Saimm 202010 oct

VOLUME 120

NO. 10

OCTOBER 2020

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Mxolisi Mgojo President, Minerals Council South Africa Honorary Vice Presidents Gwede Mantashe Minister of Mineral Resources, South Africa Ebrahim Patel Minister of Trade and Industry, South Africa Blade Nzimande Minister of Higher Education, Science and Technology, South Africa President V.G. Duke President Elect I.J. Geldenhuys Senior Vice President Z. Botha Junior Vice President W.C. Joughin Incoming Junior Vice President E Matinde Immediate Past President M.I. Mthenjane Co-opted to Office Bearers S. Ndlovu Honorary Treasurer W.C. Joughin Ordinary Members on Council B. Genc G.R. Lane K.M. Letsoalo T.M. Mmola G. Njowa S.J. Ntsoelengoe S.M Rupprecht N. Singh

A.G. Smith M.H. Solomon A.J.S. Spearing S.J. Tose M.I. van der Bank A.T. van Zyl E.J. Walls

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J.L. Porter

R.D. Beck

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J.R. Dixon

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H.E. James

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R.T. Jones

G.L. Smith

A.S. Macfarlane

W.H. van Niekerk

C. Musingwini S. Ndlovu G.R. Lane–TPC Mining Chairperson Z. Botha–TPC Metallurgy Chairperson S.F. Manjengwa–YPC Chairperson A.T. Chinhava–YPC Vice Chairperson Branch Chairpersons Johannesburg

D.F. Jensen

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N.M. Namate

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D. Muma

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VOLUME 120 NO. 10 OCTOBER 2020

Contents Journal Comment: Diamonds: Source-to-Use, 2020

by T.R. Marshall. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv President’s Corner: Looking to the future

by V.G. Duke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v SAMCODES News . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Press Release: Danfoss Energy-efficient mining for a better tomorrow

by L. McCarthy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

DIAMONDS PAPERS Diamond plant statistics, process efficiencies, liberation modelling, and simulation: The art of the possible G. Dellas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 The paper provides a reference point to assess the effectiveness of diamond plant accounting systems in the field, and establishes a baseline for ongoing education, peer technical debate, and progression to an exact science.

Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code T.R. Marshall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 The difficulties associated with evaluation and valuation of alluvial diamond and other gemstone deposits are widely known but, regrettably, often not widely understood – leading to several misconceptions over what can and cannot be expected from such deposits. The 2016 edition of the SAMREC Code includes several sections specific to the requirements of secondary diamond and gemstone deposits. This paper outlines some of the requirements and the pitfalls that need to be appreciated while estimating Diamond/Gemstone Resources and/or Reserves for such deposits.

Satellite applications in diamond exploration and mine monitoring N.C. Steenkamp, S.L. Goosen, and P.J. Bouwer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575 Satellite-based monitoring of diamond exploration, active mining operations, and mine sites post-closure is gaining traction. A variety of imagery is used to detect both kimberlite and alluvial deposits. This paper outlines the forms of imagery utilized for primary deposits, secondary deposits, operational mines, and to detect illegal and artisanal diamond diggings. The limitation of satellite applications is related mainly to the cost of obtaining images and the resolution or number of bands available on a detection platform.

International Advisory Board R. Dimitrakopoulos, McGill University, Canada D. Dreisinger, University of British Columbia, Canada M. Dworzanowski, Consulting Metallurgical Engineer, France 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

VOLUME 120

The Journal of the Southern African Institute of Mining and Metallurgy

PAPERS OF GENERAL INTEREST Next-generation, affordable SO2 abatement for coal-fired power generation – A comparison of limestone-based wet flue gas desulphurization and Sulfacid® technologies for Medupi power station A. Strickroth, M. Schumacher, G.W. Hasse, and I. Kgomo. . . . . . . . . . . . . . . . . . . . . . . . . . . 581 The SO2 abatement solution for the coal-fired power generation is classically wet flue gas desulphurization using a limestone adsorbent (WFGD L/G). In South Africa, due to the poor quality of the limestone, the gypsum by-product is unsaleable. The Sulfacid® process technology converts SO2 into saleable sulphuric acid using a catalytic process requiring only water and air, and no limestone. This paper provides a direct comparison between the two technologies for the case of Medupi power station. The results indicate that the Sulfacid® process is affordable and could be adopted by a wide range of coal-fired industries to reduce SO2 emissions to legislative limits, while also producing a valuable commercial by-product.

Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope H. Bazzi, H. Noferesti, and H. Farhadian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 The effect of blasting-induced vibrations on slope stability in open pit mines was investigated using finite element (FE) analysis. The results show that points above the fault surface have the highest displacement, while below the fault surface only minimal negligible motions occur. The intensity of the explosion has the greatest impact on motions at upper points, but the impact below the fault surface is minimal. A sensitivity analysis revealed a direct relationship between both the shear stiffness and friction angle of the fault surface and the motions of upper reference points.

The impact of equipment productivity and pushback width on the mine planning process A.S. Araya, M. Nehring, E.T. Vega, and N.S. Miranda. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 This paper challenges the current notion that pushback width during conventional mine planning processes should be set at the distance that assures maximum equipment productivity. The hypothetical case study presented shows that the value of a project may increase beyond that determined by traditional planning practices,s and that deploying more aggressive mining strategies is likely to result in greater operational complexity and thus reduced equipment productivity.

The Journal of the Southern African Institute of Mining and Metallurgy

VOLUME 120

OCTOBER 2020

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nal

Jour

ment

Diamonds: Source-to-Use, 2020

Com

T

he Diamonds: Source-to-Use 2020 conference was to have been held at the Birchwood Hotel and Conference Centre during 9–11 June 2020. It was to have comprised 1½ days of presentations/exhibitions and two technical site visits, to Multotech and Epiroc, as well as a beer tasting event at Mad Giant Craft Beers. By early March, we had 21 confirmed papers, including two keynote addresses.

And then, just as elsewhere in the world, our plans were interrupted by the hard lockdown associated with the COVID-19 pandemic. Although the SAIMM is well positioned to undertake virtual conferences, it was decided to rather wait until 2021 to hold a face-to-face meeting. Consequently, a new date of 8–10 June 2021 has been proposed. Given the difficulties of travel and the unknowns of how the pandemic will progress, we are looking at the possibility of holding a hybrid event, whereby delegates and presenters alike can either take advantage of a real conference or enjoy the benefits of virtual attendance.

The effects of the global pandemic have been seen across the entire diamond pipeline, disrupting production and sales as well as the entire downstream cutting and polishing industry.

With no-one knowing how long these impacts will be with us, and not having certainly on what the post-COVID scene will look like, the theme of the conference – Innovation and Technology – is still appropriate, if not more so. We, as the global diamond community, will certainly have to apply our minds to the changed landscape and come up with new ways of doing business. We expect that the 2021 Diamonds: Source to Use Conference will attract many stimulating papers on the new ‘normal’ within the diamond industry, which may never look the same as it has in the past.

We look forward to this new challenge with excitement and more than just a little trepidation.

In order to show our appreciation for the effort that many of the presenters dad made to get their papers completed and peer-reviewed in time for the original conference, it was decided to give them the opportunity to publish in this edition of the SAIMM Journal. The papers in this volume highlight some of the advances made across the range of exploration, mineral processing evaluation, and reporting of diamond projects.

T.R. Marshall

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President’s Corner

Looking to the future

M

ining contributed almost 16% of South Africa’s gross domestic product (GDP) in 1994, but this has since declined to about half that amount. The country has not benefitted enough from our industry, which has huge potential even when having to compete in an increasingly efficient global environment.

There are many reasons for this, but I am now seeing encouraging changes afoot which lead me to believe that we may have turned a corner, and could well see positive growth going forward.

If this is the case, then we will need to import skills, and this will be at a premium.

Many of our engineers have either emigrated or left the industry. The 2018/2019 Annual Report of the Engineering Council of South Africa (ECSA) revealed that nearly half of our 21 500 registered professionals are either retired or over the age of sixty, and that our candidate engineers don’t seem to be progressing to registration as professionals.

The data also highlights the shortage of registered professional engineers, with only one being available for every 2 800 people living in the country compared to international norms of one engineer for 40 people. This must be a concern, because the ability of our captains of industry to exploit the full potential of our orebodies and supporting resources on a sustainable basis will depend on the quantity and quality of the professional engineers available to them.

We need to develop our own capacity, and this should start at the primary schooling level. There is not enough emphasis placed on enhancing the ability of our children, at this early stage, to properly benefit from the teaching of maths and science at the secondary schooling level. If we get this right, we will have a larger pool of talent to attract into the engineering fields at our tertiary institutions. I understand that there are a number of initiatives being developed to address the relatively low levels of maths and science literacy, including the ‘Stemulator’ programme, but a lot more effort is warranted.

Our universities are sound and they are working hard on producing engineering graduates, but graduates need to be put to work so that they can be developed into professionals in a reasonable time.

It can take four to five years to produce a graduate, followed by the additional two to three years of internship and mentoring required before they are eligible for registration as an engineer.

This would be the shortest route, but our graduates can achieve this only if they are given an opportunity to work and learn. Unfortunately, many of our graduates remain unemployed and are struggling to find employment.

The SAIMM established the Young Professionals Council (YPC) to contribute to tackling this problem, but they need a lot more support from our industry’s leaders. The Johannesburg Branch of the Institute is also committed to developing our youth and has agreed to work closely with the YPC to create linkages with key people in our industry, and to extend our reach into the rest of the Southern African region where we have branches that also need support.

If our industry is indeed turning the corner, surely we can, and should, do more to support the efforts of the YPC and Johannesburg Branch. I would ask anyone in a position to make a difference to do so by contacting either Shepherd Manjengwa (sfmanjengwa@gmail.com) of the YPC, or Danie Jensen(danie.xpm@gmail.com) of the Johannesburg Branch V.G. Duke President, SAIMM

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SAMCODES NEWS ➣ Category 1 and 3B CPD credits for SACNASP registered geoscientists – see this website (https:// sacnasponlinecpd.co.za/course-promotion/) for free and paid content to obtain SACNASP CPD credits.

➣ A very successful virtual workshop, Introduction to the SAMCODES and JSE Listing Requirements, was held with 69 registered delegates. For the first time (due to the advantages of the online platform) the SAMOG Code was included for the benefit of geoscientists and engineers interested in the Oil and Gas field of practice. ➣ The SAMCODES 2021 Conference will take place on 26–27 October 2021 (the precise format, face-toface or hybrid meeting, will depend on prevailing circumstances and will be announced in due time. The conference provides Competent Persons and Competent Valuators with the opportunity to prepare and present details of recognized standards and industry benchmarks, as well as relate lessons learnt in relation to the declaration of Mineral Resources and Mineral Reserves and the preparation of valuations.

In addition to providing contributions in respect of good practices and recognized standards and industry benchmarks, the conference aims to include guidance on the complex issues in ‘grey areas’ of the Codes.

The SAMCODES conference will incorporate issues such as the implementation of the Codes by the JSE, the relevance of the Codes , some of the lessons learnt since the implementation of the Codes in 2016, aspects of SANS 10320 for the declaration of Coal Resources and Coal Reserves, and the application of the various methods of valuation and where and when they should be applied in accordance with the SAMVAL Code. ➣ On behalf of the UNECE Secretariat and the Chair of the Expert Group on Resource Management (EGRM), David MacDonald, it is with great regret that we inform you of the passing of John Etherington. John was a longstanding friend of EGRM and an active member from 2006 until 2017 when he withdrew from his roles for health reasons. He contributed to the development of the United Nations Framework Classification for Resources (UNFC) and was the founding Chair of EGRM’s Technical Advisory Group. As Chair of the Technical Advisory Group, John led the development of UNFC’s sectoral specifications and bridging documents for the national systems of Russia and China. John’s untiring efforts contributed to the global application of UNFC that we witness today. He was a doyen in international oil and gas exploration and reserves auditing with over 40 years’ experience with Mobil Oil in Canada and the USA, and more recently as Managing Director of PRA International Ltd. John advised many companies on Reserve evaluation and auditing during his long career. Industry professionals remember John’s significant role in the drafting of the 2007 Petroleum Resources Management System. (David MacDonald, EGRM Chair, Scott Foster, Director, UNECE Sustainable Energy Division, Charlotte Griffiths and Hari Tulsidas, EGRM Programme Managers) Check out the latest SAMCODES News here https://www.samcode.co.za/ Your contributions in terms of photos, articles, and content are welcome – please send to sam@saimm.co.za

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Press Release: Danfoss

Energy-efficient mining for a better tomorrow Johannesburg, 20 August 2020 Never has there been more of a need for the mining industry to opt for energy-efficient measures on the journey to sustainability as part of a successful transition towards a low-carbon future. The mining sector needs to rethink its traditional energy consumption patterns to ensure that it de-carbonizes.The decreasing costs of renewables and the proven reliability of hybrid power technologies is finally driving the interest of mining companies towards energy-efficient mining practices.

Decarbonization involves the shifting of generation, transmission, distribution, and usage towards a lower carbon future. This move is dominated by renewable energy, e-mobility (electric vehicles), energy efficiency, new and future fuels such as biofuels, and demand side management.

‘The mining industry is a major consumer of energy and is Stephen Brown responsible for more than 40% of the total industrial energy use. In subSaharan Africa, the energy-intensive user group alone consumes over 40% of the electricity produced in South Africa. Just less than 50% of the energy-intensive users in South Africa, are the mines’, according to Energy Minister - Jeff Radebe.

Given the need to increase energy supply in a globally carbon-constrained environment, the mining trade needs to improve its energy-efficient technologies, such as electrical variable-speed drives, which could reduce energy consumption drastically.

‘Conventionally, motors run at a fixed speed, regardless of actual output requirement, wast-ing a tremendous amount of energy. Energy output use can be reduced by 60% by control-ling motors with electrical variable-speed drives’ says Stephen Brown, Danfoss Drives – Mining Accounts and Business Development Manager, Turkey Middle East & Africa.

Danfoss is therefore embarking on a series of informative webinars to address the topic of energy-efficient mining, geared towards mining engineers, specifiers, buyers, mining houses, and investors. These interactive webinars will enable mining experts and learners alike to think differently about their current energy usage and appreciate how the right drives are enabling energy cost savings.

Interested in attending our mining webinars?

Sign up for our webinars: https://www.danfoss. com/en/campaigns/tma/learn-under-lockdownsign-up-for-our-webinars/#tab-overview

PR Contact Lynne McCarthy – Marketing Specialist, Danfoss Turkey, Middle East & Africa – mccarthyl@danfoss.com

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Diamond plant statistics, process efficiencies, liberation modelling, and simulation: The art of the possible G. Dellas1 Affiliation: 1 Independent Consultant and Visiting Lecturer – University of the Witwatersrand, South Africa.

Correspondence to: G. Dellas

Email:

george@dellas.co.za

Synopsis The paper brings together the language of diamond numbers and the underlying principles for calculation of diamond liberation, followed by estimation of process efficiency at circuit and complete plant levels.

In this way it provides a reference point, albeit a mixture of the theoretical and empirical, to assess the effectiveness of diamond plant accounting systems in the field. Having established today’s baseline, the wider aim is ongoing education, peer technical debate, and progression to a more exact science.

Keywords diamonds, liberation, recovery, modelling.

Dates:

Received: 11 May 2020 Revised: 31 Aug. 2020 Accepted: 23 Sep. 2020 Published: October 2020

How to cite:

Dellas, G. Diamond plant statistics, process efficiencies, liberation modelling, and simulation: The art of the possible.

Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 10, pp. 561–568.

DOI ID: http://dx.doi.org/10.17159/24119717/1213/2020

This paper will be presented at the Diamonds - Source to Use 2021 Hybrid Conference, 9–10 June 2021, The Birchwood Hotel & OR Tambo Conference Centre, Johannesburg, South Africa.

Introduction Quantification of stream content in a diamond processing plant as part of daily mass balance statistics is unlike similar exercises for other commodities. This is due to the particulate distribution of diamonds, relatively low grades, wide range of particle sizes, the indeterminate state of diamond liberation, and the absence of an assay office, among other factors. It is best described as ‘the art of the possible’, given the combination of difficult data acquisition, wide use of proxy measurements, and the uniqueness of diamond extraction.

All business entities are obliged by law to produce auditable annual financial statements. The same applies to mining businesses, and it is not just confined to the financial statements. There are equally onerous legal requirements applicable to Mineral Resource and Reserve estimates in terms of tonnages, grades, and even economic values. Does the same requirement apply to the ’metallurgical accounting statements?’ The answer is a definite ‘maybe’. The vast majority of commodities are easy to measure, be it by means of mass flows or metal/mineral content, but diamonds are very different.

The key objective of the paper is a general revision of the current status quo in terms of diamond numbers, a description of a typical process flow sheet, estimation of diamond liberation using the preferential liberation factor (PLF) deportment model, and leveraging the use of plant statistics for modelling and simulation purposes. It concludes by emphasising the need for industry-wide accepted diamond simulation guidelines and plant accounting practices.

Diamond numeracy terminology By means of a general introduction, a number of quantitative descriptors are presented, specific to diamond processing, highlighting the uniqueness of diamond numeracy. This will include diamond particle sizing, diamond sizing frequency distributions (DSFDs), ore grades, liberated and locked diamond distributions, and the prevalence of matrix calculations when using the deportment model.

Corresponding descriptors are also included for the carrier ore phase.

Diamond sieve classes Diamonds are sized according to circular aperture sieve sizes commonly referred to as diamond sieve (DS) classes, mathematically nonstandard, but generally accepted in the industry. The standard DS classes are shown in Table I; with equivalent top, bottom, and geometric mean values when mapped across to conventional square mesh sizing sieves. The last column is an indication of average diamond weight in carats per DS class, where one carat is equivalent to 0.20 g.

Above +23DS, diamonds are measured individually (carats per stone) and summarized as total carats and numbers in the size fractions +15 ct, +20 ct, +30 ct, +45 ct, +60 ct,. and +100 ct. These are classified as the special large sales ranges.

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Diamond plant statistics, process efficiencies, liberation modelling, and simulation Table I

Standard DS classes

Tag

Top size (mm)

Bottom size (mm)

11.64 9.28 7.09 5.56 4.93 4.62 3.85 3.42 2.86 2.35 2.00 1.72 1.47 1.15 1.03 0.82

9.28 7.09 5.56 4.93 4.62 3.85 3.42 2.86 2.35 2.00 1.72 1.47 1.15 1.03 0.82 0.00

+23DS +21DS +19DS +17DS +15DS +13DS +12DS +11DS +9DS +7DS +6DS +5DS +3DS +2DS +1DS –1DS

Mean size (mm)

Average mass per diamond (carats)

10.39 8.11 6.28 5.24 4.77 4.22 3.63 3.13 2.59 2.17 1.85 1.59 1.30 1.09 0.92 0.58

8.036 4.850 2.480 1.570 1.260 0.860 0.561 0.371 0.211 0.123 0.089 0.072 0.035 0.021 0.014 0.001

By means of example, Table II shows a series of sizing screens used for determination of the ore particle size distribution (PSD). Selection of screen sizes is an operator decision aligned to plant operational parameters and laboratory practices. The selection below is applicable to coarse incoming run-of-mine (ROM) ore and will change in a reducing manner deeper into the flow sheet. The location tag i refers to row position with reference to matrix calculation examples.

provides insight as to the distribution of diamonds within the orebody. This is particularly useful given the highly particulate distributions, skewness effects, and generally low grades. Table III provides such information incorporating components of Table I, the data used being purely for demonstration purposes and not referenced to any particular mining operation. The location tag j refers to column position with reference to matrix calculation examples.

The third column is an indication of average commercial value per DS class, again for illustrative purposes only, as such information is generally considered confidential and will vary across the industry. The exponential increase in value as a function of size is duly noted.

From Table III, the following deductions and observations are noted ➤ Diminishing returns if one pursues total recovery efficiency, ensuring no losses at the upper end but accepting some losses at the lower end. ➤ The average value per carat calculates to $184.95, which does not correspond to any specific DS class, highlighting the limitation of averages. ➤ The average value per particle calculates to $6.09, well below the value of the smallest DS class. Another trivial example on the limitation of averages.

Table II

Ore size classes

Ore grade The grade of a kimberlitic orebody is generally expressed as carats per hundred tons, abbreviated to cpht. In the case of marine deposits the grade is expressed as carats per square metre (ct/m2), and in the case of alluvial deposits carats per cubic metre (ct/m3) is also used. For the purpose of simplicity, a grade of 100 cpht for a hypothetical sample of 100 t has been used in the calculation examples that follow.

Diamond size frequency distribution Conversion of the scalar grade value into vector format

Location Tag Top Bottom Mean PSD tag i size (mm) size (mm) size (mm) 1 2 3 4 5 6 7 8

+150.0 +90.0 +45.0 +25.0 +8.0 +4.0 +1.0 –1.0

Total

100.00

200.00 150.00 90.00 45.00 25.00 8.00 4.00 1.00

150.00 90.00 45.00 25.00 8.00 4.00 1.00 0.00

173.21 116.19 63.64 33.54 14.14 5.66 2.00 0.71

5.00 10.00 25.00 20.00 20.00 10.00 5.00 5.00

Cumulative passing (%) 95.00 85.00 60.00 40.00 20.00 10.00 5.00 0.00

Table III

DSFD information Location tag j 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Totals

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Price ($ per carat)

DSFD

Cumulative passing (%)

Particles

+23DS +21DS +19DS +17DS +15DS +13DS +12DS +11DS +9DS +7DS +6DS +5DS +3DS +2DS +1DS –1DS

2000 1000 600 300 250 150 100 90 75 65 65 65 60 50 35 5

2 3 4 5 6 7 8 9 10 11 10 9 7 4 3 2

98 95 91 86 80 73 65 56 46 35 25 16 9 5 2 0

0.25 0.62 1.61 3.18 4.76 8.14 14.26 24.26 47.39 89.43 112.36 125.00 200.00 190.48 214.29 2000.00

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100.00

100

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Particles (%) 0.01 0.02 0.05 0.10 0.16 0.27 0.47 0.80 1.56 2.95 3.70 4.12 6.59 6.27 7.06 65.88 18 495

Mass (ct)

Value ($)

Value (%)

2 3 4 5 6 7 8 9 10 11 10 9 7 4 3 2

4 000 3 000 2 400 1 500 1 500 1 050 $800 $810 $750 $715 $650 $585 $420 $200 $105 $10

21.63 16.22 12.98 8.11 8.11 5.68 4.33 4.38 4.06 3.87 3.51 3.16 2.27 1.08 0.57 0.05

100.00

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Diamond plant statistics, process efficiencies, liberation modelling, and simulation ➤ Also note that improved efficiency in a diamond plant usually refers to improved fine diamond recovery. This will automatically reduce the average value per carat, but will improve the average dollar per ton revenue recovered. This is therefore the measure to be used for overall improved plant performance.

visible to the human eye.

Matrix distribution of diamonds – ore size class by diamond size class

Liberation circuit

Given the broad particulate distribution of diamonds, mass balances and meaningful unit process efficiency information must be derived both at a global level and per DS class. Key to this approach is the use of matrix mathematics to distribute diamonds into discrete packages based on both PSD and DSFD information.

Table IV is the integration of information displayed in Tables I, II, and III. It serves as the baseline for the PLF deportment liberation calculations that follow, using the following parameters: ➤ The number of ore size classes is 8, denoted by counter i in Table II ➤ The number of diamond size class is 16, denoted by counter j in Table III ➤ A position within the matrix is denoted by (i,j) in line with accepted notation (row, column) ➤ Sample mass 100 t ➤ Ore grade 100 cpht ➤ Total diamond content 100 ct.

