pellet production process is one of the most widely applied FeCr processes. The supplier of this technology specifies that recycling of chromite-containing dust collected from the pellet sintering off-gas and fines screened out from the sintered pellets (collectively referred to as pre-oxidized chromite fines) should be limited to a maximum of 4 wt% of the total pellet composition. However, the results presented in this paper suggest that recycling of such fines up to a limit of 32 wt% of the total pellet composition may improve the compressive and abrasion strengths of the cured pellet. In addition, electron microprobe and quantitative X-ray diffraction (XRD) analyses demonstrate that chromite grains present in the pre-oxidized chromite fines consist, at least partially, of crystalline phases/compounds that will improve the metallurgical efficiency and specific electricity consumption (i.e. MWh/ton FeCr produced) of the smelting process. Chromite, ferrochrome, ferrochromium, pre-oxidation, oxidative sintering, recycling.

Stainless steel is a vital modern-day alloy that is well-known for its corrosion resistance, which is mainly due to the inclusion of chromium (Cr) (ICDA, 2013). Stainless steel is mostly produced from recycled scrap and ferrochrome (FeCr), a relatively crude alloy of Cr and iron (Fe). FeCr is predominantly produced by the carbothermic reduction of chromite, a mineral belonging to the spinel group characterized by the unit formula [(Mg,Fe2+)(Al,Cr,Fe3+)2O4] (Haggerty, 1991; Paktunc and Cabri, 1995; Tathavakar, Antony, and Jha, 2005). Although Cr can occur in 82 different minerals, chromite is the only source of new Cr units that can be exploited in commercial volumes (Motzer and Engineers, 2004). Approximately 95% of mined chromite is used in the production of various FeCr grades, of which high-carbon and charge grade FeCr are the most common (ICDA, 2013). Beukes et al., (2017) recently presented a review of FeCr production processes.

* Chemical Resource Beneficiation (CRB), NorthWest University, Potchefstroom, South Africa. †CanmetMINING, Natural Resources Canada, Ottawa, Canada. Š The Southern African Institute of Mining and Metallurgy, 2019. ISSN 2225-6253. Paper received Dec. 2017; revised paper received Aug. 2018.

207

According to this review, FeCr is principally produced in (i) conventional open/semi-closed submerged arc furnaces (SAFs) that are mainly fed with lumpy (typically 6–150 mm) chromite ore, fluxes. and reductants, (ii) closed SAFs that are fed with oxidative sintered chromite pellets, as well as lumpy reductants and fluxes, (iii) closed SAFs fed with prereduced chromite pellets, as well as lumpy reductants and fluxes, and (iv) closed direct current (DC) arc furnaces fed with fine (typically 6 mm) chromite ore, fluxes, and reductants. Of particular interest for the present paper is the oxidative sintered pellet process, which is commercially known as the Outotec steel belt sintering process (Outotec, 2017). Various authors have previously described the oxidative sintered pellet production process (Riekkola-Vanhanen, 1999; Beukes, Dawson, and van Zyl, 2010; Basson and Daavittila, 2013). Chromite fines (typically 1mm) are wet milled together with a small percentage of a carbonaceous material that serves as an energy source during sintering. The grain size specification that must be obtained during milling is typically a d80 of 74 Οm (80% passing size of 74 Οm). Ceramic filters are used to dewater the milled slurry to a moisture content of below 9%. A fine clay binder (usually refined bentonite) is then mixed into the moist filter cake with a high-intensity mixer, and the moist mixture is pelletized in a pelletizing drum. The newly formed pellets are screened on a roller screen. Oversized pellets are broken down and recycled, together with undersized pellets. This leads to fairly homogeneously sized green (uncured) pellets with an average diameter of approximately 12 mm. The green pellets are then layered on a

Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process perforated steel belt, which carries the pellets through a multi-compartment sintering furnace. The perforated steel belt is protected against excessive temperatures by a layer of previously sintered pellets below the green pellets. The temperature of the pellet bed gradually increases as it passes through the sintering furnace, reaching a maximum of approximately 1400 to 1500°C. Sintered pellets are then cooled by air blown through the pellet bed from below. Thereafter, the sintered pellets are discharged and screened to remove < 6 mm material, but screening at a smaller aperture has also been observed by the authors during plant visits at FeCr producers applying this process. The overall process produces porous and mechanically strong pellets suitable for SAF smelting (Riekkola-Vanhanen, 1999), which results in improved SAF stability and metallurgical efficiency, as well as lower energy consumption compared with conventional SAF smelting of lumpy ore (Basson and Daavittila, 2013). The suppliers of the steel belt sintering technology indicated that chromite-containing dusts collected from the pellet sintering scrubber and fines screened out from the sintered pellets can be re-introduced into the moist material mixture that is pelletized (Basson and Daavittila, 2013). However, it is specified that the addition must be limited to a maximum of 4 wt% of the total pellet composition, since this material is believed to adversely affect pellet quality (Basson and Daavittila, 2013). The fundamental reasoning behind this limitation is not stated in the public peer-reviewed domain. A recent personal communication indicated that specifically, pellet strength deteriorates with increasing content of recycled pre-oxidized fines (Päätalo, 2018). This can be countered by increasing the clay binder and carbon contents in the green pellets. However, an increased carbon content could lead to higher sintering temperatures, which may result in a shorter operational life of the steel sintering belt (Päätalo, 2018). The authors were approached by a FeCr producer at which the generation of pre-oxidized fines, originating from the sintering scrubber and screening of the sintered pellets, exceeds the 4 wt% limitation. This has resulted in the accumulation of pre-oxidized fine chromite stockpiles. Preoxidized chromite requires less energy to metallize compared to normal chromite (Kapure et al., 2010). Furthermore, the reduction of oxidative sintered pellets requires less energy (Zhao and Hayes, 2010) and pre-oxidation of chromite fines significantly improves prereduction (solid-state reduction) of chromite (Kleynhans et al., 2016, 2017). Considering these energy-related benefits, the authors believe that all preoxidized fine chromite should be recycled on-site at FeCr producers. With this in mind, the possible recycling of preoxidized chromite fines into oxidative sintered pellets beyond the current limitation of 4 wt% pellet composition was investigated, considering both fundamental and practical aspects.

