Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings: case study from Namib Pb–Zn Mine, Namibia by S. Lohmeier, D. Gallhofer, and B.G. Lottermoser
This paper demonstrates that knowledge of primary ore mineralogy helps to understand that hosts of valuable commodities occur in rocks and that field-portable X-ray fluorescence tools provide precise and accurate determinations of the major and minor elements contained within them. Provided that the inter-mineral and inter-element relations are understood and that there is consistency in sampling and analytical methodology, portable X-ray fluorescence analysis is an effective method to evaluate the chemical characteristics of base metal tailings for a range of major and trace elements
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D by B.P. Watson, T.J. Maphosa, D.P. Roberts, and L.J. Gardner
This paper describes the results of FLAC3D modelling on chromitite Upper Group 2 Reef pillars from the Bushveld Complex of South Africa. The study aimed to find a suitable depth below the surface at which crush pillars could be safely introduced without access to the underground area for measurements. The model input parameters were determined from laboratory triaxial tests with post-failure measurements. Models were built to determine the strength and behaviour of pillars with a width-to-height ratio of approximately two. The results of the modelling are compared with the PlatMine formula for peak pillar strength and data from an instrumented pillar elsewhere in the same reef.
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings (Erongo Region, Namibia) by S. Lohmeier, D. Gallhofer, and B.G. Lottermoser ..............................................................
Base metal tailings deposits in the form of unreclaimed exposed waste piles occur at the Namib Lead and Zinc (Pb–Zn) Mine. The tailings contain major concentrations of base metals lead (Pb), zink (Zn), and sulphur (S). In addition, the tailings also reveal lower concentrations of other metals, including copper (Cu), cadmium (Cd), silver (Ag), and critical elements like antimony (Sb) and indium (In). Although challenging to reprocess, a mixed lead-zink concentrate enriched in copper and silver might be obtained. Reprocessing in future would not only generate valuable metal commodities, but would also eliminate a major metal pollution source from the local environment.
A formulation for optimum risk in open-pit mining by J. Venter, J. Wesseloo, and B. Maybee
This paper presents a design acceptance criterion that maximizes profitability in the selection of open-pit slope angles. This is achieved by defining a formulation for optimum risk that balances expected risk and reward. The formulation is unique as the essential information that must be known to quantify optimum risk is defined. The developed Mining Risk Model allows the design performance measure to be selected based on the intended goal. Slope angle decisions and pit shells can be ranked to select the best option and provide information to determine an optimum balance between risk and reward.
Development of Best Practice Guideline for the management of hot holes in surface coal mines by M. Mpofu, B. Maphalala, T. Kgarume, F. Magweregwede, and G. Stenzel
This paper is a compilation of activities concerning the development of Best Practice Guideline by Coaltech Research Association for the management of hot holes encountered in surface coal operations above old underground workings. The results indicated that management of hot holes requires a focus on pre-emptive risk assessment of mining blocks, identification of hot holes, and continuous monitoring of hot holes. Hot-hole management accessories were found to be effective in insulating the hot-hole emulsion from the rock mass temperature, thus preventing the potential for premature detonation.
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method by E. Rodovalho, T.M. El Hajj, G.C. Teodoro, G. de Tomi, and J.A.S. Tenórios
This paper presents a novel method that describes the adaptation of a room-and-pillar mining method for ore bodies with down-dip varying from 20° to 25°. This new work aimed to reduce dilution by adapting the traditional room-and-pillar mining method (TRP) to inclined ore bodies. Entitled short-hole room-and-pillar (SHRP), the results comprise comparative analyses of the operational and planned dilutions in measuring the performance of the SHRP method. The average operating dilution of the SHRP method was more than five times lower than the planned dilution according to the TRP method.
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Journal Comment
IESG and finding tools to help ‘think about things differently’
have always been fascinated by how things work, and my mother often commented that I got more pleasure from pulling my toys apart and rebuilding them than from playing with them. Learning, or should I say ‘trying to understand how things work, why they work, and then how to do it differently’ is an ongoing passion for me. The Why, How and What If may have driven my family crazy at times but they are questions that have shaped my life.
A key learning has been that if you look at a problem the same way (with the same biases!) you are likely to come up with the same solution (or perhaps a slight variant). So, to find a new solution you must look at a problem differently, from an alternative perspective. This needs to be a conscious, intentional process requiring others’ perspectives, experiences, and competencies - not just your own. Another learning is that to solve challenges we need a thinking framework, a logic construct - a ‘tool’. It has been said that a key evolutionary step in the journey to being Homo Sapiens was the use of tools My grandchildren tease me, as I so often say to them, ‘The most powerful part of your body is your brain – use it first’.
Currently I am on a journey to better understand how we could, as an industry, move from seeing ESG (environmental, social, governance) as a burden, to being a strategic enabler by changing the ‘how I see the problem’ to be able to create a new solution; while the ‘tool’ that I’m finding most useful is that of systems thinking, specifically complex adaptive systems and industrial ecology.
Moving into a little bit of theory: complex systems are systems, comprising multiple parts that may interact and whose behaviour is intrinsically difficult to model due to the dependencies, competition, relationships, or other types of interactions between their parts or between a given system and its environment over both time and location. Systems that are ‘complex’ have distinct properties that arise from these relationships that lead to collective behaviours and how the system interacts and forms relationships with its environment. Complex systems can be understood as an alternative paradigm to reductionism, which attempts to explain systems in terms of their constituent parts and the individual interactions between them. An adaptive system, or complex adaptive system, is a special case of complex systems, which can adapt its behaviour according to changes in its environment or in parts of the system itself. In this way, the system can improve its performance through a continuing interaction with its environment over time.
Industrial ecology is the study of systemic relationships between society, the economy, and the natural environment. It focuses on the use of technology to reduce environmental impacts and reconcile human development with environmental stewardship while recognizing the importance of socioeconomic factors in achieving these goals viz. sustainability, which is the goal of ESG.
So, I’ve come to a simple but profound realization: that critical shift in perspective that reshapes the pool of possible solutions. We must acknowledge that mining is a complex adaptive system, an industrial ecology, with both a temporal and spatial impact on society, environment and economy, if we are to achieve industry sustainability for the generations to come.
The thought I would like to leave you with is that I firmly believe that we as professionals should revisit our business strategies based on an industrial ecology logic and build solutions that consider the interacting components as parts of a complex adaptive system, to develop sustainability and obtain enduring social licence to operate.
G.L. Smith Pr Eng PhD SAIMM Honorary Life Fellow & Past President
President’s Corner
TArtificial Intelligence in the preparation of scientific documents
his is my final President’s Corner article and, I must admit, I feel a sense of relief that it is coming to an end. Initially, the prospect of writing eleven articles, one each month, was quite daunting. While I have extensive experience writing technical consulting reports, and research articles and case studies for conferences and journals—all of which have a clear focus, I have not written many general articles. I therefore decided to experiment with Artificial Intelligence (AI) tools to see if it could help me to produce these articles.
ChatGPT burst onto the scene in November 2022, introducing the concept of a Large Language Model (LLM) to the world. Until then, chatbots with AI were quite disappointing, but ChatGPT could answer questions sensibly and generate well-constructed sentences and paragraphs very rapidly. Being freely available and easy to use, it became widely used within a short time. LLMs are computational models capable of generating natural language. They are trained using machine-learning techniques on vast quantities of text data sourced from the internet and books. This makes them extremely powerful tools, capable of producing text in multiple languages and even generating code for computer programming. However, they simply return this information in a probabilistic manner, producing plausible outputs, without verifying the facts.
OpenAI, the developer of ChatGPT, released an upgrade called GPT-4 in March 2023. Microsoft has partnered with OpenAI and incorporated GPT-4 in Copilot, which is a specialized assistant that works with Microsoft products, but can also be used for other purposes. Generative AI is also now included in Bing and Google search engines.
I first played with ChatGPT shortly after it was released, simply generating text and poetry in English and Afrikaans for amusement. I started experimenting with GPT-4 to assist with writing the President’s Corner articles. It is very easy to generate paragraphs with simple instructions. These can then be modified with further instructions until you get something useful. You can choose between precise, creative, or balanced styles. As a test, I attempted to have GPT-4 write an entire article for me. It produced a comprehensive well written article; however, I found it challenging to get it to convey the specific message I wanted to communicate. Additionally, it generated a substantial amount of information that was unfamiliar to me and difficult to verify.
The next step was to utilize the generative AI capability in Google. I found this to be extremely useful as it generates a summary of the information along with links to additional resources, allowing you to verify the information and identify the source. The source data can include news articles, research papers, or presentations, provided they are available on the internet. This significantly accelerates the literature research process.
GPT-4 can also summarize articles very neatly and efficiently; however, I found that it did not always extract the most relevant information for my purposes and invariably required some editing. It is important to note that articles uploaded to GPT-4 for summarization are added to its database, making them accessible to everyone. This is acceptable if the article is already in the public domain and available on the internet: if it is not, there is a risk of disseminating confidential information. While there are methods to protect data while still using the GPT-4 engine, these protections are not available when using the freely accessible version.
President’s Corner (continued)
I have also found GPT-4 to be very useful for enhancing style and grammar. Typically, I jot down a few sentences quickly without focusing too much on flow or repetition, and then ask GPT-4 to rewrite the paragraph. The results are generally improved, but may still require further manual editing to ensure the correct message is conveyed. There are other tools, such as Wordtune, Paperpal, and Grammarly, that can be used for the same purpose.
The integration of AI into the realm of scientific writing has revolutionized the way researchers draft, edit, and finalize their manuscripts. A Nature survey (https://www.nature.com/articles/d41586-023-02988-6) of 1600 researchers from around the world found that AI is being used to process data, write code, and assist with the writing of papers. It is particularly helpful for researchers whose first language is not English, but need to publish their work in English journals. Scientists are using AI to improve style and grammar and to summarize other articles.
However, there is a risk that research integrity can be compromised and fake papers can be produced. This has significant implications for the peer review process and has been an important topic of discussion for the SAIMM Publications Committee. The Academy of Science of South Africa (ASSAf) have drafted guidelines for the use of AI tools and resources in research communication, taking into consideration the views of several international scientific societies and journal publishers’ websites. https://www.assaf.org.za/wp-content/uploads/2024/09/ASSAf-and-SciELO-DRAFT-Guidelines-for-the-Useof-Artificial-Intelligence-AI-Tools-and-Resources-in-Research-Communication_-4-Sept-2024.pdf
The guideline states that ‘Authors are solely responsible for ensuring the authenticity, validity, and integrity of the content in their manuscripts.’ It is essential for authors to prevent misinformation that is generated by AI tools from being included in papers, because this may impact the quality of future research and global knowledge. Any information generated by AI must be correctly cited and citations generated by AI must be checked. Where content is generated by AI and the source cannot be determined, the guideline provides recommendations on how to reference the AI tool and method of generation. Transparency is important and the use of AI tools should be disclosed; however, it is not necessary to disclose the use of tools to improve grammar and style.
The guideline also provides recommendations for editors and reviewers. In addition to their usual responsibility for validation of scientific content, editors and reviewers must consider the effects of AIgenerated content in a publication. AI tools for editing, reviewing, and plagiarism checking must be used in a responsible manner. Reviewers and editors are still required to make decisions regarding the evaluation of manuscripts.
In closing, AI tools have the potential to significantly enhance the efficiency and quality of scientific writing. However, their use must be guided by ethical considerations to ensure the integrity and reliability of scientific research. By understanding and responsibly applying these tools, researchers can leverage AI to advance their work while upholding the standards of academic writing
W.C. Joughin President, SAIMM
Affiliation:
1Institute of Disposal Research, Department of Mineral Resources, and Institute of Mining Engineering, Department of Surface Mining and International Mining, Clausthal University of Technology, ClausthalZellerfeld, Germany
2Institute for Earth Sciences, University of Graz, Graz, Austria 3Institute of Mineral Resources Engineering, RWTH Aachen University, Aachen, Germany
Correspondence to:
S. Lohmeier
Email: stephanie.lohmeier@tu-clausthal.de
Dates:
Received: 11 Mar. 2023
Revised: 31 May 2023
Accepted: 11 Jun. 2024
Published: August 2024
How to cite:
Lohmeier, S., Gallhofer, D., and Lottermoser, B.G. 2024.
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings: case study from Namib Pb-Zn Mine, Namibia. Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 8. pp. 421–436
DOI ID:
http://dx.doi.org/10.17159/24119717/2676/2024
ORCID:
S. Lohmeier
http://orcid.org/0000-0003-2556-2096
D. Gallhofer
http://orcid.org/0000-0003-2139-7847
B.G. Lottermoser
http://orcid.org/0000-0002-8385-3898
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings: case study from Namib Pb–Zn Mine, Namibia
by S. Lohmeier1, D. Gallhofer2, and B.G. Lottermoser3
Abstract
Reprocessing of historic gold tailings is a common activity in South Africa, while base metal tailings offer similar reprocessing potential. The historic base metal tailings of the Namib Pb–Zn mine (Erongo Region, Namibia) still contain valuable resources of Pb and Zn in carbonate-rich matter. Micro-analysis of primary ore minerals identifies some galena as argentiferous and sphalerite as the principal host of Cd. This study demonstrates that knowledge of primary ore mineralogy helps to reveal the hosts of valuable commodities (Ag, Cd) and that field-portable X-ray fluorescence tools allow precise and accurate determinations of major and minor elements like Zn and Cd in such carbonate-rich material. Although there are limitations to directly determine the contents of certain element (i.e., As, Sb, In), linear correlations allow prediction of the likely abundance of these elements. Providing that the inter-mineral and inter-element relations are understood and there is consistency in sampling and analytical methodology, portable X-ray fluorescence analysis is an effective method to evaluate the chemical characteristics of base metal tailings for a range of major and trace elements.
Keywords base metal tailings, trace elements, portable X-ray fluorescence analyzer
Introduction
In times of increasing demand for resources (European Commission, 2010, 2017), political and economic uncertainties, and worldwide changing supply chains, old tailings dumps are coming back into focus as a potential resource of formerly not extracted commodities. In Namibia alone, there are more than 250 abandoned mine sites (Salom and Kivinen, 2020), of which many still have potential for containing valuable commodities. Many of these Namibian tailings dumps are from colonial times and can be related to what is nowadays called small-scale mining; however, there are also younger mining residues that result from modern or large-scale mining operations and comprise large tonnages. In conjunction with changes in mining style and technologies, the focus of mining changed from time to time during production. One example is the Namib Pb–Zn mine in Namibia’s Erongo region (Figure 1), where mining focussed first only on galena, while valuable sphalerite was lost to tailings; during later production activities, a sphalerite concentrate was also produced and silver was mined as by-product. Beside Pb and Zn, which are elements of traditional industrial interest, base metal tailings bear certain potential for other commodities, which were formerly not of interest, but are nowadays desired raw materials (Mudd et al., 2017; Werner et al., 2017). In the case of Namib Pb–Zn tailings (Figure 2A, B), the old tailings dump has potential for In, Cd, and Ag, as shown by Lohmeier et al. (2024). However, the potential of old tailings dumps is frequently not known and an evaluation is often avoided due to presumably high costs related to cost-intensive and timeconsuming laboratory analyses.
The aim of this study is to show that data from primary samples allow assignment of the host(s) of trace metals to specific minerals and that portable X-ray fluorescence (pXRF) can be used to screen carbonatebearing base metal tailings, such as the Namib Pb–Zn tailings, for certain elements (e.g., Cd, Ag). Moreover, an indirect estimate of the quantities of some minor and trace elements (e.g., In, Sb) in this material is also possible, provided that the mineralogical and geochemical compositions of the tailings are understood and the hosts of these elements are known. However, this study also points out the limits of pXRF and why conventional data obtained by XRF, inductively coupled plasma mass spectrometry (ICP–MS) or atomic emission spectrometry (AES), and electron microprobe (EMP) are still needed to obtain reliable results.
Background
Mining site
The Namib Pb–Zn Mine, formerly known as Deblin Mine or Namib Lead Mine, is located within the Rössing Mountains in the Dorob National Park in Namibia’s Erongo Region (22°31'17.53''S; 14°45'41.16''E; Figure 1). The closest town is Swakopmund, about 25 km to the southwest. Access to the mining site is via the paved B2 connected to a small gravel road. The Namib Pb–Zn deposit was discovered during exploration activities in the 1930s; however, mining of base metal ores and production of a Pb concentrate only started in 1968. This was later supplemented by the production of a sphalerite concentrate and additional Ag (Snowden, 2014). After mining stopped in 1992, some exploration activities were carried out in 1992 and 1993 by Iscor Namibia and tentative reprocessing of tailings for Zn was tested by African Exploration in the mid-1990s (CCA, 2013; Snowden, 2014). The mine site was then abandoned for several years, before Kalahari Mineral Limited started drilling for primary ore and carried out a resource estimation of the potential of the tailings in 2007/2008. The mine site was then sold to North River Resources (CCA, 2013; Hahn et al., 2004; Tenova Mining and Minerals, 2014), which outlined a remaining indicated primary ore reserve of 710 000 t at a grade of 2.4% Pb, 7.0% Zn, and 50 g/t Ag related to four orebodies and an inferred resource of 409 000 t at a grade of 2.2% Pb, 6.0% Zn, and 38 g/t Ag (NLZM, 2023). There are additional resources in close-by gossans (NLZM, 2023). Limited mining and processing activities restarted in 2019; however, the mine site has been under care and maintenance since early 2020. Two tailings dumps result from the former mining activities. The northern larger tailings dump comprises about 2.75M m³ of tailings material, while the smaller southern dump contains about 1.25M m³ already tentatively reprocessed tailings material (Figure 2; Hahn et al., 2004). The remaining bulk tailings resource is estimated at 2.75M m³ at a grade of 2.54% Zn, 0.21% Pb, and 7.0 g/t Ag (northern dump) plus 1.25M m³ at a grade of 2.14% Zn, 0.15% Pb, and 7.9 g/t Ag (southern dump) by Hahn et al. (2004), while NLZM (2023) reported a measured tailings resource of 260 000 t at a grade of 0.3% Pb, 2.2% Zn, and 7.5 g/t Ag and an indicated tailings resource of 350 000 t at a grade of 0.3% Pb, 2.1% Zn, and 7.7 g/t Ag. There are no data for the inferred tailings resource by NLZM.
Materials and methods
Sampling
Eighteen surface samples, each weighing about 5 kg, were taken in 2019 from the northern tailings dump, which contains tailings material only from former processing activities; the southern tailings dump comprises already tentatively reprocessed material (Figures 1, 2). Samples were collected along vertical profiles and directly from exposed tailings faces (Figure 1B, 2A, B) and included different grey–yellow–brown–red coloured samples to account for different production cycles and thus possible geochemical heterogeneities in the tailings mass. Larger solidified chips are present, which disintegrate easily to smaller pieces/grains of silt to sand size (particle data are provided in Lohmeier et al., 2024). In addition, two ore samples, representative of the principal ore mineralization according to the mine geologists, were taken from new stockpiles (Figure 2C, D).
Sample processing and laboratory-based analysis
Tailings samples were air-dried and subsequently homogenized. A representative aliquot was milled to analytical fineness using a WC swing mill in the Department of Mineral Processing at RWTH Aachen University. Milled powders were sent to Australian Laboratory Services (ALS, Loughrea, Ireland) for conventional X-ray fluorescence spectroscopy (XRF) of major elements (Al, Ca, Fe, K, Mg, Mn, Na, P, Si, Ti), for ICP–MS after HNO3–HF–HClO4 and HCl digestions for certain trace elements (Dy, Er, Eu, Gd, Ho, Nd, Pr, Sm, Tm), and for infrared spectroscopy of C and S. Loss on ignition (LOI) was determined by sintering at 1000°C. In addition, samples were analysed at SGS Bulgaria (Bor Laboratory, Serbia) by ICP–MS after HNO3–HF–HClO4 and HCl digestion, for Ag, Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, In, K, La, Li, Lu, Mg, Mn, Mo, Nb, Na, Ni, P, Pb, Rb, Sb, Sc, Se, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, U, V, W, Y, Yb, Zn, and Zr. Samples having Ag > 10 µg/g, Pb > 10 000 µg/g, and/or Zn > 10 000 µg/g were reanalysed by AES using the same digestion approach. All sample packages included the analyses of duplicates and external and laboratory internal reference materials for quality control. Analytical data are documented in Lohmeier et al. (2024).
EMP analysis on primary ore was performed at the Institute of Disposal Research (IDR) at Clausthal University of Technology
Figure 1—A: The Namib Pb–Zn deposit is located in Namibia’s Erongo region, about 25 km ENE of Swakopmund in the Rössing Mountains in the Dorob National Park. B: Sampling points within the northern older tailings dump
Field-portable
Figure 2—A, B: Photos showing the old Namib Pb–Zn tailings dump. Colour variations of the solidified, air-dried material are apparent. The impression of centimetre-large chips is misleading because these chips easily disintegrate in hand. Photographs taken by B.G. Lottermoser in 2018 and S. Lohmeier in 2019. C: Photograph of primary sphalerite-rich ore. D: Scan of polished section of primary sphalerite-rich ore
Table I
X-ray lines, spectrometer crystals, and reference materials for cassiterite, galena, marcasite, pyrite, and sphalerite
Mineral
Field-portable
X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
(TUC) using a Cameca SX FIVE instrument to determine trace element contents of sphalerite, galena, pyrite, marcasite, and cassiterite. Reconnaissance microanalyses by energy-dispersive X-ray spectrometry (EDX) showed that no elements other than those analysed were detectable in the respective minerals. The instrument was then operated in wavelength-dispersive mode at 15 kV and 100 nA for sphalerite and at 15 kV and 20 nA for all other mineral phases. X-ray lines, spectrometer crystals, and reference materials for cassiterite, galena, marcasite, pyrite, and sphalerite are provided in Table I. All analyses were checked for line and peak overlaps and the background sides were accordingly adjusted. Detailed results of sphalerite (153 analyses), galena (80 analyses), pyrite (58 analyses), marcasite (17 analyses), and cassiterite (89 analyses) can be requested from the authors.
Portable X-ray fluorescence spectroscopy
For pXRF analysis, Chemplex sample cups were filled with milled tailings powders and then backfilled with stuffing fibre. ProleneTM thin films of 4.0 µm thinness were used to guarantee simple but comparable analytical settings. As X-ray penetration of portable devices varies for each element, usage of these very thin films guarantees sufficient X-ray penetration depth both for light and heavy elements (Demirsar Arli et al., 2020; Potts et al., 1997) so that bias is minor to negligible and is the same for all samples and reference materials. Analyses were carried out at IDR (TUC) using a Niton XL3t 900 pXRF instrument equipped with a 50 kV Ag target X-ray tube, connected to a radiation protection chamber. All analyses were done in ‘environmental mode – minerals with Cu/Zn’, with a total measurement time of 100 s and were repeated five times. Results are presented in Table II. Reproducibility and homogeneity were tested by preparing two sample cups of each pulp; results were almost identical. The device was calibrated using the following certified reference materials (CRM): (1) OREAS 24b, 24c, 36, 37, 112, 131b, 132b, 133b, 134b, 160, 623, and 932 of the OREAS pXRF Zn–Pb–Ag sulfide kit, (2) RTS-3a and MP-1b of CANMET Mining and Mineral Sciences Laboratories, Canada, and (3) SRM 2780 of the National Institute of Standards and Technology (USA).
Table II
Calibration factors, slope, and intercepts were obtained using the provided CorrectCalc software program of the Niton device. A set of six CRM (OREAS 36, 131b, 132b, 133b, 134b, 623) was analysed three times at the start and end of each measuring day and once after every sample (including duplicates) to monitor drift of the instrument and assure quality control over the measurement period of five days (Figure 3).