Generic diamond flow sheet Material flow within a typical diamond processing plant is shown in Figure 3, with emphasis on the key circuits of liberation, concentration, and final recovery.

The purpose of the liberation circuit is processing of incoming ROM ore, in order to economically release the majority of locked diamonds associated with the various ore types. This circuit employs unit operations such as comminution, fragmentation, grinding, crushing, scrubbing, and screening. Efficient liberation is a function of rock mechanical properties, fracture theory, geology, and choice of crushing and milling technology as the key variables.

Fineness of grind, as indicated by the PSD, is currently the best proxy measurement of liberation efficiency. The true quantifier of liberation efficiency by definition can only be free diamonds as a fraction of total diamonds. The latter can be determined by stage crushing of residual tailings until all the diamonds are released. In assay terms this would be equivalent to acid dissolution or fire assay, and is too costly and impractical

Diamond packet allocation per OS|DS location is calculated as follows [1] where D(i,j) Diamond content in OS class i and DS class j TD Total diamond content, the multiplication of ore grade and sample mass M(OSi) Fractional ore mass distribution (PSD) M(DSj) Fractional diamond mass distribution (DSFD).

Figure 1—Fully liberated diamonds

Locked and liberated diamond grades Unique to diamond processing is the important distinction between locked and liberated diamonds, which will be illustrated in the section dealing with deportment mathematics. A fully liberated diamond is free of any adhering gangue material as illustrated in Figure 1, while a partly liberated diamond shows residual adherence to the host rock as in Figure 2. By definition, a locked diamond is fully enclosed within the host ore and not

Figure 2—Partially liberated diamond

Table IV

Diamond allocation per OS|DS class

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Figure 3—Diamond plant material flow

in the diamond industry. Nonetheless, fineness of grind remains the best measure in combination with secondary process measurements such as percentage reduction to fine and coarse residue streams and their associated PSDs.

Concentration circuit The purpose of the concentration circuit is to separate out a diamond-rich stream which can be fed through to the final recovery circuit. Feed to the concentration circuit is from the front-end liberation circuit containing free liberated diamonds (along with residual locked diamonds), other free liberated mineral grains of variable density and mineralogical properties, waste rock particles, and residual clays and slimes depending on the quality of the upstream washing processes.

Given that concentration is currently dominated by dense medium separation (DMS), the key material property is the densimetric distribution of the incoming feed. DMS circuits can either be combined, treating the complete PSD, or split, consisting of separate fines and coarse circuits. In such cases the coarse tails above the mid cut-off size (MCO) are recirculated back to the liberation circuit for further processing. Given the advances in sensor-based sorting, coarse concentration using DMS is increasingly being replaced by X ray transmission (XRT) sorters.

Recovery circuit The purpose of the recovery circuit is targeted identification and extraction of liberated diamonds emanating from the concentration circuit. The major unit processes found in a recovery plant include sizing screens, magnetic separators, electronic sorting machines, dryers, and glove boxes. There are many variations of recovery plant flow sheets focusing either on maximum diamond recovery efficiency or maximum product grade, or both.

Understanding of the material properties of the gangue as well as the fundamentals of the candidate sensor technologies is critical to successful recovery circuit design. Alignment of these two aspects is critical in order to maximize recovery efficiency at the lowest possible yield.

grind. This in turn requires understanding of diamond liberation and associated numerical modelling of the process. This is currently done by using the diamond deportment model, which combines PSD, PSFD, grade, and the PLF to predict liberated and locked diamond content distribution within the processing plant.

In times long past, the rule of thumb for estimating diamond lock-up was the ‘4:1 rule’, indicating that the maximum nominal size of a diamond that could be locked within an ore particle was ¼ the nominal size of the particle; alternatively, the particle was four times the diamond size. This is the definition of PLF, represented as an inverse within 0 and 1. The typical range of PLF values is between 0.25 and 0.45, with 0.35 a good starting point. A low PLF value indicates reduced lock-up and easier liberation usually associated with larger diamonds, the converse applying to smaller diamonds. In applying the PLF as shown in Figure 4, a step function is used, meaning either fully liberated (1) or fully locked (0), which although simplistic has proved its robustness in industry.

This is an area in need of much research to improve from a step function to the more familiar S- curve associated with all mineral extraction processes, as shown in Figure 5. For the purposes of this narrative and associated examples the PLF will be used in its simplest step function form. As fundamental knowledge improves in the coming years, inclusive of new liberation concepts and ideas, scientific alternatives to the PLF deportment model will become possible.

The diamond deportment model and associated mathematics Calculation of liberated and locked diamond content is a five-step process, the starting point being the allocation of total diamonds into their respective OS|DS classes, as described in the derivation of Table IV, reproduced below as Matrix A.

Determination of diamond liberation While it is accepted that comminution promotes mineral liberation, with a positive correlation between fineness of grind and degree of liberation, modelling and quantification of mineral liberation is not always straightforward. In the case of diamond processing, reducing everything to ‘bug dust’ destroys the valuable species; therefore the objective becomes one of optimum

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Figure 4—Current PLF application (size-independent) The Journal of the Southern African Institute of Mining and Metallurgy

Diamond plant statistics, process efficiencies, liberation modelling, and simulation The third step is application of the PLF test (constant value of 0.35) to determine liberation status.

[3] Matrix D is the multiplication result of Matrix A by Matrix C, with the last row in Matrix D providing an estimate of the liberated DSFD. This is a new distinct mineral stream separated out from the ore stream.

Figure 5—Future PLF application (size-dependent)

[4]

The second step is calculation of diamond to ore size ratio per OS|DS class as shown in Matrix B.

Subtracting Matrix D from Matrix A, shown as Matrix E, gives the estimate of locked diamonds which remain associated with the ore classes. This in effect is the locked DSFD. [5]

[2]

Matrix A. Diamond allocation per OS|DS class

Matrix B. Diamond to ore size ratio per OS|DS class

Matrix C. Liberation status (0 = locked, 1= liberated)

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Diamond plant statistics, process efficiencies, liberation modelling, and simulation Matrix D. Liberated diamond per OS|DS class

Matrix E. Locked diamonds per OS|DS class

The information contained in the above matrices is useful in determination of diamond content across the flow sheet. Determination of value distribution is easily done by incorporating price data to generate a corresponding set of financial matrices. The combination of the two is critical in identifying the MCO for the concentration circuit, with concentration tailings above the MCO close-circuited back to the liberation circuit for additional processing. Figure 6 shows the DSFD for the example used above in terms of liberated, locked, and total distributions.

In concluding the discussion on the PLF deportment model it suffices to say that accurate knowledge of the grade in critical.

Additional to this is the interplay between the DSFD and stream PSD, as the two key drivers, in the determination of optimum grind for a diamond processing plant.

Dd Ddms Dk

Density of diamond, typically 3.52 Cut point density of DMS circuit, typically 3.10 Density of kimberlite rock, typically 2.60

Diamond lock-up model based on density differentials Reference is made to earlier methods used to estimate diamond lock-up based on the difference in densities between diamonds and the host ore, with specific application to DMS. It is premised on the assumption that an ore particle containing a locked diamond having a composite density equal to the DMS cut point will be lost to the tailings stream. This is illustrated in Figure 7, showing a spherical diamond enclosed within a spherical kimberlite ore particle.

The maximum size of a diamond that can be locked within an ore particle, expressed volumetrically, is given by Equation [6].

Figure 6—DSFD showing liberated and locked contributions

[6] where Vd Vp

Volume of diamond Volume of particle

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Figure 7—Composite spherical diamond and ore particle VOLUME 120

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Diamond plant statistics, process efficiencies, liberation modelling, and simulation Expressed in terms of particle sizes, the Equation [7] applies at the point of equilibrium.

[7] where Sd Sp

Size of diamond expressed as the diameter Size of the particle expressed as the diameter

Substituting the typical values above yields a diamond to ore size ratio of 0.82, indicating that such a situation cannot exist in terms of the PLF deportment model, which operates in the range 0.25 to 0.45 with 0.82 indicating complete liberation. It is not the purpose of this paper to critique the validity of the two approaches, other than to emphasise the need for continuous research and validation as to the fundamental mechanisms of diamond liberation, and conversely diamond lock-up. The industry remains open to new thinking.

Simulation package imperative Calculation of the metallurgical flow sheet mass balance is a daunting task at the best of times, even for single-phase commodity operations. With the advent of computers and the wide availability of simulation packages it is much easier nowadays, and many commodity-specific packages have been developed over the years. Given the relative complexity of diamond mathematics as illustrated with the diamond deportment model, the need for diamond-specific simulation packages goes without saying.

Figure 8 is a very simplistic representation of such a simulation package using off-the-shelf software as the top block, to which is interfaced custom-developed diamond tracking subroutines represented in the bottom block. The interconnectors between the two are the ore and diamond data-sets for all the streams in the flow sheet.

Diamond flow sheet simulation packages do exist, although they are generally considered to be proprietary information. This applies to producer companies, engineering design houses, and industry consultants, among others. In the author’s opinion, the critical challenge is the need for an industry-agreed package, open source and available to all participants. This will make for a single point of reference, simplified peer reviews, and improved industry technical assurance.

product streams. This should be a minimum requirement until such time that full diamond accounting systems and protocols are developed and adopted by industry.

Total plant recovery efficiency Despite the scarcity of internal stream diamond information, calculation and evaluation of the overall recovery efficiency is possible by reconciliation of diamonds recovered in stream 7 against ROM diamonds sent to the plant in ROM stream 1. This is done both at the global level for an overall plant efficiency factor and per DS class, in the understanding that recovery efficiency decreases as a function of size. Such a hypothetical control chart is shown in Figure 11. Depending on the level of available geological and plant data, coupled to the technical sophistication of associated information systems, useful insights are possible, namely: ➤ Constant under- or over-recovery across the DS spectrum, indicating inaccuracy on incoming grade ➤ Reduced recoveries in certain DS classes, indicative of process losses about those size fractions ➤ Reduced recoveries in the larger DS classes, possible evidence of diamond damage or security issues ➤ Recovery efficiencies in excess of ROM, indicative of breakage from larger DS classes into smaller DS classes or grade inaccuracies.

Figure 8—Simplified representation for a generic diamond flow sheet simulation package

Plant statistics and circuit efficiencies Plant statistics With reference to Figure 3, imagine the ideal mass balance statistics depicted in Figure 9, where all major streams are fully quantified in terms of ore and diamond throughput, with all associated PSD and DSFD information. Diamond throughput is indicated as carats per hour (c/h), while % dbw (percentage diamond by weight) is a quality measure on the final export product. Some of the information will be derived from field instrumentation and production returns, with the balance estimated by means of simulation modelling software. To add reliability to the latter would require periodic auditing of these streams through an independent bulk sample plant (BSP). This is a discussion for another day, given the decline in such capability across the industry.

The reality is closer to Figure 10, with complete ore mass balance information on the majority of key process streams, while diamond content information is limited to the ROM and final The Journal of the Southern African Institute of Mining and Metallurgy

Figure 9—Plant mass balance statistics in an ideal world

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Diamond plant statistics, process efficiencies, liberation modelling, and simulation a final recovery plant. The incoming feed is separated into a number of distinct size fractions, shown as fines, middles, and coarse. These are treated through a primary recovery circuit to produce an initial rougher concentrate which is upgraded in a secondary re-concentration circuit to produce a final product suitable for hand sorting. In comparison to the upstream circuits, recovery plants are high-security, low-throughput operations targeting liberated diamonds. Modern-day designs include sampling points, making it possible to take audit samples in order to determine process efficiencies at unit process level, and also per size stream and for the whole recovery plant. This is supplemented by the use of proxy tracers for machine set-up purposes.

Figure 11—Control chart example

Conclusions The use of control charts is widely practiced across the industry, providing high-level assurance as to plant performance and linking back to mineral resource estimates. Such charts can be compiled per ore type, defined production periods, and also over cumulative timelines.

Plant liberation efficiency Reconstitution of the outgoing stream PSD data (streams 2, 4, and 6) to calculate an in-situ plant PSD can serve as a useful proxy measurement to estimate liberation efficiency for the complete plant. This internal PSD, in combination with ROM grade and the PLF deportment algorithm, also provides a total liberated diamond profile for the plant, which in combination with control chart information can guide the plant metallurgists to identify areas of process inefficiencies.

Concentration circuit efficiency In the case of plants using DMS as the method of concentration, the circuit efficiency is determined by the use of density tracer testing, in combination with periodic washability curves of the cyclone product streams. The latter is standard practice across all commodities using DMS. In the case of diamonds, particular emphasis is placed on recovery efficiency to sinks at density point 3.52 g/cm3 specific to diamond.

Given the increasing use of electronic sorting as a way of concentration, estimation of process efficiency is done by use of proxy tracers. In operations where independent audit plants are available, tailings and concentrate samples can be taken for separate processing, to determine process efficiency.

Recovery plant circuit efficiency Figure 12 is a generic representation of material flow within

In line with the key objective of the paper, a general revision of existing information, operational practices, industry status quo, and empirical process models into a single narrative is required.

This is for the purposes of continuous learning, ongoing debate, and development into a more exact science. Some pointers into the future: ➤ Adaptation of an industry-accepted diamond flow sheet simulation package, accessible to all stakeholders, thus enhancing the assurance process ➤ Ongoing research into the fundamentals of diamond liberation as a possible alternative to the PLF deportment model currently in use ➤ Uniformity in plant statistics reporting and adaptation of minimum requirements ➤ Continuous education in the industry.

Acknowledgements The experience and learnings gained over a thirty-year career with the De Beers Group of companies is sincerely acknowledged, the numerous discussions with learned peers, fellow diamond metallurgists and professionals across the complete value chain.

A special acknowledgment to Pete Sergeant, a global thought leader in the field, for all the invaluable conversations and lessons over the years on the subject of diamond numeracy.

References

Machowski, R. 2007. Technique for estimation of diamond lockup in a diamond processing plant. Proceedings of Diamonds Source to Use Conference 2007.

Southern African Institute of Mining and Metallurgy, Johannesburg.

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. u

Figure 12—Recovery plant material flow

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Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code T.R. Marshall1 Affiliation: 1 Explorations Unlimited Johannesburg, South Africa.

Correspondence to: T.R. Marshall

Email: marshall.tania@gmail.com

Dates:

Received: 20 May 2020 Revised: 13 Aug. 2020 Accepted: 17 Aug. 2020 Published: October 2020

How to cite:

Marshall, T.R. Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code.

Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 10, pp. 569–574.

Synopsis Alluvial diamond and other gemstone deposits have, typically, been exploited by small artisanal operations with little or no geological control. Over the last decade, however, alluvial deposits have become more interesting to larger (often listed), mid-tier companies wishing to benefit from the higher incomes generated by high-quality stones.

The difficulties associated with evaluation and valuation of such alluvial diamond/gemstone deposits are widely known but, regrettably, often not widely understood – leading to several misconceptions over what can and cannot be expected from such deposits. Fortunately, there is a reasonably well-established body of knowledge on alluvial diamonds that has resulted in accepted industry-standard practices of how to evaluate these deposits.

The 2016 version of the SAMREC Code includes several sections specific to the requirements of secondary diamond and gemstone deposits, both alluvial and marine. Consequently, it is possible to define Diamond/Gemstone Resources in accordance with the major international Committee for Mineral Reserves International Reporting Standards (CRIRSCO) type codes. This paper outlines some of the requirements and some of the pitfalls that need to be appreciated while estimating Diamond/Gemstone Resources and/or Reserves on such deposits.

Keywords Reporting Codes, SAMREC, alluvial diamonds, gemstones, Resources, Reserves.

DOI ID: http://dx.doi.org/10.17159/24119717/1220/2020 ORCiD ID: T.R. Marshall https://orchid.org/000-00019662-1079 This paper will be presented at the Diamonds - Source to Use 2021 Hybrid Conference, 9–10 June 2021, The Birchwood Hotel & OR Tambo Conference Centre, Johannesburg, South Africa.

Introduction Historically, alluvial diamond and gemstone deposits (the term ‘alluvial’ in this paper includes all secondary fluvial, marine, and/or aeolian deposits) have been mined on a small-to-medium scale by artisanal operators/diggers or by so-called ‘professional diggers’. Seldom do these kinds of operators do any substantial formal exploration ahead of mining and, because they are generally not listed on a public stock exchange, they rarely have need for Competent Persons Reports (CPRs) or geological reports.

However, over the past decade, a number of mid-tier listed companies have invested significantly in alluvial diamond deposits in South Africa (Etruscan Diamonds and Rockwell Diamonds, for example) and in other parts of Africa (Zimbabwe, the DRC, Angola, Liberia, and Sierra Leone, specifically).

Marine deposits (onshore and offshore) have also been successfully mined along the South African and Namibian west coast. Examples of current, commercial alluvial diamond mining operations include those in Angola (Lucapa Diamonds NL, TransHex) along the Sewa River, Sierra Leone (Allotropes/ Newfields NL), and South Africa (TransHex). The same is true with gemstone deposits in Zambia and Mocambique (Gemfields Group Limited), Israel (Shefa Gems), Colombia/Mocambique (Fura Gems), and elsewhere.

Since most of these companies are publicly traded (in Australia, Canada, South Africa, or the UK), they require Code-compliant documentation. Although not a public company, NAMDEB (De Beers’ mining company in Namibia) reporting procedures are still Code-compliant, to assure their partners and investors of their application of best-practice, transparency, materiality, and competence.

Because alluvial diamond deposits have long been associated with artisanal operators, resource estimations for these types of deposits have historically been deemed to be impossible. The 2016

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Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code SAMREC Code (The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves), and especially the associated Diamond/Gemstone Guideline document (SAMREC Guideline Document for the Reporting of Diamond Exploration Results, Diamond Resources and Diamond Reserves (and other Gemstones, where Relevant) v.1.2) include several sections that will assist in the production of technical reports that comply with Resource/Reserve estimation codes internationally. While much of this discussion focuses on diamond deposits, the guidelines are equally applicable to gemstone deposits and such operators should take cognisance of these guidelines in the evaluation and public reporting of such deposits.

Diamond deposits are different from deposits of other commodities such as precious and base metals for a number of reasons: ➤ Their low diamond content and high heterogeneity (Lock, 2003) ➤ The term ‘quality’ cannot be substituted for ‘grade’ ➤ The widely differing nature of deposits in varying fluvial and marine environments and their associated forms of mineralization results in differing estimation methods that may be applicable under unique circumstances ➤ The specialized field of diamond valuation. The particulate nature of diamonds, and their individual physical characteristics and underlying diamond size frequency distribution (SFD) patterns, have a significant impact on diamond value.

Notwithstanding these differences, alluvial diamond deposits can be evaluated and classified using the standard SAMREC Code classification system (SAMREC Code Figures 1 and 3 – not reproduced here). In this document, both fluvial and marine deposits are included in the term ’alluvial’. It is important to note that for all diamond exploration and evaluation reporting the correct terminologies are: Diamond Exploration Results, Diamond Resources, and Diamond Reserves, as per SAMREC Figure 3.

Diamond Exploration Results For the most part, all of the provisions of SAMREC dealing with Exploration Results and Exploration Targets (Conceptual and pre-resource Mineralisation) apply equally to alluvial diamond deposits. Diamond-specific issues such as bulk sampling or trial mining, diamond size/value frequency distributions, the number of diamonds required for valuation purposes, etc are addressed in the Diamond Guidelines.

Most importantly, it must be noted that neither kimberlitic indicator minerals (KIMs) nor microdiamond data are applicable to alluvial deposits. KIMs and microdiamond data pertain specifically to primary deposits. Since alluvial deposits may be the result of erosion of any number of primary sources (both barren and diamondiferous), this data has no relevance for secondary deposits.

Diamond Resources As with any other Mineral Resources, it is fundamental for a Diamond Resource to have Reasonable Prospects for Eventual Economic Extraction (RPEEE). With reference to the requirements

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of SAMREC Table I, RPEEE needs to be demonstrated through a high-level, reasoned assessment of applicable factors and justified to the satisfaction of the Competent Person (CP).

This assessment is not a Scoping, Pre-feasibility (PFS), or Feasibility Study (FS) and cannot be used for conversion to a Diamond Reserve. However, it must include an assessment, at an appropriate level, of the geological, engineering (including mining and processing parameters), metallurgical, legal, infrastructural, environmental, marketing, socio-political, and economic assumptions which, in the opinion of the CP, are likely to influence the prospect of economic extraction.

According to the Diamond Guidelines, such an assessment must be based on the principle of reasonableness and be justifiable and defendable. The assumptions used to test for RPEEE must be within known/assumed tolerances or have examples of precedence and be applied at an appropriate and practicable scale. The principle of reasonableness shall be applied together with the primary SAMREC Code principles of Materiality, Transparency, and Competency.

In order to demonstrate that a Diamond Resource has RPEEE, some appreciation of the likely stone size distribution and value is necessary, however preliminary this estimate may be. Furthermore, spatial data distribution, as well as geological and grade continuity, must also be considered. It is also critical that project economics risk factors be clearly defined, and that these are current, reasonably developed, and based on generally accepted industry practice and experience. As an example, the potential capital cost may be relevant and should always be shown to be recoverable from project revenue.

SAMREC does not define any drill spacing or sampling number/size criteria; rather, this is determined by the site-specific geological model. Drilling and sampling must be optimized appropriate to the deposit type and style of mineralization. The programme needs to consider factors such as shape, number, and type of sedimentary facies and the average grade as well as grade variation expected. In complex deposits it may be difficult to ensure that bulk samples are truly representative of the whole deposit. As a result, drilling and sampling on such deposits may need to be significantly more extensive than for primary deposits in order to attain to the same level of resource classification. In extrapolating both stratigraphic and grade/value data, the CP needs to keep in mind the uncertainty in known or expected spatial continuity of the particular depositional environment.

Classification of a Diamond Resource requires actual sampling information from the project property or particular geological domain. Resource grades or values cannot be based on production data from adjacent properties. Neither can Resource parameters be based on unverified or unverifiable historical estimates, anecdotal evidence, or artisanal results. Estimates of quantity, grade, or value based on limited information and analogies with known deposits of similar geological character are inadequate for classification as Inferred Diamond Resources.

Note that geological evidence of continuity of mineralization based on outcrops, trenches, pits, workings and/or drill-holes is required to estimate a Diamond Resource. Geophysical anomalies, remote sensing data, satellite images and/or aerial photograph evidence alone are insufficient to estimate volumes. Any such data requires an appropriate amount of corroborating drilling The Journal of the Southern African Institute of Mining and Metallurgy

Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code and/or pitting information, with realistic extrapolation around data-points. If volumes are converted to tonnages, then sufficient density determinations need to exist – the CP will decide what is sufficient, based on the consistency of results.

The requirement of actual sampling data is one of the main stumbling blocks for acceptable Resource estimation on alluvial diamond projects. Many promoters try to ‘shoehorn’ Exploration Targets into the Inferred Resource category, where such projects lack surveyed sample volumes, verifiable production data, and/or sufficient Kimberley Process-compliant diamond sales/valuation data. Without such information, the project cannot be classified as a ‘Resource’.

The Diamond Resource classification categories (Figure 1) are fundamentally the same as for any other mineral deposit.

However, it should be noted that few diamond deposits ever get to Measured Resource classification (Lock, 2003), and marine deposits often will not even attain Indicated Resource status.

While achieving a Measured Resource classification is not impossible for an alluvial diamond deposit, it is most unlikely due to the lack of geological homogeneity and uncertainty in spatial continuity of size, grade, and price/value relationships in such deposits. This means that Proved Diamond Reserves are also most unlikely.