478564-1943/9)87.2/1 A metallurgical grade chromite ore sample was received from a large FeCr producer in South Africa, which served as the case study ore in this investigation. To obtain a

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homogeneous, representative sample, a bulk sample of approximately 320 kg was taken by manually collecting shovel loads of material at various points and various heights of the ore stockpile. The bulk sample was then split at the laboratory of the FeCr producer three times with a riffle-type sample splitter to yield a sample of approximately 80 kg, which was transported to the laboratories at the Chemical Resource Beneficiation (CRB) unit, North-West University. There, the sample mass required for a specific experiment was obtained by splitting the sample further using a vibrating feeder that fed a rotating tray of containers. Previously, Glastonbury et al., (2015) presented a detailed characterization of this case study ore, therefore such a characterization is not repeated here. In short, the ore contained 44.19 wt% Cr2O3, 24.68 wt% FeO, 14.71 wt% Al2O3, wt% 10.31 MgO, and 2.96 wt% SiO2, and had a Cr/Fe ratio of 1.58 (Glastonbury et al., 2015). This composition is representative of a typical South African metallurgical grade chromite ore (Cramer, Basson, and Nelson, 2004). It has to be considered that a substantial fraction of the chromite smelted outside South Africa originates from South Africa. In 2012 for instance, 28% of the chromite ore consumed in the rest of the world originated from South Africa (Kleynhans et al., 2016). Therefore, the results generated from the case study ore are of international relevance. A sample of chromite fines screened out from industrially produced sintered pellets was obtained from a large FeCr producer in South Africa that utilizes the oxidative sintered pellet production process. In this paper, this material is referred to as ‘pre-oxidized chromite’. This material was sampled in a similar manner as the ore. Refined bentonite (binder) and coke fines (carbon fuel source), which were used to produce oxidative sintered pellets at the aforementioned FeCr producer, were also obtained. Kleynhans et al., (2012, 2017) previously presented full characterizations of the bentonite and coke fines (sample Co5 in Kleynhans et al., 2017), therefore this information is not repeated here.

Material for pelletization experiments was prepared by dry milling a 50 g mixture, consisting of 1.5 wt% coke, with chromite the balance, for 2 minutes in a Siebtechnik laboratory disc mill. Prior to milling, all materials were dried in an oven overnight at 75°C to enable accurate and repeatable batching of the mixtures. A tungsten carbide grinding chamber was used to avoid possible Fe contamination. This milling procedure yielded a particle size distribution with a d80 of 75 Οm, which is similar to the industrial particle size specification, i.e. d80 of 74 Οm (Basson and Daavittila, 2013; Glastonbury et al., 2015). After milling, refined bentonite was added to obtain a mixture with a composition of 97.52 wt% ore, 1.49 wt% coke, and 0.99 wt% bentonite. The pelletization mixture was then vigorously blended for 15 minutes using a laboratory-scale Eirich-type mixer. As-received pre-oxidized chromite was then added to the pelletization mixture to obtain mixtures containing 4, 8, 16, 32, and 64 wt% pre-oxidized chromite. No additional coke or bentonite was added to these mixtures to compensate for the addition of pre-oxidized chromite, since this is also the practice in industry.

Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process

As previously stated, Basson and Daavittila (2013) indicated that industrially produced oxidative sintered pellets are heated to maximum temperatures of between 1400 and 1500°C. Glastonbury et al., (2015) mimicked the industrially applied process on the laboratory scale, with a temperature profile that attained a maximum temperature of 1400°C. However, these authors did not verify how deep into the pellets oxidation proceeded. Zhao and Hayes (2010) proved that only the outer chromite grains of industrially produced oxidative sintered pellets are partially oxidized, while the chromite grains deeper into the pellets are not oxidized. Heat transfer to pellets in laboratory conditions, where small amounts of pellets are sintered in batches with very effective furnaces, will be significantly superior to that in industrial furnaces, where very large quantities are treated continuously. Therefore, rather than blindly selecting a maximum sintering temperature, or applying the temperature profile used by Glastonbury et al., (2015), the authors tested various maximum temperatures. A Lenton Elite camber furnace (UK, Model BRF 15/5) with a programmable temperature controller was used for sintering. The temperature probe of this furnace was situated inside the heating chamber, which ensured that the recorded temperature and the actual pellet temperatures correlated well. Prior to oxidative sintering, the pellets (in a 99.7% Al2O3 ceramic crucible) and furnace were pre-heated to 100 and 900°C, respectively. A pellet batch (consisting of 25 pellets) was then placed in the furnace and the temperature was ramped up from 900°C to the designated maximum temperature at a rate of approximately 17°C/min. Once the designated maximum temperature was reached, the pellets were removed from the furnace and allowed to cool in the

ambient atmosphere. The sintering process also took place in an ambient gaseous environment and no dwelling time was allowed at the maximum temperature, in order to mimic the industrial process as closely as possible.

Scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectroscopy (EDS) was used to perform surface characterization of the sintered pellets in secondary electron mode. An FEI Quanta 250 FEG instrument incorporating an Oxford X-map EDS system operating at 15 kV and a working distance of 10 mm was used. Pellets were split in half and mounted on aluminium stubs, or were set in resin and polished with a SS20 Spectrum System Grinder polisher before being mounted on the stubs. The polished resin-embedded samples were coated with carbon in an Emscope TB 500 carbon coater prior to SEM analysis. Resin-embedded polished pellets were imaged in backscattered electron mode, and un-embedded pellets in secondary electron mode. Crystalline phase analysis of bulk samples was performed by X-ray diffraction (XRD) using two methods: A Rigaku D/MAX 2500 rotating-anode powder diffractometer with Cu K radiation at 50 kV, 260 mA, a step-scan of 0.02º, and a scan rate at 1/min in 2 hours from 5 to 70º. Phase identification was performed using JADE v.3.9 with the ICDD and ICSD diffraction databases A RÜntgen diffraction system (PW3040/60 X’Pert Pro) and a back-loading preparation method to determine the crystalline phases, and their percentages, present in size-partitioned samples and milled mixtures. The samples were scanned using X-rays generated by a copper (Cu) K X-ray tube. Measurements were carried out between variable divergence and fixed receiving slits. Phases were identified using X’PertHighscore plus software. Phase refinement was performed using the Rietveld method (Hill and Howard, 1987). Further analysis was undertaken by mounting crosssectioned polished pellets on glass slides and carbon-coating them prior to electron microprobe analysis (EMPA). Analysis was performed using a JEOL JXA-8900 electron microprobe fitted with five wavelength-dispersive spectrometers at an accelerating voltage of 20 kV and beam current of 26 nA.