Mineralogical and geochemical characterization of the old Namib Pb–Zn tailings
Namib Pb–Zn tailings are composed of relict galena and sphalerite, set into carbonate gangue, as the main mineral phases. In addition, minor pyrite, pyrrhotite, magnetite, quartz, graphite, apatite, biotite, phlogopite, and muscovite are present and relate to the primary ore mineralogy, as well as rare arsenopyrite, cassiterite, marcasite, rare-earth element fluorcarbonates (parisite), scheelite, and zircon. In contrast, gypsum, lepidocrocite, anglesite, and some goethite are most likely due to post-processing weathering under arid conditions (see Lohmeier et al., 2024 for more details). This mineralogical composition is also reflected by the element abundance of Fe (15.46–30.98 mass%), Ca (8.79–8.87 mass%), S (6.51–14.65 mass%), Zn (0.89–9.57 mass%), Si (2.54–6.87 mass%), Pb (0.16–5.69 mass%), Na (0.01–3.41 mass%), Mn (0.82–2.81 mass%), Al (0.56–1.57 mass%), Mg (0.25–1.31 mass%), and K (0.32–0.94 mass%). In addition, Cu (108–1479 µg/g), Sr (132–482 µg/g), Ti (420–839 µg/g), As (86–587 µg/g), P (218–349 µg/g), and Cd (32–399 µg/g) values are largely in the 100 µg/g range, whereas Ba (37–125 µg/g), Rb (35.7–93.2 µg/g), Ag (6.26–83 µg/g), Bi (3.02–45.70 µg/g), Sb (4.5–43.7 µg/g), Sn (13.1–40.2 µg/g), In (4.08–40.00 µg/g), Cr (9–22 µg/g), Ni (9.2–12.8 µg/g), V (3.7–17.7 µg/g), and Zr (3.90–8.10 µg/g) values are mostly in the 10 µg/g range or lower. Se concentrations are mostly < 2 µg/g (data are documented in Lohmeier et al., 2024). Consequently, almost all elements are present over quite large concentration ranges, although the concentration scales of individual elements are different. Pb and Zn still constitute quite large contents in these tailings and are thus clearly the elements of highest economic interest; however, Ag, Cd, In, and Sb are also present in certain quantities.
Analytical pXRF results of Namib Pb–Zn tailings. Values are mean values of five repeated analysis. Values of Cr, Mg, Sb, Se, and V are < LOD. ± values reflect the standard deviation. Abbreviation: LOD = lower detection limit
Table II (continued)
Results
Hosts of trace elements in primary ore
Backscattered electron (BSE) images revealed two different sphalerite generations by texture, which are not distinguishable in hand specimens. One generation comprises porous crystals; the other has a compact appearance (Figure 4A, B). The quite uncommon, very dark visual colour of sphalerite is attributed to very high Fe contents of ~ 7–10.5 mass% (Figure 4C), colloquially designated as marmatite. EMP analysis shows that compact sphalerite has on average 9.5 mass% Fe (8.6–10.4 mass% Fe) and 0.20 mass% Cd, while porous sphalerite has on average 8.4 mass% Fe (7.3–9.4 mass% Fe) and 0.19 mass% Cd (Figure 4C, D). Rarely, sphalerite has trace concentrations of Pb (≤ 0.3 mass% Pb; 8 analyses). Sb (≤ 0.05 mass%) and In (≤ 0.05 mass%) contents in sphalerite are below the lower analytical detection limit (LOD) of the EMP device.
There is only one galena generation present (Figure 4E, F), which has, in general, quite low trace element content, with Fe as the most abundant trace element (≤ 0.3–1.6 mass% Fe; av. 0.4 mass% Fe; Figure 4G). About one-third of all galena crystals have
trace Ag contents (max. 0.10 mass%; Figure 4H), but most Ag contents are below the LOD of the EMP device (≤ 0.06 mass% Ag). There is no obvious relation between the Ag and Fe abundance, nor are there any other apparent inter-element relations. Cd (≤ 0.08 mass%), In (≤ 0.06 mass%), Sb (≤ 0.07 mass%), and Zn (≤ 0.46 mass%) concentrations in galena are below the LOD of the EMP device.
Similar to sphalerite, one pyrite generation has a porous and the other a compact appearance (Figure 5A, B); however, both show similar trace element abundance so no differentiation is made here. The same applies to pyrite and marcasite, although the marcasite database is distinctly smaller than the pyrite database (Figure 5C). The most abundant trace element in pyrite and marcasite is Zn, varying between ≤ 0.4 and 1.9 mass% (Figure 5D). Pb concentrations in the sulfides range between ≤ 0.2 and 0.4 mass%. Only marcasite shows single Ag (~ 0.06 mass%; 2 analyses) and Cd (~ 0.05 mass%; 1 analysis) values above the LOD of the EMP device (≤ 0.05 mass% Ag; ≤ 0.04 mass% Cd), whereas In (LOD ≤ 0.05 mass%) and Sb (LOD ≤ 0.06 mass%) concentrations in pyrite and marcasite are all below the LOD of the EMP device.
Measurement of reference material in chronological order
OREAS 131b reference material f(x) = -0.00004 x + 0.00836
OREAS 132b reference material
f(x) = -0.00001 x + 0.016534
OREAS 133b reference material
f(x) = -0.00002 x + 0.031460
OREAS 134b reference material
f(x) = -0.00006 x + 0.057587
OREAS 623 reference material
f(x) = -0.0000003 x + 0.0044386
Certified value OREAS 131b
Certified value OREAS 132b
Certified value OREAS 133b
Certified value OREAS 134b
Certified value OREAS 623
Measurement of reference material in chronological order
OREAS 131b reference material
f(x) = -0.00008 x + 5.14230
OREAS 132b reference material
f(x) = -0.0009 x + 5.0940
OREAS 133b reference material
f(x) = -0.002 x + 5.099
OREAS 134b reference material
f(x) = -0.0009 x + 3.6451
OREAS 623 reference material
f(x) = 0.001 x + 5.015
Certified value OREAS 131b
Certified value OREAS 132b
Certified value OREAS 133b
Certified value OREAS 134b
Certified value OREAS 623
Figure 3—Illustration of obtained pXRF data for reference materials analysed at the start and end of each measuring day and once after every sample (including duplicates). Data are shown in chronological order and were obtained over a measuring period of five days. There was no apparent drift for Zn and Cd, however, obtained Al data vary distinctly
Cassiterite crystals (Figure 5E, F) have a very restricted trace element spectrum, with Zn (≤ 0.4–2.3 mass%; av. 1.0 mass%) and W (≤ 0.1–1.5 mass%; av. 0.5 mass%) being the most abundant substituents for Sn (74.6–78.4 mass%; av. 76.7 mass%). In addition, Fe is detected by EMP with concentrations varying between ≤ 0.19 and 0.7 mass% (av. 0.3 mass%). In concentrations vary between ≤ 0.11 and 0.24 mass% (av. 0.18 mass%). The trace element contents of Ag (≤ 0.05 mass%), Cd (≤ 0.05 mass%), S (≤ 0.04 mass%), Sb (≤ 0.05 mass%), and Pb (≤ 0.21 mass%) are below the LOD of the EMP device. The general substitution of Sn by the cations Fe, In, W, and Zn is expressed in the Sn vs. Σ(Fe + In + W + Zn) plot in Figure
5G. In concentrations should be taken with caution as there is some bias by InLα–SnLn interference. However, some crystals have trace element concentrations above 1.5× the highest obtained interference value and thus above the lower interference limit (Figure 5H).
Precision and accuracy of portable X-ray fluorescence data
The quality of the linear relationship/regression between the certified value of a CRM (the ‘true’ value) and the value obtained via pXRF is expressed by the coefficient of determination (R²), which is at best 1.00. The R² value indicates whether a specific element can be principally analysed by pXRF with acceptable quality or
Field-portable
Figure 4—A, B: Backscattered electron (BSE) images showing porous sphalerite in compact sphalerite. Columnar to spiky crystals are micas that are overgrown by pyrite. C: Fe vs. Zn plot of sphalerite. D: Cd vs. Zn plot of sphalerite. E: BSE image showing galena overgrowing cassiterite in massive sphalerite. Rare scheelite occurs in carbonate matrix. F: BSE image showing galena overgrowing cassiterite and intergrown with pyrite. Galena–pyrite–cassiterite occurs in massive sphalerite. G: Pb vs. Fe plot of galena. H: Pb vs. Ag plot of galena. C, D, G, H: Database is EMP analyses. Analyses below LOD are considered with half LOD. Abbreviations: cb = Ca–Mg–Fe(–Mn) carbonate; cst = cassiterite; gn = galena; mc = mica; py = pyrite; sch = scheelite; sp = sphalerite
not. However, similar to conventional XRF (e.g., Rousseau, 2006), it has to be assured that the matrix of the CRM is ideally the same or at least similar to that of the sample material to be analysed (De Winter et al., 2017; Hou et al., 2004; Lu et al., 2022). Very good R² values were obtained for Ag, As, Ba, Bi, Ca, Cd, Cu, K, Mn, Pb, Rb, Sb, Se, Sn, Sr, Ti, and Zn (R² = 0.99–1.00), and for Fe, Si, and Zr (R² = 0.95–0.98). Values are good for V (R² = 0.93); however, R² is low
for Cr (R² = 0.85) and Mg (R² = 0.86), and, in particular, for Al (R² = 0.53) and Ni (R² = 0.44) (Figure 6; Table III). Thus, pXRF should be principally capable of analyzing all elements having R² ≥ 0.95 with a good to acceptable quality. To eliminate, or at least weaken, the matrix effect, the calculated slope and intercept values of CRM–pXRF pairs were used for external calibration of the portable Niton XL3t 900 tool.
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
Figure 5—A, B: Photomicrographs showing porous pyrite enclosing subhedral marcasite crystals. Both sulfides are enclosed in sphalerite. The matrix is composed of carbonate and micas (reflected light). C: S vs. Fe plot of pyrite and marcasite. D: Zn vs. Fe plot of pyrite and marcasite. E: Backscattered electron (BSE) image showing cassiterite overgrown by parisite in sphalerite. F: BSE image showing cassiterite and scheelite overgrown by galena in sphalerite. Flaky minerals are micas. G: Sn vs. Σ(Fe + In + W + Zn) plot of cassiterite. H: Sn vs. In plot of cassiterite. The lower dashed line represents the obtained maximum In interference value for In(Lα)Sn(Ln). The upper line is the 1.5× interference factor. C, D, G, H: Database is m analyses. Analyses below LOD are considered with half LOD. Abbreviations: cb = Ca–Mg–Fe(–Mn) carbonate; cst = cassiterite; gn = galena; mc = mica; mrc = marcasite; pa = parisite; py = pyrite; sch = scheelite; sp = sphalerite
Field-portable
Table III
X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
Certified reference materials (CRM), coefficient of determination, and calibration factors for the pXRF device. R² values represent the coefficient of determination showing the quality of the linear regression between the certified (‘true’) value of a CRM and the value obtained by pXRF. The range of precision of the pXRF device is expressed as relative standard deviation (RSD), the accuracy is expressed as relative difference (%RD) with %RDICP-MS values referring to ICP–MS data and %RDXRF values referring to XRF data, respectively. av |%RD|ICP-MS/XRF reflects the average absolute value of the percent relative difference. R²pXRF-ICP/AES and R² pXRF-XRF values are the coefficients of determination of pXRF and ICP–MS/AES data and pXRF and XRF data, respectively, obtained for the Namib Pb–Zn tailings material
Figure 6—X–Y plots of CRM and pXRF data, showing very good correlations between ‘true’ CRM values and obtained pXRF values for the metals Ag, Cd, Cu, Sb, Pb, Zn and for Fe and Ca. In contrast, Al shows only a poor correlation. These data were used for calibration of the pXRF tool
Precision (the measure of analytical reproducibility or repeatability) and accuracy (the measure of correctness, meaning the proximity of analytical results to the true value) are common parameters used to evaluate the quality of (geo)chemical analyses. Using the criteria of Jenner (1996) and Piercy and Devine (2014), the precision of pXRF values can be assessed via the percent relative standard deviation (RSD), while the accuracy of pXRF values is
expressed via the relative difference (%RD). RSD and %RD are calculated as follows:
Using Equation [2] can lead to negative values. If negative values resulted, these values are shown in Table III for %RDICP-MS/AES and
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
%RDXRF to represent the entire value range. However, averages (av |%RDICP-MS|, av |%RDXRF|) are based on absolute values only.
The most abundant elements (Al, Ca, Fe, K, Mn, Pb, Si, Zn) and several minor and trace elements (Ag, Ba, Bi, Cd, Ni, Rb, Sn, Sr, Zr) show, on average, excellent (RSD 0%–3%) to good (RSD 7%–10%) precision, with the exceptions of As, Cu, and Ti (av. RSD ≥ 10%) (Table III). The major elements Al, Ca, Fe, K, Mn, Si, and Zn and the minor/trace elements Sn and Sr have excellent to good precision throughout, whereas precision is influenced by one outlier each for Ba, Bi, Zr, and by two outliers for Pb. The Ag and Cd values are influenced by more than two outliers. Cr, Mg, Sb, Se, and V cannot be analysed in Namib Pb–Zn tailings by pXRF due to the element contents being too low to be detected in this analytical setting.
The average accuracy of pXRF data, compared with ICP–MS/ AES and XRF data, is rather variable. When compared with XRF data, Ca, Fe, K, Mn, Ti, and Si are within an acceptable accuracy range (%RDXRF ≤ 20), while Al values (|%RDXRF| > 80) are distinctly out of range. However, except for Fe and Mn, there are outliers within all element datasets. Considering only averages, only Cd, Cu, Fe, Rb, Sr, and Zn show acceptable accuracy compared with ICP–MS/AES data, of which the Cu, Fe, and Rb datasets contain outliers. Accuracy is distinctly out of range for Al, Ba, Bi, Ni, Sn, and Zr, with |%RDICP-MS/AES| values > 100, and is similarly poor for Ag, As, K, Pb, and Ti, with |%RDICP-MS/AES| values between 23 and 67 (Table III). However, the poor average accuracy of Ag (|%RDICP-MS/AES| = 47) can be partly explained by Ag values close to the LOD of the pXRF device, because elevated Ag values (> 50 µg/g) can be analysed with generally good to excellent (|%RDICP-MS/ AES| < 10) accuracy. A similar relationship is observed for Pb, for which higher concentrations (> 4000 µg/g) can be determined with good to excellent accuracy, while accuracy is poor for lower Pb concentrations. Cd also shows this general relation; however, the accuracy is excellent for higher Cd (> 40 µg/g) and good for lower Cd contents.
Discussion
Geochemical relations
EMP analyses reveal the presence of Cd (up to 0.24 mass%) in sphalerite. In addition, few marcasite crystals have up to 0.05 mass% Cd. However, galena, pyrite, and cassiterite have no detectable Cd concentrations, identifying sphalerite as the principal Cd carrier. About one-third of all galena crystals have Ag up to 0.10 mass%. In addition, accessory marcasite can have low Ag concentrations (≤ 0.06 mass%). Marcasite abundance in the bulk ore is very low (< 0.01 vol.%, according to microscopy), so galena is the principal identified Ag carrier, not excluding the presence of other Ag-bearing minerals. In concentrations in sphalerite are < 0.05 mass%, although substitution of Zn by In and Cd in sphalerite is common (e.g., Cook et al., 2009, 2011; Johan, 1988; Xu et al., 2021) and sphalerite is the most common host of In in base metal deposits (Cook et al., 2011; Schwarz-Schampera and Herzig, 2002). Rare cassiterite has only low In concentrations, indicating that most In is in sphalerite but at concentrations below 0.05 mass%. The presence of rare cassiterite explains also the low Sn concentrations of primary ore and very likely of the tailings. Sb was not detectable by EMP in any tested mineral phase; however, Sb concentrations in tailings and primary ore are low (≤ 44 µg/g; data are in Lohmeier et al., 2024). Minor Pb can substitute for Zn in sphalerite and for Fe in pyrite, and minor Zn can substitute for Fe in pyrite and for Sn in cassiterite; however, the principal carriers of Zn and Pb are abundant sphalerite and galena. Moreover, no secondary Zn-bearing mineral is observed and
the tailings material has not been subject to any pyrometallurgical modifications so that the presence of Zn-bearing oxides is very unlikely. Anglesite is a typical weathering product of natural galena deposits and is also known from anthropogenically affected environments (e.g., Keim and Markl, 2015; Lara et al., 2011). It is a very soluble secondary Pb mineral (Keim and Markl, 2015), the presence of which indicates that weathering of the tailings material is not advanced and only slightly affected the uppermost part of the tailings dump, otherwise cerussite or pyromorphite-group minerals would be present (cf. Keim and Markl, 2015; Lara et al., 2011). Therefore, destruction of argentiferous galena and redistribution of silver from galena is very unlikely and not indicated in bulk tailings, so galena is the main identified Ag carrier in the tailings material. W is mostly within accessory scheelite, explaining concentrations of ~ 7 µg/g in the primary ore and up to ~ 66 µg/g in tailings (data in Lohmeier et al., 2024). Cassiterite can have trace concentrations of W, but these concentrations are negligible. Average As concentrations in tailings of ~ 180 µg/g can be explained, at least in part, by the presence of rare arsenopyrite (data in Lohmeier et al., 2024). We only can speculate about the host(s) of Bi and Cs; however, bulk concentrations are relatively low so most of these elements can simply occur as substituents in other minerals; e.g., Bi can occur in traces in pyrite and other sulfides with values below LOD of the EMP device or can form Bi minerals in comparable settings (e.g., Callaghan, 2001; Fitros et al., 2017; Wang et al., 2020).
Reconnaissance investigation of relict metals and other commodities of interest in fine-grained tailings materials—so-called slurries or impoundment cell material—is mostly based only on geochemical analysis of these materials, frequently by only checking for those elements of interest and leaving detailed mineralogical investigations aside. This is because slurries are usually too finegrained to allow microscopic investigations (e.g., Lohmeier et al., 2021a) so XRD is the method of choice to reveal the mineralogical composition. However, XRD fails to detect minor and trace mineral commodities (‘5% rule’ and/or the minerals have no ‘conspicuous’ XRD pattern) when many different mineral phases are present (e.g., Khan et al., 2020). Moreover, neither bulk geochemical data nor XRD data reveal the inter-element relations of the mineral phases present. In many cases, inter-element relations and the host(s) of selected trace elements can be assumed based on experience with similar material, but bulk geochemical data are sometimes misleading, resulting in erroneous assumptions.
Prediction of geochemical composition from portable X-ray fluorescence data
Laboratory-based XRF analyses combined with ICP–MS/AES analyses are well-established techniques for routine analysis of major and trace elements in geological materials. In addition, both methods have been successfully applied to determination of the composition of tailings (e.g., Hahn et al., 2004; John Morrell et al., 1996; Othmani et al., 2015; Souissi et al., 2013; Struthers et al., 1997). In the last 30 years, pXRF tools were developed and constantly improved so that they are now frequently used for screening and selecting in mining and environmental-related tasks (Lemière, 2018). However, the excitation energy (most portable tools work with 40 or 50 kV X-ray tubes (Lemière, 2018)) is too low for screening for many (critical) elements commonly present at low element concentrations (compare Gallhofer and Lottermoser, 2018). Nevertheless, comparison of laboratory-based XRF and ICP–MS/ AES data can be used to identify specific element relations that are due to intrinsic element and/or mineral relations. In case of Namib
Field-portable
Pb–Zn tailings, the trace elements In and Cd substitute for Zn in sphalerite, and Ag and probably Sb substitute for Pb in galena. Moreover, these are generally common minor or trace elements in Pb–Zn ores (Cook et al., 2009; George et al., 2015).
The chemical compositions of Namib Pb–Zn tailings, obtained by pXRF, are shown in Table II. For comparison of pXRF and ICP–MS/AES and XRF data, linear regression functions were calculated for pXRF–ICP–MS/AES and pXRF–XRF datasets. R² values are reported in Table III. Very good correlations (R² ≥ 0.95) were only obtained for Ca, Fe, and Mn, relative to XRF values, and for Ag, Cd, Cu, Fe, Pb, Sr, and Zn, relative to ICP–MS values (Ag, Cd, Pb, and Zn are shown in Figure 7A–D). K shows good correlations (R² ≥ 0.90) for both pXRF–ICP–MS and pXRF–XRF data pairs. Rb values (R² ICP-MS = 0.89) are also of acceptable quality. However, correlations for all other elements, independent of whether pXRF–ICP–MS or pXRF–XRF datasets are considered, are poor, with R² values of 0.14 to 0.64 (Table III). In this case, poor correlations were also found for metals/metalloids like As and Ni, for which very good R² pXRF-ICP-MS data pairs have previously been obtained for slags (Lohmeier et al., 2021b).
Portable XRF provides neither precise nor accurate data for any of the critical elements—As (86–587 µg/g), Bi (3–46 µg/g), and Sn (13–40 µg/g)—defined as those elements essential for (modern)
economy but (very) vulnerable to disruptions in the mining chain (USGS, 2018), in carbonate-bearing Namib Pb–Zn base metal tailings; As was only ranked amongst the critical elements for a limited time. Moreover, pXRF cannot determine Cs, In, Sb, and W using the chosen analytical setting. However, calculation of correlation coefficients for element pairs from ICP–MS and pXRF datasets reveals clear positive correlations of Zn (analysed by pXRF) with Ag (R² = 0.93), Cd (R² = 0.98), In (R² = 0.91), Pb (R² = 0.91), and Sb (R² = 0.96; analysed by ICP–MS/AES; Figure 7E–I). In addition, there is a good correlation between Zn and Pb data (R² = 0.93) obtained by pXRF (Figure 8K, L). Good, clearly positive correlations are revealed for Pb (analysed by pXRF) with Ag (R² = 1.00), Cd (R² = 0.92), In (R² = 0.90), Sb (R² = 0.94), and Zn (R² = 0.94; analysed by ICP–MS; Figure 8F–J). Moreover, good positive correlations are present for Cd (analysed by pXRF) and Ag (R² = 0.93), In (R² = 0.93), Pb (R² = 0.92), Sb (R² = 0.94), and Zn (R² = 0.99; analysed by ICP–MS; Figure 8A–E). Neither Cs nor W show good correlation with elements measurable by pXRF. The same applies for the rest of the bulk dataset, indicating that the inter-element relations in the group Ag–Cd–In–Pb–Sb–Zn are due to intrinsic element and mineral relations, explained by the substitution of Cd and In for Zn in sphalerite, and probably by the coupled substitution of (Ag+Sb) for Pb in galena.
Figure 7—A–D: X–Y plots of pXRF and ICP–MS/AES datasets, showing good correlations for the elements Ag, Cd, Pb, and Zn. E–I: X–Y plots of Zn (analysed by pXRF) and Ag, Cd, In, Pb, Sb (analysed by ICP–MS)
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
Therefore, Ag, In, and Sb can be indirectly estimated via pXRF analyses of Cd and Zn, for which both elements can be determined with good precision (Cd: av. RSD = 7.42; Zn: av. RSD = 1.19) and good accuracy (Cd: av. |%RDICP-MS| = 6.89; Zn: av. |RDICP-MS| = 5.84). However, determination via Zn values is favoured because all Zn values by pXRF are of excellent precision, while Cd values are mostly of good precision only. In addition, estimation of In and Sb via pXRF data of Pb is feasible because Pb can be analysed in this setting with good precision (av. RSD = 5.56), but only moderate accuracy (av. |%RSDICP-MS| = 39). Hence, linear regressions [3] to [5] allow semi-quantitative estimation of the In content in Namib
Pb–Zn tailings:
Inconc [%] = 2625 × Znconc, pXRF [%] – 2969; [3]
Inconc [%] = 9.93 × Cdconc, pXRF [%] − 7.95; [4]
Inconc [%] = 1330 × Pbconc, pXRF [%] – 8677. [5]
Linear regressions [6] to [8] allow semi-quantitative estimation of the Sb content in Namib Pb–Zn tailings:
Sbconc [%] = 2248 × Znconc, pXRF [%] + 1630; [6]
Sbconc [%] = 8.38 × Cdconc, pXRF [%] + 11.40; [7]
Sbconc [%] = 1142 × Pbconc, pXRF [%] – 6398. [8]
Figure 8—A–E: X–Y plots of Cd (analysed by pXRF) and Ag, Cd, In, Pb, Sn (analysed by ICP–MS). F–J: X–Y plots of Pb (analysed by pXRF) and Ag, Cd, In, Pb, Sn (analysed by ICP-MS). K–L: X–Y plots of Zn and Pb for pXRF and ICP–MS/AES datasets
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
It has to be mentioned that linear regressions [3] to [8] are only applicable for carbonate-bearing Namib Pb–Zn base metal tailings. However, when inter-element relations are known for a material of interest, linear regression functions can be developed for the respective material. These linear functions allow then an indirect semi-quantitative estimation of the element contents. This does not imply that conventional laboratory-based analyses are not necessary, because portable tools can simply not analyse all elements, as for example Na, and data are always calculated to a sum of 100% when using only the internal calibration of the portable tool. However, when financial resources are limited, acceptable data can be obtained when only a small conventional laboratory-based dataset is available, provided that inter-element relations are known for the tailings and/or primary ore material and that portable tools are calibrated with an appropriate CRM. Moreover, this indirect approach can only be used when there are clear inter-element relations, elements are not present in various mineral phases, and the material is quite homogeneous, as in case of slags (compare Lohmeier et al., 2021b). As shown for Namib Pb–Zn, Cd and In are related to sphalerite (In concentrations in accessory cassiterite and Cd concentrations in pyrite are unimportant) and Ag is related to galena (Ag concentrations in pyrite are unimportant). In contrast, the host of Bi is not known. It is very likely that the trace Bi forms no separate mineral phases, but occurs as substitutions for other elements in various minerals, so there are no explicit correlations between Bi and elements measurable by pXRF. In case of Sn, cassiterite is the major host of Sn; however, Sn can also be incorporated in other minerals and Sn concentrations are low. As primarily occurs as rare arsenopyrite (FeAsS) and is thus related to two elements that are present in diverse mineral phases, so there is no explicit correlation with another element for As.