SAMREC Clause 24 notes that where untested practices are applied, they must be justified by the CP. This is especially applicable where novel, untested, nonstandard, or littleunderstood methodologies are used for the determination of

RPEEE and Resource estimation. Where such methods are used, they need to be accompanied by peer-reviewed academic or technical references, and they require detailed explanations, discussion, and justification.

These issues apply most directly to the use of geostatistical estimation methods. While such methods can be of inestimable value (Oosterveld, 1972, 2008; Phillips, 1971; Rombouts, 1987; Sichel, 1972; Jacob, 2016), geostatistical estimations based on small samples have not been universally successful, especially on fluvial deposits. Therefore, when applied in these environments, careful consideration and justification is required.

Irrespective of the class of Resource selected, the estimate must identify separate geological domains, where applicable.

Each domain should have, at least (however preliminary), initial indications of area/volume, diamond grade, diamond size frequency distribution, diamond value, and RPEEE. This is specifically applicable to alluvial deposits where different depositional environments can have widely differing diamond carrying characteristics.

Also required for all classes of resource estimation is a size-frequency distribution (SFD) with a minimum of 100 ct recommended for a low confidence SFD. It is important for the CP to discuss the sizing definition used (i.e. DTC, Rubin/Antwerp, Christensen, grainers, square mesh, Tyler mesh, etc.) and to note whether the size classifications are the same for SFD and value data and provide correlations/conversions where more than one system has been used.

Figure 1—Classification of Diamond Exploration Results, Diamond Resources, and Diamond Reserves (SAMREC, 2016) The Journal of the Southern African Institute of Mining and Metallurgy

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Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code Diamond Resource classification In the selection of the Resource category, classification criteria need to be disclosed and justified using a systematic methodology utilizing transparent criteria which must be discussed explicitly and in sufficient detail so as to be clearly understood. The SAMREC Code stresses that higher levels in the Diamond Resource classification are dependent on both increasing geological knowledge and confidence in the geological data. Therefore, the amount of direct sampling, and the level of uncertainty in spatial continuity of size, grade, and price/ value relationships should determine the appropriate Resource category. It is incumbent on the CP to justify the classification in a transparent and professional manner.

Unlike most other commodities, for the classification of any category of Resource, there needs to be verifiable information regarding volume/tonnage, diamond grade, and diamond value.

In addition, bottom cut-off screen sizes need to be reported for each Diamond Resource statement/tabulation. As with all Mineral Resource statements, Exploration Results may not be included in a Diamond Resource statement.

Due to the importance of diamond value, the SAMREC Code provides guidance on the (run-of-mine) parcel size required for valuation. It is fundamental to appreciate that under specific circumstances, the number of carats or stones required to estimate the diamond value to a low, reasonable, or high confidence may need to be significantly different from the numbers given for guidance (typically higher, seldom lower). The CP must discuss (transparently, on an ‘if not, why not’ basis) the rationale behind the number of stones or carats selected and the level of confidence in the estimate.

SAMREC Clause 62 notes that ‘Where the valuation is used in the estimation of Diamond Resources or Diamond Reserves, the valuation shall be based on a parcel representative of the size distribution and assortment of the diamond populations in the deposit. The CP shall explain the rationale behind the parcel size that has been used in the estimation of value for the Diamond Resource or Diamond Reserve and the level of confidence in the estimate. The minimum representative size of the valuation parcel depends on the characteristic stone distribution and quality of stones in the deposit. For all valuations (irrespective of Resource classification), associated diamond size frequency distributions shall be provided, along with a discussion of the relevant applicable parcel size.’

The guidelines recommend that for an Inferred Diamond Resource, 500 ct may be sufficient for a low confidence valuation, depending on the SFD. For an Indicated Resource, 2000 ct may be sufficient for a deposit where the SFD indicates low variability of size (typically primary deposits), while more than 2500 ct would be required for a high variability environment such as an alluvial deposit (see also NAPEGG, 1997 for reference).

Under specific circumstances, parcels of more than 5000 ct may be required, and in extreme cases an adequate parcel size may never be achieved during industry-standard evaluation sampling. or even trial mining. Such cases would likely include deposits characterized by large, high-quality/value stones. A marine environment, which is typically not highly variable, may

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be valued by 500 ct (Inferred Resource) to 1000 ct (Indicated Resource), depending on how well the SFD is constrained. A revenue estimate is date-specific and linked to a value or price that should not be more than six months old. Although it might not be practicable under all circumstances, best practice would be to try and keep all evaluation parcels for future use.

For all Resource categories, the diamond parcel (for value) must come from the property (and from the deposit being estimated) – it must not be a regional value nor extrapolated from adjacent/nearby operations, nor even from a satellite deposit.

One of the challenges with any diamond deposit is that it is not possible to assay for diamond content. As shown above, geostatistical estimations of grade based on a small sample size have not always been universally successful, especially for alluvial deposits. Consequently, the most reliable information can only be obtained from bulk sampling. Typically, this involves taking large, representative samples by pitting, trenching, and/or large-diameter diameter drilling (LLD) in the land environment, and through diver sampling and probe drilling platforms offshore.

The advantage of such bulk-sampling operations is that they can grow seamlessly into trial mining (where they can form the basis of a Pre-feasibility or Feasibility Study) and, thereafter, to fullscale production.

Inferred Diamond Resources In many instances an Inferred Diamond Resource has erroneously been taken to mean almost the same as an inventory, with pretty much everything potentially occurring on a property being included. The requirements of SAMREC 2016 make it clear that a significant amount of work has to be done on an alluvial deposit before even an Inferred Resource can be estimated – an Inferred Diamond Resource is a low-level estimate, not a no-level estimate and definitely not a ‘guesstimate’. The associated increase in confidence is expected to help to increase investor confidence and the ability to raise finance from stock exchanges and other public institutions.

The SAMREC Diamond Guidelines highlight a number of features required to make certain that the Inferred (alluvial) Diamond Resource estimate is at an acceptable standard: ➤ The majority of Inferred Diamond Resources are reasonably expected to be upgraded to Indicated Diamond Resources with continued exploration. However, often an Inferred Diamond Resource is presented as covering a very large area (up to farm scale), being an average of data collected from very specific locations. Subsequently, an expanded drilling/pitting programme has shown that 80–90% of the Resource actually cannot be mined economically for one reason or another, and the resulting Indicated Resource is a very small proportion of the original. This is not an acceptable practice. ➤ An Inferred Diamond Resource is often based on interpolation between widely spaced data where there is reason to expect geological continuity of mineralization.

However, the extent of extrapolation outside of the nominal drilling or sampling grid spacing needs to be justified. The reader needs to be provided with sufficient information in respect of: The Journal of the Southern African Institute of Mining and Metallurgy

Evaluation of secondary diamond (and gemstone) deposits according to the SAMREC Code

– The maximum distance that the Diamond Resource is extrapolated beyond the sample points

– The proportion of the Diamond Resource that is based on extrapolated data

– The basis on which the Diamond Resource is extrapolated to these limits

– Diagrammatic representations (maps) should be included which clearly and unambiguously show the part of the Inferred Diamond Resource that has been extrapolated beyond actual sample points.

➤ Where the Diamond Resource being reported is predominantly an Inferred Diamond Resource, sufficient supporting information must be provided to enable the reader to evaluate and assess the risk associated with the reported Diamond Resource. ➤ Some 500 ct of diamonds (run-of-mine production) should be recovered for purposes of diamond valuation (revenue estimation). Typically, on alluvial deposits this means 500 ct sold through an applicable Kimberley Process mechanism.

Bulk sampling vs trial mining On alluvial deposits, bulk sampling programmes expand into trial mining, which typically moves seamlessly into full production mining once the techno-economic or Modifying Factors of the gravels are determined. In the interests of standardized terminology (SAMREC Diamond Guidelines): ➤ Bulk sampling is taken to be the initial period of sampling during which Exploration Targets are investigated and Diamond Resources are identified. ➤ Trial mining is the period during which the relevant mining, processing, and other economic factors (the Modifying Factors) are evaluated that may, ultimately, lead to the conversion of some or all of the Indicated Diamond Resources to Probable Diamond Reserves. Trial mining typically forms the basis of the PFS/FS. In certain circumstances (specifically marine projects), trial mining may not always be justifiable, and Modifying Factors are, typically, projected from engineering design specifications and historical performance. These are then used to estimate production with appropriate ranges which are imported into the financial and risk assessment models. The methods of mining and processing used in the bulk sampling, trial mining, and production mining phases of alluvial operation may be similar (but not necessarily so), except for the volumes processed. Full production is typically initiated once Diamond Reserves have been identified. Due to the nature of alluvial diamond deposits, it is often not possible to complete a single-stage PFS that converts all of the Indicated Resources to Probable Reserves. Ongoing production from Diamond Reserve blocks typically acts as a continuous trial mining (PFS/FS) programme for the adjacent Indicated Diamond Resource. As a result, as mining of existing Probable Diamond Reserves proceeds and as confidence in the geological and mining parameters is upheld (or increases), then surrounding Indicated The Journal of the Southern African Institute of Mining and Metallurgy

Diamond Resources can be upgraded to Probable Diamond Reserves on a continual basis, without the need for a separate technical study (see, for example Lock, 2004).

It is, generally, only in situations where new processing methodologies are introduced (or where any of the Modifying Factors change materially) that an additional PFS/FS may need to be completed.

Technical Studies There are no differences in the requirements for Technical Studies on alluvial diamond deposits and for any other mineral deposit.

The same Modifying Factors need to be considered to the same levels of confidence and in the detail required by SAMREC Table II. Indicated Diamond Resources can be converted to Probable Diamond Reserves through a PFS/FS. On alluvial diamond projects, a trial mining programme typically fulfils the role of a PFS or FS. It is worth noting that Scoping Studies require the estimation of Inferred Resources, at least. No Technical Study can be done on conceptual Exploration Targets or pre-resource Mineralisation and Scoping Studies cannot be used to convert Resources to Reserves.

However, during the early stages of exploration (to generate Exploration Targets or identify pre-Resource Mineralisation), some level of financial analysis is often carried out by a company on exploration data which might not include Diamond Resource estimates, to assess the potential for the project to proceed to the next phase of exploration. These analyses are considered to be a part of the exploration programme planning and are solely for internal company decision-making purposes. They are not for public disclosure.

Since conventional macro-diamond processing techniques are not designed to liberate or recover all contained diamonds, there is no such thing as an ‘in-situ’ grade – it is only a processed or recovered grade, which is dependent on the plant or process employed. Therefore, the relative efficiencies of sampling and subsequent mining technologies must be addressed. These recovery factors are given more confidence through the use of tailings and tracer audits.

Diamond reserves Likewise, the principles of Diamond Reserve Estimation of alluvial diamond deposits do not vary significantly from those determining Mineral Reserves on other types of deposits, and all the relevant clauses (no. 35 to 43) in the SAMREC Code apply equally to alluvial diamond deposits. Especially important in the estimation of Diamond Reserves is the degree of confidence that can be placed in the diamond revenue model.

Since a Proved Diamond Reserve is based on a Measured Diamond Resource, and since this status is seldom achieved on alluvial diamond deposits, it follows that Proved Diamond Reserves will rarely be attained.

One common mistake made in many alluvial diamond projects is the assumption that a Diamond Reserve is a fixed parameter and can be transferred from one operator to the next.

Diamond Reserve grades cannot be measured absolutely (there is no such thing as an (in-situ alluvial diamond grade’). Grades estimated through the use of one processing technology may not be applicable to another type of plant. The Modifying Factors must be determined for each specific project and operator.

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References

The SAMREC Code allows that mine planning and design may include a portion of Inferred Resources. However, there are a number of caveats and provisos that need to be borne in mind when including such Inferred Resources in a mine plan – these are the same as for any other mining operation. Modifying Factors and assumptions that were applied to the Indicated and Measured Diamond Resources to determine the Diamond Reserves must be equally applied to the Inferred Diamond Resources, if such are considered as part of the Life-of-Mine Plan. Although included in a Life-of-Mine Plan, Inferred Diamond Resources cannot be converted to Diamond Reserves and must not be stated as part of the Diamond Reserve.

Jacob, J. 2016. Contextualized risk mitigation based on geological proxies in

Compiling a CPR for an alluvial diamond project The SAMREC Code contains a section dealing specifically with the reporting of Diamond Exploration Results, Diamond Resources, and Diamond Reserves (Clauses 60 to 72), inclusive of diamondspecific classification categories. These clauses include modified definitions; probably the most important is that volume/tonnage (quantity) and grade and diamond value are all required for all classification levels of Resources and Reserves. Section 11 of Table I in the SAMREC Code lists the diamond-specific requirements that are to be included in a CPR. Such information is not to be appended as a separate chapter of the CPR but must be incorporated into the general reporting sections.

The reporting of diamond projects requires the application of the Diamond Guideline document, which provides the methodologies and definitions of the relevant terms that must be considered when preparing reports on Diamond Resources and Diamond Reserves. This Guideline document addresses various issues specific to alluvial/marine deposits. The Guideline document also contains (Appendix A) a guide to a Table of Contents that may be used when compiling CPRs. Due to the nature of alluvial diamond deposits, they often give rise to abuse of environmental, social, and/or governance issues. As a result, its is important for the CP to pay more than the usual attention to these matters. The South African Guideline for the Reporting of Environmental, Social and Governance parameters within the Solid Minerals and Oil and Gas Industries (SAMESG, 2017) has been compiled to assist CPs in completing the relevant sections of Table 1 in the SAMREC Code.

As with any mineral asset, valuation of alluvial diamond deposits must be done in accordance with SAMVAL 2016 and must be based on a SAMREC-compliant Resource/Reserve estimation report (or any CRIRSCO-based Code). Since most alluvial diamond valuations will, likely, be for projects in early-stage exploration, most valuations will be based on Cost and Market methodologies (McDonald, 2017; van der Merwe, 2017). It is only when Diamond Reserves (based on Indicated Resources and at least a PFS) have been estimated that Income-based methods, particularly the popular Discounted Cash Flow, are acceptable (Marshall, 2016). Inferred Resources can be valued by such methods only if subjected to the caveats required by SAMVAL. OCTOBER 2020

of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg.

Lock, N.P. 2003. Comparing carats to kilograms. Mining Mirror, October. pp. 36-38.

Lock, N.P. 2004. Codes and alluvials. Rough Diamond Review, vol. 6.

Marshall, T.R. 2016. Valuation of alluvial diamond deposits. Proceedings of the SAMREC/SAMVAL Companioin Volume Conference. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 151–160.

McDonald, A. 2017. Valuation of an early-stage alluvial diamond prospect - a case study. Mineral Project Valuation School. Southern African Institute of Mining and Metallurgy, Johannesburg.

NAPEGG. 1997. Repporting of Diamond Exploration Results, Identified Mineral Resources and Ore Reserves. Association of Professional Engineers, Geologists and Geophysicists of the Northwest Territories, Yellowknife, NWT, Canada.

Oosterveld, O.O. 1972. Ore reserve estimation and depletion planning. Proceedings of the 10th International Symposium on the Application of Computers in the Minerals Industry. http://www.saimm.co.za/Conferences/Apcom72/065Oosterveld.pdf Oosterveld, O.O. 2008. Wouterspan, Holpan and Klipdam, Saxendrift—size frequency and large stones for 2005–2008 period. Internal report for Rockwell Ventures Inc. Phillips, R. 1971. A method for estimating the grade of diamond deposits.

Transactions of the Institution of Mininga and Metallurgy Section B3. pp. 357–362.

Rombouts, L. 1987. Evaluation of low grade/high value diamond. Mining Magazine. pp. 217–220.

SAMESG 2017. The South African Guideline for the reporting of Environmental, Social and Governance parameters within the Solid Minerals and Oil & Gas Industries. https://www.samcode.co.za/codes/category/8-reportingcodes?download=122:samesg 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-reporting-codes?download=120:samrec

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alluvial diamond mining using geostatistical techniques. PhD thesis, Faculty

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SAMREC. (2019. SAMREC Diamond Working Group. SAMREC Guideline Document for the Reporting of Diamond Exploration Results, Diamond Resources and Diamond Reserves (and other Gemstones, where Relevant) v.1.2. https://www. samcode.co.za/codes/category/8-reporting-codes?download=121:diamondguideline Sichel, H.S. 1972. Statistical valuation of diamondiferous deposits. Proceedings of the Tenth International Symposium on the Application of Computers in the Mineral Industry. https://www.saimm.co.za/Journal/v073n07p235.pdf Van der Merwe, A.J. 2017. Applying the Cost Approach to valuation of exploration stage mineral assets.: https://www.samcode.co.za/training/reportingpractices

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Satellite applications in diamond exploration and mine monitoring N.C. Steenkamp1, S.L. Goosen1, and P.J. Bouwer1

Affiliation: 1 Pinkmatter Solutions, Pretoria, South Africa.

Correspondence to: N. Steenkamp

Email: nicolaas@pinkmatter.com

Dates:

Received: 21 May 2020 Revised: 1 Oct. 2020 Accepted: 2 Oct. 2020 Published: October 2020

Synopsis Satellite-based applications for the monitoring of diamond exploration, operational mines, and postclosure mine sites is gaining traction. A variety of imagery is used to detect both kimberlite and alluvial deposits. Hyperspectral data is utilized mainly for primary deposits, and elevation models for secondary deposits. The data is used to constrain the exploration and ground truthing efforts, resulting in savings on both cost and time. Operational mines benefit from near-real-time monitoring of mining and related activities, including include environmental and security aspects. Satellite imagery can also be used to detect illegal and artisanal diamond diggings, with particular value for ethical sourcing validation in the supply chain. Post-closure monitoring of dumps and rehabilitation reduces the on-site presence of staff. The limitations of satellite applications are related mainly to the cost of obtaining images and the resolution or number of bands available on a detection platform.

Keywords remote sensing, satellite, monitoring, diamond, exploration, kimberlite, alluvial.

How to cite:

Steenkamp, N.C., Goosen, S.L., and Bouwer, P.J. Satellite applications in diamond exploration and mine monitoring.

Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 10, pp. 575–580.

DOI ID: http://dx.doi.org/10.17159/24119717/1222/2020 ORCiD ID: N.C. Steenkamp https://orchid.org/0000-00029912-6038 This paper will be presented at the Diamonds - Source to Use 2021 Hybrid Conference, 9–10 June 2021, The Birchwood Hotel & OR Tambo Conference Centre, Johannesburg, South Africa.

Introduction Satellite applications in the diamond industry range from the exploration phase, through production to post-closure monitoring. The type of deposit, primary (kimberlite, lamproite) or secondary (alluvial), will determine the most appropriate exploration or monitoring method. Remote sensing platforms include thematic mapping, hyperspectral and multispectral imaging, and high-resolution panchromatic imaging. Primary kimberlite deposits require an integrated GIS targeting database that would typically include Landsat, Sentinel, SPOT, Radarsat, ASTER, and Hyperion satellite data to name a few. This would be supplemented with geophysical methods such as aeromagnetic and gravity survey data, digital elevation maps, and mineralogical/geochemical sampling results. This allows for thematic mapping of kimberlite indicator minerals (KIM), e.g. garnet and ilmenite, and soil index pathfinder analyses. The Sentinel 2 satellites are the highest resolution, public domain near-infrared and shortwave-infrared (VNIR/SWIR) platform, and offer multispectral imagery with a high spatial resolution of between 10 m and 60 m. ASTER has garnered notable interest as an exploration tool as it integrates reflected and emitted long-wave infrared (LWIR) signals that are diagnostic of mantle minerals spectra and can be used in large-scale geochemical surveys. Cognisance should be taken of the various spectral signatures of the various species of KIM to constrain, but not exclude, potential indicators. This sensor array also allows exploration under shallow cover and vegetation.

There tends to be an association between the richness of an alluvial deposit and its geomorphology, making high-resolution digital elevation models (DEMs) a critical data-set. The DEM is used to distinguish between paleochannels, alluvial flats, and terraces. High-resolution optical data, e.g.

Pleiades, with 0.5 m per pixel, is required to construct DEMs, for surface change monitoring of objects, operational expansions, and calculation of the volume of rock dumps and tailing storage facilities. The size and distribution of artisanal workings can also be detected and plotted to delineate the extent of an alluvial deposit.

Once mining operations have commenced, satellite-based change and environmental monitoring and volume estimation is done from high-resolution stereo images. The size and volume of excavations and areas of activity can be delineated and calculated for a reporting period. Additional data layers may include: surface water, vegetation, and settlement encroachment on the mining right area. Post-closure of the mining operations, satellite imagery is used to monitor and report on rehabilitation. Synthetic aperture radar (SAR) is utilized to monitor subsidence at underground operations and slope stability in

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Satellite applications in diamond exploration and mine monitoring opencast operations. The same technology is also used to monitor illegal and artisanal mining activities, and the information utilized in ethical sourcing reporting and the Kimberley Process.

There are, however, some limitations in the use of satellitebased platforms for exploration and monitoring, mainly related to the spatial resolution and spectral range. Cost considerations will influence the frequency of use. Free hyperspectral or multispectral data sources offer a cost-effective tool as only processing and interpretation is required, but generally have very low resolution.

High-resolution optical imagery data, which is required for elevation models, change monitoring, and specifically volume calculation, tends to be expensive.

Satellite data types There are a wide range of satellite operators, including military, research, Earth observation, communication, weather, and several other task-specific systems. Satellite constellations were previously the domain of government agencies, but since the early 2000s commercial and private operators have launched their own satellites. In the last decade, the small satellites or ‘CubeSats’ have gained a lot of interest.

Satellite sensors are used to collect data from the atmosphere, oceans, and land. The positions of vessels on the oceans are tracked by automatic identification system (AIS) and SAR sensors. Sea temperatures are recorded by microwave scanning radiometry, and precipitation radar. Sea surface winds are monitored by microwave scatterometers, and surface heights by microwave altimeters. Sea ice is tracked and monitored using microwave scanning radiometry and synthetic aperture radar (SAR) sensors.

The positions of aircraft are established using Automatic Dependent Surveillance-Broadcast (ADS-B) platforms.

Precipitation is monitored by microwave scanning radiometer and precipitation radar. Light Detection and Ranging (LiDAR) is used to track dust particles and measure wind velocity.

Land monitoring entails the use of optical sensors to obtain a visual record of conditions on the Earth’s surface, whereas SAR is used to determine changes on the Earth’s surface. Thermal infrared sensors are used to measure temperature variations on the surface. The most used satellite types in the exploration and monitoring fields are electro-optical and SAR, which are described in the next section.

Electro-optical imagery Electro-optical platforms collect visual data from the surface of the Earth and can obtain imagery in a variety of bands, ranging from visible to hyperspectral, each with a task-specific application. The limitation to these platforms is that imagery is obtained only during the day for the visible and near-visible spectra, and is affected by cloud cover. The resolution of imagery has a significant impact on the success of the method, and for many applications high-resolution imagery is needed. The processing of image data into products such as digital elevation models (stereo images) or for change monitoring (mono images) is referred to as photogrammetry. High-resolution images are required to generate these products. High-resolution optical data, e.g. Pleiades, with 0.5 m per pixel, is required for construction of digital elevation models (DEMs), for surface change monitoring of objects, operational expansion, and calculation of rock dump and tailing storage facility volumes.

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Synthetic aperture radar Synthetic aperture radar (SAR) utilizes the motion of the radar antenna of the satellite over an area of interest to provide finer spatial resolution than conventional beam-scanning radar.

The output can be used to construct two- or three-dimensional models of the land surfaces or objects (Iannacone et. al., 2018).

Interferometry SAR (InSAR) uses the phase difference between two or more SAR images acquired at different times to derive surface deformation (Iannacone et. al., 2018) and is applied to surface movement monitoring (SMM). The method requires that the surface conditions do not change throughout the period analysed and that the same reflectors appear in each image (Iannacone et. al., 2018). SAR can collect data both day and night and is not affected by cloud cover, making it ideal for use in areas with persistent cover, such as the tropical belt. This method is also able to detect surface elevations changes as small as 3 mm.