The compressive strengths of sintered pellets were determined using an Ametek Lloyd Instruments LRXplus 5 kN strength tester. NEXYGENPlus material test and data analysis software was used to capture and process the generated data, as well as to control and monitor all aspects of the system. The speed of the compression plates was kept to 10 mm/min during compressive strength tests in order to apply an increasing force on the prepared pellets. The maximum load to induce pellet breakage was recorded. Standard deviations were calculated based on 10 experimental repetitions. The abrasion resistance apparatus used in this study was based on a downscaled version of the European Standard EN 15051 rotating drum, as described by Schneider and Jensen (2008). The same drum was utilized in previous studies VOLUME 119

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Green pellets containing the various percentages of preoxidized chromite were generated using a laboratory-scale disk pelletizer. The laboratory disk pelletizer consisted of a 600 mm diameter flat steel disk with a 150 mm deep rim. The entire disk and rim were pressed from a single steel plate and the connection area between the disk and rim was rounded to limit material build-up. The pelletizer rotated at a speed of 40 r/min and a constant angle of 60° to the horizontal was maintained in all experiments. Fine water spray was introduced to the mixture with a handheld spray bottle to initiate pellet nucleation, i.e. formation of micro-pellets. Thereafter, the micro-pellets were grown by continuously wetting of their surfaces with water spray and introducing new, dry feed until the desired pellet diameter was achieved. Material build-ups on the bottom, side, and pelletizer corner (connecting area between disk and rim) were continuously loosened with a fit-for-purpose handheld scraper to facilitate the inclusion of this material into the pellets. This scraper had a flat edge to remove material build-up from the disk and the rim, as well as a rounded point to remove material buildup from the corner. Pellets with a diameter of > 11 and < 13 mm were collected by screening. Care was taken to ensure a consistent pellet moisture content, with all pellets used in further experiments containing 5.5 ¹ 0.7 wt% moisture. Pellet moisture contents were determined with an ADAM PMB 53 moisture balance. Green pellets were allowed to dry in an ambient atmosphere after collection before oxidative sintering experiments proceeded.

Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process (Kleynhans et al., 2012, 2016; Neizel et al., 2013; Glastonbury et al., 2015). During each experiment, 10 sintered pellets were abraded at 40 r/min rotational speed for 64 minutes. The pellets were screened at time intervals of. 1, 2, 4, 8, 16, 32, and 64 minutes using a sieve with an aperture of 6.5 mm. This aperture was chosen since < 6 mm material is generally classified as fines, which have to be limited in SAF feed material (Basson and Daavittila, 2013). The oversized materials ( 6.5 mm) were weighed and returned to the drum, together with the fines ( 6.5 mm), for further abrasion until the full abrasion time, i.e. 64 minutes, was reached. Standard deviations were calculated based on three experimental repetitions.

<81(-71943/9/610(11623 As previously stated, Zhao and Hayes (2010) showed that only the outer chromite grains of industrially produced oxidative sintered pellets are partially oxidized, while the grains deeper into the pellet cores remain unoxidized. Therefore, it was critical to establish the maximum temperature for the proposed laboratory sintering profile (see Pellet oxidative sintering section). Consequently, batches of pellets were cured with sintering profiles that had maximum temperatures from 1000 to 1500ยบC. SEM examination of three cross-sectioned, polished sintered pellets, which were

randomly selected from each prepared pellet batch, revealed that the outer chromite grains of pellets sintered at < 1200ยบC did not indicate signs of oxidation, while oxidation penetrated deep into pellets sintered at > 1200ยบC. A sintering profile with a maximum temperature of 1200ยบC was therefore applied within the context of this paper, to mimic the industrially applied oxidative sintered pellets process. Figure 1 presents backscattered electron SEM micrographs of a cross-sectioned polished pellet containing zero wt% pre-oxidized chromite, which was sintered at a maximum temperature of 1200ยบC. Oxidation-induced differences (alteration being indicated by the lighter greyscale patterns) were evident between the outer chromite grains and grains toward the centre of the pellet (Figure 1a). The higher magnification micrograph of the outer chromite grains (Figure 1b) clearly shows an oxidation-induced altered phase that formed on the rim of such grains, while the chromite grains toward the pellet centre were unaffected (Figure 1c). Similar oxidation-induced phase patterns have been observed during investigations considering pre-oxidation of chromite prior to direct reduction (Kapure et al., 2010), laboratory oxidative sintering of chromite pellets (Glastonbury et al., 2015), and industrially produced oxidative sintered chromite pellets (Zhao and Hayes, 2010).

In order to gain insight into the recycling of pre-oxidized chromite fines, it is vital to understand the mechanism(s) at

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Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process Table I

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play during the oxidative sintered pellet production process. Oxidation-induced alteration, which is indicated by lighter greyscale patterns in the SEM micrographs in Figure 1b, was evident on the rims of the outer chromite grains of sintered pellets containing no pre-oxidized chromite. EMPA was subsequently used to determine the chemical compositions of the transformation products. However, access to the EMPA was limited, therefore only one pellet, sintered according to the optimum sintering profile, was analysed. SEM micrographs of all three of the randomly selected pellets from the batch sintered at 1200ÂşC indicated the same phase transformations at the rim of the pellets (see Mimicking the industrial applied oxidative sintering process section), which proved that the randomly selected pellet analysed with EMPA was representative. The results of the EMPA are shown in Table I. The areas analysed are indicated as ‘grain location’ and ‘grain area’, with ‘location’ referring to the position of the chromite grain within the pellet (i.e. centre or outer grains), and ‘grain area’ referring to the zone within a specific grain (i.e. interior or rim of a specific grain, which is also indicated in Figure 1b). The Cr/Fe and Mg/Fe elemental ratios, with Fe = Fe2+ + Fe3+, as determined by EMPA, are also presented in Table I. As is evident from Table I, the interiors of grains at the centre and outer layer of the pellets had appreciably different MgO and FeO contents, whereas no appreciable differences between trivalent cations (primarily Cr3+, Al3+, and Fe3+, indicated as Cr2O3, Al2O3, and Fe2O3, respectively) occupying the octahedral sites in chromite were observed. In contrast, relatively large differences in FeO, Fe2O3, and MgO contents were evident for grain interiors and rims located in the outer layer of pellets. Grain interiors in the centre of pellets had an MgO content of 9.90 wt%, compared to 12.65 wt% at the outer layer. The FeO content of grain interiors at the centre and outer layer of pellets were 19.77 and 15.93 wt%, respectively. Consequently, the Mg/Fe ratios for grain interiors at the pellet outer layer were higher (0.46) than for grains at the pellet centre (0.30). The Mg/Fe ratio difference of grain interiors as a function of location within the pellet was indicative of Mg2+