Linear regressions [9] to [11] allow calculation of Ag in carbonate-bearing Namib Pb–Zn base metal tailings:
Ag [%] = 1293 × Znconc, pXRF [%] + 5775; [9]
Ag [%] = 4.86 × Cdconc, pXRF [%] + 25.80; [10]
Ag [%] = 684 × Pbconc, pXRF [%] – 4896. [11]
These regressions are provided because Ag can be directly analysed via pXRF with good precision, but only poor accuracy, so an additional evaluation/quality check of Ag values can be done via other elements.
Namib Pb–Zn tailings resource and reprocessing
Namib Pb–Zn tailings are still a noteworthy resource of Pb and Zn and contain Ag, Cd, In, and Sb, which are of interest for (modern) industry (Lohmeier et al., 2024). Hahn et al. (2004) state that the old tailings still contain 2.54% Zn, 0.21% Pb, and 7.0 µg/g Ag, which matches quite well with recent company data provided by NLZM (2023), with average element contents of 2.2% Zn, 0.3% Pb, and 7.5 µg/g Ag. Considering a tailings volume of 2.75M m³, as outlined by Hahn et al. (2004), and an average density of 2.42 g/cm³, this would translate into about 6.6 Mt (for density estimation, see Lohmeier et al., 2024). This resource is less than the remaining measured and indicated resource of the Namibian Rosh Pinah Mine of 19.94 Mt at a grade of 7.34% Zn, 1.83% Pb, and 27.71 µg/g Ag (Trevali, 2023) and is distinctly lower than the Zn resource of the Namibian Skorpion Mine of 24.6 Mt at a grade of 10.6% Zn (Borg et al., 2003); however, the Namib Pb–Zn tailings are easily accessible and the material is already milled. In the mid-1990s, reprocessing of these tailings was considered, resulting in the construction of the younger tailings dump (Hahn
et al., 2004). However, processing technology was less advanced in the 1990s than today and extraction of valuable quantities of Zn failed only due to problems with pyrrhotite suppression during flotation (Snowden, 2014). To date, the processing of Zn–Pb(–Cu) ores is still the most challenging of all ore types; in particular, when Fe-rich sphalerite is present, which has a similar density as pyrrhotite (Bulatovic, 2007). However, a new approach via sequential flotation, instead of the usually applied bulk Pb–Zn(–Cu) flotation, was successfully realized in some mining projects (for detail, see Bulatovic, 2007), so reprocessing of Namib Pb–Zn tailings seems possible. Moreover, valuable commodities of Ag, Cd, In and Sb will be directly extracted with Pb and Zn because these are contained in the Pb- and Zn-bearing minerals, and will upgrade a future Pb–Zn concentrate. Preparation of a saleable concentrate and further processing of such a concentrate will be challenging (see Lohmeier et al., 2024). However, the Southern African processing and metallurgical industry has experience in base metal extraction over many decades (e.g., Dworzanowski, 2019) and has already managed to treat complex tailings (e.g., Guest et al., 1988; Svoboda et al., 1988) and other kinds of Pb- and Zn-containing secondary raw materials (Reuter et al., 1997).
Conclusion
Namib Pb–Zn tailings contain elevated base metal concentrations, with Ag, Cd, In, and Sb as additional elements of commercial interest. Whenever possible, assessment of fine-grained tailings should not be performed solely on tailings, but should be combined with (detailed) mineralogical and geochemical investigations of primary ore and host rock(s) to be sure that the origin of trace elements—provided they are of interest—is understood. Portable XRF can be used as a complementary semi-quantitative tool for screening carbonate-bearing base metal tailings for target metals (Pb, Zn). Some elements of interest cannot be directly analysed via a portable tool, because element concentrations (In, Sb) are simply too low. Provided that 1) the mineralogical composition of the tailings material is known, 2) the host mineral phases of the elements of interest are known, 3) there are clear inter-mineral and inter-element relations, and 4) appropriate certified reference materials are available, then selected elements (In, Sb) can be determined via proxies (Zn, Cd, Pb) using simple linear regression functions. This does not mean that conventional geochemical analyses are not necessary, but reasonable semi-quantitative results can be obtained via portable tools, thereby reducing analytical costs and saving time. Base metal tailings like those of the Namib Pb–Zn mine are an underestimated source of elements/metals of interest for industry, particularly in times of declining resources and worldwide political and economic uncertainties.
Acknowledgements
This work was supported by the German Federal Ministry of Education and Research (BMBF) and is part of the sub-Saharan based LoCoSu project; grant number 01DG16011. Thanks to S. Garoeb and M. Punzel from Namib Pb–Zn for free access to the sampling site in 2018 and for an exciting above-ground mine visit in 2019. U. Hemmerling is thanked for preparation of polished (thin) sections (Clausthal University of Technology (TUC), Department of Mineral Resources (IMMR)). Thanks to D. Nordhausen for technical assistance and a warming coffee during many hours at the microprobe and to F. Türck for his patience and support during many long days of pXRF analysis (both: IMMR, TUC). We are grateful to L. Weitkämpfer, D. Gürsel, and P. Ihl (RWTH Aachen University, Department of Processing) for providing free access to powder preparation equipment and their never-ending patience.
Field-portable X-ray fluorescence analyzer for chemical characterization of carbonate-bearing base metal tailings
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Affiliation:
University of the Witwatersrand, Johannesburg, South Africa
Correspondence to: B.P. Watson
Email: bryan.watson@wits.ac.za
Dates:
Received: 26 Jan. 2024
Revised: 23 May 2024
Accepted: 19 Jun. 2024
Published: August 2024
How to cite:
Watson, B.P., Maphosa, T.J., Roberts, D.P., and Gardner, L.J. 2024. Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D. Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 8. pp. 437–446
DOI ID:
http://dx.doi.org/10.17159/24119717/3261/2024
ORCID:
B.P. Watson
http://orcid.org/0000-0003-0787-8767
T.J. Maphosa
http://orcid.org/0000-0003-3114-7198
D.P. Roberts
http://orcid.org/0000-0002-7517-4836
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
by B.P. Watson, T.J. Maphosa, D.P. Roberts, and L.J. Gardner
Abstract
The results of FLAC3D modelling on chromitite Upper Group 2 Reef pillars from the Bushveld Complex of South Africa are described. The model input parameters were determined from laboratory triaxial tests with post-failure measurements. These geomechanical tests were performed on rock materials within the pillars and the immediate pillar foundations. In the models, postfailure behaviour was simulated using the concept of cohesion softening. Models were built to determine the strength and behaviour of pillars with a width-to-height ratio of approximately two. The original research aimed to find a suitable depth below the surface where Impala Platinum Ltd could safely introduce crush pillars; however, the paper only describes the model results: the ideal depth of the crush pillar introduction is not discussed. The results of the modelling are compared with the PlatMine formula for peak pillar strength and with an underground instrumented pillar located elsewhere on the same reef. Some insight into the effects of grid size on strength is also provided. Further underground measurements are recommended to verify the model results.
The project described in this paper was carried out to determine the depth below the surface at which 4 m × 2 m crush pillars can be safely implemented in the conventional sections of the Upper Group 2 (UG2) reef at the Impala Platinum Rustenburg Operations (Impala). The expected mining height of these sections is 1.3 m. This paper describes the laboratory tests and numerical modelling that were done to achieve the objective. In particular, the pillar behaviour and strengths that were established by the modelling are described, but the ideal depth of crush pillar introduction is not discussed.
A series of geomechanical compression tests were conducted at various levels of confinement in the Gold Fields test laboratory at the University of the Witwatersrand. The aim of these tests was to determine the pre- and post-peak behaviour of the ‘reef’ rock types, as well as the immediate footwall and hangingwall rocks. FLAC3D modelling input parameters were determined from these tests, which, in turn, were used to simulate the pillar behaviour and determine the peak and residual strengths of the pillars.
The Impala operations are located in the Western Lobe of the Bushveld Complex. The mining operation is situated 30 km north of Rustenburg, in the Northwest Province of South Africa (Figure 1).
The Bushveld Complex is comprised of a series of shallow-dipping layers of chromitite, pyroxenite, norite, and anorthosite (Impala Mine Rock Engineering Department, 2017). At the Impala operations, these layers have an average dip of 9° towards the northeast (Impala Mine Rock Engineering Department, 2017). Two platinum-bearing orebodies are currently being mined, namely: Merensky Reef and UG2 Reef.
Most of the mining at the Impala operations is carried out using conventional labour-intensive stoping on a scattered basis, which allows for selective mining with geological losses left unmined. The regional support strategy comprises regularly spaced barrier pillars together with geological losses. In the stopes, pillar support consists of non-yielding pillars, yield pillars, and crush pillars (Impala Mine Rock Engineering Department, 2017). Figure 2 shows the typical stress–strain behaviour of in-stope pillars (Ryder and Jager, 2002). Points Y and C marked in Figure 2 indicate the operating points of yield and crush pillars, respectively.
Crush pillars offer the advantage of improving the extraction ratio and pose no pillar burst risk at their residual strength. However, pillars of crush pillar size can be susceptible to violent failure if they do not fail
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
close to the face (Watson, 2010; Watson et al., 2010). To prevent this, the stress at the face must surpass the peak strength of the pillar; otherwise crushing will not occur and the pillar becomes a hazard in the back area (Du Plessis, 2015).
The residual strength of the pillar determines how much weight the crushed pillars can carry (Watson et al., 2010). Therefore, it is crucial to estimate both the peak and residual strength of the pillar to ensure it can perform according to the design. Typically, this estimation involves costly underground measurements. This study concentrated on the UG2 reef because most research conducted on Bushveld pillars to date has focused on the Merensky pillars.
Literature review
Implications of test machine stiffness on rock test
According to Hudson et al. (1972), a testing machine is characterized as either soft or stiff for a given rock specimen. During testing, both the specimen and the machine deform as the load increases (Salamon, 1970). Salamon (1970) observed that the equilibrium between the testing machine and the sample remains stable if the machine is unable to induce further displacement in the specimen without a supply of additional external energy. This is in line with observations made by Cook (1963), which led him to conclude that the violent brittle behaviour observed during testing was due to excess energy stored in the machine. This resulted in the design of stiff testing machines. To obtain a complete stress–strain curve, the following condition must be met throughout the test to avoid abrupt violent failure (Hudson et al., 1972):
Rock specimen behaviour
Class I post-peak behaviour
Rocks that exhibit Class I behaviour require work to be done on them to induce further deformation (Wawersik and Fairhurst, 1970). Fracture propagation is stable, provided that the highest absolute value of post-peak stiffness is less than the machine stiffness (Hudson et al., 1972). Typical Class I behaviour of rock means that both the axial and lateral strain continuously increase during the deformation cycle (Oniyide, 2015).
Figure 1—Location of Impala operations (McLachlan, 2021)
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
Figure 3 shows a typical Class I stress–strain curve, with shaded regions indicating energy supplied by the testing machine and energy required to deform the rock specimen (Vogler, 2014). Additional energy would have to be supplied by the machine to continue testing from point A to point B in Figure 3. The shaded area (1 + 2 + 3) represents the energy supplied by the machine from point O to point B. The energy supplied by the machine to reach the peak at point A is represented by area (1 + 2). Area (1) represents the non-recoverable energy, including the energy required for crack formation, crack propagation, plastic deformation, heat, extension, etc. Area (2) shows the elastic energy stored in the sample at point A. The additional energy required to deform the rock sample from point A to point B is represented by Area (3). If the testing machine has sufficient stiffness, i.e., the unloading curve of the machine is equivalent or steeper than that of the rock sample, it is possible to trace out the complete stress–strain curve for Class I rock samples (Vogler, 2014).
Class II post-peak behaviour
Class II behaviour is characterized by unstable and self-sustaining fracture propagation. To obtain a complete stress–strain curve, energy must be extracted from the sample being tested (Vogler, 2014). Machine stiffness on its own is not adequate to control the failure of a Class II rock type (Vogler, 2014). A servocontrolled mechanism is required to back off the loading platens during fracture propagation. Lateral strain is used as the control measurement under these conditions (during the immediate preand post-peak phase), as it is the only variable that monotonically increases throughout the rock test (Oniyide, 2015).
Figure 4 shows a typical Class II stress–strain curve, with shaded regions indicating energy supplied by the testing machine and energy required to be removed from the rock specimen immediately after failure (Vogler, 2014). In Figure 4, the axial strain increases from point O to point A, then decreases from point A to point B, after which it starts to increase again until point C. Area (1 + 2 + 4) represents the total energy supplied by the machine to deform the rock specimen. The energy supplied by the machine to reach peak strength is represented by area (1 + 2 + 3). Area (3) represents the energy that needs to be extracted from a Class II rock sample in post-peak. Area (1) shows the non-recoverable energy. Areas (2 + 3) show the elastic energy present in the sample at point A. The energy required to complete the test from peak strength (from point A to point C) is represented by area (2 + 4) (Vogler, 2014).
Crush pillars
Crush pillars are slender pillars with a width/height (w/h) ratio < 3; they are designed to fail close to the face under stiff loading conditions. The pillars retain some strength after failure, known as residual strength (Ozbay et al., 1995). This residual strength is used to support the immediate hangingwall to prevent back breaks (Watson, 2009). They are typically used from 600 m below the surface, but can be used at shallower depths with caution (Ozbay et al., 1995). At shallower depths, they become susceptible to violent failure when the available stress at the face is too low to fail the pillar (Watson, 2010). The residual strength supports the immediate hangingwall up to the highest active parting plane, making it crucial to ensure sufficient residual strength.
Numerical simulations
FLAC3D was used in conjunction with the Mohr–Coulomb model with strain softening (Hajiabdolmajid et al., 2002) because of its proven ability to account for post-peak behaviour. An Itasca constitutive Model for Advanced Strain Softening (IMASS) has been developed (Itasca, 2023). The literature on IMASS (Itasca, 2023; Ghazvinian et al., 2020) shows the response for only one pillar, which displays characteristics that can be replicated using the Mohr–Coulomb strain-softening (MCSS) model. There was therefore no apparent advantage to using the IMASS model for modelling pillar responses, particularly given the many prior examples of successful application of the MCSS model (Watson et al., 2008; Malan and Napier, 2011; Le Bron et al., 2024).
The Hoek–Brown failure criterion was not used in the models because it provides limited softening behaviour and has not been shown to simulate the post-peak behaviour of pillars (Itasca, 2023). Significant effort was, however, expended to ensure that there was parity between calibrated Hoek–Brown and Mohr–Coulomb parameters.
Laboratory testing
Geomechanical tests and results
More than 50 cylindrical rock specimens with length/width ratios of 2.5 were prepared according to the International Society for Rock Mechanics (ISRM) specifications, as described by Ulusay and Hudson (2007). Three triaxial compressive strength (TCS) tests were conducted at confinement levels of 10 MPa, 20 MPa, and 40 MPa for each of the three rock types. Oil leakage occurred at 40 MPa
the test from peak strength (A–C) is shown by area (2 + 4)
Figure 3—Typical Class I behaviour (Vogler, 2014). Area 1 represents the non-recoverable energy; Area 2 shows the elastic energy stored in the sample at point A; Area 3 represents the additional energy required to deform the rock sample from point A to point B
Figure 4—Typical Class II behaviour (Vogler, 2014). Area 1 shows the non-recoverable energy; Areas 2 and 3 show the elastic energy present in the sample at point A. The energy required to complete
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
in the anorthosite samples, so the maximum confinement in this rock type was limited to 30 MPa. The tests were carried out using a servo-controlled stiff testing machine (MTS 815) to allow for the monitoring of post-failure behaviour. Unconfined compressive strength (UCS) tests were also conducted, but no post-failure deformations were measured in these tests.
Triaxial compressive strength test results
Examples of the TCS test results are provided in Figure 5. The anorthosite and pyroxenite rock types were sourced from the immediate footwall and hangingwall of the pillars, respectively. They both demonstrated transitional behaviour between Class I and Class II post-failure behaviour at confinement levels of 10 MPa and 20 MPa. At higher confinements, these rock types exhibited Class I behaviour. Interestingly, the pegmatoid and chromitite samples showed Class I behaviour at 10 MPa and 20 MPa, and transitional behaviour at 40 MPa. Further testing should be done to confirm these behaviour patterns.
Test data analysis
In Figure 6, the average peak strength results from the TCS and UCS tests are plotted as a function of confining stress. A linear regression with a coefficient of fit is provided for each rock type. The test results from the Impala database (Gardner and Bosman, 2014) provided similar peak strength results.
The residual strength of each rock type was taken as the constant strength that the rock retains after failure. The residual strength is similarly influenced by confinement to the peak strength. Figure 7 illustrates the effect of confinement on the average residual strength of the various rock types. The graph demonstrates that the residual strength rises with higher confining stress as a linear function.
The cohesion and friction angle were determined using Equations [2] and [3] (Jaeger and Cook, 1979), respectively: [2]
Figure 5—Stress-strain curves for (a) pyroxenite, (b) pegmatoid, (c) chromitite, and (d) anorthosite
Figure 6—Plot of peak strength as a function of confining pressure
Figure 7—Effect of confinement on residual strength
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
where σc is the UCS of the rock type, m is the strengthening parameter (gradient) shown in Figures 6 and 7, c is the cohesion of intact rock, and Φ is the internal friction angle.
Crush pillar model
The results of the rock tests were downrated to account for the rock mass by using the geological strength index (GSI), as described by Hoek (1994). Rock mass values of cohesion and friction were determined by using the RocLab software program (2007). Impala considers a GSI value of 60 to provide a reasonable description of the rock mass conditions across the mine (Gardner, 2022). The disturbance factor (D), to account for blasting damage, was originally set at 0.3 (Oniyide, 2015), but it was found that there was only a marginal difference of about 3 MPa in peak strength between D-values of 0.3 and zero. The FLAC3D (Itasca, 2023) models were therefore run using the laboratory-determined parameters, downrated by RocLab (2007) (using a GSI of 60) with D set to zero. These results were then compared with models that were run without downrating or data manipulation. The models were set up using a built-in structured hexahedral grid generation.
The pillar dimensions in the model were 4 m × 2 m and 1.3 m high, and the stope span between pillars was 30 m, as provided by the mine. The hangingwall and footwall thicknesses were 5 m and 4.8 m, respectively, as shown in Figure 8. A 1.5 m holing was cut every 4 m to ensure 4 m long pillars, and gully dimensions of 1.3 m wide and 1.8 m deep were also included (Carollo, 2022), as shown in Figure 9. The constitutive model was set to the maximum Coulomb shear stress (MCSS) criterion, based on the cohesion-weakening model (Hajiabdolmajid et al., 2002). Roller boundaries were applied to the model sides and base, to simulate repeating geometries along both axes. A velocity of 10−6 m/s was applied to the top of the model, and the average pillar stress was calculated across the centre height of the pillar.
Figure 9 shows the modelled area in plan view, with shaded rectangles representing the crush pillars modelled for two scenarios. Scenario 1 had a 2 m siding between the pillar and the gully and Scenario 2 had no siding. Both scenarios assumed zero dips to simplify the model. The plastic strain input parameters were estimated by analysing FLAC3D models of the laboratory triaxial tests and back-fitting the stress–strain curves obtained from these tests. An example is provided in Figure 10.
The final input values of cohesion, dilation angle, and friction angle are provided as a function of plastic shear strain in Figure 11
Figure 11—Strain-softening curves used in modelling pillar behaviour. The solid lines represent the friction angle, the dashed lines represent the cohesion, and the dotted lines represent the dilation angle
for the different rock types. The solid lines represent the friction angle, the dashed lines represent the cohesion, and the dotted lines represent the dilation angle.
Watson et al. (2008) found a relationship between element size and brittleness in FLAC models. Fractures develop more easily in a model with a finer grid. The research showed that brittle models were weaker than comparatively more ductile materials (Watson et al., 2008). It was therefore necessary to conduct a sensitivity
Figure 9—Schematic (plan view) showing the two modelled scenarios. The modelled areas are shown by the light-red and purple rectangles
Figure 10—Curve fitting for the chromitite samples
Figure 8—3D view of a model showing the hangingwall and footwall thicknesses used in the models Pegmatoid
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
analysis on strength as a function of element size for this research. The results are shown together with model run time in Figure 12. Clearly, there is a linear relationship between stress and grid size. The most appropriate compromise on grid size between accuracy and run-time was a length of 0.100 m, a width of 0.050 m, and height of 0.0325 m, shown as unity in Figure 12. There was only about 7 MPa difference between an infinitesimal grid size and the grid that was used. This difference is within the chromitite range of UCS strength determined in a test laboratory (Watson et al., 2021a).
Scenario 1
Scenario 1 represents a case where a 2 m siding is present between the gully and the pillar. Sidings are used to change the fracture patterns resulting from high face stress and to protect people from falling rocks by having some distance (1–2.5 m) between the crush pillars and the gully, which is used as a travelling way (Du Plessis and Malan, 2018). Figure 13(a) shows the configuration of Scenario 1. The result of the model estimates a peak and residual pillar strength of 55 MPa and 9 MPa, respectively.
Figure 13(b) shows the modelled failure progression through the pillar, while Figure 13(c) shows the variation of stress as the pillar fails. Pillar failure initiated at the edges of the pillar and progressed towards the core. The stress at the pillar edge was exacerbated by the bending of strata around the pillar, causing the so-called draping effect (Watson, 2010). As the pillar approached peak strength, high stress concentrated in the core of the pillar. Confinement was provided by the frictional drag of the foundations and the draping effect. At this stage, significant damage was observed in the foundations (Figure 13(b)).
The pillar enters the post-peak phase when peak strength is exceeded, and load shedding initiates. In the post-peak phase, the average stress in the pillar reduces as the pillar approaches residual strength (Figure 13(c)). The extent of damage in the foundations is also observed to increase as the pillar approaches residual
strength (Figure 13(b)). At residual strength, little further damage progression is observed. The stress concentration in the pillar is at its lowest, because its peak strength has been exceeded.
Scenario 2
Scenario 2 represented a condition where no siding was left between the pillar and the gully, as shown in Figure 14(a). The adjacent gully changes the pillar height on one side, effectively increasing the overall pillar height. This, in turn, reduces the effective w/h ratio, making the pillar weaker. The model results estimate a peak strength of 46 MPa and a residual strength of 9 MPa (Figure 14(b)). The peak strength of Scenario 2 is significantly lower than that of Scenario 1. Evidence of draping can be seen in the early stages of pillar deformation (Figure 14(c)), as the edges of the pillar were under high stress while the core was at low levels of stress. In Scenario 2, the progression of damage was more concentrated in the footwall. This was probably caused by the lack of confinement due to the presence of the gully. Hangingwall damage was observed, but not to the extent of the footwall.
Figure 12—Effects of grid size on strength and model run time
Figure 13—Model results for scenario 1 showing (a) pillar stress-strain curve, (b) progression of pillar failure (shown by the contours of plastic shear strain), and (c) variation of ZZ (vertical) stress for three stages from pillar edge failure to residual strength
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
Key
Pegmatoid
Anorthosite
Pyroxenite
Chromitite
The failure progression pattern was similar to Scenario 1 in the early stages of deformation. The pillar failure initiates from the edges of the pillar and progresses towards the core. As the stress levels approached peak, more ‘failure’ was observed in the footwall
progressing towards the gully (Figure 14(d)). In the post-peak region, once the pillar reached residual strength, very little change in stress was observed (Figure 14(e)). The stress was high in the core of the pillar as the load approached peak strength. In the post-peak
Figure 14—Model results for Scenario 2, showing (a) model setup, (b) model results, (c) draping effect, (d) progression of failure (shown by the contours of plastic shear strain), and (e) stress variation
(e)
(d)
(c)
(b)
(a)
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
region, the stress level dropped gradually until it reached residual strength. The deformation and failure distribution suggest that the pillar would fail into the gully.