Databases Satellite data cannot be used in isolation; it is a value-add product within the larger database. A typical diamond mining data-set would contain maps, aerial photos and satellite images, and results from ground truthing and geochemical surveys. Remote sensing platforms include thematic mapping, hyperspectral and multispectral imaging, and high-resolution panchromatic imaging. Exploration targets require an integrated geographic information system (GIS) targeting database, that would typically include, for example, Landsat, Sentinel, SPOT, Radarsat, ASTER, and Hyperion satellite data.

Ground truthing data would include the results from geophysical methods such as aeromagnetic and gravity surveys, surveyed maps, and mineralogical and geochemical sampling results. This allows for thematic mapping of kimberlite indicator minerals (KIM) e.g. garnet and ilmenite and soil index pathfinder analyses.

Exploration Kimberlite exploration Hyperspectral satellite data has been extensively used to delineate features and trends, combining these with geomorphology, drainage patterns, and structural zones that are considered favourable for hosting kimberlite intrusions.

Extensive work has been done using satellite imagery in India, e.g. in the Mahabubunagar area (Nandhagopal et al., 2015) and Chhatarpur district (Guha et al., 2018). Ishmukhametov (2016) suggested that LANDSAT-7 ETM+ satellite images could be used in conjunction with geological, geophysical, and mineralogical data to delineate diamondiferous kimberlite pipes in the poorly explored areas of Siberia.

A combination of airborne magnetics, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Landsat Thematic Mapper images was used to identify potential kimberlite targets in South Africa’s Northern Cape and Free State provinces (Tessema, Nefale, and Sebake, 2012). The nature of each kimberlite target was evaluated and ranked based on a comparison of the strength of the magnetic anomaly and the size and geometry of the magnetic signature. The spectral angle mapping (SAM) method was applied to the most promising targets, with the first nine ASTER bands, to distinguish The Journal of the Southern African Institute of Mining and Metallurgy

Satellite applications in diamond exploration and mine monitoring kimberlite indicator minerals (KIM): ilmenite, serpentine, olivine, and phlogopite (Tessema, Nefale, and Sebake, 2012). The ilmenite and phlogopite distributions form linear patterns, which coincide with palaeochannels.

Pendock (2018) suggested that kimberlite weathering products and some of the main KIM can be mapped using ASTER long-wave infra-red (LWIR) imagery. This is used as a proxy for large-scale geochemical mapping. The relatively low resolution of 90 × 90 m per pixel makes it viable only as an guide to large areas of interest, whereas a kimberlite intrusion would measure in the order of a couple of hundred metres in diameter or manifest as extensive dyke development (Pendock, 2018).

Pendock (2019) also suggested that thermal data from MODIS, supplemented with ASTER hyperspectral data, can be used as a high-level proxy for gravity surveys.

Alluvial deposit exploration

There tends to be an association between the richness of an alluvial deposit and its geomorphology (de Wit and Thorose, 2015), making high-resolution DEMs a critical data-set. The DEM is used to distinguish between paleochannels, alluvial flats, and terraces. The grades in alluvial flats and floodplains tend to be higher than in terraces. Figure 1 shows an example of a digital surface model (DSM) generated on the FarEarth Change Monitor platform. The heat map indicates elevation differences, with cool colours demarcating the higher elevations and warm colours the lower elevations. The hill shading option provides a textured appearance to aid in the visual evaluation of the model.

High-resolution optical images are used to identify potential trap sites, such as depressions or potholes or other geomorphological structures that can act as natural traps. The size and distribution of artisanal workings can also be detected and plotted to delineate the potential extent of an alluvial deposit, utilizing temporal satellite imagery. Fresh excavations tend to have lighter coloured spoil heaped on the edges, cleared vegetation, and are not filled with water. Artisanal workings will tend to follow the trend of recovery of the largest stones, which could be used to infer the setting of the paleochannel, and this can also be used to project potential deposit areas upstream or downstream.

Pendock (2019) suggests that paleo-gravel deposits could be identified by estimating the resistivities from SAR imagery as a proxy for ground penetrating radar (GPR). It is further suggested that L-band radar with 23.5 cm wavelength from a high-resolution sensor (e.g. 2.5 m) could also be used to identify gravel deposits (Pendock, 2019), owing to a high correlation with electromagnetic (EM) data from the Orange River alluvial deposits.

Life-of-mine Monitoring of operations during the life-of-mine ranges from change detection to estimation or calculation of volumes. Change detection is most often used to keep track of changes on the surface, from infrastructure to vegetation and surface water, with purpose-built platforms such as FarEarth Change Monitor.

High-resolution optical satellite images are incorporated into the platform. In the case study, barren ground is indicated in orange and development on the mining area in red, with water in blue (Figure 2).

Once a reference DEM has been generated, photogrammetry can be applied to calculate volume gains and losses. Volume calculations can be done ‘on the fly’ by cloud-hosted platforms like Stack Insight. As a first pass, volume gains or losses are indicated by cool or hot colours respectively. Volume changes can be calculated between two acquisitions over an area of interest, defined by a polygon. In the example in Figure 2, the volume increases of the dumps and volume decreases in the open pits are shown along with the relative changes in volume over the selected area.

Comparative results between high-resolution Pleiades imagery DEM products and LiDAR-derived products have indicated a volume difference of less than 2% at Sishen mine (Airbus, 2017) as part of a validation study. Volumetic accuracy was measured over areas ranging from 30 000 m2 to 180 000 m2, with volumes ranging between 200 000 m3 and 3 million m3 (Airbus, 2017) and it was found that as the volume increases, the error decreases. An elevation accuracy study was conducted over areas ranging from flat horizontal plains to areas being actively mined. The size of the areas ranged from

Figure 1—Example of a digital surface model (DSM) with elevation heat and hill shading generated on the FarEarth Change Monitor platform The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 2—Vegetation changes and barren ground change monitoring on an active mine, as displayed on the FarEarth Change Monitor platform

700 000 m² to 2 million m² (Airbus, 2017). A statistical comparison found the difference measured between the mean elevation of the LiDAR- and Stack Insight elevation model to be between 22 cm and 56 cm, which can be attributed to different reference planes used (absolute error). The standard deviation of the difference in elevation is an indication of the accuracy of and noise in the data (Airbus, 2017). Other factors that may influence accuracy include: quality of the ground truthing, or homogeneous areas with few recognizable features (Airbus, 2017), e.g. due to thick snow or vegetation cover that obscures the surface Stability monitoring of dumps that have reached their design capacity is done by InSAR. InSAR measures the difference in coherence over a temporal period, with an accuracy down to several millimetres. Mining-induced subsidence can also be monitored with InSAR. In both cases, subsidence is indicated as heat maps on platforms such as FarEarth Change Monitor and included as part of the risk assessment and remediation process where the mining-induced subsidence may pose a risk to surface infrastructure or to communities close to the active or closed operation.

Post-closure

Most countries require a post-closure monitoring and rehabilitation programme for the mine. Optical satellite imagery obtained at regular intervals can be used to monitor any surface changes, such as erosion or illegal mining of dump material or illegal entry into the mining area, or to evaluate the success of re-vegetation. In the example in Figure 3, the gain of vegetation on part of the rehabilitation project is indicated in green, and the area where a loss of vegetation occurred over the same period is indicated in red. A platform like FarEarth Change Monitor will use a series of temporal images to indicate notable surface changes between acquisitions, which the monitoring team can respond to. The basis of the change monitoring is the difference in the Normalized Difference Vegetation Index (NDVI) between acquisitions. Vegetation indices employ a difference formula to quantify the density of plant growth (Equation [1]). Calculations

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of NDVI for a given pixel always result in a number that ranges from –1 to +1; however, an absence of green leaves gives a value close to zero (NASA, 2000).

NDVI = (NIR — VIS)/(NIR + VIS) [1] where NIR is near-infrared radiation and VIS is visible radiation.

In the case study (Figure 3) a gain between acquisitions is indicated in green and areas of vegetation loss in red. It is also possible to display gains and losses as a time series and phenology curves.

Ethical sourcing Awareness of the issue of conflict diamonds led to the creation of the Kimberley Process (KP), with the aim of preventing conflict diamonds from entering the market (Kimberley Process, 2020).

Optical satellite imagery is employed to detect and monitor artisanal diggings in proximity to mining operations and known conflict areas. Artisanal diggings can be identified by clearing of vegetation, increases in bare ground areas, and ponding of surface water for gravel washing. Artificial intelligence (AI) systems such as FarEarth Change Monitor, where propriety algorithms have been developed, can be trained to detect clusters of these features over large monitoring areas and alert authorities to new or resumed activities.

The detection and the monitoring of artisanal diggings is done by comparison of element differences between two acquisitions. As most artisanal diggings are in virgin territory, the most notable effect is loss of vegetation, which can be detected through changes in the NDVI, textural changes, or surface colour changes. This type of data is obtained from high-resolution optical imagery, such as Pleiades with 0.5 m resolution and four bands (red, blue, green, and near-infrared). Pagot et. al. (2008) proposed that a bi-temporal data-set allows information on the evolution of the activity to be derived from an object-oriented classification of sets of satellite imagery. Mapping of these diggings to produce DSMs would, however, require metadata to be incorporated from unmanned aerial vehicle (UAV) imagery.

The Journal of the Southern African Institute of Mining and Metallurgy

Satellite applications in diamond exploration and mine monitoring

Figure 3—Gains (green) and losses (red) of vegetation in a mine rehabilitation project. Analyses performed and displayed on the FarEarth Change Monitor platform

Figure 4—Diamond mining activity detected, and notifications issued based on images acquired on 8 November 2017, 17 November 2018, 26 March 2019, and 21 June 2019

Chirico and DeWitt (2017) used DSMs derived from highresolution ortho-images from wide-angle and narrow field of view camera systems to map artisanal pits.

The case study (Figure 4) demonstrates the detection of new diggings in an abandoned alluvial diamond mine located in the North-West province of South Africa. A series of temporal optical satellite images indicates the locations of disturbed surface features.

Other applications would potentially include monitoring the extent and increase of illegal or artisanal activity in proximity to diamond operations in risk areas. By comparing the volume of The Journal of the Southern African Institute of Mining and Metallurgy

material processed by the legal operation and the reported carats per 100 m3 and the logged purchases from registered small-scale operators supplying the mine, it is possible to do a reconciliation of the volumes of the parcels offered for auction. If there is a notable discrepancy, the operation might be placed under scrutiny. There is, however, the risk that this method would not account for any potential variation in grade, specifically when the operators move from a high-grade area to a low-grade area.

It is therefore critical that diamond recovery by the operator is diligently recorded, and that mineral and geological reports are kept up to date, audited, and ready for submission.

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Satellite applications in diamond exploration and mine monitoring The introduction of the blockchain led to the development of several proof-of-concept systems. The ‘Tracr’ blockchain (www.tracr.com) is being developed by De Beers with the aim of of tracking individual diamonds from source to sale. Satellite imagery could potentially be used as part of the provenance body of evidence.

Limitations The limitations to satellite applications range from physical to economic constraints. Optical images offer the most value in conditions of little or no cloud, fog cover, or smog. Dense vegetation cover also affects the quality of the product, specifically if the aim is for change detection or generation of value-add products like digital elevation modelling. Tasking of satellites to obtain current imagery is costly and this will influence the frequency with which images are collected.

Synthetic aperture radar for subsidence or movement monitoring is viable only if the surface changes are minimal and the temporal InSAR series attains a high degree of coherence.

The implication is that active areas cannot be easily monitored for subsidence or movement.

Satellite-based exploration, monitoring. and detection need to be confirmed with ground truthing. The imagery and valueadd products aid in reducing costs and time by constraining an area of either high potential or risk. They also reduces risk by largely eliminating the need for staff to enter potentially dangerous areas. Human intervention is still required, however, to investigate, remediate, or resolve the identified issue. The last limiting factor is the relatively high cost associated with obtaining tasked, specifically high-resolution imagery, and processing.

Airborne systems Airborne systems are used as a supporting method to obtain higher spatial resolution data once the main areas of interest have been identified. Airborne systems offer higher resolution imagery, but also entail a relatively high cost to fly the survey and produce the higher levels of processed data. Airborne data acquisition is done from light aircraft at altitude and can cover approximately 800 km2 to 1800 km2, depending primarily on the pixel size required, and can have a resolution of between 3 m and 12 m. The imagery is then processed to various levels and a mineral map produced that could indicate mineral species, abundances, and geochemistry. This data is then merged with supporting ground truth and GIS data in a similar manner as the satellite imagery. De Beers pioneered airborne hyperspectral SWIR systems for diamond exploration. Similar systems have been developed by mining companies such as Anglo American (Köstlin, 2000) and commercial companies for mineral exploration, mineral mapping, and remote sensing.

Conclusions Satellite-based methods can be applied to the entire life of a diamond project, from exploration, throughout the lifeof-mine, and beyond closure. The type of imagery and the pairing with other data are determined by the type of deposit, either primary kimberlite or secondary alluvial. Mine planning and reconciliation, along with survey, can be supplemented with satellite imagery and value-add products. Secondary applications range from environmental monitoring and safety to demonstrating compliance with ethical sourcing requirements.

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Post-closure activities are supported through satellite-based change monitoring. Satellite data helps to focus efforts, resulting in cost and time savings along with increasing safety. The main limitations relate to the ability to acquire imagery and the associated cost.

Acknowledgement The contributions of an anonymous reviewer to the first draft of this paper are acknowledged with gratitude.

References Airbus. 2016. Remote monitoring of iron ore mining pits with stack insight. https://www.intelligence-airbusds.com/en/6988-case-study-gallerydetails?item=43473#.Xa70f-gzaUk [accessed 1 October 2019].

Chirico, P.G. and DeWitt, J.D. 2017. Mapping informal small-scale mining features in a data-sparse tropical environment with a small UAS. Journal of Unmanned Vehicle Systems, vol. 5. pp. 69–91.

De Wit, M. and Thorose, E. 2015. Diamond-bearing gravel along the Lower Kwango River DRC. Geology and Resources of the Congo Basin, De Wit, M.J., Guillocheau, F., and de Wit, M.C.J. Springer-Verlag, Berlin-Heidelberg. pp. 341–360.

Guha, A., Rani, K., Bhusan Varma, C., Sarwate, N., Sharma, N., Mukherjee., Vinod Kumar, K., Pal, S., Saw, A., and Jha, S. 2018. Identification of potential zones for kimberlite exploration – an Earth observation approach. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-5. p. 201. Proceedings of the ISPRS TC V Mid-term Symposium ‘Geospatial Technology – Pixel to People’, Dehradun, India, 20–23 November 2018. pp. 239–250.

Iannacone, J.P., Lato, M., Troncoso, J., and Perissin, D. 2018. InSAR monitoring of active, inactive and abandoned tailing facilities. Tailings 2018. Proceedings of the 5th International Seminar on Tailings Management. Santiago, Chile, 11–13 July 2018. Gecamin, Santiago. Abstract volume. 7 pp.

Ishmukhametov, V.T. 2016. Predicting kimberlite diamond deposits in the north of the Siberian Platform on the basis of interpretation of satellite imagery. Moscow University Geology Bulletin, no. 71. pp. 368–371.

Köstlin, E.O. 2000. Technological advances in mineral exploration and exploitation: Staying ahead in the mining industry with geophysics. Proceedings of the 2000 SEG Annual Meeting, Calgary, Alberta, 6–11 August. Abstracts Volume. 2 pp.

NASA. Not dated. Measuring vegetation. https://earthobservatory.nasa.gov/ features/MeasuringVegetation/measuring_vegetation_2. php [accessed 22 October 2019].

Nandhagopal, N., Kumar, R.S., Kannadasan, T., and Bawanhun Mawthoh, M. 2015.

Inferences from satellite images for locating kimberlite: Mahabubnagar area, Telangana, South India. Elixir Earth Science, vol. 84. pp. 33547–33553.

Pagot, E., Pesaresi, M., Buda, D., and Ehrlich, D. 2008. Development of an objectoriented classification model using very high resolution satellite imagery for monitoring diamond mining activity. International Journal of Remote Sensing, vol. 29. pp. 499–512.

Pendock, N. 2018. Regional diamond exploration under cover. Proceedings of Diamonds – Source to Use 2018, Johannesburg, South Africa, 11–13 June 2018. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 103–112.

Pendock, N. 2019. Unpublished Linkedin post. https://www.linkedin.com/in/neilpendock-41854a58/detail/recent-activity/shares/ [accessed 22 October 2019].

Tessema, A., Nefale, N., and Sebake, D. 2012. The use of high-resolution airborne magnetic, ASTER and Landsat 7 ETM+ images for identification of kimberlite pipes in the northwestern Free State Province, South Africa. International Journal of Remote Sensing, vol. 33. pp. 4356–4373.

The Kimberley Process. 2020. https://www.kimberleyprocess.com/ Tracr. https://www.tracr.com/ [accessed: 22 October 2019].

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The Journal of the Southern African Institute of Mining and Metallurgy

Next-generation, affordable SO2 abatement for coal-fired power generation – A comparison of limestone-based wet flue gas desulphurization and Sulfacid® technologies for Medupi power station A. Strickroth1, M. Schumacher1, G.W. Hasse2, and I. Kgomo2 Affiliation: 1 Carbon Process & Plant Engineering S.A., Grand Duchy of Luxembourg, Europe.

2 EPCM Global Engineering (Pty) Ltd, Centurion, 0157, South Africa.

Correspondence to: G.W. Hasse

Email:

gunther@epcm.co.za

Dates:

Received: 14 Jun. 2020 Revised: 14 Sep. 2020 Accepted: 15 Sep. 2020 Published: October 2020

How to cite:

Strickroth, A., Schumacher, M., Hasse, G.W., and Kgomo, I. Next-generation, affordable SO2 abatement for coal-fired power generation – A comparison of limestone-based wet flue gas desulphurization and Sulfacid® technologies for Medupi power station.

Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 10, pp. 581–590.

DOI ID: http://dx.doi.org/10.17159/24119717/1252/2020 ORCiD ID: G.W. Hasse https://orchid.org/0000-00034912--0305

Synopsis Coal is used to generate more than three-quarters of South Africa’s electricity, while numerous coal-fired boilers are employed for steam generation in industrial processes. However, coal-fired power generation is responsible for the release of the largest quantities of SO2 emissions to the atmosphere and leads to detrimental health and welfare effects in communities in the proximity of coal-fired plants. The classical industrial SO2 abatement solution for the coal-fired power generation industry is wet flue gas desulphurization, which uses a limestone adsorbent and produces a gypsum by-product (WFGD L/G). In South Africa, due to the poor quality of the limestone the gypsum product is unsaleable and is co-disposed with coal ash. In comparison, the Sulfacid® process technology converts SO2 contained in industrial flue gas into saleable sulphuric acid using a catalytic process requiring only water and air. This process does not require limestone. The scale of the latest commercial applications of the Sulfacid® SO2 abatement technology in the chemical, fertilizer, and copper mining industries demonstrates the potential and readiness of this technology to be employed in the coal-fired electricity and steam production sectors.

This paper provides a first-order direct comparison between the techno-economic aspects of the WFGD (L/G) and Sulfacid® technologies using the requirements specified for the 6 × 800 MWe Eskom coal-fired Medupi power station. The results indicate that affordable flue gas desulphurization technology exists that could be adopted by the South African industry to reduce SO2 emissions to legislative limits and beyond.

Keywords SO2 abatement, coal-fired power, and heat generation, sulphuric acid, wet fluidized gas desulphurization, Sulfacid®, waste-to-chemicals.

Introduction Coal-fired power generation in South Africa remains indispensable for maintaining economic activity now and into the foreseeable future, even with the introduction of renewable energy. During the period April 2018 to March 2019, approximately 77% of all electricity in South Africa was generated by 15 coal-fired power plants which, in addition to gas, hydro-, and nuclear power, formed part of a total of 92% Eskom-generated electricity supplied to the national grid (Stats SA, 2018a, 2018b, 2019; Eskom, 2019). Flue gas from coal-fired power plants contains sulphur dioxide (SO2) that originates from the sulphur in the coal, and which has detrimental health and welfare effects on communities living in the proximity to the plants.

Sulphur is contained in coal predominantly as organic sulphur (S) and pyritic sulphur (Calkins, 1994), with typical total sulphur contents of 0.54% (by mass) for thermal export coal, > 1% for Sasol syngas production coal, < 2% for Eskom thermal power generation coal, and 2% (range 0.4–3.0%) for discard coal (Hall, Eslait, and den Hoed, 2011; Steyn and Minnitt, 2010; Makgato and Chirwa, 2017). During pulverized coal combustion, the organic and pyritic sulphur is converted mostly into SO2, and in small quantities into sulphur trioxide (SO3) (Müller, Schnell, and Scheffknecht, 2013), with only approximately 10% of the sulphur captured in the coal ash (Harrison, 2006). The SO2 flue gas concentration for Eskom power plants typically ranges from 1 623 mg/Nm3 (dry, 10% O2) for 0.7% S (air-dried basis) at Kriel power station to a maximum of 3 934 mg/Nm3 for 1.8% S at Medupi power station (Harris, 2014; Girmay and Chikobvu, 2017; Kolker, Senior, and Alphen, 2016). Likewise, the SO2 emissions for the Eskom coal-fired power plant fleet (single point sources) range from 26 Mt/a for Komati power station to 429 Mt/a for Matimba power station (van Geuns, 2018; Mathebula, 2017).

Most of the Eskom coal-fired power stations are located in, and impact the air quality in, the Highveld and Waterberg Priority Areas (South Africa, 2007, 2012a, 2012b, 2015).

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Next-generation, affordable SO2 abatement for coal-fired power generation The atmospheric dispersion, transportation, and conversion of concentrated SO2 (Harrison, 2006) from these power plant stacks result in diluted SO2 concentrations ranging from 20 μg/m3 to more than 500 μg/m3 at ground level (WHO, 2005; South Africa, 2009) in the form of dry (gas and particulates) and wet depositions (droplets) to which humans, nature, and infrastructure are exposed (Hazi, Heikkinen, and Cohen, 2003; Pretorius, Piketh, and Burger, 2017). Human inhalation of SO2 is associated with both short- and long-term adverse health problems, affecting the nose, upper respiratory tract, and lung function (WHO, 2005). In fact, integrated research reviews by the US Environmental Protection Agency (EPA) found a causal relationship between respiratory morbidity (illnesses), particularly in individuals with asthma, and short-term exposure to SO2 (EPA, 2017). The EPA also found that cardiovascular effects and mortality are caused by short- and long-term human exposure to particulate matter (PM) with a nominal aerodynamic diameter < 2.5 μm (PM2.5) that is composed primarily of sulphate (SO42-) and nitrogen oxides (NOx) (EPA, 2009). Based on these types of studies, human mortality and morbidity rates for communities living in the proximity of Eskom coal-fired power stations could be estimated (Langerman and Pauw, 2018).

Despite South African legislation to limit the coal-fired power plant stack and ground-level ambient SO2 concentrations, multiple exceedances of atmospheric emission licence (AEL) limits are reported by environmental groups (Sahu, 2019) and by the Department of Environment, Forestry and Fisheries (DEFF) (Gwase, 2019). Government’s failure to implement the Highveld Priority Area air quality management plan, in view of the constitutional right of citizens to an environment that is supportive of their health and wellbeing (South Africa, 2016), has led to pending litigation by the Centre for Environmental Rights (CER) against the President of South Africa, the Minister of Environmental Affairs, and Chief Air Quality Officer (CER, 2019). Furthermore, in an apparent attempt by government to help the industry with legislative compliance, the SO2 limit of 500 mg/Nm3 for existing coal-fired plants that was set in 2010 was relaxed to 1 000 mg/Nm3 in 2020 (South Africa, 2010, 2020).