enrichment at the expense of Fe2+ in grains at the outer layer. Fe2+ occupies the tetrahedral sites in normal (untreated) chromite spinel. High-temperature oxidative treatment of chromite caused Fe2+ to oxidize and diffuse towards the grain rim, generating tetrahedral site vacancies within the particle interior. Vacancy formation occurs according to the following reaction (Lindsley, 1991; Tathavakar, Antony, and Jha., 2005) [1] where O2-0 represents oxygen anions in the cubic closed packed lattice, Vcat is the cation vacancies, and h a ‘hole’, respectively. Vcat + 2h collectively represent mobile point defects, from which mass transfer occurs. These tetrahedral vacancies were filled by Mg2+ (originating from the particle rim) via a Mg-Fe exchange reaction, which resulted in the observed 0.31 wt% MgO content at the grain rim in the pellet outer layer. The Fe2+ that migrated to the grain rim in the pellet outer layer was subsequently oxidized to Fe3+, or alternatively the Fe2+ was oxidized to Fe3+ which then migrated to the surface – currently it is impossible to say with certainty which occurs first. The generalized oxidative spinel phase decomposition is described by Equation [2] (Alper, 1970; Tathavakar, Antony, and Jha, 2005) [2] The products of complete oxidative decomposition of chromite (Mg,Fe)[Al,Cr,Fe]2O4 at elevated temperatures are free oxides of the various species, as indicated by Equation [2]. However, in this study complete oxidative decomposition was not the intention, nor was it achieved. The products of chromite oxidation are strongly dependent on temperature, oxygen partial pressure, and the chemical composition of the chromite. In the presence of oxygen, Fe2+ will be oxidized to Fe3+ at sufficiently high temperatures. This explains the decrease in FeO content of 15.39% to 0.21% and increase in Fe2O3 content of 6.75 to 33.15% from the grain interior to VOLUME 119

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Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process

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the rim of the pellet outer layer. Ultimately, this results in the exsolution of a Fe2O3-rich sesquioxide phase along the chromite grain rims and cleavage planes, as shown in Figure 1b and identified in Table I. Such Widmänstatten intergrowths of sesquioxide phase (M2O3, with M representing a series of trivalent ions such as Cr3+, Al3+, and Fe3+) during chromite oxidation, have been identified by various authors (Tathavakar, Antony, and Jha, 2005; Kapure et al., 2010; Zhao and Hayes, 2010; Glastonbury et al., 2015). It is believed that the sesquioxide Fe2O3 phase is initially present as metastable, intermediate maghemite ( -Fe2O3) exsolved precipitate, which forms by changing an ABC packing into an ABAB packing by dislocations along the (111) chromite spinel plane. The maghemite phase then decomposes to the more stable hematite (Fe2O3) phase at elevated temperatures (> 600ºC) in the presence of oxygen (PO2 = 0.2 atm.) (Tathavakar et al., 2005; Pan, Yang, and Zhu, 2015). The presence of Al3+ cations increases the oxidative decomposition temperature as they can occupy

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vacancies present in the maghaemite structure (Hellwege and Hellwege, 1970). Furthermore, chromite oxidation resulted in variations in the Cr/Fe ratio at the grain interiors and rims as a function of grain location within the pellet. The unoxidized chromite had a Cr/Fe ratio of 1.64, which correlated well with typical Cr/Fe ratios of South African chromite ore originating from the Middle Group 1 and 2 seams of the Bushveld Complex (Basson, Curr, and Gericke, 2007; Kleynhans, 2011). Chromite near the grain rim in the outer layer of sintered pellets had a Cr/Fe ratio of 1.42 as opposed to 1.99 near the grain interior, which resulted from Fe diffusion from the grain interior towards the grain rim.

Quantitative Rietveld refined XRD analysis was performed on the as-received pre-oxidized chromite fines. The phases found (in order of decreasing abundance) were chromite 39.9 wt%, chromian spinel 42.9 wt%, haematite 5.1 wt%,

Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process and enstatite 12.0 wt%. The chromite phase represents residual chromite. The chromian spinel chromite formed at 1200ยบC in the presence of oxygen. Tathavakar et al., (2015) stated that South African chromite decomposes into two spinel phases upon heating, i.e. (Mg1-x,Fex)(Cr1-y,Aly)2O4 and (Fe1-a,Mga)(Cr1-b,Alb)2O4. The two observed chromite phases, i.e. chromite and chromian spinel, were in agreement with phases detected by Tathavakar, Antony, and Jha (2015). Furthermore, the detection of hematite (Fe2O3) indicated that free Fe2O3, which formed as described in the section Mechanism of chromite oxidation, was present in the pre-oxidized chromite fines. The enstatite phase likely originated from siliceous gangue minerals, clay that was added as a pellet binder, and ash from the coke (in-pellet carbon fuel source).