Discussion
The modelling suggests that where a 2 m wide siding was left, the gully had little effect on the pre-peak pillar behaviour (Figure 13(b)). However, in post-failure, damage to the material in the siding was observed. This is probably due to the lack of confinement and frictional drag as the failed pillar dilated into the gully. Where no siding was left between the pillar and the gully, it was observed that the gully excavation significantly affected pillar behaviour (Figure 14(d)). Curved fractures appear to have developed at an early stage of loading from the narrower side of the pillar towards the bottom of the gully. Such fractures have also been observed underground (Watson et al., 2009).
The common empirical pillar strength formulae do not account for rectangular-shaped pillars. However, Wagner (1974) developed the ‘perimeter rule’ for converting rectangular pillars to square pillars, where W is the width and L is the length:
[4]
Using Wagner’s rule, the 4 m × 2 m pillars were ‘converted’ to an equivalent square pillar of 2.7 m × 2.7 m. At a stoping height of 1.3 m, the pillars with a siding have an effective w/h ratio of 2.1. The commonly used formula for pillar strength design on the UG2 Reef is a modified version of the Hedley and Grant (1972) formula, with the initial k value set to 35 MPa (Malan and Napier, 2011):
[5]
The modified formula predicts a strength of 46.9 MPa for the pillars with a siding, which is lower than the 55 MPa shown by the model when a GSI value of 60 was used (Hedley and Grant in Figure 15). The PlatMine formula suggests a strength of 119.8 MPa (Equation [6]), which is significantly stronger than predicted by this model (PlatMine 1 in Figure 15). A comparison between the calculated and various modelling results is provided in Figure 15.
[6]
The planned pillar heights at the Impala operations fall slightly outside the range of the PlatMine database for UG2 pillar strengths (Watson et al., 2021). Therefore, a strength prediction was calculated assuming a pillar height within the range of the database, but at the
same w/h ratio as the Impala pillars. A height of 1.5 m was used in the formula and a strength of 128.3 MPa was calculated (PlatMine 2 in Figure 15). Subsequently, further models were run with the same input parameters, but the GSI was varied to see the effect of GSI on the modelled strength.
The numerical results suggest that the pillars are stronger than predicted by the traditional modified Hedley and Grant (Malan and Napier, 2011) formula, and this has been confirmed by several researchers (Oates and Malan, 2023; Rajpal, 2012). The GSI value of 60, which was provided by the mine, may be low, because other mines have suggested higher values. A value of 70 was estimated for the Booysendal instrumentation site (Watson et al., 2021). The pillar strength predicted by the PlatMine formula (Watson et al., 2021) may be considered high for the pillars at Impala when compared with the models. Importantly though, the calculated strength was lower than predicted by the model without GSI manipulation (GSI = 100). It is recommended that underground measurements be done to verify the numerical results.
The novelty of the research described in this paper is in the use of laboratory tests to determine both peak and residual strengths of the pillar and foundation materials. The post-failure behaviour of the laboratory samples was used to establish material properties that simulated pillar behaviour as failure progressed through the pillars. It was impossible to use visual or underground measurements to calibrate the models because the research aim was to determine the depth at which crush pillars could be introduced. At the time of modelling, these pillars were not available for observations or instrumentation. The models were therefore confirmed using the PlatMine formula (Watson et al., 2021a), which was developed using observations made on other similar Bushveld mines. An instrumented pillar on the eastern side of the Bushveld Complex was used to confirm the PlatMine formula (Watson et al., 2021b), and the results are compared with the FLAC3D model without a GSI downrating in Figure 16.
Conclusions and recommendations
The investigation aimed to estimate the peak and residual strength of the planned 4 m × 2 m UG2 pillars at the Impala operations. FLAC3D models with MCSS were calibrated using laboratory tests with post-failure behaviour and employed in the strength evaluation. Some practical guides into the methodology of conducting tests on rock samples in post-failure were developed during the programme, as described in the paper. The modelled pillar height was 1.3 m, which effectively created pillars at a w/h ratio of 2. The mine typically uses an average GSI value of 60, which was adopted for the strength modelling. The models provided insights into the effects of grid size on predicted strength and
Figure 15—Comparison between the FLAC3D model with GSI values of 60, 80, and 100, the Platmine formula (Watson etal., 2021) and the modified Hedley and Grant formula (Malan and Napier, 2011)
Determination of strength of UG2 chromitite pillars at Impala Platinum from laboratory tests and FLAC3D
the progression of failure through the pillars and foundations as the applied stress was increased. The most suitable grid size was established and used in all subsequent analyses. The findings of the models where a GSI of 60 was used were:
➤ crush pillars with sidings had peak and residual strengths of 55 MPa and 9 MPa, respectively;
➤ crush pillars without sidings had peak and residual strengths of 45 MPa and 9 MPa, respectively.
Subsequent analyses on the effect of GSI suggested that a value of 60 may have been too low. The results of several GSI values were compared with the modified Hedley and Grant formula with the k-value set at 35 MPa (Malan and Napier, 2011) and the PlatMine formula (Watson et al., 2021). The modified Hedley and Grant (Malan and Napier, 2011) formula underestimated the strengths suggested by the modelling. It could be argued that the PlatMine formula (Watson et al., 2021) overestimated the strength. However, it is important to note that the PlatMine formula strength was lower than predicted by the model without manipulated material strengths (GSI = 100). The novelty of the research was the use of laboratory tests with post-failure measurements to determine input parameters for the FLAC3D modelling. It is recommended that further underground measurements be carried out at Impala to verify the modelling results.
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Figure 16— Comparison between the FLAC3D model without a GSI and downrating instrumentation results at Booysendal Platinum Mine (Watson 2021b)
Determination
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Affiliation:
1Institute of Disposal Research, Department of Mineral Resources, and Institute of Mining Engineering, Department of Surface Mining and International Mining, Clausthal University of Technology, ClausthalZellerfeld, Germany
2Institute for Earth Sciences, University of Graz, Graz, Austria
3Institute of Mineral Resources Engineering, RWTH Aachen University, Aachen, Germany
Correspondence to:
S. Lohmeier
Email: stephanie.lohmeier@tu-clausthal.de
Dates:
Received: 11 Mar. 2023
Revised: 31 May 2023
Accepted: 11 Jun. 2024
Published: August 2024
How to cite:
Lohmeier, S., Gallhofer, D., and Lottermoser, B.G. 2024. Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings (Erongo Region, Namibia). Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 8. pp. 447–460
DOI ID:
http://dx.doi.org/10.17159/24119717/2724/2024
ORCID:
S. Lohmeier
http://orcid.org/0000-0003-2556-2096
D. Gallhofer
http://orcid.org/0000-0003-2139-7847
B.G. Lottermoser
http://orcid.org/0000-0002-8385-3898
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings (Erongo Region, Namibia)
by S. Lohmeier1, D. Gallhofer2, and B.G. Lottermoser3
Abstract
In Southern Africa, historic mining and mineral processing of base metal deposits have almost exclusively focussed on the extraction of major metals, leading to the loss of remaining valuable raw materials into tailings dumps and waste rock piles. At the Namib Pb–Zn mine (Erongo Region, Namibia), historic base metal tailings deposits are present as unreclaimed exposed waste piles. The tailings comprise silt- (fraction A: d50 = 25 to 48 μm) to sand-sized (fraction B: d50 = 86 to 185 μm; fraction C: d50 = 210 to 230 μm) material and contain major concentrations of base metals (Pb av. 1.15 mass%, Zn av. 3.20 mass%), S (av. 9.95 mass%), as well as lower values of other metals (Cu av. 490 µg/g, Cd av. 133 µg/g, Ag av. 22 µg/g), and critical elements like Sb (av. 14.7 µg/g) and In (14.3 µg/g). Former mineral processing only targeted the extraction of galena and sphalerite. As a consequence, qualitative mineralogical composition of the tailings is similar to that of the primary ore. Ca–Fe–Mg(–Mn) carbonates, quartz, micas, chlorite, minor graphite, magnetite, and rare parisite relate to the former host rock and gangue matrix, whereas Fe-rich sphalerite, galena, magnetite, pyrite with minor pyrrhotite, rare arsenopyrite, marcasite, cassiterite, and accessory scheelite are original constituents of the primary ore. Reprocessing of such a material would be challenging, but a mixed Pb–Zn concentrate enriched in Cd and Ag might be obtained. In future, possible reprocessing of Namib tailings and associated disposal of wastes into an appropriately designed repository would not only generate valuable metal commodities, but such activities would also eliminate a major metal pollution source from the local environment.
Keywords
Namib Pb–Zn deposit, tailings, base metals, resource, reprocessing
Introduction
Modern processing technologies allow a recovery of 80%–90% of ore, depending on grinding and the flotation agents used (Dold, 2010). However, many historic tailings dumps in Southern Africa originate from mining activities at the end of the 20th century or the colonial era or are even older, and have remained untouched for many years. In addition, about 75% of all worldwide mining projects close prematurely before ore is mined out, so valuable resources remain untouched or are even lost (Laurence, 2011). In times of increasing demand for resources (European Commission, 2010, 2017), such historic tailings dumps are of potential economic interest because the former ores were originally processed for particular metals, leaving other potential resources behind in the tailings (Lèbre et al., 2016; Lei et al., 2015). However, if such metalliferous tailings are left uncapped for extended periods, then they may become potential sources for metal contamination and thus likely hazards for human health and the environment (e.g., Festin et al., 2019; Harrison et al., 2010; Liakopoulos et al., 2010; Lupankwa et al., 2004), even in arid environments (Blight, 2007).
Nowadays, mining still focusses on the most profitable elements/metals, avoiding, at times, the extraction of other elements/metals as by-product(s), instead of using the whole potential of the ore as financial interests prevail (Mudd et al. 2017; West, 2020), even though full recovery of valuable components is often technically feasible and economic (Jahanshahi et al., 2007). In Namibia alone, there are more than 250 abandoned mine sites (Salom and Kivinen, 2019), of which many have potential to contain elements of economic interest. Pb–Zn ores, for example, have high potential for In, Cd, and Ga, which are elements of interest for modern technologies (Mudd et al., 2017; Werner et al., 2017).
The aim of this study is to document the general geochemical and mineralogical characteristics of the historic Namib Pb–Zn tailings at the Namib Pb–Zn mine site and to show the potential of these tailings as a
Geochemical
and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
secondary metal resource (Figure 1). Hence, this study contributes to our understanding of the resource potential of historic tailings dumps with respect to relict metals that were not previously extracted.
Background
Mining site
The Namib Pb–Zn Mine is located about 25 km east of the town of Swakopmund and west of the Rössing Mountains in the Dorob National Park of Namibia’s Erongo Region (22°31'17.53''S; 14°45'41.16''E; Figure 1A, B). Discovered during exploration activities between 1932 and 1934, underground mining for Pb, and later also for Zn and Ag, took place between 1968 to 1992 in the mine, formerly known as Deblin Mine or Namib Lead Mine, down to 210 m below surface (Snowden, 2014). Between 1992 and 1993, Iscor Namibia carried out some exploration activities. The mine site was then abandoned for several years, except for a short attempt to reprocess tailings for Zn by African Exploration in the mid-1990s (CCA, 2013; Snowden, 2014). However, recovery of Zn failed due to technical problems in suppressing flotation of pyrrhotite (Hahn et al., 2004; Snowden, 2014). In 2001, the tailings were granted to local geologists (CCA, 2013; Snowden, 2014), before Kalahari Mineral Limited carried out a detailed evaluation of the dumps via a reverse circulation drilling programme in 2008 (Snowden, 2014). At the same time, Kalahari Minerals Limited commenced drilling work on primary ore in 2007, before North River Resources took over the project in 2009, starting with cleaning the mine site and dewatering and surveying the underground workings as no remedial works were done prior to abandonment (CCA, 2013; Hahn et al., 2004; Snowden, 2014; Tenova Mining and Minerals, 2014). Since mid2021, the Castlelake Group has held the largest stake of the Namib Pb–Zn mine, with North River Resources as minor stakeholder (NLZM, 2023). In 2014, a pre-feasibility study was published, followed by several optimization studies, outlining a remaining indicated primary JORC mineral resource of 710 000 t at a grade of 2.4% Pb, 7.0% Zn, and 50 g/t Ag related to four orebodies (South, Junction, North, and N20), plus an inferred resource of 409 000 t at a grade of 2.2% Pb, 6.0% Zn, and 38 g/t Ag and additional resources in close-by gossans (NLZM, 2023). Moreover, a preliminary JORC resource of 689 000 t at a grade of 32.1 g/t In (Snowden, 2014) or 0.9 Mt at a grade of 29 g/t In (Werner et al., 2017) was announced. Following limited mining and mineral processing in 2018 and 2019, the mine site remains under care and maintenance to this date.
Two tailings (slurry) dumps are located on site, having been produced during earlier mining and mineral processing activities, when ~100 000 t of Pb and Zn concentrate were produced from ~700 000 t of ore (CCA, 2013; Figure 1B-F). The former Ag production is estimated to exceed 1.1 Moz Ag. The older northern dump (~ 2.75 Mm³; Hahn et al., 2004) traces back to the older mining activities, whereas the younger southern dump (~ 1.25 Mm³; Hahn et al., 2004) is from the first reprocessing activities in the mid-1990s (CCA, 2013; Snowden, 2014). The remaining measured bulk tailings resource is estimated at 260 000 t at a grade of 0.3% Pb, 2.2% Zn, and 7.5 g/t Ag plus an additional indicated resource of 350 000 t at a grade of 0.3% Pb, 2.1% Zn, and 7.7 g/t Ag by NLZM (2023). However, Hahn et al. (2004) estimated the resource at 2.75 Mm³ at a grade of 2.54% Zn, 0.21% Pb, and 7.0 g/t Ag (old dump) plus an additional 1.25 Mm³ at a grade of 2.14% Zn, 0.15% Pb, and 7.9 g/t Ag (new dump). In the 2010s, Mintek proved recovery of Zn from the tailings to be uneconomic at that time (Snowden, 2014).
Slurries were largely preserved on-site due to the semi-arid climate in this part of the Namib desert after processing stopped. However, due to rare heavy rainfall events, as well as constant and, at times, strong winds resulting in wind erosion, the surrounding topsoils are covered, in particular in the downwind direction, to a certain degree by wind-blown material up to 8 km away from the site and up to 35 cm in thickness (CCA, 2013; Salom and Kivinen, 2019; Snowden, 2014). In addition, slurries are transported along drainage lines and contaminate stream sediments with Pb, Zn, As, and Cd (Hahn et al., 2004; SLR-EC, 2013). There are only ephemeral rivers in the surroundings: no acid mine drainage (AMD) has developed and no leaching of metals has taken place to date and is unlikely to develop, because AMD would be buffered by the carbonate host rock (SLR-EC, 2013). However, there is a distinct risk that seepage from the tailings dumps will influence groundwater quality through the addition of metals (SLR-EC, 2013). In future, covering/sealing of tailings dumps by marble waste rock is envisaged, which would reduce mobilization of remaining metals (e.g., CCA, 2013; Moreno and Neretnieks, 2006; Souissi et al., 2013).
Local geology and mineralization
At the Namib Pb–Zn Mine site, the Pb–Zn mineralization occurs within the calcitic ‘mine marble’, just above the contact with the underlying Arandis Formation (Basson et al., 2018; CCA, 2013). The mineralization is generally stratabound, but can cut across lithologies. Prolate and rhomb-shaped ore shoots dip between 45° and 90°, range in width between 2.5 and 13.6 m (av. 5.9 m), and have strike lengths of 9.6 to 91.2 m (av. 24.9 m; Basson et al., 2018; Snowden, 2014). The known minimum vertical extent of the ore shoots is > 210 m, corresponding to the current deepest mine level (Basson et al., 2018). Ore consists of sphalerite–galena–pyrrhotite–pyrite with locally abundant magnetite and fluorite (SLR-EC, 2013). In addition, Basson et al. (2018) reported locally anomalous In and Sn in the ore, without reference to the host mineral(s). Locally, gossans are characterized by ferruginous goethite- and hematite-bearing material with some occurrence of galena, cerussite, and smithsonite, which can extend down to 10 m depth below surface (Basson et al., 2018; Snowden, 2014). In general, oxidation extends down to ~ 16 m below surface (Snowden, 2014). Originally, the mineralization was defined as being of (remobilized) Mississippi Valley or sedimentary-exhalative type, evolved in the vicinity of a syn–rift growth fault (Basson et al., 2018; Frimmel and Miller, 2009; Snowden, 2014, and references therein), and was reinterpreted by mine geologists to be of intrusive-related carbonate replacement or manto type (CCA, 2013; SLR-EC, 2013). Based on structural investigations, Basson et al. (2018) described mineralization as remobilized and redeposited ore sequestered from a primary orebody in a tectonically activated stress field; however, mineralization has not yet been investigated in detail and is thus not fully understood.
The host rocks are mostly massive, white, coarse-grained marbles with intercalated fine-grained quartz–biotite–(feldspar–cordierite) schist and pegmatites (Badenhorst, 1987; CCA, 2013). Only very locally, weak lamination or colour-banding is developed (Lehtonen et al., 1996). Intercalations of calc-silicate layers and thin chert lenses or quartzite layers are rare (CCA, 2013; Lehtonen et al., 1996; Miller, 2008). Locally, 1–2 mm large graphite flakes (up to 5 vol.% of the mineral assemblage; Lehtonen et al., 1996), muscovite, and phlogopite can be abundant in the marble (Miller, 2008; MME, 1996), imparting a speckled appearance (Lehtonen et al., 1996). Fine-grained mylonite zones with and without graphite characterize the transition to the overlying Kuiseb schists (Miller, 2008).
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
Figure 1—A: The Namib Pb–Zn deposit is located about 25 km east of the town of Swakopmund in Namibia’s Erongo Region. B: Currently, the mine site comprises two smaller (new) spoil dumps and two large tailings dumps set onto a relatively flat underground. The older northern tailings dump, composed of solidified air-dried material, is distinctly higher than the younger southern tailings dump. Samples were taken from the older northern tailings dump because the reprocessing potential of this dump is assumed to be more promising. C–F: Photos showing the, higher, older northern tailings dump. Orange to brown colour variations of the solidified, air-dried material are apparent. The impression of centimeter-large rock pieces is misleading: the processing remains are largely composed of coarse silt to medium sand size (‘powder size’) material. ‘Rock pieces’ disintegrate during transport. Photographs taken by B.G. Lottermoser in 2018 and S. Lohmeier in 2019.
G: Photograph showing primary Namib Pb–Zn ore composed of (visible) sphalerite (dark brown), galena (greyish), and pyrite intergrown with carbonate.
H: Photograph showing sphalerite-dominated Namib Pb–Zn ore. G, H: Photographs taken by S. Lohmeier in 2022
Methodology
Sampling
Two large tailings dumps are located in the southern part of the Namib Pb–Zn mine site, SE of the main entrance to the underground mine (Figure 1B). Tailings range in grain size from clay to sand size (‘powder size’). Larger hardened tailings blocks are present; however, these easily disintegrate to smaller pieces/
grains (Figure 1E, F). In 2019, sampling focussed on the differently coloured tailings of the northern tailings dump to obtain different materials that represented different production cycles. Samples were collected along vertical profiles and directly from the surface of the tailings dump. In total, 18 tailings samples, each weighing ~ 5 kg, were taken. Two additional ore samples, representative of the principal ore mineralization according to the mine geologists, are from new stockpiles (Figure 1F, G).
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
Sample processing and analysis
Geochemical analysis
Tailings samples were air-dried and subsequently homogenized. A representative aliquot was milled to analytical fineness using a WC swing mill in the Department of Mineral Processing at RWTH Aachen University. Milled powders were sent to Australian Laboratory Services (ALS; Loughrea, Ireland) for X-ray fluorescence spectroscopy (XRF) of major elements (Al, Ca, Fe, K, Mg, Mn, Na, P, Si, Ti) for inductively coupled plasma mass spectrometry (ICP–MS) after HNO3–HF–HClO4 and HCl digestion for certain trace elements (Dy, Er, Eu, Gd, Ho, Nd, Pr, Sm, Tm), and for infrared (IR) spectroscopy of C and S. Loss on ignition (LOI) was determined by sintering at 1000°C. In addition, samples were analysed at SGS Bulgaria (Bor Laboratory, Serbia) by ICP–MS after HNO3–HF–HClO4 and HCl digestion for Ag, Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, In, K, La, Li, Lu, Mg, Mn, Mo, Nb, Na, Ni, P, Pb, Rb, Sb, Sc, Se, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, U, V, W, Y, Yb, Zn, and Zr. Samples having Ag > 10 µg/g, Pb > 10 000 µg/g, and/or Zn > 10 000 µg/g were reanalyzed by atomic emission spectroscopy (AES) using the same digestion approach. All sample packages included the analyses of duplicates and external and laboratory internal reference materials for quality control. Analytical data are given in Table I. X-ray diffraction (XRD) was performed on tailings samples and primary ore samples, using a X’Pert Pro (PANalytical) instrument with data collector and a X’Pert HighScore system equipped with a Co-LFF (Empyrian) tube and an automated divergence slit at the Institute of Disposal Research (IDR) at Clausthal University of Technology (TUC). Qualitative evaluation was carried out using the X’Pert HighScore software from PANalytical. Transmitted and reflected light microscopy was carried out on primary ore to correlate bulk geochemical data of tailings with mineralogical data.
Laser diffractometry and tailings density
Laser diffractometry in dry mode was applied after conventional wet sieving, screen washing, and laser diffractometry with hydrodispersion failed due to clogging of sieves/screens and/or the analytical unit by the fines in a very short time. Moreover, laser diffractometry is the recommended method when a large quantity of particles is smaller than fine-sand size. All samples were dried and homogenized and lumps were gently comminuted before analysis. Particle size analysis was performed at the Institute of Mineral and Waste Processing, Recycling and Circular Economy Systems (TUC) using a HELOS H2387 Mastersizer instrument in dry mode. After initial test runs, the size range was set to 1 to 875 μm to include the whole particle size spectrum. No prescreening was necessary. The instrument was run at 4 mbar external pressure and 44 mbar internal negative pressure as the dispersion method. Material charge (about 1.5 full spoon; ≤ 1 g) was by a vibration doser with a feed rate of 40% at a gap width of 7 mm. Analysis started when Copt was ≥ 0.1% and stopped when Copt was ≤ 0.1% for 2 s. All runs were repeated three times. Evaluation was done using the PAQXOS 4.3 software of HELOS. It should be noted that results of sand-sized material are comparable with those obtained by the classical sieving-pipette method, but deviations for clay-sized material may occur (e.g., Beuselinck et al., 1998; Konert and Vandenberghe, 1997; Miller and Schaetzl, 2011). It was intended to give a general overview of the Namib Pb–Zn tailings so that data collected by laser diffraction are of sufficient quality. However, a certain bias by platy particles, such as graphite flakes and micas, cannot be excluded.
The density of the tailings material was semi-quantitatively determined in two different ways. These approaches were chosen because the material loosened on transport, losing its original compact state. Thus, the bulk density of the tailings can be only approximately determined after liberation of the material from the tailings pile and transport. The first approach was by filling an 80 mL flask with the loosened material and subsequent weighing the material to determine the bulk or powder density without any additional compaction. The gross density was then calculated under consideration of the decompaction factor, which was set here to 0.6, equivalent to material of medium density (Dachroth, 2017; DIN 18300). The second approach was by calculating the density from bulk geochemical data and mineral proportions.