Eskom is in the process of implementing wet flue gas desulphurization (WFGD) technology on its 6 × 800 MWe Kusile coal-fired power station that is under construction in the Mpumalanga region and is planning to retrofit the same SO2 abatement technology to its operational 6 × 800 MWe Medupi power station in the Waterberg region (Bagus, van Wyk, and Chang, 2018; Harris, 2014). Besides water, power and steam, the WFGD (L/G) abatement process requires limestone as an adsorbent, generates additional taxable CO2 during the SO2 abatement process, and produces unsaleable gypsum as a byproduct (Bagus, van Wyk, and Chang, 2018). Owing to the lack of a market for the gypsum by-product, additional volumes of waste (in addition to coal ash) need to be handled, increasing the life-cycle cost for waste disposal (Gruenewaldt, 2013; Campbell, 2015; Vosloo, 2018; Koralegedara et al., 2019). From an economic viewpoint, this means that WFGD (L/G) incurs a capex (capital expenditure at the start of its life-cycle and sustains a net negative opex (operational expenditure during its life, resulting in a net negative cost of ownership at the end of its life-cycle. Furthermore, the scarcity, quality, and transportation cost of limestone in South Africa poses a threat to the further implementation of WFGD (L/G) for SO2 abatement by Eskom

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(Stephen et al., 2014). In fact, Steyn and Kornelius (2018) argued in a recent paper that the implementation of the WFGD technology in the Highveld Priority Area will not provide a net benefit over a 30-year period from 2020 to 2050, mainly because of the continued ne negative opex.

The challenge is, therefore, that while South Africa is dependent on coal-fired power and heat generation that generates harmful SO2 emissions, the WFGD (L/G) technology currently being implement at Kusile power station is not economically viable for further roll-out to the Eskom coal-fired power plant fleet.

Fortunately, next-generation SO2 emission abatement technology is becoming available that seems to provide a net positive opex, resulting in a break-even and net profit during lifetime deployment. The back-end (before the stack) Sulfacid® process technology, developed by Lurgi AG and improved and patented by Luxembourg company Carbon Process & Plant Engineering S.A. (CPPE) directly converts SO2 and SO3 contained in any industrial flue gas, using only water and air, into saleable sulphuric acid (H2SO4) of various grades using a fixed bed of activated carbon, without requiring any limestone or producing CO2 or gypsum (Strickroth, 2017a, 2017b).

The purpose of this paper is to conduct a first-order technoeconomic comparison between the WFGD (L/G) and Sulfacid® SO2 abatement technologies as applied to Eskom’s 6 × 800 MWe Medupi coal-fired power plant, using publicly available data, to determine whether affordable SO2 abatement could be implemented on coal-fired power plants in South Africa.

The Medupi power plant has been chosen for this comparative study because Eskom’s financial loan conditions require the implementation of an SO2 abatement technology (World Bank, 2015), the sulphur content of the coal and resulting SO2 concentrations are the highest in the Eskom fleet, and because this new station has a remaining life of 60 years (Cheng, van Wyk, and Bagus, 2018).

The following sections of this paper provide a historical literature review of the WFGD (L/G) and Sulfacid® technologies, followed by a description of the method of high-level first-order comparison that was used. The results from the techno-economic comparison are presented and discussed. The results indicate the possibility of affordable next-generation SO2 abatement for coal-fired power generation. The limitations of this study are highlighted, and conclusions drawn suggesting that further comparative analysis be conducted by academia, industry, and government.

Literature survey Flue gas desulphurization technologies Flue gas desulphurization (FGD) technologies remove oxides of sulphur (SOx) from flue gases, generally by reaction or absorption with alkaline absorbents, and can be categorized by the nature of the process flow (thermal or chemical regeneration of absorbent or non-regenerative one pass through), water usage (wet, semi-dry, and dry) and by-product production (commercially marketable commodity or landfill waste) (Lisnic and Jinga, 2018). Different absorbents are used for the wet (limestone, lime, caustic soda, ammonia, and seawater), semidry (limestone and lime), and dry process variants (limestone and dolomite) of the FGD technologies (Lisnic and Jinga, 2018).

Therefore, WFGD (L/G) could be categorized as a wet, limestoneThe Journal of the Southern African Institute of Mining and Metallurgy

Next-generation, affordable SO2 abatement for coal-fired power generation based absorbent, non-regenerative one pass through technology producing a gypsum by-product. Sulfacid® could be described according to this classification as a wet, water-based absorbent, regenerative one pass through technology producing a sulphuric acid by-product.

waste treatment plant where usable water is separated for recycle and the final liquid waste sent for disposal. The WFGD (L/G) technology and solution is therefore a three-step process: (a) limestone slurry preparation, (b) SO2 absorption, and (c) gypsum dewatering (Stephen, 2017).

Evolution of the WFGD (L/G) technology

WFGD (L/G) process chemistry and quantities

The first WFGD installation was in 1931 at the Battersea coalfired power station in London (UK) with the spraying of water on counterflow flue gases to convert some of the SO2 into an acidic sulphite solution (Biondo and Marten, 1977). In 1935 a modified WFGD system was installed at the Swansea coal-fired power station, where a lime slurry was mixed with water and sprayed onto counterflow flue gases. Further upgrades of the FGD process and the installation at the Fullham coal-fired power station in 1938 led to the extraction and disposal of gypsum as a by-product (Biondo and Marten, 1977). Many WFGD installations were completed worldwide for SO2 abatement of coal-fired flue gases, with the limestone forced oxidation (LSFO) or limestone – gypsum (L/G) process variant being chosen by most of the utilities, primarily due to the abundance and low price of limestone (Lisnic and Jinga, 2018). In 2012 a WFGD (L/G) installation at the Rovinari coal-fired power plant in Romania was able to reduce the SO2 flue gas concentration to 250 mg/ Nm3 (Lisnic and Jinga, 2018), and this technology is continually undergoing further developed (Dragomir et al., 2017).

The WFGD (L/G) installation at Eskom’s Kusile power plant achieved an SO2 removal efficiency of only 93% during performance testing (Ezeh, 2018). Nevertheless, the required WFGD efficiency of > 98%, water consumption of < 0.21 l/kWh, and worldwide installed capacity of > 80% as in 2012 (Carpenter, 2010) played a major role in Eskom’s decision to implement WFGD (L/G) at Medupi (Cheng, van Wyk, and Bagus, 2018). The major drawback of applying this technology in South Africa is the requirement for large volumes of scarce and costly high-quality limestone (Stephen et al., 2014; Haripersad and Swart, 2015), production and co-disposal of unsaleable gypsum with coal ash (Gruenewaldt, 2013; Vosloo, 2018; Koralegedara et al., 2019), as well as the production of additional (taxable) CO2 that results in a net negative opex and a net negative cost of ownership during the installation’s life-cycle.

The overall balanced chemical reaction for the WFGD (L/G) technology solution is given by Pimenta (2010, p. 6) for absorber operational conditions at 60°C and pH 5, as shown in Equation [1]. SO2 reacts with calcium carbonate/limestone (CaCO3), oxygen (O2), and water (H2O) to form a gypsum slurry/calcium sulphate dihydrate (CaSO4 · 2H2O) and carbon dioxide (CO2).

[1] According to Equation [1], one ton of SO2 reacts with 1.56 t of limestone (reagent), 0.25 t of oxygen, and 0.56 t of water to form 2.13 t of gypsum, 0.56 t of slurry water, and 0.69 t of CO2.

The SO2 in the untreated flue gas is removed to allow the treated flue gas to exit the chemical process.

The commercial WFGD (L/G) process parameters and quantities calculated for the abatement of sulphur dioxide using 85% pure limestone for Medupi power station are given by Cheng, van Wyk, and Bagus (2018).

Evolution of the Sulfacid® FGD technology The CPPE Sulfacid® FGD technology differs from classical FGD technologies as categorized by Lisnic and Jinga (2018) as it does not require an alkaline absorbent such as limestone but converts sulphur oxides into sulphuric acid by adsorption in a cold wet catalytic process on a fixed bed of activated carbon, requiring only water and air. Particulate matter and heavy metals are removed in a water-based quench step to condition the SOx-containing flue gas before it is release across the activated carbon bed. Intermittent spraying of water on the bed washes out the formed sulphuric acid and thereby regenerates the bed.

The stationary activated carbon bed is guaranteed for 10 years

Description of the WFGD (L/G) technology employed for Medupi power station WFGD (L/G) process description The WFGD (L/G) SO2 emission abatement system consists predominantly of an absorber, as shown in Figure 1.

It is anticipated that the limestone-forced-oxidation (LSFO) version of the WFGD technology will be implemented on Medupi power station (Stephen, 2017). In this technology, limestone is pulverized and slurried with process water. The slurry is sprayed onto the untreated flue gas, reacting with SO2 to form a gypsum slurry (Figure 1). Excess oxygen is provided during this process to ensure ‘the oxidation of sulphite species to form sulphates’ (Stephen, 2017, p. 35) and gypsum is formed as a by-product.

In this manner, the sulphur dioxide from the untreated flue gas is absorbed by the limestone slurry and the desulphurized gas exits the absorber to the stack. The gypsum slurry by-product is then removed from the absorber and dewatered before it is added to the boiler ash stream for disposal on the ash dump. The liquid stream from the gypsum dewatering plant is fed to the liquid The Journal of the Southern African Institute of Mining and Metallurgy

Figure 1—Schematic layout of the absorber of the WFGD (L/G) SO2 abatement system (adapted from Stephen, 2017) VOLUME 120

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Next-generation, affordable SO2 abatement for coal-fired power generation of optimal operation during steady-state and fluctuating volume flows (start-up and shutdown transients) as well as fluctuating SOx flue gas concentrations.

The first commercial Sulfacid® plant was built in 1966 in Germany and operated for flue gas desulphurization at a detergent manufacturing facility (Scheidel, 1968; Grüpner, 1970). This was followed by Sulfacid® plants capable of treating increased flue gas volume flows for an oil-fired generator in 1967 and sulphur-burning and titanium dioxide plants in 1968 (ibid.).

Since then, Sulfacid® plants have been installed in more than 20 countries for SOx abatement in the chemical, pigment, smelting, medical catalyst recovery, and fertilizer manufacturing industries.

Interestingly, in 1972 a pilot Sulfacid® plant was installed and operated successfully at the 4 × 110 MWe Prunerov coal-fired power plant in the Czech Republic (Svejcar, 1976).

Scheidel (1968) cites the much higher volume flow, unfamiliarity of chemical processes, and a lack of on-site use of sulphuric acid as reasons why coal-fired utilities have chosen WFGD with limestone as the preferred SO2 abatement technology until now. This situation is about to change, due to: ➤ The size and scale of the latest commercial applications of the Sulfacid® SO2 abatement technology in the chemical, fertilizer, and copper mining industries (up to 1 000 000 Nm3/h), which demonstrate its potential and readiness to be employed for coal-fired plants ➤ The economic pressure to install SO2 abatement systems that are financially viable, environmental pressures that require lower scrubbing limits of flue gases, and the requirement for systems that form part of integrated multipollutant abatement (NOx and Hg) and CO2 carbon capture and utilization solutions.

For example, in Morocco, one of the recently completed Sulfacid® plants achieved approximately 98% SO2 flue gas reduction from > 600 ppmv (1254 mg/Nm3, 10% O2) to < 15 ppmv (31 mg/Nm3, 10% O2), against a legislative emission limit of 157 ppm (328 mg/Nm3, 10% O2), while also producing marketable sulphuric acid, using only water and air (Africa Outlook Magazine, 2019). In fact, it has been demonstrated that the Sulfacid® technology is able to remove and convert SO2 into sulphuric acid for SO2 levels in flue gases from 3 000 ppmv (6 272 mg/Nm3, 10% O2) down to < 9 ppmv (19 mg/Nm3, 10% O2) (Strickroth, 2017a, 2017b). This indicates that the Sulfacid® technology has the potential to remove SO2 contained in the flue gases of coal-fired power plants to meet current and future legislative requirements.

could be disposed or potentially utilized in construction materials (Rastogi and Kumar Paul, 2020). The separated liquid is further treated in a liquid waste treatment process, after which most of the liquid is returned to the venturi quench while a small stream is disposed.

The cooled, dedusted flue gas then enters the Sulfacid® reactor, where a special activated carbon-based catalyst bed converts SO2 and SO3 into H2SO4. The activated carbon bed is continuously sprayed at the top with process water for countercurrent regeneration of the catalyst. Dilute sulphuric acid (15 wt.%) is continuously produced by the process, and the gas phase SO2 is reduced to the specified concentration (the design can be adapted to meet current or future concentrations for SO2 < 50 mg/Nm3).

Activated carbon in the Sulfacid® reactor provides a buffering capacity to maintain reactor performance for normal variations in SO2 concentration and flow rate that may be expected during the operation of a power generation unit. The treated flue gas then exits the reactor to the stack. The concentration of the sulphuric acid from the Sulfacid® reactor could be increased from 15 wt.% to 50 wt.% and more using a mechanical vapour compression process that requires steam and electrical power. The recovered water is re-used as process water in the Sulfacid® system.

Sulfacid® process chemistry and quantities The conversion of SO2 into sulphuric acid on the activated carbon catalyst takes place according to the balanced chemical reaction as shown in Equation [2].

[2] According to Equation [2], one ton of SO2 reacts with 0.25 t of O2 and 0.28 t of water to form 1.53 t of H2SO4 with a 100 wt.% concentration.

The process parameters and quantities for the commercial Sulfacid® process designed for the Medupi power plant are given

by CPPE (2019).

Methodology Approach for first-order techno-economic comparison of WFGD (L/G) and Sulfacid® technologies for Medupi power station The Eskom data, as employed for the WFGD (L/G) system for Medupi power station, was used in an unchanged format. A new conceptual design of the Sulfacid® system for Medupi power

Description of the Sulfacid® technology as could be applied to Medupi power station The Sulfacid® SO2 emission abatement system consists of a packed bed type venturi quench and a fixed bed activated carbon Sulfacid® reactor, as shown in Figure 2.

Untreated flue gas from the baghouse enters the venturi quench where the temperature of the flue gas is reduced, the gas stream is saturated through evaporation, water-soluble heavy metals are removed, and the flue gas de-dusted (PM removal) to provide optimal process conditions for the downstream Sulfacid® reactor. Adequate removal of PM from the flue gas prior to entry into the Sulfacid® reactor protects the activated carbon bed from blockage and increased pressure drop. The venturi quench solution that contains PM and heavy metals is drawn off regularly for liquid/solid separation. The dewatered solids

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Figure 2—Schematic layout of the venturi uench and Sulfacid® reactor of the Sulfacid® SO2 abatement system (CPPE, 2019) The Journal of the Southern African Institute of Mining and Metallurgy

Next-generation, affordable SO2 abatement for coal-fired power generation station was done using the same design parameters. Process flow data and quantities for the WFGD (L/G) SO2 abatement system to retrofit the 6 × 800 MWe Eskom Medupi coal-fired power plant units were obtained from the publicly available FGD retrofit basic design report (Harris, 2014), supplemented by information contained in a published master’s thesis for the same design configuration (Stephen, 2017). Similarly, data on capex, quantities, and unit costs that comprise opex was obtained from the publicly available Medupi FGD technology selection study report (Cheng, van Wyk, and Bagus, 2018).

The CPPE Sulfacid® SO2 abatement system conceptual design (process flow, quantities, and 3D configuration) as well as costing (CPPE, 2019) were based on the same input and boundary condition information as for the WFGD (L/G) SO2 abatement system as specified in Harris (2014), Stephen (2017), and Cheng, van Wyk, and Bagus (2018) to ensure common and comparable flue gas input and output conditions.

Assumptions for technical data Specified input data for untreated flue gas Input data for the untreated flue gas exiting the Medupi baghouse for a single 800 MWe unit, as given in Table I, was assumed as the input boundary condition for the WFGD (L/G) and Sulfacid® abatement system designs.

Required output data for treated flue gas The required SO2 concentration in the Medupi flue gas after abatement is specified as 400 mg/Nm3, dry at 6% O2 (Harris, 2014).

Assumption related to installation position The installation position for both SO2 abatement systems is after the baghouse ID fans with a tie-in to the existing emission stack ducting.

Assumptions for economic data Cost estimation accuracy, inclusions, and exclusions ➤ All cost estimates are based on a ‘conceptual-level accuracy of ±30% in 2017 South African Rand’ (Cheng, van Wyk, and Bagus, 2018, p. 27). ➤ ‘The cost estimates include allowances for auxiliary electricals, control system upgrades, and other required Table I

Input data at the baghouse outlet of the 800 MWe Medupi power plant unit Description Volume flow* SO2 (max.)* NOx as NO2** PM** SO3* HCl* O2** CO2** H2Ovapour** N2** Ar** Gasified ash** Annual operational hours*

Value

Unit

2 590 000 5 855 650 50 53 160 6.0 13.3 8.8 79.4 0.9 5.4 7 884

Nm3/h mg/Nm3 mg/Nm3 mg/Nm3 mg/Nm3 mg/Nm3 vol.% vol.% vol.% vol.% vol.% t/h h

Note: All values for dry flue gas at 6% O2; * Harris (2014); ** Stephen (2017) The Journal of the Southern African Institute of Mining and Metallurgy

BOP [balance of plant] system upgrades.’ (Cheng, van Wyk, and Bagus, 2018, p. 28). ➤ Further details on cost assumptions (inclusions and exclusions) are given in Cheng, van Wyk, and Bagus (2018).

Specific assumptions related to capex ➤ ‘The capital cost estimates include direct and indirect costs as an overnight price, but exclude Owner’s costs.’ (Cheng, van Wyk, and Bagus, 2018, p. 28). ➤ The same capex of R 17.677 billion for both systems is assumed for the design, construction, and commissioning of six unitized SO2 abatement systems and includes indirect costs, contingency, and escalation (Cheng, van Wyk, and Bagus, 2018).

Specific assumptions related to opex ➤ ‘The operating cost estimates were based on operation at full-load conditions. The annual operating costs also account for increases in auxiliary power requirements, additional labour requirements, water costs, and additional costs for consumables (Cheng, van Wyk, and Bagus, 2018, p. 28). ➤ The process quantities for the WFGD (L/G) system were used for a 96% pure limestone absorbent as given in Harris (2014, pp. 61–63, Table V). ➤ The unit costs for process inputs and outputs were used as specified in Cheng, van Wyk, and Bagus (2018, p. 32, Table VII) for both systems. ➤ The required labour for operating the Sulfacid® system is deemed to be 50% that of a WFGD (L/G) system. This is due to the absence of limestone-related equipment such as offloading, conveying, storage, milling, and water mixing. ➤ No sale of the gypsum by-product is assumed for the WFGD (L/G) system. ➤ It is assumed that all the produced sulphuric acid is sold for each financial year. The sales price of sulphuric acid (50 wt.%) was taken as R800 per ton as obtained from a prominent chemicals distributor in South Africa. ➤ An effective CO2 emission tax rate of R48 per ton was assumed (National Treasury, 2018), although CO2 emissions are taxable at R120 per ton (South Africa, 2019). Emissions tax was calculated only for CO2 generated from the abatement chemistry and not for the required auxiliary electricity consumption or process steam.

Specific assumption for life cycle cost estimation Cost of ownership (COO) is estimated using the simplified relationship between capex, annual opex, and life cycle as shown in Equation [3].

[3] ➤ A life cycle period of 30 years (2020–2050) was assumed, similar to Steyn and Kornelius (2018) in their economic assessment of the reduction of SO2 on the South African highveld.

Results The results for the techno-economic comparison are given in Tables II, III, and IV. VOLUME 120

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Next-generation, affordable SO2 abatement for coal-fired power generation Table II

C omparison of selected process quantities for the WFGD (L/G) and Sulfacid® SO2 abatement systems to retrofit 6 × 800 MWe Medupi coal-fired power plant units Description

WFGD (L/G) (quantity per annum)

Sulfacid® (quantity per annum)

Difference (relative to WFGD (L/G)

991 1 839 459 31 445 185 120 9 299 0.25 60 54.45 429 284 0 124

0 0 0 0 221 92 560 8 704 0.23 60 60.00 473 040 2 081 376 705

–100% –100% –100% –100% –50% –50% –6% –6% 0% 10% 10% 100% 468%

Limestone reagent/sorbent (kt/a) Gypsum disposal (kt/a) Abatement process-generated CO2 (kt/a) Crystallizer salts disposal (kt/a) Wastewater disposal (kt/a) Operating labour (h/a) Process water consumption (thousand m3/a) Process water consumption rate (l/kWh) Pre-treatment solids disposal (kt/a) Auxiliary power rate (MWh/h) Auxiliary power consumption (MWh/a) Sulphuric acid (50 wt.%) (t/a) Steam (kt/a)

Table III

C omparison of operational costs using relevant process quantities for the WFGD (L/G) and Sulfacid® SO2 abatement systems to retrofit the 6 × 800 MWe Medupi power plant units

Description

Cost per unit

WFGD (L/G) WFGD (L/G) (quantity per annum) (expense / income per annum)

Limestone reagent/sorbent (R/t) –R 475 991 295 Gypsum disposal (R/t) –R 30 1 838 940 CO2 carbon tax (R/t) –R 48 459 120 Crystallizer salts disposal (R/t) –R 1000 31 351 Auxiliary power (R/MWh) –R 421 429 284 Process water (R/m3) –R 21 9 299 178 Wastewater disposal (R/t) –R 477 444 658 Steam (R/t) –R 91 124 173 Pre-treatment solids disposal (R/t) –R 680 59 911 Operating labour (R/hr) –R 240 185 120 Annual opex Sulphuric acid sales (50 wt.%) R 800 0 Net annual opex

Table IV

C omparison of life cycle cost for WFGD (L/G) and Sulfacid® SO2 abatement systems to retrofit 6 × 800 MWe Medupi coal-fired power plant units Description Capex Opex (annual) Net opex (annual) Cost of ownership (30 years) Break-even (payback in years)

WFGD (L/G)

Sulfacid®

–R17.677 billion –R1.266 billion –R1.266 billion –R55.657 ‬billion No break-even

–R17.677 billion –R0.616 billion R1.049 billion R 3.793 billion 16.9

Discussion The techno-economic comparison between the WFGD (L/G) and Sulfacid® technologies and systems applied for the retrofitting of the 6 × 800 MWe Medupi power plant indicate the possibility of affordable SO2 abatement for a modern coal-fired power plant using the Sulfacid® technology. The affordability originates from the process quantities that result from the two different technologies as well as the opex and life cycle costs.

Comparison of process quantities The major difference between the two technologies and systems

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Sulfacid® Sulfacid® (quantity per annum) (expense / income per annum)

–R471 million 0 –R55 million 0 –R22million 0 –R31million 0 –R181 million 473 040 –R196 million 8 703 936 –R212 million 221 409 –R11 million 704 830 –R41 million 59 911 –R44 million 92 560 –R1 265 million 0 2 081 376 –R1 266 million

0 0 0 0 –R199 million –R184 million –R106 million –R64 million –R41 million –R22 million –R616 million R1 665 million R1 049 million

becomes apparent when comparing the process quantities in Table II. The Sulfacid® process does not use limestone, does not generate CO2 from the process chemistry, nor a gypsum byproduct, and there is no need to dispose of crystallizer salts.