If pre-oxidized chromite fines were to be recycled into oxidative sintered pellets beyond the current limitation of 4 wt% (Basson and Daavittila, 2013), such pellets must be physically strong enough to prevent fines formation. Figures 2 and 3 present the cured compressive and abrasion resistance strengths of pellets containing 4, 8, 16, 32, and 64 wt% pre-oxidized chromite fines, compared to pellets containing zero wt% pre-oxidized chromite fines (indicated by the y-axis). From Figures 2 and 3, it is evident that pellet compressive and abrasion resistance strengths remained the same, or improved, compared to the base case (zero wt% preoxidized chromite fines) as the pre-oxidized chromite fines content increased from 4 to 32 wt%. However, pellets containing 64 wt% pre-oxidized chromite fines were substantially weaker. It thus seems feasible to include up to 32 wt% pre-oxidized chromite fines in oxidative sintered pellet mixtures without adversely affecting cured pellet strengths. However, it is unlikely that such large additions would ever be required. There might be various reasons why the cured sintered pellets containing pre-oxidized chromite fines (at least up to 32 wt% containing pellets) were stronger than the base case. However, the most obvious is that the cured sintered pellets

containing pre-oxidized chromite fines contained a larger proportion of ultrafine particles, as is evident from a comparison of Figures 4a and 4b. These ultrafine particles filled interparticle gaps and improved particle contact, which enhanced cured pellet strength. The presence of the ultrafine particles in the pre-oxidized chromite fines can be related to the phase/chemical nature thereof. EMPA indicated that sesquioxide Fe2O3 phases were formed in outer layer pellet chromite grains (see Mechanism of chromite oxidation section, Table I) during pellet oxidative sintering. Quantitative Rietveld refined XRD analysis even indicated the presence of haematite (XRD characterization of pre-oxidized chromite fines section) in the pre-oxidized chromite fines. Zhao and Hayes (2010), who also investigated oxidative sintered chromite pellets, found that the phase transformation responsible for Fe2O3 formation resulted in a shear mechanism (i.e. crystal stress/strain build-up) that can cause crystalline mechanical break-up, thereby generating the ultrafine particles observed in the pre-oxidized chromite fines (Figure 4b). In addition to the particle size-related enhancement of cured pellet strength, particle surface morphology was also considered. Surface SEM micrographs of pellets containing zero and 16 wt% pre-oxidized chromite fines pellets are presented in Figures 5a and 5b, respectively. The most notable difference in surface morphology is the enhanced interparticle sintering of the cured sintered pellets containing pre-oxidized chromite fines. This suggests that some additional degree of melting occurred during sintering, probably due to the presence of pre-oxidized chromite fines. Obviously, this can at least in part be attributed to the better interparticle contact and reduced interparticle voids, but it was also important to investigate the chemical nature of the interparticle bonding. SEM-EDS analyses of the areas highlighted with the white blocks in Figures 5a and 5b indicated similar chemical compositions. This suggests that the siliceous gangue minerals, clay as the pellet binder, and ash from the coke (in-pellet carbon fuel source) jointly contributed to binding the particles together via the formation of a molten phase. Figure 6a present a SEM micrograph of a typical pre-oxidized chromite fines particle,

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Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process

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while the white box in Figure 6b indicate the presence of a aluminosilicate type particle (SEM-EDS composition of 4.0 wt% Al, 8.0 wt% Si, 7.7 wt% Mg, and 0.5 wt% Ca). Considering that the interparticle bridges in the samples with and without the pre-oxidized chromite fines were compositionally similar, the difference in their sintered compressive and abrasion strengths can most likely be attributed to the presence of more ultrafine particles in the pre-oxidized chromite fines.

230-(16231 The findings presented in this paper suggest that preoxidized chromite fines, originating from the oxidative pellet sintering scrubber, and fines screened out from oxidative sintered pellets may be re-introduced into the moist material mixture that is pelletized in excess of the current 4 wt% limit indicated by the technology supplier. EMPA and quantitative XRD analysis verified the formation of Fe2O3-rich sesquioxide phases and/or liberated Fe2O3. Such phases will metallize at lower temperatures during smelting compared to normal chromite; hence possibly enhancing metallurgical efficiency and reducing electricity consumption. The authors

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VOLUME 119

therefore recommend that pre-oxidized chromite fines can be recycled in excess of the current 4 wt% limitation, but only after considering the following aspects. An appropriate techno-economic study should be conducted to assess the financial viability of the changes to the process. Practical implications, such as equipment modifications, as well as equipment and/or process licensing/warranty issues. For instance, the crushing of oversized green pellets and the operational lifetime of the steel sintering belt were not considered in this paper. Currently, oversized green pellets are crushed with a roller before the moist material is recycled to the pelletizing drum. This roller might be damaged by larger hard particles in the pre-oxidized chromite fines if this material is mixed with the moist material that is pelletized. This can be avoided by replacing the roller by a more robust crushing device, or alternatively milling the pre-oxidized chromite fines with the ore and carbonaceous material to prevent roller damage. As previously mentioned, increased recycling of preoxidized chromite fines might also require higher

Recycling pre-oxidized chromite fines in the oxidative sintered pellet production process

0 32'-8/*8)8371 The authors thank Sarel Naude from the Department of Mechanical Engineering for assistance during sample preparation. Thanks are also given to Derek Smith and Dominique Duguay at CANMET for XRD and electron microprobe analyses, respectively. The financial assistance of the National Research Foundation (NRF) towards the PhD study of S.P. du Preez (grant number 101345) is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.

<8+8583081 ALPER, A.M. 1970. High Temperature Oxides, Academic Press, New York. BASSON, J., CURR, T., and GERICKE, W. 2007. South Africa's ferroalloys industrypresent status and future outlook. Proceedings of the 11th International Ferroalloys Congress (INFACON XI). Das, R.K. and Sundaresan, T.S. (eds). Indian Ferro Alloys Producers Association, New Delhi. pp. 3–24. BASSON, J., and DAAVITTILA, J. 2013. High carbon ferrochrome technology. Handbook of Ferroalloys: Theory and Technology. Gasik, M. (ed.). Elsevier Science, UK. Chapter 9. pp. 317–363. http://dx.doi.org/10.1016/B978-0-08-097753-9.00009-5 BEUKES, J.P., DAWSON, N.F., and VAN ZYL, P.G. 2010. Theoretical and practical aspects of Cr(VI) in the South African ferrochrome industry. Journal of the Southern African Institute of Mining and Metallurgy, vol. 110, no. 12. pp. 743–750. BEUKES, J.P., DU PREEZ, S.P., VAN ZYL, P.G., PAKTUNC, D., FABRITIUS, T., PÄÄTALO, M., and CRAMER, M. 2017. Review of Cr (VI) environmental practices in the chromite mining and smelting industry–relevance to development of the Ring of Fire, Canada. Journal of Cleaner Production, vol. 165. pp. 874– 889. https://doi.org/10.1016/j.jclepro.2017.07.176 CRAMER, L.A., BASSON, J., and NELSON, L.R. 2004. The impact of platinum production from UG2 ore on ferrochrome production in South Africa. Journal of the South African Institute of Mining and Metallurgy, vol. 104, no. 9. pp 517–527. GLASTONBURY, R.I., BEUKES, J.P, VAN ZYL, P.G, SADIKI, L.N, JORDAAN, A., CAMPBELL, Q.P, STEWART, H.M, and DAWSON, N.F. 2015. Comparison of physical properties of oxidative sintered pellets produced with UG2 or metallurgical-grade South African chromite: A case study. Journal of the Southern African Institute of Mining and Metallurgy, vol. 115, no. 8. pp. 699–706. http://dx.doi.org/10.17159/2411-9717/2015/V115N8A6 HAGGERTY, S.E. 1991. Oxide mineralogy of the upper mantle. Reviews in Mineralogy and Geochemistry, vol. 25, no. 1. pp. 355–416. HELLWEGE, K.H., and HELLWEGE, A.H. 1970. Landolt–Bornsten Numerical Data and Functional Relation in Science and Technology. Springer-Verlag. b52, a18-a20.