Results
Primary ore
Primary ore mineralogy
Primary Namib Pb–Zn ore is characterized by visible massive dark to very dark coloured sphalerite and galena (Figures 1G, F, 2A) set into a Ca–Mg–Fe(–Mn) carbonate matrix. Microscopy reveals the presence of minor micas, including phlogopite, biotite, and muscovite, as well as quartz, zircon, apatite, and graphite flakes (Figure 2B) as part of the matrix. Pyrite is the most abundant minor sulfide mineral and occurs mostly as small patches enclosed in sphalerite or in small fissures and fractures crosscutting sphalerite (Figure 2C, D) and overgrowing micas. The presence of rare relict anhedral to partially subhedral marcasite enclosed in pyrite reveals that the paragenesis galena–sphalerite–pyrite is post-marcasite. At least two different pyrite generations could be identified using microscopy. The younger pyrite generation comprises fine-crystalline ‘porous’ pyrite crystals, whereas the older pyrite generation is characterized by anhedral to partly subhedral pyrite. Marcasite is consistently associated with ‘porous’
Figure 2—A–D: Photomicrographs of primary Namib Pb–Zn ore. A: Massive sphalerite set into carbonate matrix. B: Graphite flakes in massive sphalerite with carbonate matrix. C: Small pyrrhotite crystals enclosed in massive pyrite. Pyrite–pyrrhotite are enclosed in massive sphalerite with carbonate matrix. D: Pyrrhotite enclosed in massive pyrite and as small rounded inclusions, together with galena, in massive sphalerite. A: Transmitted light, straight polars; B–D: Reflected light. Abbreviations: cb = Ca–Mg–Fe(–Mn) carbonate; gn = galena; gr = graphite; mc = mica; po = pyrrhotite; py = pyrite; sp = sphalerite
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
Table I
Table I 1
Bulk rock analytical results for Namib Pb–Zn tailings. Values of Ge are < 5 µg/g, of Se mostly < 2 µg/g, of Te mostly < 0.05 µg/g. Primary ore samples were only analyzed by ICP–MS. Tailings analyses were analyzed by (1) XRF, (2) ICP-MS, (3) AES, (4) IR, and (5) Loss on ignition
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
pyrite crystals, suggesting that the formation of marcasite was subsequently followed by transformation of the metastable marcasite polymorph into the more stable pyrite polymorph. In addition, the ore paragenesis comprises minor anhedral pyrrhotite that is mostly enclosed in massive pyrite, but can occur also as discrete minerals in sphalerite (Figure 2C–D). Arsenopyrite, cassiterite, and parisite are rare. Scheelite is an accessory. Cassiterite and scheelite are either (partly) overgrown by galena and/or pyrite or occur in the carbonate matrix, so they are pre-galena–sphalerite–pyrite. Parisite locally overgrows cassiterite and is enclosed in pyrite, identifying parisite as post-cassiterite and pre-galena–sphalerite(–pyrite). The temporal relations of arsenopyrite cannot be constrained because arsenopyrite is only observed as inclusions in massive sphalerite. No gypsum and no goethite are observed in massive primary Namib Pb–Zn ore.
Primary ore geochemistry and bulk enrichment
The two primary ore samples have quite similar geochemical composition. Based on ICP–MS and AES data, Zn (33.06 and 33.80 mass%), Fe (10.01 and 10.04 mass%), and Ca (3.88 and 5.72 mass%) constitute by far the largest proportions of bulk samples (Figure 3A; Table I). Relatively high contents are also present for Pb (0.91 and 0.93 mass%), Al (0.87 and 0.88 mass%), K (0.50 and 0.51 mass%), and Mn (0.48 and 0.49 mass%), whereas Mg (both: 0.15 mass%) and Cd (both: 0.10 mass%) concentrations are somewhat minor. All other elements have concentrations of ≤ 400 µg/g. Average P (~ 400 µg/g), Ti (~ 150 µg/g), and Cu (~ 110 µg/g) concentrations are in the range of 100 to 400 µg/g, while In (~ 86 µg/g), Ce (~ 62 µg/g), La (~ 45 µg/g), Rb (~ 37 µg/g), Ba (~ 32 µg/g), Sn (~ 24 µg/g), Sr (~ 24 µg/g), Nd (~ 23 µg/g), Cr (~ 22 µg/g), Ag (~ 19 µg/g), As (~ 19 µg/g), and Ga (~ 11 µg/g) average contents are between 10 and 90 µg/g. In contrast Sb
(~ 7.7 µg/g), W (~ 7.7 µg/g), V (~ 7.3 µg/g), Pr (~ 6.3 µg/g), Co (~ 5.7 µg/g), Y (~ 5.7 µg/g), Ni (~ 5.4 µg/g), Zr (~ 5.0 µg/g), Eu (~ 4.4 µg/g), Sm (~ 3.3 µg/g), Gd (~ 2.8 µg/g), Tl (~ 2.4 µg/g), Li (~ 2.4 µg/g), Nb (~ 2.0 µg/g), U (~ 1.7 µg/g), Dy (~ 1.3 µg/g), Sc (~ 1.3 µg/g), and Th (~1 .2 µg/g) averages are between 8 and 1 µg/g, and Cs, Er, Mo, Yb, Tb, Hf, Ho, Be, Bi, Lu, Tm, and Ta averages are < 1 µg/g. By far the most enriched element in primary ore, compared with bulk crustal abundance, is Cd, with an enrichment of 12 600×. Additionally, Zn (4643×), In (1646×), Pb (835×), and Ag (330×) are strongly enriched and thus the same elements as in tailings material (see below). A distinct enrichment is also seen in Sb (38×), As (16×), and Sn (14×), while W (8×), Mn (6×), and Tl (5×) show a very slight enrichment compared with bulk crustal abundance. All other elements are not enriched or enrichment is ≤ 4×.
Tailings
Particle size and tailings density
The particle-size distribution curves of Namib Pb–Zn tailings cover the size spectrum from clay to medium sand, as is typical for fine-grained tailings (average data for three runs are provided in Table II). Three different particle fractions can be distinguished by median/mean values and curve shapes (Figure 4). Fraction A (six samples) comprises tailings with a dominant silt component, whereas Fraction B (eight samples) and Fraction C (three samples) comprise samples with a prevailing sand component. Fraction B is thereby largely composed of fine sand-sized particles while also having a relatively high silt component, whereas Fraction C has a quite narrow particle range with most particles in the medium sand-size range.
Figure 3—A: Element ranges of tailings and primary ore samples. Major elements are in mass%, whereas minor and trace elements are in µg/g. B: Enrichment of elements of Namib Pb–Zn tailings in comparison with bulk continental crust (data from Rudnick and Gao, 2003). Elements are assigned to major elements, large-ion lithophile elements (LILE), high-field strength elements (HFS), rare earth elements (REE; including here only the lanthanides), and other elements. There is probably some bias concerning W because a WC mill was used during sample preparation. A, B: For major elements XRF data was used. Sulfur values are by IR. ICP-MS data was used for minor and trace elements
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
Table II
Results of laser diffractometry of Namib Pb–Zn tailings (averages of three runs per sample), particle size statistical parameters based on sieving curves, and bulk density. (1) Mass of 80 mL flask (g); (2) powder density by weighing (g/cm³); (3) gross density by weighing in (g/cm³); (4) density calculated from geochemical data (g/cm³)
≤
Fraction A is characterized by median (d50) values of 25 to 48 μm and corresponding mean values ((d25 + d75)/2) of 48 to 62 μm, reflecting material of largely coarse-silt size. The graphical coefficient of uniformity U (U = d60/d10) is between 14.50 and 38.18, indicating a non-uniform to very non-uniform particle size spectrum. A wide range of particle sizes, pointing to very poorly sorted material, is also reflected by sorting values (S0; with S0 = √(d75/d25)) of 2.91 to 3.72. Skewness Sk (Sk = (d75 + d25)/(d50)2) is consistently strongly positive, with all values ≥ 0.62. Kurtosis Kqa (Kqa = (d75 − d25)/(2(d90 − d10)) is consistently ≤ 0.25 and thus very platykurtic. In contrast to Fraction A, Fraction B comprises material with d50 values of 63 to 180 μm and mean values of 86 to 185 μm, reflecting material of mainly fine-sand size. Like Fraction A, the fine-sand tailings material is largely very poorly sorted (S0:
1.76–3.35), but U (U: 1.29–26.83) varies considerably between rarely uniform (one value), non-uniform (two values), and very non-uniform (six values). Sk is strongly positive (Sk: 0.50–0.78) and kurtosis (Kaq: 0.30–0.41) consistently very platykurtic. The mediumsand size material of Fraction C is characterized by d50 values of 210 to 230 μm and corresponding mean values of 220 to 240 μm. Although sorting is only poor, expressed by S0 values of 1.37 to 1.49, the material has a uniform particle spectrum reflected by U values of ≤ 4.81. Like Fractions A and B, Fraction C has a strongly positive Sk (Sk: 0.90–1.03) and Kaq (Kaq: 0.26–0.31) is platykurtic.
The tailings material has a relatively uniform gross density, varying between 2.02 and 2.95 g/cm³ when using the powder density as base (data are provided in Table II). In contrast, the calculated density, based on geochemical and mineralogical data,
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
Figure 4—Particle size distribution curves of Namib Pb–Zn tailings based on laser diffraction data. The tailings material can be divided into three fractions based on median/mean values of 25–48 μm/48–62 μm (Fraction A), 63–180 μm/86–185 μm (Fraction B), and 210–230 μm/220–240 μm (Fraction C), reflecting material of largely coarse-silt, fine-sand, and medium-sand size, respectively. However, all fractions are, at most, poorly sorted
varies considerably between 2.90 and 5.77 g/cm³, where most tailings samples have a calculated density in the range of 2.03 to 3.27 g/cm³ and only four samples have a density > 4 g/cm³. However, the mean density obtained by both semi-quantitative approaches is in a similar range of 2.4 to 2.9 g/cm³, although a higher gross density does not necessarily correspond to a higher calculated density.
Tailings mineralogy
Namib Pb–Zn tailings are fine-grained, with most material in the coarse silt to fine sand fraction, which precludes the direct macroscopic and microscopic identification of the mineralogical phases present. Relict galena, sphalerite, pyrite, pyrrhotite, magnetite, hematite, calcite, and siderite were identified using XRD
Table III
(Figure 4; Table III). However, differentiation of Ca–Mg–Fe(–Mn)bearing carbonate phases is difficult by XRD, so the presence of other carbonates, including dolomite–ankerite solid solutions, is very likely. Moreover, XRD indicates the presence of quartz, graphite, apatite, the micas biotite, phlogopite, and muscovite, as well as plagioclase, and chlorite, which belong to the primary host mineral assemblage. In contrast, gypsum, lepidocrocite, and anglesite are interpreted to result from post-processing weathering under arid conditions. Some goethite is probably also of secondary origin; however, most goethite and jarosite originate from gossans mined at surface. The origins of rare anhydrite, chalcocite, and halite remain dubious. Halite likely results from evaporation of processing water and chalcocite is a common weathering product of primary copper minerals. Anhydrite might result from dehydration of gypsum. The presence of other minor and trace phases cannot be excluded, but identification is difficult at abundances of < 5 vol.% or the lack of a distinctive XRD pattern (e.g., anglesite, gypsum; Khan et al., 2020). Overall, the tailings mineralogy is similar to that of the primary ore, highlighting the fact that the former processing technologies, including flotation and the used flotation agents, did not modify the mineralogical assemblage.
Tailings geochemistry and bulk enrichment
The chemical composition ranges of the tailings samples are given in Figure 3A, showing major elements analysed by XRF, S by IR, and minor and trace elements by ICP–MS and AES. The older tailings are mainly composed of Fe (~ 16–31 mass%), Ca (~ 9–19 mass%), and minor Si (~ 3–7 mass%), Mn (~ 1–3 mass%), and Al (~ 1–2 mass%). K, Mg, Na, P, and Ti contents are insignificant, with average values < 0.6 mass%. Calculating all Ca as CaCO3 and all Fe as FeCO3 would translate into an average 35 mass% content of calcite and 41 mass% content of siderite in tailings, explaining the extremely high LOI values of ~ 9 mass% to 20 mass%. The tailings are very rich in S, which ranges from ~ 6.5 to 14.6 mass%, attributed
X-ray diffraction results of Namib Pb–Zn tailings (samples starting with NBT-) and primary Namib Pb–Zn ore (samples starting with NAM-)
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
to the presence of sulfide mineral phases and minor sulfates. The primary Pb–Zn ore signature is reflected by high Pb and Zn values of ~ 0.2 to 5.7 mass% (av. 1.2 mass%) and ~ 0.9 to 9.6 mass% (av. ~3.2 mass%), respectively, classifying them as major components in these tailings. Tailings show high contents (av. values > 100 µg/g) of Cu, Sr, As, and Cd. In addition, Ba, Rb, W, Ce, Sn, Ag, La, Sb, In, Nd, Ni, Co, Li, Cr, and V values are > 10 µg/g. Average Cs and Bi values are slightly below 10 µg/g. This indicates that minor to very low concentrations of the critical elements (As,) Bi, Cs, In, Sb, Sn, and W, which are essential for modern economy but are very vulnerable to supply disruptions in the mining chain according to the classification of the USGS (2018), and noteworthy Zn and Pb proportions are still present in these tailings. (Arsenic was formerly ranked amongst the critical elements, which was later modified, and is therefore shown in brackets).
Compared with average crustal abundance (data from Rudnick and Gao, 2003), Namib Pb–Zn tailings are significantly enriched in Cd (~ 1660× on av.). Pb, (~ 1050×), Zn (~ 445×), Ag (~ 400×), In (~ 280×), and S (~ 250×). Sb (~ 75×), As (~ 72×), Bi (~ 50×), and W (~ 50×) show also a distinct enrichment. However, W values have to be regarded as semi-quantitative, because a WC mill was used for pulp preparation, but accessory scheelite is detected in primary Namib Pb–Zn ore. A moderate to slight enrichment is also observed
for Cu (18×), Sn (15×), and Cs (5×). All other elements show no notable enrichment or have averages close to crustal abundance or even below. Amongst the most enriched elements in Namib Pb–Zn tailings is the critical element In (80–770×). In addition, the critical elements Sb (~ 20–220×), As (~ 35–240×), W (~ 30–70×), and Bi (~15–250×) are distinctly enriched, and Cs (2–14×) shows a minor enrichment (Figure 3B).
Discussion
Geochemical relations
Principally, the general geochemical compositions of primary Namib Pb–Zn ore and related tailings material are similar for several elements, however, the relative proportions of some elements deviate due to the extraction of sphalerite and galena, and associated elements. Consequently, primary ore has at least 10× higher Zn, 7× higher Cd concentration, and about 6× higher In concentration than tailings material, based on our data. In contrast, the chalcophile elements As, Bi, and Cu have distinctly to slightly higher concentrations in tailings material than in primary ore samples (~ 125–5×). Likewise, slightly higher concentrations in tailings material are shown by Cs, Ta, W, and Li (14–5×). However, a slight bias due to contamination during milling cannot be excluded
Figure 5—Selected X-ray diffraction results of Namib Pb–Zn tailings and primary Namib Pb–Zn ore. A: Indexed XRD pattern of tailings sample NBT-11. B: Indexed XRD pattern of primary ore sample NAM-1
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
for W and the primary ore database is small. All other elements occur in similar concentration ranges in primary ore as well as in tailings.
Reprocessing potential of Namib tailings
The tailings composition directly reflects the primary ore mineralogy, although mineral abundances have been modified due to the extraction of the mineral(s) of interest (i.e., sphalerite, galena) in a quantitative sense. Post-processing weathering resulted only in the formation of rare sulfates and probably some Fehydroxides. Namib Pb–Zn tailings have not been subject to any pyrometallurgical modification because only a simple classical enrichment/concentration of ore material was done. The focus during the first years was on the production of a galena concentrate; production later included a sphalerite concentrate and by-product Ag of undescribed origin. One major attempt of extracting sphalerite from the old tailings dump in the mid-1990s led to the construction of the younger tailings dump (Hahn et al., 2004). However, processing technology was less advanced in the 1990s than today and extraction of noteworthy quantities of sphalerite failed due to problems with pyrrhotite suppression during flotation (Snowden, 2014).
Today, processing of Zn-Pb(-Cu) ores is still the most complex of all ore types because processing characteristics have to be adjusted to individual ore characteristics (Bulatovic, 2007) and Pb production can have hazardous impacts on the environment and human health (Nayak et al., 2021). However, during the last 20 years, no new large Pb–Zn deposits have been exploited (Mudd et al., 2017), so recovery from existing resources becomes important because both metals are wanted on international markets. In particular, processing of pyrrhotite-bearing Zn–Pb(–Cu) ore is challenging because Fe-rich sphalerite behaves very similarly to pyrrhotite (e.g., Tang and Chen, 2022), so a sequential or differential (froth) flotation approach has to be chosen. During sequential flotation, (Cu–)Pb minerals are first floated and recovered and then Zn minerals are activated (e.g., Lang et al., 2018), with pyrrhotite going into the rejects. If Pb minerals have to be separated from Cu minerals, then this is done on upgraded bulk concentrate (Bulatovic, 2007). Another processing approach is by bulk (Cu–)Pb–Zn mineral flotation followed by (Cu–)Pb–Zn mineral separation (e.g., Basilio et al., 1996; Luo et al., 2016), with pyrrhotite going into the rejects. The sequential approach usually performs better when precious metals, like Ag, should be recovered (Bulatovic, 2007). In contrast, bulk (Cu–)Pb–Zn mineral flotation is, in general, more economic (Bulatovic, 2007) and more suitable for low-grade sulfide ores with complex mineral intergrowths (e.g., Lang et al., 2018). Different reagent schemes have been successfully tested in recent years, including bisulfide, starch/ lime, and soda ash/SO2 or lime/SO2 methods for the sequential approach. A combination of different depressants, including NaCN, ammonium sulfate, and ZnSO4 was tested successfully for bulk flotation of different Pb–Zn(–Cu) ores with Fe-rich sphalerite and high pyrrhotite contents (Bulatovic, 2007; Bulatovic and Wyslouzil, 1995, 1999; Tang and Chen, 2022; Wang et al., 2019). However, the collectors most currently used are xanthates and dithiophosphates (e.g., Kohad, 1998; Li and Zhang, 2012; Tang and Chen, 2022; Yuan et al., 2012) with sodium polyacrylate and/or sodium hexametaphosphate as dispersants for sphalerite flotation (e.g., Silvestre et al., 2009) and pine oil as frother (Nayak et al., 2021) because cyanides and other formerly used chemicals are effective, but toxic (e.g., Lang et al., 2018; Nayak et al., 2021). Flotation is performed at high alkalinity (pH 10.5–12) to prevent
activation of pyrrhotite (Tang and Chen, 2022; Wills and NapierMunn, 2005) and a pre-aeration prior to the addition of a collector was successfully performed to lower the floatability of pyrrhotite (e.g., Becker et al., 2010; Tang and Chen, 2022). Moreover, flotation columns are nowadays recommended because recovery of finely disseminated mineral particles is more effective on columns than via the traditional flotation cells (Kursun and Ulusoy, 2012; Lang et al., 2018; Mittal et al., 2000). In addition, there are promising approaches using high-gradient magnetic separators to preconcentrate Fe-rich sphalerite and to separate Fe-rich sphalerite and pyrrhotite (e.g., Jeong and Kim, 2018) because the magnetic susceptibility of sphalerite increases with increasing Fe content (e.g., Keys et al., 1968; Pearce et al., 2006).
Flotation of highly variable ore with pyrrhotite, quartz, dolomite, and siderite was successfully performed, for example, at the Renison Bell tin mine, Tasmania, with desliming conducted at 6 µm, and old tailings of the South African Union and Rooiberg mines, South Africa, were also successfully floated (Bulatovic, 2010). During recent years, mining and recovery of the remaining lowgrade Rosh Pinah, Namibia, Zn–Pb ore (cut-off Zn 3.0%, cut-off Pb 1.95%) with trace to minor amounts of pyrrhotite and chalcopyrite (Alchin and Moore, 2005; Fourie et al., 2007) was successfully tested. A selective approach was chosen (Sehlotho et al., 2018) to produce Zn and Pb mineral concentrates. In general, Zn mineral concentrate from Rosh Pinah was exported to the South African Zincor smelter in Springs, while the Pb mineral concentrate was traded on the international markets (Fourie et al., 2007). A similar approach also seems feasible for Namib Pb–Zn tailings.
The older Namib Pb–Zn tailings dump comprises ca. 2.75 Mm³ of re-processible material with average Zn and Pb contents of 3.20 mass% and 1.15 mass%, respectively, based on our preliminary data, so Zn and Pb will be the principal targets during reprocessing. The tailings contain minor In (av. 14 µg/g in bulk sample) and Sb (av. 15 µg/g in bulk sample), two critical elements, which are of interest for modern ‘green’ industry applications. In addition, there is some Ag (av. 22 µg/g in tailings) in galena (Lohmeier et al., 2024), which is an economically attractive by-product, and Cd (av. 133 µg/g in bulk sample) in sphalerite (Lohmeier et al., 2024). Ag and Cd will be directly extracted as part of sphalerite and galena during production of saleable (combined) Pb and Zn concentrates. Smelting and production of pure metals on site at Swakopmund or at the Tsumeb smelter, the only smelter in Namibia—focussed on the production of Pb and Cu from sulfidic ore (Lohmeier et al., 2021a and references therein)—is probably not feasible. However, there are smelters abroad capable of smelting carbonate-hosted base metal concentrates to obtain Pb, Zn, and by-products, such as Ag, Sb, and In (see Alfantazi and Moskalyk, 2003); Zn concentrates of variable composition were previously processed at the South African Zincor smelter (Van Niekerk and Begley, 1991). Extraction of the contained critical commodities (As,) Bi, and Cs is not feasible as concentrations are very low. Production of by-product Ag and Cd might be feasible. Considering a remaining combined measured and indicated tailings tonnage of about 610 000 t (NLZM, 2023), this would translate into a resource containing about 19 530 t Zn, 7 030 t Pb, 14 t Ag, 9 t Sb, and 9 t In, plus 300 t Cu and 80 t Cd. However, using the determined average gross density of about 2.42 g/cm³ (2.90 g/cm³; results obtained by the calculated density are shown in brackets) and the tailings resource outlined by Hahn et al. (2004) of 2.75 Mm³, this would translate into a tonnage of ~ 6.65 Mt (~ 7.97 Mt) containing about 213 100 t Zn (255 350 t
Geochemical and mineralogical characterization and resource potential of the Namib Pb–Zn tailings
Zn), 76 680 t Pb (91 900 t Pb), 3260 t Cu (3900 t Cu), 885 t Cd (1060 t Cd), 150 t Ag (180 t Ag), 98 t Sb (120 t Sb), and 95 t In (115 t In). This distinct discrepancy between the two resource estimates can be explained by lacking company data for the inferred tailings resource. Our data distinctly deviate from that provided by Hahn et al. (2004), with average element values of 2.54% Zn, 0.21% Pb, and 7.0 g/t Ag for the old tailings dumps, and from data provided from NLZM (2023), with average element values of 0.3% Pb, 2.2% Zn, and 7.5 g/t Ag for the combined measured and indicated tailings resource. These differences are due to the fact that we took mostly surface samples, which are representative of the surficial part of the old tailings dump, but are not necessarily representative for the bulk of the tailings pile, whereas data by NLZM (2023) and Hahn et al. (2004) are largely based on drilling activities. Thus, some bias might be induced by wind erosion removing less dense particles and leaving heavier ones behind. Nevertheless, it becomes clear that the Namib Pb–Zn tailings dump represents a noteworthy metal resource. Future resource and reserve estimates should establish tailings heterogeneities and zonations.
This study demonstrates that tailings characterization solely relying on XRD and bulk geochemical data of tailings can be misleading. For example, the XRD data of Namib Pb–Zn tailings do not indicate the presence of rare to accessory marcasite, cassiterite, and scheelite, which have been detected in primary ore. Moreover, the bulk geochemical data do not reveal the siting of trace elements (Lohmeier et al., 2024). Therefore, whenever possible, assessment of fine-grained tailings, like slurries and impoundment cell material, should not be performed solely on tailings, but should be combined with (detailed) mineralogical and geochemical investigations of primary ore and host rock(s). It is quite likely that marcasite, cassiterite, and scheelite were not detected by XRD because concentrations are low and all three minerals do not have striking XRD patterns (see Khan et al., 2020). However, if larger quantities of pyrite or marcasite were erroneously overlooked, a potential risk for AMD would stay undetected.
On the one hand, the old Namib Pb–Zn tailings dump contains a certain resource that is of economic interest (Ag, Cd, Pb, Zn; e.g., Mudd et al., 2017; Werner et al., 2017); on the other hand, the tailings dump is a contamination source of environmentally significant As, Pb, and Cd, which may become hazardous to humans and the environment (Mudd et al., 2017). North River Resources cleaned the mine site in the late 2000s, however, the principal source of pollution remains as long as it is not safely sealed from wind erosion (e.g., Blight, 2007; Salom and Kivinen, 2019). The effects of climate change on metal mobility from mine waste repositories are difficult to estimate (Northey et al., 2017). Currently, tailings dispersal only affects the immediate surroundings, but strong winds, temporal rainfalls, and potential seepage may mobilize environmentally significant As, Pb, and Cd into surface soils, sediments, as well as ground and surface waters. Reprocessing of the Namib tailings would have obvious environmental as well as economic benefits. In fact, the southern African mineral processing industry has demonstrated that it is capable to recover additional metals from old tailings dumps (e.g., Craven, 2001; Guest et al., 1988; Jones et al., 2002; Svoboda et al., 1988; Watson and Beharrell, 2006).