From a by-product point of view, both processes produce similar bulk quantities, i.e. WFGD (L/G) produces 1.8 Mt gypsum while Sulfacid® produces 2.1 Mt sulphuric acid. The Sulfacid® process produces 50% less disposable wastewater because it does not require water to generate a lime slurry stream, and also uses 50% less labour due to the simplicity of the process equipment compared to WFGD (L/G), since no limestone-related equipment is required. Both processes require similar process water quantities but the Sulfacid® process converts the process water into a saleable product. Furthermore, the Sulfacid® process uses 10% more auxiliary power, predominantly to overcome the pressure drop across the fixed bed of activated carbon. For the Sulfacid® process to re-use some of the process water, the sulphuric acid is concentrated from 15 wt.% to 50 wt.% using mechanical vapour compression. This compression process requires auxiliary power and also steam, and therefore the Sulfacid® process requires 468% more steam than the WFGD (L/G) process. Most process quantities for the Sulfacid® process in Table II are less than or similar to the WFGD (L/G) process, except for the steam requirements.

The Journal of the Southern African Institute of Mining and Metallurgy

Next-generation, affordable SO2 abatement for coal-fired power generation Comparison of operational costs The differences in the process quantities between the two technologies, systems, and processes multiplied by the process quantity unit cost results in a net negative annual opex for WFGD (L/G) compared to a net positive opex for Sulfacid®, as shown in Table III. The absence of costs for limestone, gypsum disposal, taxable CO2, and disposal of crystallizer salts for the Sulfacid® process results in an annual opex saving of approximately –R579 million compared to the WFGD (L/G) process. These savings, in addition to the production of approximately 50% less wastewater, leads to the total annual opex of the Sulfacid® process being approximately 50% lower than for WFGD (L/G) technology, i.e.

–R616 million vs. –R1 266 million. The sale of the sulphuric acid by-product results in a net positive annual opex for the Sulfacid® process (R1 049 million) compared to a net negative annual opex for the WFGD (L/G) process (–R1 266 million).

Comparison of life cycle cost The positive net annual opex of R1 049 million for the Sulfacid® process makes it possible to realize a payback of the assumed capex of R17.677 billion over 16.9 years for a 30-year plant life cycle as shown in Table IV.

Sulphuric acid markets and sales prices Globally, sulphuric acid is used in the manufacturing for fertilizers (68%), petroleum refining (24%), metal mining (5%), and other industrial applications (3%) (Modiselle, 2013). The global sulphuric acid market is expected to grow by 2.3% (CAGR for 2019–2027) (Business Wire, 2019b) and by 3.8% (CAGR for 2019–2024) in the fertilizer industry (Business Wire, 2019a).

The growth in the fertilizer market is fuelled by the increase in the global population and the reduction in the availability of arable land, which requires an increase in crop production per hectare (IFA, 2018).

However, when large quantities of sulphuric acid become available in the South African market for local use and export due to the deployment of the Sulfacid® technology, it could be expected that the sales price of R800 per ton/t, which leads to a break-even of 16.9 years, may drop. To maintain the affordability of the Sulfacid® process with a break-even of 30 years, given the assumptions of this study, the price of sulphuric acid needs to be above R579 per ton.

A future perspective Expanding the function of a coal-fired power plant beyond electricity generation and stream production towards the on-site manufacturing of chemical commodities from flue gas (wasteto-chemicals) (Deloitte and VCI, 2017) demands a deliberate paradigm shift (Kuhn, 1970) towards a sustainable circular economy (Potting et al., 2017) from utility owners, policymakers, financiers, and governments. Sulphuric acid from converted coal-fired flue gases creates an opportunity not only for security of supply, regional sales, and export, but also for on-site beneficiation to fertilizer and other products and the formation of new, viable, special economic industrial zones and clusters.

Sulphuric acid derived from the Sulfacid® SO2 abatement system circumvents the need to construct new sulphuric acid production plants that burn pyrite (Runkel and Sturm, 2009) thereby avoiding emissions and greenhouse gases associated with additional industrial plants. Furthermore, the ability of the Sulfacid® SO2 abatement system to reduce SO2 flue gas The Journal of the Southern African Institute of Mining and Metallurgy

concentrations beyond compliance limits (< 19 mg/Nm3, 10% O2) to produce sulphuric acid demonstrates that coal-fired plants could be operated with negligible environmental impact.

Finally, the Sulfacid® SO2 abatement system forms part of the CPPE suite of modular activated carbon reactors that are able to capture hazardous pollutants such as Hg, Cd, dioxins, and furans and convert NOx and CO2 into ammonium nitrate and ammonium bicarbonate fertilizer products to enable complete coal-fired flue gas abatement and conversion.

Limitations of the comparative study The first-order techno-economic comparison between the WGFD (L/G) and Sulfacid® SO2 abatement technologies as applied to the 6 × 800 MWe Medupi power station needs to be followed up by a deeper level, comprehensive comparison executed by independent experts in this field to verify the results reported in this paper.

Conclusion This paper provided a first-order techno-economic comparison between the WFGD (L/G) (wet flue gas desulphurization using limestone and producing a gypsum by-product) and Sulfacid® technologies using the same input parameters and assumptions and applied for SO2 abatement of the 6 × 800 MWe Medupi power station. The Sulfacid® technology converts SO2 into saleable sulphuric acid and uses approximately the same water and power inputs as the WFGD (L/G) technology, but without the need for limestone or the adverse effects of producing unsaleable gypsum or additional CO2. Furthermore, for the same capex, the Sulfacid® technology shows a break-even of 16.9 years and a net positive cost of ownership over its life cycle.

The benefit of the utilization and roll-out of the CPPE Sulfacid® technology for coal-fired power generation in South Africa could have benefits for the environment, the economy, and society. SO2 emissions from coal-fired power stations could be reduced to < 50 mg/Nm3 (current legislation requires 1000 mg/Nm3) while producing saleable sulphuric acid. The drastic reduction in SO2 emissions in the Waterberg, Highveld, and Vaal Priority Areas would improve the working and living environment. The addition of further modular CPPE reactors will convert SO2, NOx, and CO2 into saleable fertilizer salts for the agricultural sector, and create the Chemistry 4.0 circular economy.

Saleable products from coal-fired flue gas will avoid stranded assets, promote the formation of economic growth points at power stations, create new asset classes, provide security of commodity supply and promote export opportunities. This in turn, will lead to job retention in the current coal value chain, new job creation, and the sustainable and environmental friendly utilization of South Africa’s. vast coal reserves.

Given a ‘conceptual-level accuracy of ±30% in 2017 South African rand’ (Cheng, van Wyk, and Bagus, 2018, p. 27) and using the assumptions as specified by Eskom for the WFGD (L/G) system and assumptions for the Sulfacid® system for Medupi power station, this comparative study shows that affordable SO2 abatement is possible by employing the CPPE Sulfacid® technology on a modern coal-fired power plant. It is recommended that independent assessments be done by academia, industry and government to verify the findings of this study.

Acknowledgement Dr Günther Hasse conceptualized the paper and wrote most of VOLUME 120

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Next-generation, affordable SO2 abatement for coal-fired power generation the text. Dr Alain Strickroth and Dr Marc Schumacher provided and verified all process data and quantities related to the CPPE Sulfacid® technology. Dr Alain Strickroth, Dr Marc Schumacher, and Itumeleng Kgomo conducted multiple comprehensive reviews of the draft paper. CPPE is the owner of the Sulfacid® technology and EPCM Global Engineering is the Southern African representative of CPPE.

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Facilitating skills development Next-generation, affordable SO abatement for coal-fired power generation opportunities for a rewarding career in the mining and minerals sector 2

Digging with Skills and Knowledge!

T

he Mining Qualifications Authority (MQA) is the Sector Education and Training Authority (SETA) responsible for skills development training in the mining and minerals sector.

The purpose of the MQA is informed by various objectives defined within a range of correlated sector frameworks. These are underpinned by the following six strategic objectives which also reinforce the MQA’s vision of a competent, health and safety orientated mining and minerals workforce: ➣ Promote efficient and effective governance and administration ➣ Improve skills development planning and decision-making through research ➣ Promote work-based skills development to support transformation in the mining and minerals sector ➣ Facilitate access to occupationally directed learning programmes for the unemployed ➣ Support mine community training initiatives to access economic opportunities ➣ Ensure the delivery of quality learning programmes in the mining and minerals sector.

The objectives of the National Skills Development Plan (NSDP) as determined by the Department of Higher Education, and Training (DHET) are supported by the mandate of the MQA. The MQA also supports the objectives of the Mining Charter in terms of the Minerals and Petroleum Resources Development Act (No. 29 of 1996).

The objective of the Mining Qualifications Authority is to address the skills needs in the South African mining and minerals sector to improve health, safety, employment equity and productivity.

The MQA is also responsible for maintaining the quality of standards, qualifications and learning provision, as well as for disbursing grants from the Skills Development Levy.

The MQA supports the career progression of individuals in the mining and minerals sector through various learning interventions and opportunities. These include career guidance outreach programmes targeting excelling maths and physical science learners in grades 9 to 12, with a special focus on learners situated in rural and mining communities.

The MQA Bursary Scheme supports learners in grade 12 (matric), who excel in mathematics and physical science, with financial assistance in the following qualifications and artisan trades:

Qualifications including core disciplines → → → → → → →

Metallurgical engineering Jewellery design and manufacturing Geology Mining engineering Mechanical engineering Mine surveying Electrical engineering (heavy current)

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Artisan trades Chemical engineering (mineral processing) Environmental health and management Analytical chemistry Electro mechanical engineering Industrial engineering Occupational health and safety Occupational hygiene

For employees in the mining and mineral sector, other disciplines will be considered (only 10%) for funding under the Bursary Scheme.

Graduates with qualifications in core mining disciplines from institutions of higher learning can benefit from participating in the MQA internship programme to gain structured work experience.

Learners can also participate in the work experience programme targeting learners that are seeking relevant work experience to pursue careers in the mining and minerals sector. The following is a list of core disciplines supported by the MQA as part of the work experience programme.

For more information on these and other MQA learning programmes, 2020 VOLUME 120 ▶ 590 please contact theOCTOBER Mining Qualifications Authority:

Telephone: 011 547 2600 • Website: www.mqa.org.za • Enquiries: info@mqa.org.za

→ → → → → → → →

Boilermaker Fitting (including machinery) Fitting and turning Rigger ropesman Diesel mechanic Electrician Millwright Instrumentation mechanician

Learnerships, with a structured learning component and practical work experience, are also available to assist learners to gain a recognised mining related qualification.

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Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope H. Bazzi1, H. Noferesti2, and H. Farhadian2 Affiliation: 1 Master of Science in Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

2 Assistant professor of Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

Correspondence to: H. Noferesti

Email:

hnoferesty@birjand.ac.ir

Dates:

Received: 11 Dec. 2019 Revised: 24 Aug. 2020 Accepted: 7 Sep. 2020 Published: October 2020

How to cite:

Bazzi, H., Noferesti, H., and Farhadian, H. Modelling the effect of blastinduced vibrations on the stability of a faulted mine slope.

Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 10, pp. 591–598.

DOI ID: http://dx.doi.org/10.17159/24119717/1066/2020 ORCiD ID: H. Bazzi https://orchid.org/0000-00032424-0394 ORCiD ID: H. Farhadian https://orchid.org/0000-00029948-0731

Synopsis Blasting, which is carried out regularly in open-pit mines, causes considerable ground vibration in the vicinity of the blasting site. These vibrations may affect the stability of mine slopes, causing problems in safety and constituting a hazard to life and property. In this study, the effect of the blasting-induced vibrations on slope stability was investigated using finite element (FE) analysis. A pit slope containing a fault was examined under seismic loading caused by successive explosions with varying intensity. Some reference points were selected above/under the fault surface and their motions recorded during the FE analysis. The results show that the points above the fault surface have the greatest displacement, while below the fault surface, only minimal (negligible) motions occur. Also, the intensity of the explosion has the greatest impact on motions at the upper points, but below the fault surface, the effect of the blast intensity was minimal. Usually, each explosion causes only small displacements in the mine slopes, but the destabilizing effect of repeated weekly blasts is significant, as confirmed in the present study. A sensitivity analysis proved a direct relationship between both the shear stiffness and friction angle of the fault surface and the motions of upper reference points. Likewise, in the presence of underground water pressure, the blasting-induced movements increase sharply.

Keywords blasting, slope stability, finite element analysis, dynamic analysis.

Introduction In surface mines, blasting is necessary for fragmenting of medium to hard rocks. However, there are side effects like ground vibration, air vibration, and fly rock that may cause damage to buildings and other structures in the vicinity of the mining site. In a blasting operation, around 20–30% of the explosive energy is spent on rock fragmentation, and the large remaining fraction is lost through ground vibration, fly rock, air vibration, noise, and back-break (Murmu, Maheshwari, and Verma, 2018).

Ground vibration is a major side effect of blasting operations, and special regulations are in place to minimize damage to nearby structures. These regulations depend mainly on the maximum particle velocity (Görgülü et al., 2013; Ozer et al., 2008; Azizabadi, Mansouri, and Fouché, 2014; Saadat, Khandelwal, and Monjezi, 2014). The US Bureau of Mines proposed the first rules for maximum particle velocity (Siskind et al., 1980). The legal constraints in ground vibration are related to the largest mass of explosive per delay and distance from the explosion site.

The impact of the explosion on civil structures has been well studied, and there are guidelines for the frequency of shaking, maximum charge mass, and the minimum distance to avoid any damage to buildings (Siskind et al., 1980; Singh and Roy 2010; Faramarzi, Farsangi, and Mansouri, 2014).

However, no such precise guidelines exist to minimize the explosion damage on the mine slopes, which are much closer to the blasting location. This is mainly due to the complexity of rock masses, and the issue of the cyclic nature of loading on mine slopes by successive explosions.

In this study, the effect of repeated explosions in a regular mining operation on the stability of a typical mine slope is investigated using a dynamic analysis method.

The importance of the dynamic analysis of the mine slopes has been emphasised in various studies (Terzaghi 1950; Stewart, Blake, and Hollingsworth, 2003; Bray and Travasarou 2009; Clough and Chopra 1966; Jibson Harp, and Michael, 2000; Newmark 1965). Terzaghi (1950) did pioneering research on the effects of seismic motion on slope stability, and various methods for investigating slope stability under seismic loading have subsequently been proposed. The conventional techniques for slope stability analysis in seismic conditions include pseudo-static analysis (Terzaghi 1950), Newmark displacement analysis (Newmark 1965), and dynamic numerical analysis (Seed 1979; Finn et al., 1986).

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Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope Pseudo-static stability analysis is a widely used limit equilibrium method that replaces the effect of dynamic earthquake loads by a constant equivalent-static acceleration (Terzaghi 1950). Newmark displacement analysis is a straightforward method in which an estimation of the permanent deformation during an earthquake is made and then compared to what is regarded as acceptable deformation (Newmark 1965).

For large mine slopes under complex seismic loading, the use of dynamic numerical analysis is recommended. This type of analysis is usually performed using the finite element or finite difference method (Roy, Dayal, and Jain, 2007). In this paper, using an acceleration-time wave typical of a mine blast, the effect of blast-induced vibrations on the stability of a faulted mine slope was investigated through a dynamic analysis with the finite element method.

Dynamic numerical analysis

Dynamic numerical analysis essentially involves estimation of the deformation behaviour of a geotechnical slope using the finite element or finite difference method (Roy, Dayal, and Jain, 2007).

A meshing scheme is used to represent the deformation of a slope during a seismic event. In each node of the mesh, displacement values are estimated as the result of external loads applied to the model. Usually in two-dimensional models, triangular (three or six nodes) or quadrilateral (four or eight nodes) elements are used. In comparison with pseudo-static and Newmark methods, dynamic numerical analysis is an exact method, yet the accuracy of this approach depends on the type of elements, mesh quality, mesh design, and structure of model (Melo and Sharma 2004).

An advanced modelling procedure and high-quality input data are required to acquire reliable results from the dynamic numerical analysis. Using the dynamic numerical method, a detailed representation of slope behaviour during a seismic event is plausible. In this regard, a decision must be made about numerous parameters, with damping coefficient, dynamic boundaries, and deformation modulus of materials being the most critical.

Damping coefficient

A dynamic system constantly loses its kinetic energy to reach a static condition. The damping rate of the system determines the amount of energy loss per unit time. The damping rate of geotechnical systems is related to internal friction and slip along weak structural surfaces (Bargi, 2010). In practice, it is hard to determine the damping data for dynamic simulations, and if available, use of experimental data is very helpful. Rayleigh damping is a straightforward method to define the damping of vibrating systems, in which the damping of a system (C) relates to its mass (m) and stiffness (k):

C = αm × m + βk × k where, αm and βk are constants with units of s-1 and s

[1]

respectively.

A system with several degrees of freedom has a large number of natural frequencies, but a constant level of damping is not possible for all frequencies because of the nature of the problem (Rocscience, 2020). If Rayleigh damping is applied, a damping ratio is set for two selected frequencies, and a damping predictor like the Lagomarsino model (Lagomarsino, 1993) is used to define the rest of the frequencies. With the Lagomarsino model, the frequencies between the two fixed frequencies hold a damping ratio less than the set value and frequencies outside this range are damped more heavily.

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By selecting a damping ratio in two fixed frequencies, the alpha and beta values of Rayleigh damping can be calculated.

Alternatively, the alpha and beta values may be determined directly. Setting the damping coefficients to zero corresponds to an undamped system, which results in the transient response of the system never dissipating (Rocscience Inc, 2019).

Dynamic boundaries Realistic and fictitious boundaries are used in dynamic numerical analysis of geotechnical systems. Fictitious boundaries do not exist physically but are used to encapsulate infinite volumes.

Using these boundaries in a dynamic analysis prevents the reflection of retreating waves and their return to the modelling domain. The main types of boundaries that are often used in dynamic models are absorbing, transmitting, damper, nodal mass, tied, and hydro mass.

Stability assessment of mining slope against a simulated blasting The southwestern final pit wall of Sungun copper mine with a hypothetical fault in it was modelled using RS2 software as shown in Figure 1. RS2 (formerly Phase2) is a 2D finite element program that can be used for a wide range of engineering projects, including advanced dynamic analysis of slopes. A wide array of material models is built in RS2. Additional new material models from well-known numerical tools like FLAC and PLAXIS are included. The RS2 program has been successfully applied to slope stability analysis (Hammah et al., 2009; Allan, Yacoub, and Curran, 2012; Noferesti and Hazegh, 2018). Results from RS2 models for slope stability analysis are verified against standard FLAC models (Rocscience Inc., 2020), UDEC models (Hammah et al., 2007), and limit equilibrium or analytical models (Hammah et al., 2005; Azami, Yacoub, and Curran, 2012).

A total of 1500 six-node triangular elements are present in this model. The Mohr-Coulomb strain-softening model was defined as the failure criterion of the medium. The fault boundary was assigned appropriate strength and stiffness characteristics to determine its response to stress. Elastic displacement and inelastic slip were allowed along the fault surface. The bottom and lateral sides of the slope model in Figure 1 are fictitious boundaries that attempt to simulate the infinite boundary effect of the earth medium. The lateral sides of the model were set as the transmit boundary that permits the incoming waves to enter the system while absorbing shear and pressure waves that would be moving out of the model domain. The bottom of the model was set as an absorb boundary type that absorbs the exiting wave from the system.

For dynamic analysis of a mining slope, an accelerationtime record of a blasting event is needed. In this study, an acceleration-time record presented by Hudson, Alford, and Iwan, (1961) has been used. As seen in Figure 2 this record lasts for 6 seconds, and its maximum absolute value is equal to 0.24 g.

The blast position is situated in other areas of the mine so that the blast-holes do not intersect the fault surface. However, it is assumed the blasting event causes a vibration in the slope model, as shown in Figure 2. The input geotechnical parameters of the mine slope model are given in Tables I and II. Before performing a dynamic numerical analysis, the following three steps should be completed (Rocscience Inc, 2019):

Step 1: Deconvolution of the seismic input Earthquake ground motions are usually provided in terms of The Journal of the Southern African Institute of Mining and Metallurgy

Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope

Figure 1—FE model of the Sungun pit slope with a hypothetical fault (orange line)

Earth surface acceleration data. However, for an RS2 analysis, the seismic input must be applied in the form of velocity data to the base of the model rather than the Earth’s surface. Therefore, ‘deconvolution’ of the given data is needed, such that once it is applied at the base of the model, it correctly simulates the ground motion.

The following steps are performed in deconvolution of the adopted acceleration-time history for a compliant base model: Figure 2—Horizontal acceleration data for a typical mining blast (Hudson, Alford, and Iwan, 1961)

Table I

Geotechnical properties of the Sungun porphyry rock (Tajadodian, 2005) Unit weight (kN/m3) 23.2

σci E GSI cm ϕm Ru (MPa) (GPa) value (kN/m2) (degree) 55

20

30

456

31

0.4

Table II

Geotechnical properties of a hypothetical fault Normal stiffness Shear stiffness cj ϕj Tensile strength Ru (MPa/m) (MPa/m) (kN/m2) (degree) (MPa)

100

10

0

24

0

The Journal of the Southern African Institute of Mining and Metallurgy

0.4

1. Integration of the acceleration-time history by applying the acceleration-time history to the top of a one-dimensional column model and measuring the resulting velocity data at a query point on top of the column.

2. Obtained velocity data is divided by 2. For a compliant base model, the upward-propagating wave train should be used. The upward-propagating wave is ½ the outcrop motion (Mejia and Dawson, 2006).

3. The halved velocity data, as shown in Figure 3, is converted to a stress wave and then applied to the base of the slope model.

Step 2: Filtering the input velocity of seismic loading When modelling seismic loading, the numerical accuracy of wave transmission is determined by the frequency content of the input wave and the wave speed of the system. For the exact representation of wave transmission within a model, the spatial element size should be (Kuhlemeyer and Lysmer 1973):

Element size ≤ λ/10 [2]

where λ is the wavelength related to the highest frequency portion that holds significant energy.

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Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope

Figure 3—Horizontal particle velocity data for a typical mining blast

To satisfy Equation [2], an unusually fine mesh may be needed that may lead to an excessive amount of computing time.

Fortunately, for most seismic events, the powerful part of the input velocity wave is carried in the lower-frequency portions (Rocscience Inc., 2019). By filtering the input wave and excluding high-frequency sections, a coarser mesh may be used without significantly influencing the results. The filter frequency for the adopted seismic event was selected as 4.5 Hz, and frequencies above this value were excluded from the dynamic analysis.

Step 3: Rayleigh damping In the present study, the Rayleigh coefficients were defined explicitly as alpha = 0.5 and beta = 0.0036.

After completing the above steps, a dynamic analysis was conducted on the mining slope using the acceleration data of the assumed blast. Results are presented in the following sections:

Effect of number of explosions The volume of minerals extracted in large surface mines is very high. So, usually, one blast per week is done in these mines.

The ground vibration due to each blast increases the likelihood of instability of the surrounding mine slopes. Identifying the main rock fractures and studying the blocks formed by these discontinuities is very important in the investigation of the stability of the mine slopes.

The FE model of the mining slope was analysed under the loading effect of a single blast. Figure 4 shows the motion of

four points on the mine slope during the loading process of a single blast, which lasts for 6 seconds. The point designated 4, located below the fault position, experienced the least effect from the blast while three other points, located over the fault, show considerable movement. Point 3, which is the closest point to the fault surface, shows the most displacement. The significant difference between the motion of points 4 and 3 confirms the severe effect of blasting on fault movement.