HILL, R., and HOWARD, C. 1987. Quantitative phase analysis from neutron powder diffraction data using the Rietveld method. Journal of Applied Crystallography, vol. 20, no. 6. pp. 467–474. https://doi.org/10.1107/S0021889887086199 ICDA. 2013. Statistical Bulletin 2013 Edition. International Chromium Development Association, Paris, France. KAPURE, G., TATHAVADKAR, V., RAO, C.B., RAO, S.M., and RAJU, K.S. 2010. Coal based direct reduction of preoxidized chromite ore at high temperature. Proceedings of the 12th International Ferroalloys Congress (INFACON XII), Helsinki, Finland. Vartiainen, A. (ed). Outotec Oyj. pp. 293–301. KLEYNHANS, E.L.J. 2011. Unique challenges of clay binders in a pelletised chromite pre-reduction process – a case study. MSc dissertation, NorthWest University, Potchefstroom, South Africa. KLEYNHANS, E.L.J., BEUKES, J.P., VAN ZYL, P.G., KESTENS, P.H.I., and LANGA, J.M. 2012. Unique challenges of clay binders in a pelletised chromite prereduction process. Minerals Engineering, vol. 34. pp. 55–62. https://doi.org/10.1016/j.mineng.2012.03.021 KLEYNHANS, E.L.J., NEIZEL, B.W., BEUKES, J.P., and VAN ZYL, P.G. 2016. Utilisation of pre-oxidised ore in the pelletised chromite pre-reduction process. Minerals Engineering, vol. 92. pp. 114–124. https://doi.org/10.1016/j.mineng.2016.03.005 KLEYNHANS, E.L.J., BEUKES, J.P, VAN ZYL, P.G, and FICK, J.I.J. 2017. Technoeconomic feasibility of a pre-oxidation process to enhance prereduction of chromite. Journal of the Southern African Institute of Mining and Metallurgy, vol. 117, no. 5. pp. 457–468. http://dx.doi.org/10.17159/2411-9717/2017/v117n5a8 LINDSLEY, D.H. 1991. Reviews in Mineralogy. Mineralogical Society of America. Washington DC. Vol. 25. pp. 323–350. MOTZER, W.E., and ENGINEERS, T. 2004. Chemistry, geochemistry, and geology of chromium and chromium compounds. Chromium(VI) Handbook. Guetin, J., Jacobs, J.A, and Avakian, C.P. (eds.). CRC Press. Boca Raton, FL. pp. 23–91. NEIZEL, B.W., BEUKES, J.P, VAN ZYL, P.G., and DAWSON, N.F. 2013. Why is CaCO3 not used as an additive in the pelletised chromite pre-reduction process? Minerals Engineering, vol. 45. pp. 115–120. http://dx.doi.org/10.1016/j.mineng.2013.02.015 OUTOTEC. 2017. OutotecŽ steel belt sintering plant. https://www.outotec.com/products/sintering-and-pelletizing/steel-beltsintering-plant [accessed 24 October 2017]. PÄÄTALO, M. 2018. Head of Research and Development, Outokumpu, Finland. Personal communication. PAKTUNC, A.D., and CABRI, L. 1995. A proton-and electron-microprobe study of gallium, nickel and zinc distribution in chromian spinel. Lithos, vol. 35, no. 3. pp. 261–282. https://doi.org/10.1016/0024-4937(95)99071-4 PAN, J., YANG, C., and ZHU, D. 2015. Solid state reduction of preoxidized chromite-iron ore pellets by coal. ISIJ International, vol. 55, no. 4. pp. 727–735. http://doi.org/10.2355/isijinternational.55.727 RIEKKOLA-VANHANEN, M. 1999. Finnish expert report on best available techniques in ferrochromium production. Helsinki. SCHNEIDER, T., and JENSEN, K.A. 2008. Combined single-drop and rotating drum dustiness test of fine to nanosize powders using a small drum. Annals of Occupational Hygiene, vol. 52, no. 1. pp. 23–34. https://doi.org/10.1093/annhyg/mem059 TATHAVAKAR, V.D., ANTONY, M., and JHA, A. 2005. The physical chemistry of thermal decomposition of South African chromite minerals. Metallurgical and Materials Transactions B, vol. 36, no. 1. pp. 75–84. https://doi.org/10.1007/s11663-005-0008-1 ZHAO, B. and HAYES, P. 2010. Effects of oxidation on the microstructure and reduction of chromite pellets. Proceedings of the 12th International Ferro Alloys Congress (INFACON XII), Helsinki, Finland. Vartiainen, A. (ed). Outotec Oyj. pp. 263–273. VOLUME 119

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carbon additions in the green pellets, which could be shorten the operational lifetime of the steel sintering belt. However, our results indicate that increased recycling of pre-oxidized chromite fines up to a very high percentage does not adversely influence sintered pellet strength. Such decisions (how to introduce larger, hard pre-oxidized particles and the effect of increased recycling of pre-oxidized fines on the steel sintering belt) will be guided by the techno-economic study of the process alteration options, and additionally by equipment and/or process licensing/warranty issues. However, the possible metallurgical and energy benefits associated with recycling of pre-oxidized chromite fines, which have up to now largely been ignored, should also be considered.