Conclusion
Clay- to sand-sized Namib Pb–Zn tailings were produced during mineral processing in the 1960–1990s of sphalerite–galena–pyrite ore, which also contained minor pyrrhotite, rare marcasite,
cassiterite, and arsenopyrite, as well as accessory scheelite set into a carbonate-rich host rock. In addition to Pb and Zn, the older tailings dump contains trace concentrations of the critical elements In and Sb. Ag and Cd could be extracted and concentrated together with Pb and Zn. Any future processing of Pb–Zn(–Cu) tailings would be challenging, and might result in a combined Pb–Zn mineral concentrate or even two separate Pb and Zn mineral concentrates with valuable by-products. It is not realistic that the old tailings dump will soon be the sole target on the Namib Pb–Zn mining site, but it is definitely worth considering reprocessing when mining and extraction of primary Pb–Zn ore continue. Presently, the historic tailings dumps are not covered and are therefore considered as a point source of ongoing metal contamination. Consequently, any reprocessing of tailings, with subsequent disposal of wastes in an appropriately designed mine waste repository, would also eliminate a major metal pollution source. Pb–Zn-containing base metal tailings dumps have to be considered as secondary raw material sources following the principle of circular economy.
Acknowledgements
This work was supported by the German Federal Ministry of Education and Research (BMBF) and is part of the sub-Saharan based LoCoSu project; grant number 01DG16011. We thank S. Garoeb and M. Punzel from Namib Pb–Zn for free access to the sampling site in 2018 and for an exciting above-ground mine visit in 2019. U. Hemmerling is thanked for preparation of polished (thin) sections (Clausthal University of Technology (TUC), Department of Mineral Resources (IMMR)) and we are grateful to L. Weitkämpfer, D. Gürsel, and P. Ihl (RWTH Aachen University, Department of Processing) for providing free access to powder preparation equipment and their never-ending patience. Thanks to M. Gamenik (Institute of Mineral and Waste Processing, Recycling and Circular Economy Systems, TUC) for assistance with laser diffractometry.
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ESG IN THE MINERALS INDUSTRY CHALLENGES AND OPPORTUNITIES
DATE: 16-17 OCTOBER 2024
VENUE: GLENBURN LODGE AND SPA, MULDERSDRIFT
BACKGROUND
Environmental, Social, and Governance (ESG) considerations have become increasingly important in the business world, as it contributes to long-term sustainability and responsible corporate behaviour.
In the minerals industry, ESG considerations are particularly important due to the sector’s significant environmental and social impacts. Mining operations involve land use, energy and water consumption, and waste generation, which have lasting effects on ecosystems. Additionally, the industry faces challenges related to labour practices, community engagement, and the impact on indigenous populations.
An ESG-driven strategy is not only a responsible approach to business but also a strategic imperative for long-term success. It can contribute to risk mitigation, enhances reputation, attracts capital, and fosters innovation, making it a competitive advantage in today’s business landscape. In the mining industry, ESG considerations are crucial for addressing environmental and social challenges and ensuring the industry’s sustainable development.
The role of the Southern African Institute of Mining and Metallurgy (SAIMM) in the promotion of ESG is based on the premise that sustainability, and the contribution of the mining and minerals industry to society, is dependent on the professional and ethical conduct of minerals industry professionals – our members.
On this basis, the purpose and focus of this conference is to influence professional behaviour, and foster industry dialogue on sustainability and responsible mining through Environmental, Social, Governance, and Sustainability-related matters.
We invite you to share your knowledge and experience with an audience of like-minded individuals to inspire growth and change.
2Australian Centre for Geomechanics, The University of Western Australia, Perth, Western Australia
3Curtin University of Technology, Perth, Western Australia
Correspondence to: J. Venter
Email: JuVenter@AngloGoldAshanti.com
Dates:
Received: 23 Nov. 2023
Revised: 30 May 2024
Accepted: 15 Jun. 2024
Published: August 2024
How to cite:
Venter, J., Wesseloo, J., and Maybee, B. 2024. A formulation for optimum risk in open-pit mining.
Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 8. pp. 461–472
DOI ID:
http://dx.doi.org/10.17159/24119717/3201/2024
ORCID:
J. Venter
http://orcid.org/ 0009-0008-5456-8319
A formulation for optimum risk in open-pit mining
by J. Venter1, J. Wesseloo2, and B. Maybee3
Abstract
The selection of open-pit slope angles involves high-value decisions with small changes in slope angle, often representing significant changes in net present value. The existing methods used to define design acceptance criteria have evolved from factor of safety to probability of failure (P[F]) to risk consequence to risk frontiers, which represents current state of the art. Current design acceptance measures are based on references to published tables representing the experience and judgement of their authors, who did not specify the purpose of the measures contained therein. As the purpose of the published design acceptance criteria is not specified (i.e., definite stability, marginal stability, optimization, etc.), such tables cannot be used to achieve optimum slope angles that maximize profitability. This paper develops a design acceptance criterion that maximizes profitability by defining a formulation for optimum risk that balances expected risk and reward. The model developed is titled the Mining Risk Model (MRM), and is applied to open-pit slope angle selection through an equation for optimum probability of downside (P[D]O) that balances the upside or opportunity, the P[F], and the downside impact. This formulation for optimum risk is unique, as many authors have presented objective functions for their risk models that can be optimized, but none of the sources reviewed contained a formulation for optimum risk. The MRM is sufficiently flexible to allow the design performance measure that drives P[F], and hence P[D]O, to be selected based on the intended goal. Furthermore, the essential information that must be known to quantify optimum risk is defined. This allows users to determine what information to collect for optimum risk decisions. A further benefit of the MRM is that slope angle decisions and pit shells can be ranked to select the best option, and a threshold is provided that separates acceptable from unacceptable decisions. Finally, the workflow and information required to determine optimum risk are presented.
Keywords optimum risk, benefit, probability of failure, Mining Risk Model, slope stability
Introduction
Open-pit slope angles have a direct impact on the economics of a mining project as they determine the amount of waste rock mined, which drives the stripping costs to recover a given amount of ore. Selecting an appropriate slope angle for open pits is consequently a profit maximization decision that requires a balance between the reduction in stripping costs achieved by selecting steeper slope angles and the potential costs of managing slope instability.
According to current practice, the decision to accept or reject a given slope angle is made with reference to a slope design performance measure, such as factor of safety or probability of failure (P[F]) (see the Glossary in Appendix A for definitions). These factors of safety or P[F] are evaluated for acceptance against existing guidelines, such as that of Wesseloo and Read (2009), who provided a summary of guidelines in existence at the time for the Large Open Pit Project (LOP).
Other guidelines for design acceptance criteria (DAC) exist, with the majority being published or summarized in Kirsten (1983), Priest and Brown (1983), Swan and Sepulveda (2000), Sullivan (2006), and more recently Pothitos and Li (2007), Wesseloo and Read (2009), and Macciotta et al. (2020). There are three major shortcomings with all these sources. The first is that they provide no basis for the threshold values they provide. The recommended values are presented based on the authors’ experience, or summarized from other sources who based their recommendations on their experiences. The second major shortcoming is that none of these sources declare the inherent performance goal that the threshold value is
A formulation for optimum risk in open-pit mining
trying to achieve. It is not clear from the existing guidelines how the P[F] thresholds were selected, or whether adhering to the suggested thresholds will result in slopes that are robustly stable with a high error tolerance, marginally stable, or slopes that are optimized, and, if so, for which attribute, e.g., stripping ratio, net present value (NPV), or other attributes. The last major shortcoming is that they exclude the potential reward for accepting higher risk from their threshold criteria. If optimum risk is to be defined, the upside or U (i.e., opportunity) must also take part in the solution, not only the downside (D).
To overcome these limitations, Terbrugge et al. (2006), Steffen et al. (2008), and Wesseloo and Read (2009) made the argument that the choice of slope angle reflects a business risk, the acceptance of which is the domain of mine or business management, not the geotechnical engineer. These authors all proposed a fault event tree methodology, with Contreras (2015) proposing further refinements to amalgamate the many risks present in a pit, or extraction plan, into a single risk frontier. An example of such a risk frontier is presented later. The risk frontier is a tool to quantify the consequences of slope instability in terms of business risk, which can then be used to inform decisions. These authors showed that open-pit slope angle selection is not an engineering decision, but rather a business decision. The question remains, however, how much business risk should be accepted?
To define the business risk decision, Terbrugge et al. (2006) highlighted several business-related risks flowing from slope instability, which directly impact a mine. As a result of mandatory reporting codes, such as JORC (2012) and SAMREC (2016), an additional business risk should be added to the list in the form of compliance against reporting standards and investor expectations. From a business risk perspective, the following five main geotechnical risks are ever present for overall and inter-ramp scale slopes:
➤ safety of personnel and equipment in the pit;
➤ compliance against reporting standards and investor expectations;
➤ force majeure;
➤ contractual default; and
➤ loss of profit.
Loss of profit is unique among these in that greater spending to reduce the risk of loss of profit, in itself, represents an increase in loss of profit. As such, the search for an optimum risk formulation must focus on the risk of loss of profit. Such a value would represent a balance between spending more money on physical measures that reduce loss of profit against spending less money in order to directly reduce loss of profit (i.e., aiming to manage costs). The other four risks can be ameliorated by providing more resources to manage those risks until the desired threshold is reached.
Ryan and Pryor (2000) studied the integration of slope angle decisions into mine cash-flow models and presented a risk model for optimum slope angle assessment. Their analysis considered kinematic inter-ramp failure mechanisms only, with the failure volume for each mechanism converted to failure cost and multiplied by its P[F]. The summed failure costs are then built into the mine cash-flow model schedule and converted to NPV using the corporate discount rate. The analysis is repeated for various slope angles until the maximum NPV is obtained.
The method proposed by Ryan and Pryor (2000) provides a methodology to optimize slope angles, but requires a full integration of slope angles and their associated P[F] with the mine cashflow model schedule to obtain an NPV for each slope angle. The
requirement to integrate slope angles into a mine cash-flow model before accepting or rejecting a potential design slope limits the method’s application to slopes where such cash-flow models are available, while slowing down the decision process as cash-flow models are iteratively updated.
Heslop and Milne (2003) used a similar approach to Ryan and Pryor (2000), but replaced optimization of the NPV with that of mining volumes to reduce the need for cash-flow model integration. Such a simplified analysis allows slope stability decisions to be made without consultation of the mine’s cash-flow model, but still falls short of providing a DAC or a formulation for optimum risk. A further limitation is that the Heslop and Milne (2003) approach cannot be adapted to cater for P[F] based on design performance measures other than factor of safety against slope collapse.
This lack of definition in the existing DAC leaves room for improved DAC that factor in expected consequences of slope instability (as opposed to slope scale-based categories only), a risk benefit trade-off identifying risks that can be accepted and those that should be avoided, and a definition for optimum risk, as opposed to recommended risk thresholds only. Such improvements are necessary if the most profitable slope angles are to be selected.
This paper develops a formulation for optimum risk and consequently optimum P[F], and optimum P[downside] or P[D]O as DAC to determine a general solution for the most profitable strategy to select overall slope angles for open pits. The formulation includes:
➤ the minimum information required to quantify optimum risk for open pits;
➤ a system to rank open-pit slope angle decisions;
➤ a risk threshold separating desirable slope angles or pit shells from undesirable ones;
➤ the formulation of optimum risk, optimum P[D] for open-pit slope stability.
This paper uses concepts from the fields of geotechnical engineering and economics, so a Glossary of Terms is provided as Appendix A to provide selected background information to readers not familiar with the topics. Consequently, technical terms are not explained in text.
A worked example is not provided here as the focus of this paper is the derivation of the optimum risk formulation
The optimum risk equation and the Mining Risk Model
The optimum risk formulation is derived by first defining the Mining Risk Model (MRM) objective function, followed by the risk–benefit strategy space, and the P[D]O
Mining Risk model objective function
The MRM is based on the idea of offsetting the probability weighted cost (probability × expected value) of D against the probability weighted value of U when mining a given overall slope angle in a slope sector.
To achieve this, the expected values of D and U for each slope sector in the extraction plan are summed in the objective function. The objective function for the MRM is defined by the risk-adjusted value, which is given by Equation [1]. The MRM objective function follows the framework of the Dembo and Freeman (1998) model, adapted here for use in a mining context. The main difference is that Dembo and Freeman (1998) require a real option price valuation of U and D, combining subjective probability and value, while the MRM requires an engineering evaluation of U and D, with the P[D] being calculated using analytical techniques, such as slope stability analysis methods and fault/event trees:
A formulation for optimum risk in open-pit mining
where U is the value of a stable slope or upside if all goes well, D is the total cost of slope design failure or downside, and λ is the risk preference factor. U for a slope sector can be expressed as NPV, mining contribution (revenue – mining cost), gross margin (see Equation [4]), or tonnes mined, and is used to evaluate the outcome of the slope angle decision. D can be expressed as the total slope design failure management cost in the same measurement units as used for U. D is used to quantify the consequences should slopes in a slope sector fail, i.e., exceed a slope performance measure threshold at inter-ramp or overall scale in the pit shell under consideration. The risk preference factor λ > 1 denotes risk aversion, λ = 1 denotes risk neutral, and λ < 1 denotes risk-seeking preferences.
P[D] is the probability of one or more slopes in the slope sector experiencing economic consequences as a result of exceeding their design performance measure thresholds. Typically, such signs are evaluated by using performance measures and their associated design thresholds, such as a factor of safety < 1 or exceeding the defined displacement thresholds.
Note that the benefit term includes the slope reliability, which is equal to 1 – P[D], and the risk is scaled up or down using λ to reflect risk appetite.
Quantifying consequences
U and D can be based on any measure of consequence, such as NPV, mining contribution, tonnes mined, gross margin, or carbon emissions, to name a few, provided U and D use the same units of measure. The mining surplus for a pit shell is selected for this paper, given by Equation [4], as it allows a simple isolation of the geotechnical decision while still factoring in the full mining cost.
Revenue is the ore units × ore unit price for a given slope sector in a pit shell and the mining cost is the total mining cost for that slope sector. The advantage of using the gross margin as defined here, as opposed to the NPV, is that it allows the slope angle decision to be decoupled from extraction schedules and cash-flow models, while still factoring in processing cost. Where schedule options are considered important, the present value of the gross margin can be used.
D reflects the total unsatisfactory outcome of slope design failure for the slope sector under consideration and is given by Equations [8] and [9]. For slope stability purposes, D is split into slope collapse costs and costs for infrastructure requiring maintenance or replacement should displacement thresholds at infrastructure locations be exceeded.
In Equation [8], instability management refers to all expected costs that may occur following slope design failure, such as fines, mining license impairments, rehabilitation, and compensation payments.
Slope sector and pit shell risk and reward
The uncertainty for all potential slope failure mechanisms, the decision threshold, and the project timeframe feed into the P[F] for each failure mechanism and infrastructure piece in a slope sector, which is then converted to the P[D] through consideration of postinstability modifying factors that affect the P[D], the consequence of instability, and the ability to plan for contingencies. Event tree methodologies can be used to convert P[F] into P[D].
Where displacement thresholds for a slope sector exist, the P[D] has to be evaluated for both the slope collapse and the slope exceeding displacement threshold cases.
To combine all individual uncertainties for a single slope sector, including uncertainties based on natural events such as earthquakes, into a slope sector P[F] and P[D], fault tree methodologies, such as those presented by Terbrugge et al. (2006) and Steffen et al. (2008), can be used to evaluate the relevant information for use as input into the MRM. For evaluation of the MRM inputs, the information flow between geotechnical parameters, failure mechanisms, P[F], and P[D] are shown in Figure 1.
The risk and benefit for the extraction plan require all slope sector U, D, and P[D] values in a pit shell to be combined into a single risk and benefit value representing the pit shell under consideration. This is achieved through further development of the risk frontier concept proposed by Contreras (2015).
Contreras (2015) showed that all individual slope sector risks for a pit shell can be combined into a single line on a graph, called a risk frontier, with P[Exceedance] on the horizontal axis and D on the vertical axis. An example of such a risk frontier is demonstrated in Figure 2. The advantage of aggregating risks using a risk frontier is that individual risks and all possible combinations of risks are accounted for, giving a complete picture of the downside part of risk. Contreras (2015) posited that risk combinations can be calculated using either a closed-form solution for a small number of risks or alternatively through a Monte Carlo simulation, which is easier to use for larger numbers of risks. He also provided an equation for combining different risk frontiers into a single representative risk frontier. The example provided by Contreras (2015) used extraction schedule years for individual risk frontiers, which were then combined into a risk frontier for a life-of-mine plan. For the MRM, individual risk frontiers are created for each slope sector by applying Equation [3] to each failure mechanism at overall and inter-ramp scale, and then combining the results into a risk frontier using the calculations provided by Contreras (2015). The slope sector risk frontiers can then be combined into a pit shell risk frontier using Contreras (2015).
Contreras (2015) did, however, not make allowance for a benefit frontier, which is required for the MRM. The benefit frontier for each slope sector and pit shell can be calculated from the
A formulation for optimum risk in open-pit mining
risk frontier using Equation [2], with U determined by applying Equation [4]. The risk frontier then represents the cumulative probability diagram of D, and the benefit frontier the cumulative probability diagram of U.
In cases where slope sector U and D values are defined by a number of simulations, such as obtained when constructing the risk and benefit frontiers using a Monte Carlo process, risk is given by the mean simulation value of all simulation outcomes below the threshold value, and the benefit is given by the mean simulation value of all simulations with outcomes above the threshold value. Using a Monte Carlo process to generate the risk and benefit frontiers allows correlations and other relationships between individual risks to be catered for.
Risk preference factor λ
Utility curves of wealth, as defined by the utility theory introduced by von Neumann and Morgenstern (1944), present the net worth in financial units on the horizontal axis and the utility (subjective value for the decision-maker) on the vertical axis. The MRM includes utility in the form of the risk preference factor λ applied to D, which represents risk appetite. As λ is an escalation factor applied to D, it allows each downside dollar to be scaled up or down compared with an upside dollar. It is possible to apply the MRM using a calibrated utility curve, but that requires first establishing
a utility curve, which is often not available. In contrast, λ is easy to estimate and can be measured to some degree of accuracy through production records by comparing the cost of mining intact rock with that of mining failed material with consideration of all factors that influence the cost of mining failed rock. The most important of these are secondary blasting, production delays, schedule gaps, and access re-establishment.
Mining Risk Model risk–benefit strategy space
In the context of the MRM, the word benefit communicates the riskadjusted value of U, and risk communicates the risk- and utilityadjusted value of D, whereas U and D reflect the unadjusted values.
Visualization of the MRM objective function in a strategy space can occur in a number of ways, depending on the decision that needs to be made. As this section focuses on the geotechnical risk of a pit shell comprising slope sectors, the strategy space presented in Figure 3 shows risk plotted on the vertical axis and benefit on the horizontal axis. The risk and benefit plotted in Figure 3 are shown with dimensionless monetary units from 0 to 100; however, the scale can be adjusted to suit the size of the pit shell and slope sectors (i.e., $millions, $10 millions, $100 millions, tonnes, etc.).
The risk acceptance threshold (RAT) shown in Figure 3 represents a line in the strategy space with a gradient of one dollar of risk ($R) for one dollar of benefit ($B), or a risk-to-benefit (RB) ratio of 1. It follows that slope sector designs or pit shell designs with a RB ratio above the RAT represent slope designs that are expected to cost more than the revenue they generate on a riskadjusted basis, and should not be pursued: pit shell designs above the RAT can be considered to be gambles. Conversely, pit shell designs below the RAT are expected to cost less than the revenue generated and are expected to be profitable: risks or decisions below the RAT can be considered calculated risks. The further below the RAT a pit shell design lies, the more profitable it is expected to be. For this reason, lines of constant RB ratio are shown as solid grey lines to allow pit shell designs below the RAT to be ranked. For the purpose of this paper, the strategy space shown in Figure 3 is called the MRM risk benefit strategy space
To evaluate a pit shell design, each slope sector design and pit shell design can be plotted as points in the MRM strategy space, presented as Figure 3. To illustrate, two fictitious open pit designs A and B are shown with four slope sectors each. Both pits have positive gross margins, unadjusted for risk.
The Pit A design, however, has a negative risk-adjusted value
Figure 1—Information flow from geotechnical uncertainties to P[D]
Figure 2—Risk and benefit frontier concept (unit of measurement in million dollars gross margin)
A formulation for optimum risk in open-pit mining
due to higher slope sector P[F] values, causing it to lie above the RAT, and so should not be pursued in its current form. It may be possible to change Pit A by changing the attributes of slope sectors A1 and A2 because they lie above the RAT, meaning they do not add value to the portfolio. To understand what needs to be changed, the factors that drive risk or benefit can be considered. It may be that with flatter slope angles or a shallower pit and the associated decrease in P[F], sectors A1 and A2 may improve. Alternatively, the P[D] can be decreased by desensitising sectors A1 and A2 to slope instability. Depending on the cause of the high risk, this may mean maintaining larger stockpiles, opening up additional pits or mining fronts to maintain ore feed in case of instability, or creating an alternative or additional access ramp. If all amelioration options have been explored and sectors A1 and A2 remain above the RAT, Pit A should be removed from the extraction plan because it is expected to cost more than its value, even though it has a profitable gross margin.
Pit B has a risk-adjusted value below the RAT and so is worth pursuing. However, it may be that additional value can be gained by re-considering the pit geometry of Sector B1, because it lies above the RAT. Where sector B1 cannot be moved below the RAT, it may be worth keeping it unchanged in cases where such a slope sector unlocks value in other slope sectors (provided the pit as a whole lies below the RAT), such as providing access ramps or contingency ramps for other slope sectors. Such relationships need to be considered when visualizing the MRM. Where a slope sector lies above the RAT, but allows value in other slope sectors below the RAT to be unlocked, it is worth considering all slope sectors in an open pit together as a unit to make sure the risk accepted is more than offset by the benefit gained, and the combined value remains below the RAT.
Should a slope sector lie on the RAT, the extraction plan should be indifferent to its inclusion, except where it enables additional value in other slope sectors to be unlocked.
The MRM visualization is used to evaluate a pit shell design on a risk-adjusted basis and to rank slope sectors in terms of risk efficiency. The most risk-efficient slope sector is the one with the lowest RB and represents the efficient frontier (see Figure 3 for illustration). Slope sectors above the efficient frontier can be targeted for further optimization to improve the overall pit shell risk efficiency and overall profitability.
Such an understanding can prevent mine plans such as Case A in Figure 3 from being accepted where the combined RB ratio for Pit A lies above the RAT, in spite of two profitable sectors and two expected loss-making slope sectors. Both pits can display positive mining surplus values if geotechnical risks are not accounted for in the design and may seem like profitable pits. After risk adjustment, however, only one of the two pits appears profitable.
Mining Risk Model and the optimum P[D]
The previous section applied the MRM to pit shell risk for an extraction plan to create the MRM strategy space (Figure 3). For a practical application to slope stability analysis, a target P[D] is needed for slope sector stability analysis. This section applies the MRM to the derivation of an optimum P[D], or P[D]O, for a single slope sector, and provides an MRM visualization for slope stability. The P[D]O occurs when the risk-adjusted value for the slope scenario being considered is maximized. To maximize the riskadjusted value, the benefit must be maximized and the risk minimized by changing the factors that drive the P[D] until the P[=D]O is reached. This can take the form of changing the slope angle, reducing the factors that drive uncertainty about the stability of the slope, or reducing the impact should the slope fail. As both benefit and risk are functions of, among others P[F], the riskadjusted value can be maximized by defining the RB ratio, as in Equation [10]: [10]
For a slope scenario where the benefit is greater than the risk, the RB ratio will be less than 1; where the risk is equal to the benefit, the RB ratio will equal 1; and where the risk is greater than the benefit, the RB ratio will be greater than 1. The RB ratio can be minimized by changing the P[D] and its associated U and D values until the minimum RB ratio is achieved. The minimized RB ratio will give the same value for P[D] as simply maximizing the riskadjusted value.
Evaluating the full benefit and risk terms in the RB ratio may include many U and D cost factors in the gross margin that have no direct bearing on the optimum P[D] for a slope, which is unnecessarily cumbersome. These additional factors can be removed from the calculation by considering a process starting with a scenario comprising a very flat slope and consequently a low P[D].
Figure 3—Mining Risk Model risk–benefit strategy space showing contours of risk/benefit
A formulation for optimum risk in open-pit mining
In such a case, the RB ratio is not optimized and the P[D] is below optimum. The slope angle is then increased in small increments, say 1° increments, and the benefit of increasing the slope angle is calculated for each increment as well as the risk associated with increasing the slope by an increment, as per Equation [11]. For each increment, if the incremental RB ratio is less than 1, the incremental benefit is greater than the incremental risk and the new steeper slope should be accepted over the previous one. As each slope angle is evaluated in the process, the P[D] will increase with increasing slope angle, resulting in a higher incremental RB ratio for each incremental step until an incremental RB ratio of 1 is found. This point indicates a state of equilibrium where the incremental benefit is equal to the incremental risk, which represents the minimum RB ratio because further increases in P[D] will result in smaller incremental benefits and larger incremental risks, causing a higher RB ratio.