While the destabilizing effect of a single blast is negligible, the impact of repeated weekly blasts may be considerable. Figure 5 shows the motion of points 1 to 4 on the same mine slope after 50 blasts, an estimate for one year of mining. By comparing the motion values of reference points between two states (i.e., single and fifty blasts) interesting results are obtained. Point 4 in both cases shows negligible movement, which implies that single or repeated blasts in mining operations do not have much effect on massive intact slopes. Points 1 to 3, however, exhibited a large progressive movement towards the pit after 50 blasts. This indicates that in real mining situations a fault under repeated blasts may finally become unstable and a large slope failures occur.

Impact of blast intensity

The effect of blast intensity on mine slope model was studied by scaling up and down the horizontal acceleration data of Figure 2. The original acceleration wave of Figure 2 was scaled to the degree that the maximum acceleration reached to 0.05 g, 0.1 g, 0.2 g, and 0.5 g in each case. Figure 6 shows the motion of points 1 to 4 after 20 blasts of different intensities. Point 4 again shows no movement, even for the strongest blasts, but in the case of points 1 to 3, which lie over the fault, the displacement values increase monotonically with the blast intensity.

Impact of fault surface stiffness

Fault surface stiffness is a critical parameter, affecting fault behaviour under static/dynamic loading conditions, and is defined in two directions, i.e., normal and parallel to the fault surface. The shear (parallel) stiffness of the fault surface (ks) was changed from 0.5 MPa/m to 1.5 MPa/m and displacement of reference points was recorded after ten blasts. In the FE model the normal stiffness of the fault (kn) was considered to be considered ten times the shear stiffness. The maximum movement for reference points (Figure 7) was observed in the case of the least shear stiffness.

Figure 4—The horizontal displacements of reference points after a single blast

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Figure 5—The horizontal displacements of reference points after fifty blasts

Figure 6—Horizontal displacements of points 1–4 after 20 blasts of different intensities

Figure 7—The effect of shear stiffness of fault surface (ks) on horizontal displacement The Journal of the Southern African Institute of Mining and Metallurgy

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Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope Impact of friction angle of fault surface The friction angle of fault surface was reduced gradually from 30° to 24° and the displacement of the reference points recorded after ten blasts. When the friction angle was reduced to 26° a sudden increase in the motion of the reference points was observed, except for point 4 (Figure 8). Further reduction of friction angle in the FE model resulted in extraordinarily large displacement values for points 1 to 3. On the other hand, for friction angle values of 27° and larger, insignificant movement of points located above the fault surface was observed after ten blasts (Figure 8). This implies that in surface mining operations for any given fault there is a critical friction angle, below which failure can be expected after repeated blasting.

Impact of pore water pressure The ratio of pore water pressure to overburden pressure, Ru, was changed from 0 to 0.4 in the FE models and displacements of reference points recorded after ten blasts. In all cases, points 1 to 3 indicated a continuous increase in motion as greater Ru values were applied to the mine slope models (Figure 9). For Ru values ≤ 0.35 point 4 was almost stable and indicated minimal motion change, but for Ru = 0.4 a sudden large motion was observed as the seismic load was applied to the model. It seems that a Ru value between 0.35 and 0.4 is the critical value above which the intact rocks in the mine slope collapse under blast loading.

Conclusions In the FE model of a mine slope under the seismic effects of a mining blast, the largest motions were observed for the reference points above the fault surface, and below the fault surface only negligible movement was detected. A stronger blast caused higher displacements in the mine slope, especially in the presence of a fault, the points’ motions increased remarkably.

The shear stiffness of the fault surface is another factor affecting the behaviour of a mine slope close to a blasting

event. Increasing the shear stiffness of the fault decreased the displacements, but with repeated blasts a sudden increase of movement was observed at first, but then the rate of increase slowed.

Reducing the friction angle of the fault surface significantly increased the amount of displacement, so that at a lower friction angle the mine slope became unstable. Investigation of the effect of pore pressure showed that the points above the fault surface underwent higher displacements as pore pressure increased. At Ru = 0.25, the fault surface showed large movements, and the mine slope collapsed.

Although each blast causes small displacements in the mine slope, with repeated blasts the movements will gradually become significant, which may lead to slope failure. When deciding on an blasting pattern, the amount of explosive per delay should be selected with great care so that the explosion-induced shock wave does not cause permanent displacement in the mine slopes.

Even so, the effect of repeated blasts on the movement of the mine slopes should not be forgotten. A detailed numerical study, similar to the present one, is required in each case.

References

Allan, F.C., Yacoub, T.E., and Curran, J.H. 2012. On using spatial methods for heterogeneous slope stability analysis. Proceedings of the 46th US Rock Mechanics / Geomechanics Symposium, Chicago, IL, 24-27 June 2012.

American Rock Mechanics Association, Alexandria, VA. Azami A., Yacoub T.E., and Curran J.E. 2012. Effects of strength anisotropy on the stability of slopes. Proceedings of the 65th Canadian Geotechnical Conference, Winnipeg, Manitoba, 30 September - 3 October 2012. https://www.rocscience. com/assets/resources/learning/papers/Effects-of-Strength-Anisotropy-on-theStability-Analysis-of-Slopes.pdf Azizabadi, H.R.M., Mansouri, H., and Fouché, O. 2014. Coupling of two methods, waveform superposition and numerical, to model blast vibration effect on slope stability in jointed rock masses. Computers and Geotechnics, vol. 61. pp. 42–49.

Bargi, K. 2010. Fundamentals of Earthquake Engineering. Tehran University Press, Tehran.

Bray, J.D. and Travasarou, T. 2009. Pseudostatic coefficient for use in simplified seismic slope stability evaluation. Journal of Geotechnical and Geoenvironmental Engineering, vol. 135. no. 9. pp. 1336–1340.

Figure 8—The effect of friction angle of fault surface (phi) on horizontal displacement of mentioned points

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Figure 9—The effect of pore water pressure on horizontal displacement Clough, R.W. and Chopra, A.K. 1966. Earthquake stress analysis in earth dams.

Journal of the Engineering Mechanics Division, ASCE, vol. 92, no. 2. pp.197–212.

Faramarzi, F., Farsangi, M.A.E., and Mansouri, H. 2014. Simultaneous investigation of blast induced ground vibration and air blast effects on safety level of structures and human in surface blasting. International Journal of Mining Science and Technology, vol. 24. no. 5. pp. 663–669.

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Modelling the effect of blast-induced vibrations on the stability of a faulted mine slope

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The Southern African Institute of Mining and Metallurgy (SAIMM), the Canadian Institute of Mining, Metallurgy and Petroleum (CIM,) and the Australasian Institute of Mining and Metallurgy (AusIMM) will jointly convene a World Gold Conference every two years. In 2021 the conference will be held in Johannesburg, South Africa and hosted under the auspices of the SAIMM. The conference will be delivered as a hybrid event which will allow for contact and Online attendance and presentation.

Several important aspects of the current mining environment will constitute opportunities and threats for the industry in the foreseeable future. These include: • • • • • • •

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The impact of equipment productivity and pushback width on the mine planning process A.S. Araya1, M. Nehring2, E.T. Vega3, and N.S. Miranda4 Affiliation: 1 Department of Metallurgical and Mining Engineering, Universidad Católica del Norte, Antofagasta, Chile.

2 School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD, Australia.

3 Department of Metallurgical and Mining Engineering, Universidad Católica del Norte, Antofagasta, Chile.

4 Department of Metallurgical and Mining Engineering, Universidad Católica del Norte, Antofagasta, Chile.

Synopsis Conventional mine planning processes result in the selection of pushback widths that maximize equipment productivity. This paper challenges the current notion that pushback width should be set at the distance that assures maximum equipment productivity. A hypothetical case study is presented, which shows that the value of a project may increase beyond that determined by traditional planning practices. It was found that it may be better to deploy more aggressive mining strategies, which are likely to result in greater operational complexity and thus reduced equipment productivity. A higher equipment productivity, which often corresponds to wider (and therefore a lower number of) pushbacks, will also often result in later ore extraction and require higher capital costs.

Keywords open pit mining, pushback width, mine planning, schemes of exploitation, mining rate, equipment utilization.

Correspondence to: m.nehring@uq.edu.au

Email: M. Nehring

Dates:

Received: 17 Jun. 2020 Revised: 9 Sep. 2020 Accepted: 11 Sep. 2020 Published: October 2020

How to cite:

Araya, A.S., Nehring, M., Vega, E.T., and Miranda, N.S. The impact of equipment productivity and pushback width on the mine planning process.

Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 10, pp. 599–608.

DOI ID: http://dx.doi.org/10.17159/24119717/1256/2020 ORCiD ID: M. Nehring https://orchid.org/0000-00020404-5332

Introduction The value that is generated during the mining planning process should take into account multiple variables that interact with each other in a very complex system. One of the complexities of this task involves investigating the impact of different strategies on the economic value of the project, and finally identifying the optimum strategy while evaluating multiple trade-offs to increase the value of the mining business.

The open pit optimization process is generally completed in three sequential steps. Firstly, the final pit is defined. Secondly, the pushbacks that provide the exploitation sequence are defined. Finally, the production schedule is generated. Computer software and other tools are available to aid the decisionmaking process across these steps; however, the experience and aversion to risk of the planning team will determine the final mine plan.

This paper indirectly focuses on the second step of the process described. It addresses the impact of equipment productivity and associated mining rate, which in turn results from an alteration in pushback width. It is commonly accepted that the minimum width that maximizes equipment productivity and thus the mining rate should be used in pushback design. This is the geometrical width at which equipment is able to transit and operate without major difficulties. This width generally results in high equipment productivities and mining rates, which leads to reduced operating costs.

The increase in the size of equipment and therefore the need for additional working space has resulted in an increase in the width of pushbacks over time. During this investigation, mine planners from Chile’s mining industry were contacted, who asserted that after considering geomechanical variables and targeted ore feed to a process plant, the minimum width of each pushback was usually never less than 80 m, as operation of the equipment for high productivity was assured at this width.

This paper challenges the current notion that pushback width should be set at the distance that assures high equipment productivity. This project considered the evaluation of pushback width that is not reliant solely on optimal equipment performance, but driven more by the time related to accessing cash flows. This evaluation includes schemes that may be considered more aggressive in that they maintain a higher sinking rate in the operation.

Background Mine planning is one of the tasks where it is possible to add considerable value to a mining business.

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The impact of equipment productivity and pushback width on the mine planning process The work has to associate all the different processes in the mining value chain (Figure 1). For this, there are different techniques available depending on the company’s objectives and strategy, e.g. maximizing the NPV, extending the life of the mine, and minimizing risks, among others. However, regardless of the objectives to be attained, the working sequence for long-term mine planning (LMP) is similar in any company. Figure 2 shows a simplified diagram of the LMP process for the case of an open pit mine, not considering the cyclicality of the process.

Figure 2 shows that after the processes that define the geological model and final pit, the next step is to design the pushbacks, or phases, and their proper scheduling and extraction sequencing. Finally, the necessary investments and mining operational costs are determined, with which the project must be re-evaluated to give the project’s NPV. The multiple variables that must be considered for the determination of the project, in addition to the cyclicality of the process, generate difficulties in the actual evaluation of the impact of these variables on the project’s NPV and its optimization.

According to Castillo (2009), the problem of open pit mining optimization can be approached from at least two perspectives in mine planning. The first is a traditional approach, and the one most used by the industry. It starts with the definition of the final pit, then divides the pit into phases or pushbacks, and finally determines the extraction sequence. A variation of this first approach establishes the sequencing after the definition of the final pit, and this information is then used to define the project’s pushbacks. A second approach is ‘integral block sequencing’.

This new approach aims to determine the ideal block extraction sequence at the same time that the final pit limits are defined, but this approach is still being developed.

Final pit After defining the mining resources, the future economic variables must be defined, and with them the ore reserves that will establish the economic feasibility of the project are generated.

It is possible to define the final pit as the resulting shape, projected from the extraction of the ore and waste from an ore deposit, that maximizes the NPV of an open pit project.

Two algorithms are normally used to determine the final pit, and both work with the block model that contains the geological information. Both algorithms positively or negatively evaluate

the blocks to be mined. The first, based on graph theory, is the Lerchs-Grossmann algorithm (Lerchs and Grossmann, 1964).

The algorithm considers the spatial position of the block and its content (grade), and evaluates the extraction of a block considering all the blocks immediately above that need to be extracted to extract that block. Finally, the Lerchs-Grossmann algorithm indicates the envelope and final shape of the extraction that would maximize the project’s economic value. Another algorithm commonly accepted in the mining industry is the Kovorov algorithm, usually known as the floating cone algorithm.

This algorithm works by positioning an inverted cone around the blocks with a positive economic value, and then calculates the result of extracting a block, considering all the blocks that are inside the cone that must be removed. Some authors have discussed this algorithm and presented modifications for improving it in the last few years.

Pushbacks

Phases or pushbacks can be defined as each of the divisions that can be generated between the surface and the final pit in a mining project; as whole, they are also called nested pits. The generation of pushbacks is one of the first tasks in the mine planning process, and it divides the final pit into more manageable units for the various mine planning stages and evaluations (Couzens and Pincock, Allen & Holt, 1970; Hustrulid, Kuchta, and Martin, 2013; Meagher, Dimitrakopoulos, and Avis, 2014). In addition, using pushbacks can lead to an increased NPV while minimizing the stripping ratio for the project, depending on the characteristics of the mineralization and dimensions of the final pit (Sabanov and Bearre, 2015).

The definition of pushback contains the ‘worst case’ and ‘best case’ concepts, which are explained by David Whittle (2011). Both terms refer to the order of exploitation of an ore deposit. The ‘worst case’ (Figure 3a) is when an ore deposit is removed sequentially bench by bench, that is, the upper bench is completely extracted and once this is completed extraction of the bench immediately beneath follows. This extraction strategy gives the lowest NPV for the project because the ore cannot be accessed until a large amount of waste is removed (upper benches). In the ‘best case’ (Figure 3b) the ore deposit is mined sequentially pit by pit (nested pits, phases, or pushbacks), which generates the best NPV for the project because access to the ore

Figure 1—Steps in the mining project value chain (Whittle, 2010)

Figure 2—Long-term mine planning process

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Figure 3—Different mining sequences (Whittle, 2011)

Figure 4—Pit-by-pit chart

is achieved quickly. The difference in the NPVs generated by the ‘worst case’ and the ‘best case’ determines the feasibility of, or opportunity for, applying a pushback. If the NPV is similar for both cases, the implementation of a pushback will not add value to the project. However, if there is a difference between the cases, the implementation of a pushback should be studied. The analysis is done by means of a pit-by-pit chart (Figure 4).

Mathieson (1982, cited by Hustralid, 2013), indicates that three aspects should be considered before starting to define a project’s pushback: (1) The geomechanical variables; for example, final pit angles, inter-ramp angles, and bench face angles; (2) the maximum extraction rate required of the pushback (ore and waste); and (3) the size and type of operating equipment, which determines the minimum width requirements of the operational benches. If the geomechanical conditions are favourable for any design and the rate of extraction required is achieved by the fleet of equipment available, the final design of the pushback is restricted to the minimum space required to operate the equipment.

According to Crawford (2001, cited by Songolo, 2010), some factors can affect pushback width, and the minimum possible design width depends on them: ➤ The flexible operation of equipment. If the operation is flexible, the mine can have more faces and this provides a greater selection of ores with different properties, in addition to simplifying balancing of the waste and ore feeds. If there are more faces, a problem at one face will not stop production; instead, the other faces can be exploited. ➤ The ease with which scheduling can be achieved. Multiple faces could cause problems for the mine planners, who have to be certain that the upper level worked on does not overtake the lower levels.

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➤ The

mine operating costs. Productivity in large spaces is higher than in small ones, therefore the cost is lower.

Mine planners often design using this approach for the evaluation of the initial plan. ➤ Slope stability. Typically, narrower pushbacks generate shallower overall pit slopes than wider pushbacks. Wider pushbacks are often used at the ultimate slope design limits, where failures can occur, and can be used early in the life of the mine to test the ultimate slopes. ➤ Deposit geometry and stripping ratio. The costs of different pushbacks can be similar or vary, depending on the orebody shape. Two pushbacks may contain different amounts of ore and waste. ➤ Cost of capital at the corporate level. Companies have to be aware of the cost of using narrower pushbacks, especially when making decisions about this indicator. This approach can have higher operating costs than wider pushbacks, but a strategic mix using both in line with the fluctuation of the commodity price could be considered. ➤ Spatial continuity of ore zones. A well-defined disseminated deposit involves a lower risk than an erratic deposit. Therefore, a narrower pushback increases the risk if the deposit is erratic due to the possibility of not finding the ore, or it not being completely exposed for stripping. ➤ Management preferences. The decision regarding which approach to use depends on the complexity of the company. The concern of small businesses is commonly their daily operation. In larger companies with more specialized engineering capabilities, the approach involves sophisticated simulations of different strategies There are several proposals for generating pushbacks.

In his thesis project, Ramazan (1996) reviewed the different algorithms proposed for that period and proposed a new one.

His project finally tested them in two study cases. All the algorithms studied gave different results for both cases and none of them considered pushback width as an important variable.

Meagher, Dimitrakopoulos, and Avis (2014) reviewed open pit mining design, pushbacks, and the gap problem. This last term is used to describe the size inconsistencies that occur between two successive pushbacks. This research concluded that it is important to develop algorithms that project or use a determined size for the phases to eliminate the gap problem and obtain the optimal NPV. Currently, to resolve the problem of defining pushbacks, mining engineers divide the problem into two parts. The first, and one of the most widely used methods, is the variation of economic conditions such as commodity prices, costs, or cutting grades. This method consists of first assigning low values and VOLUME 120

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The impact of equipment productivity and pushback width on the mine planning process then gradually increasing them. If it is decided to vary the price of the commodity, for example from a low value to a high value (a technique often used by software packages), a number of pits can be generated, increasing the size each time and reducing the average value of the ore constituents of interest contained in the pit (Dagdelen, 2001). The second step is to select the appropriate pushback for the project, but this decision is the responsibility of the supervising engineer and is done by trial and error, considering the project’s needs and objectives.

According to Meagher, Dimitrakopoulos, and Avis (2014), the current pushback design methods present at least four problems, leading to a suboptimal production schedule:

with truck traffic in parallel. Figure 8 shows a shovel advancing parallel to the bench, but this time with the transit from or to the shovel; once the truck is loaded with the material it must return along the route it used to enter the sector.

The operating costs for loading and haulage are directly related to the speed with which the material is loaded and transported from the extraction point to its final destination.

When considering the strategy to be used in the operation, loading from both sides of the power shovel, which increases productivity by reducing waiting times for truck positioning, should be considered. However, it also increases the space requirements for the operation (Figure 9).

➤ The grade and ore quality requirements are not considered in the design ➤ The uncertainty of the in-situ grade is ignored ➤ There are large variations in the size of the pushbacks ➤ They do not consider the discounted value during optimization and assume that a ‘greedy’ approach will maximize the discounted value.

Production scheduling To calculate the value of a mining project, an extraction sequence must first be decided on, and then the pit must be extracted conceptually, accumulating income and costs as it progresses. If

Finally, it is possible to identify the practical steps for pushback design. The process consists of first determining whether a pushback should be implemented (from the difference between the best and worst cases), followed by the evaluation, using a computer tool, of the pushback section for the project.

At the same time, the project’s requirements and restrictions (technical and economic) must be defined, and finally, after crossvalidation and several tests, the pushback that can improve the value of the project can be selected or modified.

Equipment operation and requirements Industry trends show a progressive increase in the capacity and dimensions of mining equipment. This directly affects the operational design of a mine and its extraction schedule, both tasks that are the purview of mine planning. Currently, there are mining industry trucks with capacities of over 267 m3 and electric shovels with bucket capacities of up to 67 m3. The dimensions in area units exceed 140 m2 for trucks and 225 m2 for loading equipment. This is the result of economies of scale that affect the system (Bozorgebrahimi, Hall, and Morin, 2005; Rojas Seguel, Castillo, E., and Cantallopts, 2015) and their impact on the reduction of operating costs is of considerable interest to mining companies, especially in times of low commodity prices (Bozorgebrahimi, Hall, and Blackwell, 2003).

The growing trend in the work capacity of mining equipment has undoubtedly had a considerable impact on mining and operations design as the dimensions of the equipment must be considered in the mine planning process for effective performance.

Hustrulid (2013) explains the strategies for the expansion process that are directly related to pushback width design.

Figures 5 and 6 show the frontal cut approach, which comprises a frontal cut to the face of the bench. This option is usually used to start a new mine level. This strategy can be used in large spaces where it is necessary to expand exploitation over large, horizontal directions. When the space is sufficient, more than one item of equipment (shovel) is usually used in the sectors where this is feasible (Figure 6).

For mine expansions in which work must be done in limited spaces, the strategy that is used comprises parallel advance to the face of the bench. This strategy entails variants in the transit of trucks. Figure 7 shows a shovel advancing parallel to the bench

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Figure 5—Frontal cut operation (Hustrulid, Kuchta, and Martin 2013)

Figure 6—Two shovels working on the same face (Hustrulid, Kuchta, and Martin, 2013)

Figure 7—Parallel cut with drive-by loading ((Hustrulid, Kuchta, and Martin, 2013) The Journal of the Southern African Institute of Mining and Metallurgy

The impact of equipment productivity and pushback width on the mine planning process Typically, once the final pit and pushback have been defined, the extraction schedule is defined. This process depends on the information provided by the pushback design. Some proposals have been published and discussed, but this topic is currently being developed.

Case study

Figure 8—Parallel cut with single back-up loading ((Hustrulid, Kuchta, and Martin, 2013)

Figure 9—Parallel cut with double back-up loading (Hustrulid, Kuchta, and Martin, 2013)

the time value of money is to be included, that is, the fact that one dollar today is more valuable than one dollar next year, then revenues and costs must be discounted by a factor that increases over time ( Whittle, 1990).

The objective of production scheduling of a mining project is to maximize the present value and return on investment that could be derived from the extraction, concentration, and sale of a commodity from an ore deposit (Bohnet, 1990). To achieve this, the block removal sequence must be determined, considering the year in which it has to be removed (Mousavi Nogholi, 2015).

The evaluation methodology for this work considers a common hypothetical ore deposit (with the same block model and final pit) for testing different scenarios. The equipment to be used in the different pushback designs also remain the same, however, its performance will vary with the bench width it is operating on.

After defining and designing all the pushbacks, the next step is to determine the production plan for each case. The production plan is used to calculate the mining equipment required for each scenario. Finally, the NPV for each design is calculated and the results are compared (Figure 10).

The block size for the hypothetical deposit is 5×5×5 m.

The model is 1555 m long, 1500 m wide, and 750 m deep. The density of the rock is considered to be fixed at 2.6 t/m3.

The same mineralization and basic design parameters are considered for all cases. The deposit contains a total of 879 Mt of oxide ore, with a grade of 1.1% copper for each block. A total of 2369 Mt of material is extracted. Mine production is 118.4 Mt/a, and a maximum of 54.7 Mt of ore is fed to the plant for all cases.

The metallurgical recovery from the ore is 85%. The final product is electro-refined cathodes. The mine design considers a bench 15 m high for each of the exploitation designs with different pushbacks. The final level (at the base of the pit) has a width of 65 m, and the final pit has a slope angle of 44 degrees.

This work considers the same fleet of shovels and trucks.

The selected loader is a Komatsu P&H 4100XPC power shovel.

The truck model selected is a CAT 797F. Typically, the minimum width needed for the equipment selected is 80 m, taking into consideration the size of the machinery and its clearance and turning diameter (Figure 12) in optimal circumstances.

Figure 11—Hypothetical ore deposit

Figure 10—Work methodology The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 12—Diagram of shovel-truck operation in open pit mining

Figure 13—First movements of the truck in a reduced bench space

Figure 14—Second movement of the truck in a reduced bench space

The estimation of minimum space is completed using the equipment’s technical manual and considering a minimum space possible in the operation. This reduced space means that the truck has to manoeuvre more than usual. In the first movements, the truck must position in front of the bench and then reverse (Figure 13).