NINTH INTERNATIONAL CONFERENCE ON DEEP AND HIGH STRESS MINING 2019 24–25 JUNE 2019 - CONFERENCE 26 JUNE 2019 - SARES 2019 27 JUNE 2019 - TECHNICAL VISIT MISTY HILLS CONFERENCE CENTRE, MULDERSDRIFT, JOHANNESBURG, SOUTH AFRICA

BACKGROUND The Ninth International Conference on Deep and High Stress Mining (Deep Mining 2019) will be held at the Misty Hills Conference Centre, Muldersdrift, Johannesburg on 24 and 25 June 2019. Conferences in this series have previously been hosted in Australia, South Africa, Canada, and Chile. Around the world, mines are getting deeper and the challenges of stress damage, squeezing ground, and rockbursts are everpresent and increasing. Mining methods and support systems have evolved slowly to improve the management of excavation damage and safety of personnel, but damage still occurs and personnel are injured. Techniques for modelling and monitoring have been adapted and enhanced to help us understand rock mass behaviour under high stress. Many efficacious dynamic support products have been developed, but our understanding of the demand and capacity of support systems remains uncertain.

OBJECTIVE To create an international forum for discussing the challenges associated with deep and high stress mining and to present advances in technology.

WHO SHOULD ATTEND

Rock engineering practitioners Mining engineers Researchers Academics Geotechnical engineers Hydraulic fracturing engineers

High stress mining engineers Waste repository engineers Rock engineers Petroleum engineers Tunnelling engineers

Sponsors

For further information contact: Camielah Jardine, Head of Conferencing, SAIMM, Tel: +27 11 834-1273/7, Fax: +27 11 833-8156 or +27 11 838-5923 E-mail: camielah@saimm.co.za, Website: http://www.saimm.co.za

NATIONAL & INTERNATIONAL ACTIVITIES 2019

2–3 April 2019 — Global Mining Standards and Guidelines Group Forum 2019 Wits Club, University of the Witwatersrand, Johannesburg, South Africa Contact: Yolanda Ndimande 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 22–23 May 2019 — Mine Planner’s Colloquium 2019 ‘Skills for the future – yours and your mine’s’ Glenhove Conference Centre, Melrose Estate, 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 27–29 May 2019 — The 9th International Conference on Sustainable Development in the Minerals Industry Park Royal Darling Harbour, Sydney , Australia Contact: Georgios Barakos Tel: +49(0)3731 39 3602, Fax: +49(0)3731 39 2087 E-mail: Georgios.Barakos@mabb.tu-freiberg.de Website: http://sdimi.ausimm.com/ 5–6 June 2019 — New Technology Conference and Trade Show ‘Embracing the Fourth Industrial Revolution in the Minerals Industry’ Driving competitiveness through people, processes and technology in a modernised environment The Canvas Riversands, Fourways, Johannesburg, South Africa Contact: Yolanda Ndimande 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 24–27 June 2019 — Ninth International Conference on Deep and High Stress Mining 2019 Misty Hills Conference Centre, Muldersdrift, 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 4–5 July 2019 — Smart Mining, Smart Environment, Smart Society ‘Implementing change now, for the mine of the future’ Midrand Contact: Yolanda Ndimande 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 31 July–1 August 2019 — Entrepreneurship in Mining Conference 2019 The Canvas Riversands, Fourways, Johannesburg, 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

5–7 August 2019 — The Southern African Institute of Mining and Metallurgy in collaboration with the Zululand Branch is organising The Eleventh International Heavy Minerals Conference ‘Renewed focus on Process and Optimization’ The Vineyard, Cape Town, South Africa Contact: Yolanda Ndimande 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 18–21 August 2019 — Copper 2019 Vancouver Convention Centre, Canada Contact: Brigitte Farah Tel: +1 514-939-2710 (ext. 1329), E-mail: metsoc@cim.org Website: http://com.metsoc.org 19–22 Augusts 2019 — Southern African Coal Processing Society 2019 Conference and Networking Opportunity Graceland Hotel Casino and Country Club, Secunda Contact: Johan de Korte Tel: 079 872-6403 E-mail: dekorte.johan@gmail.com Website: http://www.sacoalprep.co.za 28–29 August 2019 — International Mine Health and Safety Conference 2019 ‘Beyond Zero Harm’ 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 11–12 September 2019 — Surface Mining 2019 Conference ‘Better ¡ cheaper ¡ safer’ Glenhove Conference Centre, Melrose Estate, 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 8–9 October 2019 — SAMCODES Conference 2019 Best Practice ‘An Industry Standard for Mining Professionals in South Africa’ Birchwood Hotel & OR Tambo Conference Centre, 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 16–17 October 2019 — Tailing Storage Conference 2019 ‘Investing in a Sustainable Future’ Birchwood Hotel & OR Tambo Conference Centre, 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 13–15 November 2019 — XIX International Coal Preparation Congress & Expo 2019 New Delhi, India Contact: Coal Preparation Society of India Tel/Fax: +91-11-26136416, 4166 1820 E-mail: cpsidelhi. india@gmail.com, president@cpsi.org. inrksachdevO1@gmail.com, hi.sapru@monnetgroup.com

$ %#6--!666666666666666666666666666666666666666

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11–13 March 2019 — 7th Sulphur and Sulphuric Acid 2019 Conference Swakopmund Hotel, Swakopmund, Namibia 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

3M South Africa (Pty) Limited

Expectra 2004 (Pty) Ltd

Murray and Roberts Cementation

AECOM SA (Pty) Ltd

Exxaro Coal (Pty) Ltd

Nalco Africa (Pty) Ltd

AEL Mining Services Limited

Exxaro Resources Limited

Namakwa Sands(Pty) Ltd

Air Liquide (PTY) Ltd

Filtaquip (Pty) Ltd

Ncamiso Trading (Pty) Ltd

Alexander Proudfoot Africa (Pty) Ltd

FLSmidth Minerals (Pty) Ltd

New Concept Mining (Pty) Limited

AMEC Foster Wheeler

Fluor Daniel SA (Pty) Ltd

Northam Platinum Ltd - Zondereinde

AMIRA International Africa (Pty) Ltd

Franki Africa (Pty) Ltd-JHB

OPTRON (Pty) Ltd

ANDRITZ Delkor(Pty) Ltd

Fraser Alexander (Pty) Ltd

PANalytical (Pty) Ltd

Anglo Operations Proprietary Limited

G H H Mining Machines (Pty) Ltd

Anglogold Ashanti Ltd

Geobrugg Southern Africa (Pty) Ltd

Paterson & Cooke Consulting Engineers (Pty) Ltd

Arcus Gibb (Pty) Ltd

Glencore

Perkinelmer

Atlas Copco Holdings South Africa (Pty) Limited

Hall Core Drilling (Pty) Ltd

Polysius A Division Of Thyssenkrupp Industrial Sol

Aurecon South Africa (Pty) Ltd Aveng Engineering Aveng Mining Shafts and Underground Axis House Pty Ltd Bafokeng Rasimone Platinum Mine Barloworld Equipment -Mining BASF Holdings SA (Pty) Ltd BCL Limited