[11]
In practice, it is not necessary to follow this process for every slope option. A formulation for the P[D]O for all slope stability cases where incremental changes in D with changing slope angles can be considered small (i.e., less than the confidence interval for the estimation of D is derived in Appendix B, and presented as Equation [12]:
[12]
where ΔU is the incremental difference in U with each incremental slope angle increase.
This formulation is derived from Equation [11], as proposed above, for cases where the value of D is the same for each increment and the value of U increases with increasing slope angle increments. These assumptions are representative of most slope scenarios where it can be demonstrated through sensitivity analysis that the estimation of D is insensitive to changes in slope angle due to the uncertainty in estimating D. The resulting P[D]O is presented as Equation [12].
In Equation [12], λD/(ΔU) can be reflected as a dimensionless ratio for universal application to calculate the P[D]O. The result is illustrated as Figure 4A, which shows λD/(ΔU) plotted on the vertical axis and P[D]O on the horizontal axis to provide the optimum risk strategy space. To allow greater precision for many practical applications when using Figure 4A, Figure 4B is included, showing Figure 4A zoomed to a maximum λD/(ΔU) value of 50 and maximum P[D]O of 20%.
To use Equation [12] or Figure 4, ΔU can be obtained through mine planning sensitivity analysis that provides the value-add for each degree of increase in the slope angle from a base case for a given slope sector. The value of D can be obtained by estimating the likely slope failure volume and then estimating the cost of managing the slope instability. The λ factor can be obtained after discussion with mine management and consideration of alternative options for ore feed. Based on this information, the geotechnical inter-ramp and overall scale slope stability analyses can then target the P[D]O calculated using Equation [12] or read from Figure 4. As there is a difference between the P[F] and P[D], as shown in Figure 1, the relevant fault trees need to be consulted to back-calculate the optimum P[F] from the P[D]O. This process requires the pit layout, such as ramps and infrastructure, to be evaluated before an optimum P[F] can be determined.
Mining Risk Model in practice
To apply the MRM in practice, a process is required to ensure the correct data are gathered and processed in an appropriate manner before decisions are made. Such a process is presented in two parts as Table I, using the same order as they are likely to be applied. The first part (Steps 1 to 4) establishes baseline properties and identifies the optimum slope angle for input into mine optimization software. The second part (Steps 5 to 12) optimizes the pit shell as a whole and quantifies the MRM risk–benefit for a pit shell.
The process presented in Table I is used to optimize and evaluate a pit shell in terms of risk–benefit by maximising the benefit per unit of risk. The process first individually optimizes each slope sector, then combines the optimized slope sectors into an optimized pit shell. The pit shell is then evaluated against the RAT to determine if it is expected to be profitable on risk-adjusted basis. As the individual slope sectors are already optimal, pit shells above the RAT cannot be further optimized by changing slope angles, but may be desensitized to risk by changing the risk factors that drive the pit shell above the RAT. Examples of such risk factors may be the location of external infrastructure, such as public roads or crushers, or the location of ramps in high-risk slope sectors.
Discussion
As the MRM strategy space is based on risk and benefit, it can be used to benchmark the risks associated with pit shells against those representative of other scenarios, such as playing the lottery and gambling in a casino. To illustrate this, several gambling risks were added to the MRM strategy space and are presented as Figure 5.
As expected, the Australian Powerball lottery displays a high level of risk with an RB ratio of 9 when the jackpot is $20 million, placing it well above the RAT. Closer to the RAT are casino risks such as playing craps, where using “7 Out” bets has an RB ratio of 1.25, and roulette in a European casino (one zero on the wheel), where placing bets in the first column has an RB ratio of 1.04. Both these values are close to the RAT, which has an RB ratio of 1. For the less sophisticated gambler, the Australasian Gaming Council, which regulates gambling, mandates a Minimum Return to Player payout (RTP) of 90c to the dollar for, among others, slot machines in Western Australia (Australasian Gaming Council, 2019), which translates to an RB ratio of 1.11. The proximity of gambling risks to the RAT suggests that project acceptability thresholds in terms of risk–benefit should be placed well below the RAT, as opposed to near the RAT, to allow for changes in conditions as mining progress. For example, a pit shell with an RB of 0.9 (below the RAT) can easily move to an RB of 2.25 (above the RAT) if the P[D] changes from 10% at design to 20% during implementation as a result of poorly managed blasting damaging the slope. An RB of 2.25 places the pit shell risk between a craps game and the Powerball—a risk level unlikely to impress investors.
The implication is that accepting risks above the RAT is not only expected to lose money in the long run (i.e., has a negative expected net value), but can be more risky than gambling in a casino. Consider the shareholder response to an executive team declaring that their extraction plan geotechnical risk is riskier than roulette or playing a slot machine.
The RAT separates desirable slope angle choices (calculated risk) from undesirable slope angle choices (gambling). The difference between them is that: slope angles selected below the RAT (i.e., calculated risk) may result in costly slope collapse from time to time, but on average, will remain profitable; while slope
A formulation for optimum risk in open-pit mining
(b)
4—(a) Mining Risk Model optimum risk strategy space for (A) P[D]O and (b) P[D]O zoomed in
angles selected above the RAT (i.e., gambling) may result in low cost slopes from time to time, but on average, will remain unprofitable.
Finally, the derivation of the P[D]O equation in Appendix B, which is presented as Equation [12] meets the fourth goal for this paper. To the best knowledge of the authors, this is the first equation for P[D]O derived in any discipline.
The advantage of using optimum P[F] and optimum P[D]O, as opposed to experiential guidelines or rigorous cash-flow integrated sensitivity studies, is that the lowest RB ratio is achieved without having to carry out an elaborate sensitivity study. The experiential guidelines published to date, for instance Macciotta et al. (2020) and Wesseloo and Read (2009), are based on an evaluation of P[F] with a simple categorization of risk and no consideration of reward. As reward is absent, such methods cannot be used to demonstrate that the proposed risk acceptance has a positive expected value, i.e., is below the RAT or represents an optimum. This deficiency is demonstrated by Figure 6, which compares the MRM P[D]O with the maximum acceptable P[F] given by many existing guidelines, as
summarized by Wesseloo and Read (2009). In Figure 7, the existing guidelines cater for P[F] values ranging from 5% for overall scale slopes with high consequences of instability to 25% for inter-ramp scale slopes with low consequences of instability. As these guidelines are agnostic of utility or upside, it is possible to select P[F] values that are above or below the optimum for many slopes, and even worse, those that have negative expected values. The typical ranges of λD/(ΔU) for open pits, shown in Figure 6, demonstrate that if the Wesseloo and Read (2009) or Macciotta et al. (2020) guidance is followed, it is more likely that a P[F] will be selected above the optimum than below the optimum.
This is problematic because making decisions such as accepting pit shells with negative expected values on a regular basis will result in a downturn in company profitability and resultant loss in shareholder value. Where P[F] values above optimum are accepted, risk is accepted without being suitably rewarded; where P[F] values below optimum are accepted, additional value is destroyed. Consequently, a P[F] value on or near optimum with a positive
Figure
(a)
A formulation for optimum risk in open-pit mining
Table I
Pit shell risk-based optimization steps
Step no. Task
1 Evaluate λ for each open pit by compiling the mining cost per tonne of waste to that of mining failed material, given that an inter-ramp or overall scale slope instability occurs. The minimum value for λ can then be estimated as the unit failure cost/unit stripping cost for a given slope sector. Further adjustments to λ can be made to incorporate optionality available to manage a slope instability, such as stockpiles and alternative ore sources, and other corporate risk preferences.
2 Using the ore resource shape and depth as a starting point, define slope sectors for a potential open pit. For each sector, define a range of toe position scenarios to serve as basis for slope stability analysis prior to mine planning optimization. Each toe position scenario will reflect a pit depth/toe position combination. Also define the slope sectors likely to contain ramps and that may impact infrastructure.
3 Carry out a slope stability sensitivity analysis for each toe position scenario in each slope sector to a range of slope angles. For this sensitivity analysis, incorporate all significant failure mechanisms and combine their P[F] values into a single P[D] value for each slope angle using fault trees. Increase the slope angle by 1° and repeat the P[F] analysis. Define the value of D and ΔU for all the slope sectors based on the results of the first two slope stability analyses. Read off the P[D]O for each slope sector from Figure 4, and iteratively find the slope angle for each slope sector corresponding to its P[D]O
4 Using the optimum slope angles and toe location Scenarios from Step 3 as input for mine optimization software, create a series of pit shells and select one or more options for further analysis. For each sector in each selected pit shell option, evaluate the waste mining unit cost, ore mining unit cost, unit gross margin each for each sector and the pit as a whole.
5 Using the exact geometry of the pit shell options selected in Step 4:
a) Evaluate the P[F] for each significant failure mechanism at inter-ramp and overall scale inside each sector.
b) Evaluate the probability of exceeding the design displacement threshold, P[Displacement > Threshold] for each infrastructure piece impacted in each sector.
6 a) Evaluate the failure size in tonnes for each of the failure mechanisms evaluated in Step 5.
7 a) Based on the results of Step 6 and with the aid of the event tree methodology presented by Terbrugge et al. (2006) or Steffen et al. (2008), evaluate the DownsideCollapse consequence for each sector evaluated in Step 6 in terms of sterilized ore, access re-establishment, infrastructure damage, production gaps, and any other cost that would be incurred should an instability occur in a given schedule period.
b) Assess the DownsideDisplacement cost components for each piece of infrastructure with a displacement threshold.
8 Amalgamate all P[Failure] and P[Exceeding Displacement Threshold] values with their corresponding consequence valuations into a risk frontier for each slope sector, using either a closed form solution or Monte Carlo analysis, as proposed by Contreras (2015).
9 Create the benefit frontier for each sector from the risk frontier and gross margin using Equations [1] to [4].
10 Calculate the risk and benefit for each sector based on the results of Steps 8 and 9. In each case, the risk and benefit are equal to the mean outcome (risk or benefit) of all Monte Carlo simulations for the sector under consideration.
11 The combined pit shell risk and benefit can also be calculated using the risk frontier addition equation presented by Contreras (2015).
12 Plot the risk and benefit on the MRM in Figure 3 for all sectors and evaluate the pit shell. Amend slope geometries where needed and return to Step 5 to re-evaluate new pit shell.
Deliverable
• Unit mining cost of failed rock
• Unit mining cost of intact rock
• λ
• Preliminary plan with slope sectors
• Toe location scenarios defined
• Potential ramp locations and external infrastructure positions
• Fault tree for slope sectors
• Optimum slope angles for each toe location scenario in each sector
• Final pit shell and volumes
• Per sector ore and waste volumes and unit costs
• P[F] for each failure mechanism
• P[Displacement > Threshold]
• Table with failure sizes
• Table with consequence costs for each sector
o Buried ore
o Access re-establishment
o Infrastructure damage
• Cost of production gaps
• Risk frontier for each sector
• Benefit frontier for each sector
• Table with risks and benefits for each sector
• Pit shell risk and benefit frontiers
• Pit shell risk and benefit
• Final pit shells
A formulation for optimum risk in open-pit mining
expected value is the only defendable design criterion for the risk of loss of profit in open-pit slopes.
When using the optimum risk concept to select slope angles and pit shells in practice, additional factors that were outside the scope of this paper must also be considered.
Safety, compliance, contractual default, and force majeure outcomes were not evaluated in this paper because their evaluation follows an entirely different line of reasoning.
The loss-of-profit outcome is akin to the question of whether a particular investment is a good deal, but it does not address the question of whether one can afford the good deal. For example, a mansion in a billionaire’s suburb may be selling at a bargain price, but that does not mean one can afford it. The “can I afford it?” question is typically addressed by most risk assessment systems: this paper only addressed the “is it a good deal?” question.
The development of the MRM presented in this paper merges economics and geotechnical engineering and, as such, requires a lot of additional theory to be presented in more detail than would have been needed for most papers to cater for the different backgrounds of readers. The content can benefit from a practical example to demonstrate the process and application thereof. This was excluded to limit the length of the paper, but is planned for future publication.
The calculation of two of the inputs required is not straightforward. These are the P[F] and the utility factor λ. While a
significant body of work is available on the calculation of P[F], the same is not available for λ. In both cases, more specific guidance to open-pit slope stability will add value.
Conclusion
The most important shortcomings of existing DAC are that they provide no basis for the threshold values they provide, they do not specify the inherent performance goal that the threshold value is trying to achieve, and the potential reward for accepting risk is excluded.
The MRM developed in this paper addresses these shortcomings in current state-of-the-art slope performance indicators by achieving all four goals defined for this paper. The minimum information required to calculate the optimum risk using the MRM is defined as:
➤ a measure of performance for the decision outcome (factor of safety or displacement thresholds for infrastructure);
➤ a benchmark separating upside from downside, such as factor of safety = 1 and/or a displacement threshold for infrastructure;
➤ selecting a timeframe within which the outcome will be measured;
➤ selecting the utility factor λ;
Figure 6—Mining Risk Model optimum risk compared with guideline summary by Wesseloo and Read (2009)
Figure 5—Mining Risk Model strategy space showing various gambling scenarios for reference (λ = 1)
A formulation for optimum risk in open-pit mining
➤ A P[D] based on an event tree starting with the P[F] as top fault that incorporates the relevant uncertainties and likely loss-of-profit impacts of slope instability.
The MRM provides a system to rank geotechnical risk, a threshold to separate desirable from undesirable risk, and a formulation for optimum risk. Consequently, the MRM can be successfully used to understand, quantify, and optimize the geotechnical risk related to open pits. In this capacity, the MRM can also provide P[F] analysis targets for open-pit slope design. The MRM relates the slope risk in such a way that business decision makers can use the MRM strategy spaces to understand, select, and communicate risk and benefit targets that can be related to geotechnical design performance indicators, such as P[F] and probability of exceeding displacement thresholds.
Acknowledgements
This paper is dedicated to the memory the late Dr Oskar Steffen, friend and mentor to Julian and Johan. Oskar was one of the founders of SRK Consulting who pioneered the ideas of designing open pits and mine schedules on the basis of Risk and Consequence. Oskar introduced us to Risk Consequence-based design and encouraged us to continue developing this field.
We thank Mr Emrich Hamman, Vice President Geotechnical Engineering, at AngloGold Ashanti for supporting Julian’s research presented in this paper and his feedback and encouragement. Julian also thanks his wife Tia, and two children: Julian Jnr and Isabella for all their support during the late-night work sessions. This research is supported by an Australian Government Research Training Program (RTP) Scholarship.
References
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Appendix A – Glossary of terms
Corporate discount rate – the official corporate rate at which cash flows are discounted to account for the time value money for project evaluation purposes.
Downside – is the expected value of all outcomes that are below the threshold value for a single scenario in a risk–benefit analysis.
Expected value – is the average of a group of outcomes weighted by their individual probabilities of occurrence within that group, i.e., the probabilities of occurrence for outcomes in a group must sum to 100%.
Factor of safety – Factor of safety in engineering is generally defined as the capacity over the demand. For slope stability this often takes the form of the sum of resisting forces divided by the sum of driving forces for a given failure mechanism, or the allowable displacement divided by the calculated displacement. The factor of safety for a given slope can be calculated with standard methods given the slope geometry, geology, and geotechnical properties.
Failure mechanism – is a technical term denoting how slopes can cease to perform their intended function. Open-pit examples of failure mechanisms are rotational failure through rock mass, plane failure along bedding, wedge failure along geological faults, complex failure through a combination of rock mass and faults, and excessive displacement by squeezing of soft layers. Slopes that fail typically fail through only one failure mechanism, however all credible failure mechanisms are considered at design stage.
Force majeure – is a French term meaning greater force and is used in mining to describe an event that results in a mine ceasing operations. In the open-pit context, an example would be a large ramp slope collapse that requires more capital to remediate than a mine can afford.
A formulation for optimum risk in open-pit mining
Large Open Pit Project (LOP) – is a research committee funded by many of the largest mining houses that drives geotechnical research for open-pit mining.
Mine cash-flow model – to evaluate and operate mining projects the expected costs and revenues are defined, and built into a mine cash-flow model to evaluate project feasibility, and to plan future cash flows. See also NPV.
Net present value (NPV) – is the present value, after accounting for the time value of money and the corporate discount rate, of a series of cash flows representing a project.
Outcome – is the value of the performance measure for one singlepoint estimate in a group of single-point estimates that serve as input into a probabilistic analysis.
Performance measure – is an index describing the state of nature selected to analyse the outcomes in a scenario, in order to understand the performance of a given scenario. Common performance measures are factor of safety, displacement at defined locations, NPV, and gross margin. Note that a probability such as P[F] is not a performance measure because it does not describe a state of nature, but a state of knowledge.
P[D] – is a variable that quantifies the uncertainty that an outcome in a scenario or group of scenarios falls below the respective threshold values for each scenario. Where multiple scenarios are considered, for instance all design sectors in a pit, the P[D] is the probability of at least one scenario materialising an adverse outcome. P[D] = 1 – Reliability.
Probability of failure or P[F] – is a variable that quantifies the uncertainty that the performance measure will fall below the threshold value for a given scenario. P[F] = (sum of all outcomes < threshold value)/(sum of all outcomes) for a given scenario. P[F] can be defined for any chosen performance measure, e.g., factor of safety or displacement threshold.
Probabilistic analysis – is an analysis of a scenario by combining all the outcomes of that scenario in such a way that the P[F] can be calculated. Several methods are available for a probabilistic analysis, such as Monte Carlo, point estimate, and Taylor series.
Reliability – is a variable that quantifies the uncertainty that no outcomes in a scenario or group of scenarios fall below the respective threshold values for each scenario. Where multiple scenarios are considered, for instance all design sectors in a pit, the reliability is the probability of no scenarios materialising a downside value. Reliability = 1 – P[D].
Reward – is an expression of the expected value of the favourable outcome of a scenario or group of scenarios and is given by the equation: Reward = Reliability × Upside.
Risk – is an expression of the expected value of the adverse outcome of a scenario or group of scenarios and is given by the equation: Risk = P[D] × Downside.
Risk-adjusted value – the generic term for the probability of an outcome multiplied by the value of the outcome. Specific examples are risk and reward.
Scenario – is a group of single-point estimates in a probabilistic analysis unified by a common property, e.g., 1:50 year flood scenario, 1:400 year earthquake scenario, poor slope depressurization scenario.
Single-point estimate – is the evaluation of a single outcome out of many for a scenario.
Stripping rate – is the ratio of the tonnes of waste rock per tonne of ore that has to be mined. For example, a stripping rate of 3:1 means that for every tonne of ore, three tonnes of waste must be mined. Threshold value – is the value of the performance measure that separates downside from upside.
Uncertainty – is a measure of the state of knowledge, at a given time, limited by a given amount of information, based on a defined analysis, about a specific property or parameter. Uncertainty is communicated as a probabilistic distribution, which includes minimum and maximum limits, a standard deviation, a mean, and any other parameters needed to define the probability distribution. Upside – is the expected value of all outcomes that are above the threshold value for a single scenario in a risk–benefit analysis. Utility – is the perceived benefit obtained by consuming a product or service. In terms of investment income problems, such as discussed in this paper, utility is used to denote the difference in perceived benefit between making another dollar vs losing the last dollar made. Utility theory was first published by von Neumann and Morgenstern (1944), and has found a variety of applications in investment analysis.
Variability – describes how a certain parameter varies from location to location in space and time or any other index.
Appendix B – Optimum risk derivation
A solution for the optimum risk can be found with reference to Equation [8] by setting the incremental risk equal to the incremental reward, as shown in the following derivation.
For the slope stability case, it is assumed that the downside estimate remains approximately constant, regardless of slope angle. For each incremental slope angle increase, if the angle increase is accepted, the upside is the incremental upside compared with the previous slope angle increment, but the downside remains the failure of the slope being considered: it is not just the extra slope angle increment that will fail.
With this in mind, the derivation starts by setting the incremental risk equal to the incremental reward: Incremental risk=Incremental reward.
Breaking each term down into its components gives:
Abbreviating P[Downside] to P[D], Downside to D, and UpsideIncremental to ΔU and multiplying out gives:
P[D] × D + P[D] × ∆U = ∆U
Dividing by ΔU gives:
Simplified, this becomes:
Finally, isolating P[D] to the left of the equation and applying λ to D gives:
BACKGROUND
SANCOT SYMPOSIUM 2024
31 OCTOBER – 01 NOVEMBER 2024
CULLINAN DIAMOND LODGE, CULLINAN, PRETORIA
Advances in Tunnelling - A Portal to the Future
With the continued pace of urbanisation, economic and population growth, the availability of space for necessary infrastructure in the urban environment is a major challenge. This, in conjunction with climate change and a focus on reducing impact on the environment, are the key factors driving the necessity and relevance of tunnelling. Tunnels are increasingly seen as a means to providing sustainable, safe and reliable transport, electricity, gas, water, sewage facilities and extraction of raw materials. Whilst the public and private sectors come to terms with the high capital expenditure required for tunnel construction, we live in an age of continued technological development and the application of these technologies presents an opportunity to better and more cost-effectively design, construct, and monitor tunnels. Furthermore, it is imperative that tunnelling consultants and contractors keep up to date with rapidly changing tunnelling technologies in order to remain viable in a competitive industry.
THEME
This conference concentrates on advances in the tunnelling industry, current best practice and how technology has improved tunnelling design, construction, supervision and monitoring. The conference will be held in Cullinan, world famous for its Diamond mines and the discovery of the Cullinan Diamond, the largest rough diamond of gem quality ever found. Diamond mining in Cullinan has transformed in recent years from an open pit operation to an underground operation, a common progression in the Southern African mining industry, and one that has utilised the benefits of advancing tunnelling technological solutions. This highlights the evolution and development of the tunnelling industry at one of South Africa’s oldest mines.
Gugu
Affiliation: CSIR, South Africa
Correspondence to: M. Mpofu
Email: MMpofu@csir.co.za
Dates:
Received: 12 Oct. 2023
Revised: 6 Mar. 2024
Accepted: 12 Mar. 2024
Published: August 2024
How to cite:
Mpofu, M., Maphalala, B., Kgarume, T., Magweregwede, F., and Stenzel, G. 2024. Development of Best Practice Guideline for the management of hot holes in surface coal mines. Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 8. pp. 473–482
DOI ID:
http://dx.doi.org/10.17159/24119717/3159/2024
Development of Best Practice Guideline for the management of hot holes in surface coal mines
by M. Mpofu, B. Maphalala, T. Kgarume, F. Magweregwede, and G. Stenzel
Abstract
Surface coal operations in which mining activities are conducted above old underground workings experience hot holes after drilling. In a coal mine, hot holes are defined as shot holes, which after being drilled have an in-hole ambient temperature of 40°C or above or show a temperature increase of 3°C or more during monitoring. Hot holes and other cavities, such as cracks, pose health and safety risks to workers, such as exposure to hot air and high concentrations of noxious gases released from these holes. In addition, workers may be exposed to premature detonation of explosives caused by in-hole temperature increases and chemical reactions. To this end, Coaltech Research Association commissioned a project to develop a Best Practice Guideline for the management of hot holes. This paper is a compilation of the activities conducted in the development of the Best Practice Guideline between 2021 and 2022. The activities included a review of standard operating procedures and hot-hole temperature-measuring and -monitoring devices, and the assessment of current hot-hole procedures at selected mines. The results indicated that management of hot holes requires a focus on pre-emptive risk assessment of mining blocks, identification of hot holes using the correct temperature-measuring devices, and continuous monitoring of hot holes from the time of drilling until just before blasting. Hot-hole management accessories, such as polyvinyl chloride sleeves, were found to be effective in insulating the hot-hole emulsion from the rock mass temperature, thus preventing the potential for premature detonation.
Surface coal mining activities that occur above old underground workings present the risk of intercepting hot ground or underground fires through cavities, such as deep cracks and sinkholes in the ground, and by the drilling of blast holes. In such mining areas, lower coal seams were mined first through underground mining methods in the past and the upper coal seams are currently being mined using surface mining methods, hence the occurrence of these cavities. The cavities provide ingress for oxygen and an outlet for noxious gases and heat generated by spontaneous combustion in the previous underground bord-andpillar coal mines (Sloss, 2015). When air seeps through cracks on the ground or through blast holes and encounters coal, oxidation occurs. Oxidation is an exothermic reaction, in which heat is released. Because the heat is trapped in underground workings, it leads to a constant increase in the temperature of the rock mass. The magnitude of coal oxidation increases with an increase in the surface area of the coal that is exposed to air; thus, more hot holes are found when drilling on the coal seam than when drilling on overburden. Loading of explosives under such high temperatures is a dangerous process that can cause premature detonation or failure of explosives and initiating systems.