After that, the truck must move forward and then turn again, stopping and reversing, to finally be in the correct position for loading by shovel (Figure 14). The truck can then proceed to the road without any further problems.

Finally, the pushback widths for four different cases were defined, each with their own predetermined productivity parameters given the available space. In the first case, the pushback width is only the minimum space of 65 m needed for the equipment. In this case, the truck must manoeuvre more;

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in fact, the truck must carry out two steps (prior manoeuvres) before reaching its final loading position. In this strategy, loading is done only on one side of the shovel. In the second case, the truck must manoeuvre, but it only needs one step to reach the final position. The strategy in this case is also single back-up loading, but this strategy requires lower driver skills. The third and fourth designs correspond to widths of 120 and 160 m; in these the space is sufficient for the truck to reach the final loading position without any problems. In the pushback with a width of 120 m, the strategy is single back-up loading. For the pushback with a width of 160 m, the strategy is double back-up loading.

The four different mining planning scenarios are generated considering pushback widths of 65, 80, 120, and 160 m (Figure 15). To show more clearly the real impact of the pushback width on the project’s NPV, the scenarios use fixed widths for each case.

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Figure 15—Different pushback designs

Table I

Operating costs considered for the project Mine costs Item Unit Drilling Blasting Loading Hauling Aux. equip. & services Administration Open pit cost

US/t mov US/t mov US/t mov US/t mov US/t mov US/t mov US/t mov

Base Value

Plant costs Item Unit

0.06 Metallurgy recovery % 80 0.16 Total cost US/lb 19.50 0.13 0.45 Sales cost Base 0.17 Item Unit Value 0.19 G&A US/lb 0.24 1.16

Investment items Item Unit Value

Economic parameters Item Unit

Plant Truck Shovel

Copper price Tax (investors) Interest rate

million US$ million US$ million US$

Base Value

1700 4.5 25

The cost and performance of the equipment are adjusted according to the pushback width for each particular economic evaluation. For the above, the operating performance characteristics of a fleet of real mine equipment will be considered. The base cost to consider (Table I) corresponds to the pushback width of 80 m (base). For the other pushbacks, these costs are increased or decreased according to the performance of the fleet in each design.

The different pushback design strategies affect the times required for operating equipment in a single pass (80 m). For the 65 m pushback width, a 50% longer truck manoeuvring time and a 50% longer waiting time for equipment positioning are required. For the 120 m width scenario, both the truck manoeuvring time and the waiting time for equipment positioning are 20% shorter. Finally, for the 160 m pushback width, a reduction of 20% in manoeuvring and final positioning times is possible. The variations in the operating times of the fleet are applied to the variables indicated in Table II. Similar considerations were taken for the costs of loading and hauling. For the 65 m pushback width scenario, the hauling cost is 25% higher and the loading cost 20% higher. For the 120 m width scenario, hauling and loading costs are 10% lower. Finally, for the 160 m pushback width, a reduction of 20% in hauling and loading costs is possible. The variations in costs of the fleet are applied to the variables indicated in Table II. The Journal of the Southern African Institute of Mining and Metallurgy

Base Value

US$/lb % %

3.0 25 25

After the necessary adjustments to the operating performance of the mine equipment are made, the corresponding extraction sequence can be generated for each scenario, maintaining the plant feed restrictions and the mine production.

In this work, the extraction sequence is in the order of the pushbacks designed. First, the shorter pushback (pit) is extracted, and after that, the following one. Extraction is independent of the specific period as mine planning will always complete the extraction scheduled for the mine. As a general rule, prior to extracting ore the extraction of at least 50% of the waste

Table II

ariations in shovel and truck operating times for each V pushback design Variable

65 m

Pushback width

80 m (base case) 120 m 160 m

Truck

Manoeuvring time (%)

1.50

1

0.80

0.80

Power shovel

Equipment positioning time (%)

1.50

1

0.80

0.60

Total cost (%)

1.25

1

0.90

0.80

Total cost (%)

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0.80

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The impact of equipment productivity and pushback width on the mine planning process in the first pits and at least 25% in the final pits is necessary in each pushback, which provides certainty that ore will always be exposed.

The extraction of ore is not fixed. Mine planning attempts to extract the ore necessary for the plant, but if this is not possible the plant will just process the ore actually extracted. With this approach, we will know the impact on ore extraction of each pushback design. The mine plan for economic evaluation is generated using this information.

The next step is to calculate the equipment required to maintain the production goal previously established in each scenario. Finally, the economic evaluation of each scenario is carried out considering the difference in operating costs, as well as the difference in equipment necessary for exploitation (investment).

Implementation The implementation of this work was completed in MS Excel®. To evaluate the different designs, a worksheet was created with each cell corresponding to one block, with the dimensions indicated (5×5×5 m), and then using a colour code the different pushbacks were defined for the four final pits, all with the same dimensions.

The differentiation between ore and waste is done numerically, using positive numbers for ore and negative numbers for waste. The ore and waste for each pushback are then calculated and the extraction scheduling is begun, always following the guidelines established previously. When the extraction schedule is finished for each period, the corresponding calculation of gross revenue is carried out.

The cost of haulage is considered to increase over time. For all designs, a 2% increase in the cost of haulage is considered for each period. The other mine costs are taken to be fixed in time, because in the future mining companies may generate new business strategies or integrate new technologies that help to decrease or maintain costs.

The fleet required for loading and haulage is determined by the capacity of each item of equipment. In addition, different operating times are considered for each pushback design to evaluate the number of shovels and trucks needed. More time is needed for the 65 m pushback width, and the time is decreased for each design as the space for operation increases (pushback widths of 80, 120, and 160 m).

Finally, the cash flow for each design is calculated. The evaluation considers the same investment plan and the same costs for shovels and trucks for all the designs, but in accordance with the equipment required for each pushback design. In this work, the replacement of equipment is not taken into account, because this only increases the investment necessary in the evaluation of all the designs, and the impact of this item on the value of the project is not important in establishing the value of each design.

Results and discussion The ore production with the different pushback designs shows large differences in the first years in the cases evaluated (Figure 16). All the designs extracted only waste in the first year and started ore production in the second year, with the exception of the 160 m width pushback, which extracted only 3.5 Mt of ore in the second year. Therefore, the first two years are considered to be the pre-stripping period in the 160 m pushback design. For the 65 m pushback width, there is quick access to the ore, but in this approach the maximum planned production is reached only after eight years. In contrast, the 160 m pushback width enables more stable ore and waste extraction, providing the processing plant with a continuous feed of ore that is less variable over time.

There are no great differences in the fleet of equipment necessary for the different designs. Only for the 65 m pushback width does the number of trucks increase over time, because the haulage profile used (mean distance) was the same for each pushback design and only the production performance was

Figure 16—Mine extraction with the different pushback designs

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The impact of equipment productivity and pushback width on the mine planning process Table III

Final economic result Pushback width 65 m 80 m 120 m 160 m

Number of power shovels

Max. number of trucks

5 4 4 4

66 55 54 54

Mine operating cost year 1 (US$/t) Mine operating cost year 20 (US$/t) NPV (US$) 1.305 1.160 1.102 1.044

1.327 1.177 1.118 1.058

934 937 056 927 473 044 809 324 513 477 228 965

mining performance. International Journal of Surface Mining, Reclamation and Environment, vol. 19, no. 1. pp. 41–56. https://doi.org/10.1080/1389526041 2331326821 Bozorgebrahimi, E., Hall, R.A., and Blackwell, G.H. 2003. Sizing equipment for open pit mining – a review of critical parameters. https://doi. org/10.1179/0371784032250035 Castillo, L. 2009. Modelos de optimización para la planificacion de mineria a cielo abierto. Undergraduate thesis, Universidad de Chile.

Couzens, T. and Pincock, Allen & Holt. 1970. Aspects of production planning: operating layout and phase plans. Open Pit Mine Planning and Design.1st edn. Crawford, J. and Hustrulid, W. (eds), Society of Mining Engineers of the

Figure 17—Number of trucks per period in the different pushback designs

American Institute of Mining, Metallurgical, and Petroleum Engineers, New York. pp. 217–231.

Dagdelen, K. 2001. Open pit optimization - Strategies for improving economics of mining projects through mine planning. Proceedings of the 17* International

considered to be different in each evaluation. For the pushback widths of 80, 120, and 160 m, the number of trucks is similar, although different performance was considered in each case, reducing the time for manoeuvring and waiting for correct truck positioning.

The economic results obtained for each design (Table III) show a reduction in value with smaller pushback widths. In cases in which ore extraction is late, the cost of the project increases in the first period, because the equipment moves only waste, and gross revenue is generated late, which decreases the value of the project.

Conclusion This paper challenges the current notion that pushback width should be set at the distance that assures high equipment productivity. The results of the case study show that the value of the project increases with a greater number of pushbacks (or smaller pushback widths), although this requires equipment to operate at suboptimal productivity. When pushbacks that offer minimal space for equipment transit are used, this leads to poorer productivity and subsequently high operating costs. However, as this study has shown, higher values than in situations with fewer pushbacks may still result. This is due to the time value of money – by delaying expenditure associated with pre-stripping while bringing forward access to ore as a result of a faster sinking rate.

Table II contains the assumed productivity adjustment factors applied to equipment operating under the various pushback widths considered in this study.

The traditional belief that generating ’optimal’ exploitation models requires planners to seek lower operating costs is thus not always valid. Planners should seek out value increases rather than cost reductions.

References

Bohnet, E.L. 1990. Optimun production scheduling. Surface Mining. Kennedy, B.A. (ed.). 2nd edn. Society for Mining, Metallurgy & Exploration, Littleton, CO. Bozorgebrahimi, A., Hall, R., and Morin, M. 2005. Equipment size effects on open pit The Journal of the Southern African Institute of Mining and Metallurgy

Mining Congress and Exhibition of Turkey. pp. 117–122. http://www.maden. org.tr/resimler/ekler/1259a0cb2431834_ek.pdf Hustrulid, W.A., Kuchta, M., and Martin, R.K. 2013. Open Pit Mine Planning & Design. CRC Press, Boca Raton, FL. https://www.mendeley.com/researchpapers/open-pit-mine-planning-design-two-volume-set-cdrom-pack/ Lerchs, H. and Grossmann, I. 1964. Optimum design of open-pit mines. CIM Transactions, vol. 68. pp. 17–24.

Mathieson, G.A. 1982. Open pit sequencing and scheduling. Proceedings of the the First International SME-AIME Fall Meeting, Honolulu. Society of Mining Engineers of the American Institute of Mining, Metallurgical, and Petroleum Engineers, New York.

Meagher, C., Dimitrakopoulos, R., and Avis, D. 2014. Optimized open pit mine design, pushbacks and the gap problem—a review. Journal of Mining Science, vol. 50, no. 3. pp. 508–526. https://doi.org/10.1134/S1062739114030132 Mousavi Nogholi, A.A. 2015. Optimisation of open pit mine block sequencing. PhD thesis, Queensland University of Technology. http://eprints.qut.edu.au/86697/ Ramazan, S. 1996. A new push back design algorithm in open pit mining. MS thesis, Colorado School of Mines.

Rojas Seguel, D., Castillo, E., and Cantallopts, J. 2015. Caracterización de los costos de la gran minería del cobre. https://www.cochilco.cl/Listado Temtico/062016 Seguimiento Costos.pdf Sabanov, S. and Bearre, M. 2015. Open pit scheduling features to improve project economy. Proceedings of the 23rd International Symposium on Mine Planning and Equipment Selection (MPES 2015), vol. 1. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 1033–1040.

Songolo, M. 2010. Pushback design using genetic algorithms. MEng thesis, Curtin University. https://doi.org/10.13140/2.1.4065.6169 Whittle, D. 2011. Open-pit planning and design. SME Mining Engineering Handbook. Society for Mining, Metallurgy & Exploration, Littleton, CO. pp.

877–901.

Whittle, G. 2010. Enterprise optimisation. Proceedings of Mine Planning and Equipment Selection (MPES), Fremantle, WA, 1-3 December 2010.

Australasian Institute of Mining and Metallurgy, Melbourne. pp. 105–117. http://www.whittleconsulting.com.au/documents/articles/Enterprise%20 Optimisation.pdf Whittle, J. 1990. Open pit optimization. Surface Mining. 2nd edn.Kennedy, B.A. (ed.). Society for Mining, Metallurgy & Exploration. Littleton,CO. pp. 470–475.

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SOUTHERN AFRICAN

RARE EARTHS

INTERNATIONAL CONFERENCE 2021

18-20 OCTOBER 2021

SWAKOPMUND HOTEL AND ENTERTAINMENT CENTRE, SWAKOPMUND, NAMIBIA

Driving the future of high-tech industries ABOUT THE CONFERENCE The global demand for rare earth elements (REEs) and their alloys has increased enormously in the last few decades. REEs are critical materials in high-technology applications due to their unique chemical, catalytic, electrical, magnetic, and optical properties.

In particular, REEs are used in emerging and niche technologies such as medical devices, electric vehicles, energy-efficient lighting, wind turbines, rechargeable batteries, catalytic converters, flat screen televisions, mobile phones. and disk drives. In fact, the 4IRdriven digital revolution will not be possible without the critical rare REEs.

The supply security of rare earth metals is of global concern. The need to diversify the supply of REEs thus creates significant opportunities for southern Africa to contribute to the global supply.

In fact, as one of the regions with large REE resources, southern Africa can exploit this window of opportunity and significantly contribute to the sustainable supply of these high-tech materials.

PLEASE NOTE THAT SPONSORSHIP OPPORTUNITIES AND EXHIBITION SPACE IS AVAILABLE.

The need to fully participate along the REE value chain has also inspired interest in developing downstream capacity for refining, through the Southern African Centralized Rare Earth Refinery (SACREF). Thus, in order to maximize value from the REEs industry in the region, further discussions on optimizing the REE value chain are needed.

This conference, focusing on the optimization of the primary production and refining of rare earth metals, is designed to stimulate debate on growth, creating opportunities for the southern African rare earths industry.

FOR FURTHER INFORMATION, CONTACT: â&#x2013;śâ&#x20AC;&#x201A; 608

Camielah Jardine, Head of Conferencing OCTOBER 2020

E-mail: camielah@saimm.co.za Web: VOLUME 120 www.saimm.co.za The Journal of the Southern African Institute of Mining and Metallurgy

NATIONAL & INTERNATIONAL ACTIVITIES 2021 20–22 October 2020 — Exclusive Online Customer Training Workshop Fines Processing and Disposal 2020 Website: https://www.multotec.com

6–9 June 2021 — The 16th International Ferroalloys Congress (INFACON XVI) Clarion Hotel & Congress Trondheim infacon2021@videre.ntnu.no

28–30 October 2020 — Hydropress 12th International Conference on Process Hydrometallurgy 2020 Sheraton Hotel, Chile E-mail: hydroprocess@gecamin.com

9–10 June 2021 — Diamonds – Source To Use — 2021 Hybrid Conference ‘Innovation And Technology’ The Canvas, Riversands, Fourways, 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

2–5 November 2020 — CIM Capital Projects Virtual Symposium 2020 hydroprocess@gecamin.com 4–5 November 2020 — Second International Conference on Underground Mining Technology Online Event (UMT 2020) Australia E-mail: info-acg@uwa.edu.au 10–11/17–18 November 2020 — Preconcentration Digital Online Conference 2020 Website: https://precon.ausimm.com 9–27 November 2020 — ALTA 2020 Online Nickel-Cobalt-Copper, Uranium-REE, Gold-PM, In-Situ Recovery, Lithium & Battery Technology Conference and Exhibition 2020 E-mail: allisontaylor@altamet.com.au 19–20 November 2020 — The Rise of the Phoenix | Make SA Manufacturing Great Again Virtual Summit 2020 E-mail: stead.nick@gmail.com 23–25 November 2020 — Geographical Information System (GIS) for 21st Century Mining 2020 Wits Mining Institute, Chamber of Mines Building, University of the Witwatersrand E-mail: lileen.lee@wits.ac.za 9–11 December 2020 — MassMin2020 Virtual Conference E-mail: jgutierrez@ing.uchile.clv

2021 18–22 April 2021 — IMPC2020 XXX International Mineral Processing Congress Cape Town, 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

28–30 June 2021 — Renewable Solutions for and Intensive Industry Hybrid Conference 2021 Mintek, Randburg, 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 13–16 July 2021 — Copper Cobalt Africa Incorporating The 10th Southern African Base Metals Conference Avani Victoria Falls Resort, Livingstone, Zambia 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 29–30 July 2021 — 5th Mineral Project Valuation Colloquium Glenhove Events Hub, Melrose Estate, Johannesburg Contact: Gugu Charlie Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 16–17 August 2021 — Worldgold Hybrid Conference 2021 Misty Hills Conference Centre, Muldersdrift, 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

Owing to the current COVID-19 pandemic our 2020 conferences have been postponed until further notice.

We will confirm new dates in due course.

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NATIONAL & INTERNATIONAL ACTIVITIES 29 August–2 September 2021 — APCOM 2021 Minerals Industry Conference ‘The next digital transformation in mining’ Misty Hills Conference Centre, Muldersdrift, 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 21–22 September 2021 — 5th Young Professionals Conference 2021 ‘A Showcase of Emerging Research and Innovation in the Minerals Industry’ The Canvas, Riversands, Fourways, 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 27–30 September 2021 — 8th Sulphur and Sulphuric Acid Conference 2021 The Vineyard Hotel, Newlands, Cape Town, 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 4–6 October 2021 — The 8th International PGM Conference 2021 ‘PGMs – Enabling a cleaner world’ Sun City Resort, Rustenburg, 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

18–20 October 2021 — South African Rare Earths International Conference 2021 ‘Driving the future of high-tech industries’ Swakopmund Hotel And Entertainment Centre, 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 20–21 October 2020 — MINSA Online Symposium 2021 E-mail: minsa@gssa.org.za 26–27 October 2021 — SAMCODES Conference 2021 ‘Good Practice and Lessons Learnt’ Industry Reporting Standards Glenhove Events Hub, Melrose Estate, 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 8–10 November 2021 — Global Tailings Standards and Opportunities Hybrid Conference 2021 ‘For the Mine of the Future’ Sun City Resort, Rustenburg, South Africa Contact: Gugu Charlie Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 9–11 December 2021— Massmin 2020 Eight International Conference & Exhibition on Mass Mining Virtual Conference 2020 Santiago, Chile, Contact: J.O. Gutiérrez Tel: (56-2) 2978 4476 Website: www.massmin2020.com

Owing to the current COVID-19 pandemic our 2020 conferences have been postponed until further notice.

We will confirm new dates in due course.

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Company affiliates The following organizations have been admitted to the Institute as Company Affiliates 3M South Africa (Pty) Limited AECOM SA (Pty) Ltd AEL Mining Services Limited African Pegmatite (Pty) Ltd Air Liquide (Pty) Ltd Alexander Proudfoot Africa (Pty) Ltd AMEC Foster Wheeler AMIRA International Africa (Pty) Ltd ANDRITZ Delkor(Pty) Ltd Anglo Operations Proprietary Limited Anglogold Ashanti Ltd Arcus Gibb (Pty) Ltd ASPASA Atlas Copco Holdings South Africa (Pty) Limited Aurecon South Africa (Pty) Ltd Aveng Engineering Aveng Mining Shafts and Underground Axiom Chemlab Supplies (Pty) Ltd Axis House (Pty) Ltd Bafokeng Rasimone Platinum Mine Barloworld Equipment -Mining BASF Holdings SA (Pty) Ltd BCL Limited Becker Mining (Pty) Ltd BedRock Mining Support (Pty) Ltd BHP Billiton Energy Coal SA Ltd Blue Cube Systems (Pty) Ltd Bluhm Burton Engineering (Pty) Ltd Bond Equipment (Pty) Ltd Bouygues Travaux Publics Castle Lead Works 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) Data Mine SA Digby Wells and Associates DRA Mineral Projects (Pty) Ltd DTP Mining - Bouygues Construction Duraset Elbroc Mining Products (Pty) Ltd eThekwini Municipality

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The Journal of the Southern African Institute of Mining and Metallurgy

8th

SULPHUR AND SULPHURIC ACID CONFERENCE | 2021 27 SEPTEMBER 2021 - WORKSHOP

Sulfuric Acid Catalysis - Key Parameters to Increase Efficiency and Lower Costs

28-29 SEPTEMBER 2021 - CONFERENCE 30 SEPTEMBER 2021 - TECHNICAL VISIT THE VINEYARD HOTEL, NEWLANDS, CAPE TOWN, SOUTH AFRICA

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, safe transportation and conversion of sulphur, sulphuric acid, and SO2 abatement in metallurgical and other processes, to be held in September 2021 in Cape Town.

FORMAT OF THE EVENT At this point in time, the event is planned as a full contact conference with international participation through web links. It is also planned to hold technical visits to nearby facilities.

However, as we are still in Stage 2 Lock down as a result of COVID-19, this will be constantly reviewed, and if it appears that the effects of the pandemic are still such as to pose a threat to the health and safety of delegates, this will be changed to a digital event.

OBJECTIVES •

•

•

•

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

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.

WHO SHOULD ATTEND The Conference will be of value to: 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

EXHIBITION AND 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 E-mail: camielah@saimm.co.za Tel: +27 11 834-1273/7 Web: www.saimm.co.za

Transportation

WORKSHOP SPONSOR

SAIMM HYBRID CONFERENCE

CONFERENCE MINTEK, RANDBURG, SOUTH AFRICA

28-29 JUNE 2021 — CONFERENCE 30 JUNE 2021 — TECHNICAL VISIT

RENEWABLE SOLUTIONS FOR AN ENERGY INTENSIVE INDUSTRY The is SAIMM proud to host the Conference on Renewable Solutions for an Energy Intensive Industry in Randburg, South Africa, as well as online, from 28-30 June 2021. This conference provides an opportunity for interested parties to present their ideas on the future of energy-intensive industries in the light of increased pressure to reduce greenhouse gas emissions, and against a background of rising electricity costs in South Africa. Discussions are invited on the renewable technologies available, their practical application, and the challenges of implementing these solutions in mining, extractive metallurgy, and metal production to address environmental concerns while maintaining profitability.

Objective

To provide an opportunity for energyintensive industries to engage with companies and researchers in the renewable energy field and vice versa. To inform on the state of technology development and promote the adoption of renewable energy solutions in more sustainable, profitable processes. Renewables are seen as including renewable electricity, renewable process heat, and secondary processing (recycling) technologies.

Head of Conferencing: Camielah Jardine

E-mail: camielah@saimm.co.za www.saimm.co.za

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 820561

The conference will be co-located with the second PRÉMA workshop. The workshop theme is ‘Building Partnerships in Renewable Projects’ and will include presentations on global renewable projects. The workshop then call for suggestions on partnership models for renewable projects in Southern Africa, applicable to local constraints, and conclude with an update on progress and results of the PRÉMA project to external stakeholders. The PRÉMA project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 820561 to investigate solar thermal preheating technology in manganese ferroalloy production in order to reduce greenhouse gas emissions and electricity consumption. Come and find out about these and other renewable solutions at the Conference.

Who should attend

The Conference is aimed at delegates from operating mines, smelters, kilns, and other minerals processing facilities requiring heating, cooling, or electricity, as well as academics, researchers, and companies providing renewable solutions, including: • First line, middle, and senior management • First line, middle, and senior management • Plant/production engineers • Process/development engineers • Design engineers • Environmental and sustainability specialists • Renewable technology providers • Academics and researchers.