Hatch (Pty) Ltd

Precious Metals Refiners

Herrenknecht AG HPE Hydro Power Equipment (Pty) Ltd Immersive Technologies

BedRock Mining Support Pty Ltd BHP Billiton Energy Coal SA Ltd Blue Cube Systems (Pty) Ltd

Redpath Mining (South Africa) (Pty) Ltd Rocbolt Technologies

IMS Engineering (Pty) Ltd

Rosond (Pty) Ltd

Ivanhoe Mines SA

Royal Bafokeng Platinum

Joy Global Inc.(Africa)

Roytec Global (Pty) Ltd

Kudumane Manganese Resources

RungePincockMinarco Limited

Leco Africa (Pty) Limited

Becker Mining (Pty) Ltd

Rand Refinery Limited

Rustenburg Platinum Mines Limited

Longyear South Africa (Pty) Ltd

Salene Mining (Pty) Ltd

Lonmin Plc

Sandvik Mining and Construction Delmas (Pty) Ltd

Lull Storm Trading (Pty) Ltd Maccaferri SA (Pty) Ltd Magnetech (Pty) Ltd

Sandvik Mining and Construction RSA(Pty) Ltd

MAGOTTEAUX (PTY) LTD

SANIRE

Maptek (Pty) Ltd

Schauenburg (Pty) Ltd

MBE Minerals SA Pty Ltd

Sebilo Resources (Pty) Ltd

MCC Contracts (Pty) Ltd

SENET (Pty) Ltd

MD Mineral Technologies SA (Pty) Ltd

Senmin International (Pty) Ltd

MDM Technical Africa (Pty) Ltd

Smec South Africa

Metalock Engineering RSA (Pty)Ltd

Sound Mining Solution (Pty) Ltd

Metorex Limited

SRK Consulting SA (Pty) Ltd

Metso Minerals (South Africa) Pty Ltd

Technology Innovation Agency

Data Mine SA

Minerals Council of South Africa

Time Mining and Processing (Pty) Ltd

Department of Water Affairs and Forestry

Minerals Operations Executive (Pty) Ltd

Timrite Pty Ltd

Digby Wells and Associates

MineRP Holding (Pty) Ltd

Tomra (Pty) Ltd

DRA Mineral Projects (Pty) Ltd

Mintek

Ukwazi Mining Solutions (Pty) Ltd

DTP Mining - Bouygues Construction

MIP Process Technologies (Pty) Limited

Umgeni Water

Duraset

Modular Mining Systems Africa (Pty) Ltd

Webber Wentzel

Elbroc Mining Products (Pty) Ltd

MSA Group (Pty) Ltd

Weir Minerals Africa

eThekwini Municipality

Multotec (Pty) Ltd

Worley Parsons RSA (Pty) Ltd

Bluhm Burton Engineering Pty Ltd Bouygues Travaux Publics CDM Group CGG Services SA Coalmin Process Technologies CC Concor Opencast Mining Concor Technicrete Council for Geoscience Library CRONIMET Mining Processing SA Pty Ltd CSIR Natural Resources and the Environment (NRE)

viii

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The Southern African Institute of Mining and Metallurgy

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7th Sulphur and Sulphuric Acid 2019 Conference 11–12 March 2019 Conference 13 March 2019 Technical Visit (Swakop Uranium) Swakopmund Hotel, Swakopmund, Namibia

BACKGROUND The production of SO2 and sulphuric acid remains a pertinent topic in the Southern African mining and metallurgical industry, especially in view of the strong demand for, and increasing prices of, vital base metals such as cobalt and copper. The electric car revolution is well underway and demand for cobalt is rocketing. New sulphuric acid plants are being built, comprising both smelters and sulphur burners, as the demand for metals increases. However, these projects take time to plan and construct, and in the interim sulphuric acid is being sourced from far afield, sometimes more than 2000 km away from the place that it is required. The need for sulphuric acid ‘sinks’ such as phosphate fertilizer plants is also becoming apparent. All of the above factors create both opportunities and issues and supply chain challenges. To ensure that you stay abreast of developments in the industry, the Southern African Institute of Mining and Metallurgy invites you to participate in a conference on the production, utilization, and conversion of sulphur, sulphuric acid, and SO2 abatement in metallurgical and other processes, to be held in March 2019 in Namibia.

OBJECTIVES

WHO SHOULD ATTEND

> To expose delegates to issues relating to the generation and handling of sulphur, sulphuric acid, and SO2 abatement in the metallurgical and other industries.

The Conference will be of value to:

> Provide an opportunity to producers and consumers of sulphur and sulphuric acid and related products to be introduced to new technologies and equipment in the field. > Enable participants to share information about and experience in the application of such technologies. > Provide an opportunity for role players in the industry to discuss common problems and their solutions

> Metallurgical and chemical engineers working in the minerals and metals processing and chemical industries > Metallurgical/chemical/plant management > Project managers > Research and development personnel > Academics and students > Technology providers and engineering firms > Equipment and system providers > Relevant legislators > Environmentalists > Consultants

EXHIBITION/SPONSORSHIP There are a number of sponsorship opportunities available. Companies wishing to sponsor or exhibit should contact the Conference Co-ordinator. For further information contact: Camielah Jardine Head of Conferencing, SAIMM, P O Box 61127, Marshalltown 2107 Tel: (011) 834-1273/7 Fax: (011) 833-8156 or (011) 838-5923 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za

SPONSORS

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