The other source of the heat found in hot holes, not covered in this study, is reactive ground. The term reactive ground refers to ground that contains mainly iron and copper sulfides and, to a lesser extent, coal sulfides. This ground undergoes a spontaneous exothermic reaction when it encounters nitrates of ammonium, calcium, and sodium (found in explosives products). The reaction involves chemical oxidation of the sulfides, resulting in the release of heat (Sharma, 2010; White, 2018; AEISG, 2020).
Figure 1 illustrates the processes involved in the generation of underground heat and fires when mining over old underground workings.
Considering the risks associated with hot holes and their effects on people, equipment, and infrastructure, Coaltech Research Association commissioned a project to develop a Best Practice Guideline (BPG) for the management of hot holes.
Development of Best Practice Guideline for the management of hot holes in surface coal mines
Previous research by authors that include Eroglu et al. (1999), Eroglu (2003), Otter et al. (2005), Uludag (2007), Phillips et al. (2011), Sloss (2015), Genc and Cook (2015), Oageng (2016), Onafide and Genc (2019), and Ngwenyama and de Graaf (2021) extensively describes spontaneous combustion. Their work covered a wide scope of spontaneous combustion aspects that include the causes, prevention, prediction, and monitoring of spontaneous combustion on different mine areas. In addition, the previous research applies to varying spontaneous combustion sources, such as spoil dumps, coal stockpiles, underground coal mining faces, goaf zones of longwall mining methods, open pit highwalls, and, to a lesser extent, on drill holes on benches. However, the concepts for managing spontaneous combustion are similar and relevant to the concepts for the management of hot holes in surface mines located over old underground workings. These concepts include solutions such as the use of sealing agents to minimize the time a coal surface is exposed to air, thus preventing spontaneous combustion. Similarly, for the management of hot holes, the holes are sealed at the collar with a cone or sealing agent, such as an expanding foam, to prevent the ingress of air into the bottom of the hole. Another example is the practice of ‘just-in-time drilling’, which ensures that newly uncovered coal is drilled, blasted, and immediately excavated after exposure to the air. This is similar to the hot-hole management practice of charging, tying up, and blasting as soon as drilling is completed to minimize the time spent time on the block (Eroglu, 2003; Phillips, et al., 2011; Sloss, 2015; Ngwenyama and de Graaf, 2021).
Other research related to the management of hot holes has been conducted by explosives manufacturing companies, such as African Explosives and Chemical Industries (AECI) and Bulk Mining Explosives (BME). Rorke and Conradie (2018) reported on small-scale tests, carried out by BME, to characterize the behaviour
of two explosive product types at high temperatures (up to 750°C). The tests showed the reactions undergone by the products and the associated temperatures; for example, the boiling point of the emulsion was determined to be 150°C and heat generation (or the occurrence of an event) started at 320°C. Rorke and Conradie concluded that the risk of premature detonation in hot holes is likely to be caused by charging accessories, such as initiators, that are sensitive to detonation above 80°C. Tose (2018; 2022) reported on small- and large-scale tests conducted by AECI to investigate the behaviour of explosives in hot environments. The tests enabled the determination of temperatures at which:
➤ grey-white fumes start to form (110°C);
➤ emulsion product is ejected (140°C–220°C);
➤ brown fumes are released (180°C);
➤ detonation/ deflagration occurs (220°C–260°C).
The Department of Minerals Resources and Energy (DMRE) defines hot holes as drilled shot holes that have an in-hole ambient temperature of 40°C or above, or show a temperature increase of 3°C or more during monitoring (Department of Mineral Resources, 2015; Mine Health and Safety Council, 2019). Working in such environments may expose workers to hazards such as (AEISG, 2020; Tose, 2022):
➤ hot air exhausted from underground;
➤ high concentration of noxious gases, such as carbon dioxide, carbon monoxide, and sulfur dioxide;
➤ premature detonation due to:
• temperature increase, which affects the chemical composition of explosives products;
• softening and/or melting of initiating system components.
Figure 2(a) and (b) illustrate hot holes that are venting; Figure 2(c) shows a hot hole with fire at its bottom.
Cracks and holes allow more air into the mine
Coal and pyrite oxidated by air, producing heat
Roof collapses, creating cracks and holes to surface
Coal pillars burn away and weaken roof of workings
Heat causes coal to catch fire
Oxygen
fire
Oxygen & water
Figure 1—Cycle of processes that occur when mining over old underground workings (Tose, 2022)
Figure 2—(a, b) Venting holes and (c) a fire in a hot hole at a mine that has hot-hole challenges
OXYGEN & WATER
OXYGEN & WATER
SURFACE DEPRESSION
SINKHOLE
SINKHOLE
Development of Best Practice Guideline for the management of hot holes in surface coal mines
Premature detonation may lead to injuries, loss of life, damage to equipment, and the destruction of infrastructure. Moreover, the high temperatures may reduce the effectiveness of the explosive products, causing suboptimal blasts. Mining on ground containing hot holes poses a significant safety risk to the workforce, and it is critical for surface coal mines to have sound hot-hole management practices to mitigate all associated risks. Chapter 4.16(5) of the Mine Health and Safety Act (1996) mandates that an employer, in consultation with the explosives manufacturer or supplier, must prepare and implement a procedure to prevent persons from being exposed to the significant risks associated with hot holes. South African coal mines affected by these risks have implemented such procedures; however, as shown in Table I, incidents related to hot holes and/or reactive ground continue to occur. The causes of such incidents include limited site-specific knowledge on the interaction between a hot hole and explosives products, explosives accessories, and hot-hole management instruments, limited knowledge about the temperature profile along the entire length of a shot hole, and, in some cases, lack of compliance with procedures. These factors contributed to the need for developing a BPG that would assist to mitigate the risks associated with hot holes.
It is difficult to determine whether a drill hole is hot or not by just observing it from the surface. There are indicators, such as venting or smoking of the holes, that are sometimes used to identify a hot hole (Rorke & Conradie, 2018; AEISG, 2020). However, the indicators obtained from this technique do not provide adequate
Table I
Examples of local and international incidents caused by hot holes and/or reactive ground (adapted and modified from AEISG (2020))
Year Location Incident description
2020 South Africa Premature detonation in a hot hole
2019 South Africa Premature detonation in a hot hole, one fatality
2018 South Africa Uncontrolled detonation of two blast holes caused by an extreme hot hole
2016 Indonesia Melted booster in a hot hole
2014 Indonesia Premature detonation in a hot hole and reactive ground area
2014 Chile Premature detonation in a hot hole
2014 Canada Mass detonation in a hot-hole area
2014 Australia Melted downlines in a hot hole and reactive ground area.
2013 Chile Premature detonation in a hot hole
2013 Australia Premature detonation in a hot hole
2013 Australia Melted downlines in a hot hole area
2011 Mongolia Premature detonation in a hot hole and reactive ground area
2010 Australia Premature detonation in a hot hole and reactive ground area
2010 South Africa Mass premature detonation in a hot-hole area
2009 Australia Premature detonation in a hot hole
2009 South Africa
Premature detonation in a hot hole, one fatality
information, such as the exact temperature of the holes, that would allow implementation of appropriate control strategies to prevent the risk of accidental detonation of explosives. Therefore, temperature-measurement and -monitoring instruments are used by mines to assess hole temperatures from the time of drilling to just before blasting, as per the regulations (Anthony and Grobbelaar, 2009). Temperature-measuring and -monitoring devices detect the temperature and display the output on a type of a monitor or screen. They are generally used to take readings from the time a hole is drilled until charging. These devices are of different types, and vary according to the method for which they are designed to be used in detecting the temperature (AEISG, 2020). The methods of detection include:
➤ in-hole air temperature measurement;
➤ in-hole rock surface temperature measurement;
➤ non-in-hole/surface temperature measurement devices. Explosive temperature-monitoring devices are disposable devices designed for use during charging to monitor the temperature change of emulsion (Australasian Explosives Industry Safety Group, 2021). They are implemented as a safety measure to enable the early detection of the risk of premature detonation due to the increase of explosives’ temperature. If the temperaturemonitoring device detects an increase of temperature to that above the set safe-to-work temperature cut-off (e.g., 55°C, 80oC, 95oC), workers are immediately evacuated from the mining block. It should be noted that some devices have the functionality to be used for both temperature measurement before charging and the monitoring of the emulsion temperature during charging. Anthony and Grobbelaar (2009) developed and tested a probe recording-monitoring system that continuously monitored the temperature and pressure when explosives were loaded into hot holes. The system consisted of three temperature probes and two pressure transducers, a monitoring station, and a recording station located at a safe distance from the hot holes. The tests assisted in determining the different temperatures at which emulsion reacts in a hot hole, such as the emission of nitrous gases, which the authors correlated with a drop in temperature. The tests also showed that the temperature along the profile of a hot hole is not necessarily equal to the temperature at the bottom of the hole.
While Anthony and Grobbelaar (2009) used temperature probes to measure the temperature of hot holes, there are various other types of temperature-measurement and -monitoring instruments that include thermocouples, infrared guns, and infrared cameras. The instruments have varying limitations that include inaccurate temperature readings due to the presence of water, smoke, or dust, the requirement for intensive labour, and lack of functionality to detect the temperature throughout the depth of a hole. It is important for mines to select an appropriate device that suits the site conditions and provides accurate results that may be used to reduce the risk associated with hot holes. Additionally, use of the device should not impede productivity on the bench.
Methodology
Development of the BPG adopted elements of both qualitative and quantitative research, usually called mixed research methods. A qualitative research approach is exploratory and seeks to gain a deep understanding of a problem, as explained by Creswell and Creswell (2018). This approach helped to investigate the subject of hot holes, ensuring that independent thoughts of mining stakeholders and their in-depth understanding of the subject was captured. The qualitative aspects included a review of standard
Development of Best Practice Guideline for the management of hot holes in surface coal mines
operating procedures (SOPs), hot-hole temperature-measuring and -monitoring devices used locally and internationally, monitoring and observing current hot-hole procedures at selected mines, and assessing the effectiveness of various hot-hole management accessories, such as gas bags and polyvinyl chloride (PVC) sleeves (Figure 3). These documents and apparatus were used by the various mines at the time of the projection execution between 2021 and 2022.
Quantitative aspects of the methodology involved collection of temperature data using different types of temperature-measuring devices during various experiments conducted with the different hot-hole management accessories. Further details on the methodology and the protocols for the tests can be accessed on the BPG report found on the Coaltech Research Association website (Coaltech Research Association, 2022). The data were analysed, and the generated insights were integrated with the results from the qualitative research to develop the BPG. Two study mines were
selected based on the existence of hot-hole risk in their operations and the presence of controls implemented to prevent, reduce, and/or eliminate the risk.
It is noteworthy that, for each experiment, a specific protocol or methodology was designed and used. Similarly, for evaluating the performance of the different types of temperature-measuring and -monitoring devices, assessment criteria were developed, as shown in Table II.
Results and discussion
Results from SOP reviews, the evaluation of temperaturemeasuring and -monitoring devices, the behaviour of hot holes from drilling to charging, and from the tests on hot-hole management devices are discussed in this section. It should be noted that these results are based on the SOP versions that were used by the various mines during the time of the project between 2021 and 2022.
Table II
Acceptance criteria for temperature measuring or monitoring devices
Criteria
Accuracy
Response time
Temperature range
Length of temperature-measuring wire, cable, or string
Durability of the wire, string, cable, or the device or probe
Instrument set-up time
Position of temperature measurement in the hole (single or multiple point)
Visibility of temperature readings and warning lights
Audibility of warning/alarm system
Definition
The accuracy of temperature readings relative to a standard calibrated instrument.
The time it takes for a probe/sensor to make a measurement and display it on the output device (screen/alarm light).
The range of temperatures at which the instrument functions without failure. (In the preliminary temperature-measuring tests, temperatures of up to 500°C were measured at a coal mine.)
The length of the wire/string/cable or any part of the instrument to be immersed into the hot hole.
This is a qualitative criterion to assess the visible damage suffered by the wire, string, cable, or the device or probe during temperature measurement/monitoring.
The time it takes to set up the device and its accessories before a temperature measurement can be made in a hot hole.
This refers to the temperature profile of the hole. A device can either produce a profile of temperatures along the depth of the hole due to multiple sensors or probes or produce a single temperature reading at one point in the hole.
The visible display of the control system output (monitor, LCD screen, LED, lights, etc.) temperature readings visibly in different environmental conditions (sunlight, rain, darkness, dust, etc.)
This refers to whether the warning or alarm system of the device can be clearly heard on a bench.
Time taken to take a reading per hole This refers to the duration required to immerse the wire, string, cable, or probe into the hole to the required depth, take a reading, and retract it from the hot hole.
Figure 3—Contents of the research methodology followed in the development of the Best Practice Guideline
Development of Best Practice Guideline for the management of hot holes in surface coal mines
Review of Standard Operating Procedures
The SOP review process involved collating and reviewing SOPs from three South African coal mines (Mine A, Mine B, and Mine C) owned by three different mining companies and a Code of Practice (COP) from the Australian mining industry. In the review process, SOP contents were categorized into five themes for analysis. The themes are in alignment with regulation; risk assessment; identification of hot holes; treatment of hot holes, and hot-hole charging and blasting.
With respect to the first theme on alignment with regulation, it was important for the research team to identify how a mine defines the term ‘hot hole’ in comparison with the regulatory definition, which states that a hot hole is a shot hole, in a coal mine, which after being drilled has an in-hole ambient temperature of 40°C or above or shows a temperature increase of 3°C. Furthermore, it was important to establish whether the mine SOP satisfied the DMRE’s definition found in Mine Health and Safety Council (2019) requirements, which stipulate that, at any surface mine, ‘a competent person appointed by the employer in writing should measure the temperature of the shot hole in the event of a significant risk of hot holes in that environment’ and that ‘the temperatures of the shot holes should be measured at any point throughout the length of the shot hole and recorded prior and during charging up operation’.
Some gaps and misalignments were found in the SOPs of the three mines with respect to regulation terminology. The definition of hot holes in some SOPs was not in alignment with the latest (2018) definition provided by the DMRE. However, this did not seem to influence the actual procedures for managing hot holes in those SOPs. All SOPs reviewed contained sections on the requirement to measure and record the temperature of holes. However, there was no explicit emphasis on the importance of measuring the in-hole ambient temperature along the depth of the hole. In all SOPs from the different mines that were reviewed, the responsibility for measuring the temperature of the holes was given to specific competent persons, depending on the complexity of the procedure.
The second SOP review theme, risk assessment, focused on what activities are conducted during bench preparation, when bench preparation is conducted (on the same or different day as blasting), and who is responsible for the activities. This was key to understanding the safety measures put in place by the individual mines in preparation for a blast on hot ground, considering that there is a possibility that the in-hole temperature of drill holes changes (increases or decreases) between the time of bench preparation and charging of the holes. All SOPs that were assessed contained sections on the need to perform some form of a risk assessment that includes identifying hazards, declaring the area safe, and limiting access to the bench. These activities are vital to hothole management. For Mine B and Mine C, it was not clear whether bench preparation is conducted prior to or on the day of charging. For Mine A, bench preparation is a standalone activity in the drilland-blast cycle and is performed on the preceding day to charging. On this day, temperature measurements are taken and recorded, and are used as benchmark for further measurements after charging and blasting. According to the SOPs of the different mines, specific competent persons are responsible for bench preparation.
The SOP review theme on hot-hole identification had the purpose of establishing the methods followed in identifying hot holes and the associated accessories that are used. At Mine A, a preemptive risk assessment strategy is used to determine, in advance,
whether the succeeding cut or blocks have risks associated with hot holes. Furthermore, at Mine A and Mine B, the SOPs reveal that the risk of hot holes is associated with old underground workings. This is consistent with literature and observations made during the visits to the two selected mines. Spontaneous combustion is referred to as a risk that is linked to hot holes in the SOPs of some of the mines. In all SOPs, identification of hot holes occurs between the time of drilling and the time of charging. The differences lie in the frequency of measurement and recording, the classification of the holes based on the measurements, and the devices used to take the temperature measurements. At Mine A, it is stated in the SOP that two independent temperature measurement devices should be used. Additionally, at this mine and at Mine C, it is prescribed in the SOP that a Blast Eye monitoring device be used after charging of the holes.
The treatment of hot holes, the fourth theme on the SOP review, as anticipated, was found to vary per mine depending on the hot-hole classification. According to the different SOPs, holes that are found to be in the hottest classification band are sealed off and not charged. There is a vast difference in the temperature of the holes that may be charged or sealed off at these various mines. For example, the SOP for Mine A states that holes of up to 90°C may be charged and those above this temperature should be sealed off. In contrast, at Mine B, holes that have a temperature greater than 60°C are sealed off and not charged. Different accessories are prescribed in the SOPs for use in hot holes of varying classifications. These accessories include water, cooling agents, gas bags, foam expander plugs, and PVC sleeves. The procedure for the treatment of drill holes that hole through into underground workings was found to be identical in the two mines that were visited. A procedure from Mine A involves the sealing of these holes at the collar, using expanding foam plugs, whereas Mine B uses drill chippings and/ or sand to seal the holes off. Other holes that are treated, according to the reviewed SOPs, are holes that vent. At Mine B, a venting hole is sealed off and an adjacent second hole is drilled 1 m away and 2 m shorter than the initial hole. This procedure is based on the assumption that the source of heat and smoke is at the bottom of the hole; thus, a 2 m gap would prevent venting in the adjacent hole. At Mine A, venting holes are sealed off with foam expander plugs and it is the responsibility of the Blasting Supervisor to ensure that these treatment processes are followed. Similarly at other mines, the responsibility of overseeing the treatment process lies with a competent person, such as a Miner.
Under the fifth SOP review theme, charging and blasting of hot holes, the aim of the research was to understand the differences in the procedures used for hot holes (or benches) and normal holes (or benches). The review focused on the type of explosives products used, the use of stemming material, and the designation of the responsibilities for the different procedures.
At Mine A, the procedures for charging and blasting are centred around working safely and promptly at those areas in which hot holes are found. This is shown by the requirement to use three explosives trucks or two trucks with rapid reload system functionality to reduce the time spent on charging and, subsequently, the time spent by workers on the block. In addition, personnel that are either not involved in the charging or those that are not trained and appointed are removed from the block to reduce the number of people exposed to the risk. Other safety precautions contained in the SOPs include the use of explosives that contain urea or inhibitors to charge hot holes, and the charging and
Development of Best Practice Guideline for the management of hot holes in surface coal mines
blasting of holes on the same day (no sleep-over holes). Detonators that trigger at temperatures between 80°C and 110°C are not used and the drill holes are not stemmed on a blast block that has hot holes. These safety precautions are common in all SOPs that were reviewed except for one precaution: the stemming of holes, which is practiced at Mine B and Mine C. Another common feature of the SOPs is the use of a temperature-monitoring device (the Blast Eye) to monitor the temperature of bulk/emulsion after charging the holes. Monitoring of the in-hole emulsion temperature informs the evacuation procedure: the mining block is cleared if an alarm, which is set to trigger at specific threshold temperature (e.g., 80°C), is reached.
Internationally, similar practices to those contained in the SOPs of local surface coal mines were identified. In Australia, a COP developed by explosives manufacturers quotes the Australian Standard (2187.2) definition for elevated temperature as material that is above 55°C. Materials above 55°C are divided into hot ground (ground with material above 55°C, but less 100°C) and high-temperature ground (material with a temperature of 100°C or more). In these areas, similar to the DMRE regulations, the temperature should be measured along the length of the hole and the highest measured temperature should be recorded as the temperature of that particular hole.
On the identification of hot holes, the COP recommends that mine SOPs should contain a method that would be followed in the identification of which holes to measure, when and how often to measure them (e.g., test every hole, test every hole in a certain known hot area, test 24 h apart to check for increasing temperatures), which instruments to use, and defining the site cut-off temperatures for the mine. Similar practices, including the classification of the holes into temperature categories, were also found in the local mine SOPs. Furthermore, the selection of a measurement device with a suitable temperature range and a measuring system suitable to the conditions (e.g., infrared may not be effective in wet holes or steaming holes) is recommended. Other recommended practices to enhance safety on a block with hot holes include using specific explosives products in line with the different temperature classifications and minimising the sleep time of hot holes that are loaded with explosives. The sleep time, which is the time that explosives are left loaded in hot holes, is an important factor to consider in preventing heating up of explosives products, which may result in premature detonation.
Evaluation of temperature-monitoring and -measuring devices
Various temperature-measurement and -monitoring devices, including the Blast Eye (Figure 4(a)), were evaluated according to the criteria developed in the test protocols. The results showed that
there is no one device that is a perfect fit for the purpose of hot-hole measurement and/or the monitoring of emulsion temperature in hot holes. This is attributed to the ongoing innovations and design adaptations made to these devices for the unique field of hot-hole management. Additionally, in the hot-hole environment, factors such as dust, water, mud, and smoke impede the optimal performance of these devices. For example, an infrared device (Figure 4(b)) measures the in-hole rock temperature, which is useful because it is the rock that will be in direct contact with explosives during charging; however, the accuracy of the temperatures it measures is affected by the presence of water, dust, and smoke, and its temperature range is narrower than that of thermocouples. In contrast, thermocouples (Figure 4(c) and (d)) measure the in-hole air temperature, which may not be a true reflection of the highest temperature in the hole. Therefore, a balance needs to be found between the strengths and weaknesses of the different devices. This may be achieved through further technological design changes to the current instruments or alternatively, as practiced in one of the host mines, the use of two distinct types of temperature measurement devices (infrared and thermocouples) to provide more reliable in-hole temperature characteristics.
Field test results
The tests conducted to understand the behaviour of holes, from immediately after drilling until prior to charging, displayed varying results. In some mining blocks, the in-hole air temperature of most holes increased (with some holes increasing by up to 40°C) over a period of three days of observation. On one mining block, the in-hole air temperature of the majority of the holes decreased over time; however, a minority of holes increased in temperature by up to 13°C. For all blocks, there were some holes in which the temperature remained constant, with either a decrease or increase in the in-hole air temperature of 2°C or less. The results show that, within a mining block, individual holes behave differently from the time of drilling until charging. The increase in temperature in the holes is a major safety hazard and the main factor of consideration in the management of hot holes. Even though most holes on a block may display minimal temperature changes, a single hole may be the cause of an incident due to a sudden large increase in temperature. This is the hole that may lead to self-detonation or premature detonation of explosives on the block.
The tests conducted on two gas bag products were inconclusive due to the small sample sizes. These sample sizes were limited by the availability of the gas bags at the mine. A larger sample size of 20 hot holes was used for the tests on the most commonly used gas bag at the host mine. The results suggested that these gas bags do not last for a period of 24 h or longer in a hot hole of temperatures greater than 43°C. The gas bags failed by either deflating or rupturing. Thus, the use of gas bags overnight, as a solid base for emulsion in
Figure 4—Examples of various temperature measurement and monitoring devices. (a) Blast Eye, (b) infrared, (c) and (d) thermocouples
Development of Best Practice Guideline for the management of hot holes in surface coal mines
holes that breakthrough to underground workings (also known as bhoboza holes) or to seal off the bottom of venting holes, is not recommended. However, it is noteworthy that a larger sample size may have resulted in a different outcome. As a recommendation, future tests to determine how much weight the gas bags can carry should be conducted.
Tests conducted on expanding foam and observations made at the mining blocks revealed that venting of holes is indeed stopped when using expanding foam plugs. However, when the foam is placed near the heat source in the hole, it burns, releasing white smoke, after which venting re-surfaces. Thus, the use of expanding
Table III
Summary of Best Practice Guideline contents
Mining process stage BPG stage Risk management activities
Before drill and blast activities
Pre-emptive risk assessment
After drilling Identification of hot holes
During charging Hot-hole charging and blasting
form, as a solid base for emulsion in bhoboza holes or to seal off the bottom of venting holes, is not recommended. The foam plugs that were tested expanded by between 0.4 m and 1.2 m in the holes.
Summary of the Best Practice Guideline
The BPG was compiled from literature, experimental results, and engagements with mining industry stakeholders. The full Guideline can be accessed on the Coaltech Research Association website (Coaltech Research Association, 2022). The Guideline covers hot-hole management aspects such as risk assessment, the identification of hot holes, the treatment of hot holes, and the
• Classify mining blocks according to their potential to have hot holes (e.g., those over old mining workings).
• Classify the mining blocks into hot and normal blocks and according to the risk assessment matrix of the operation.
• Develop broad risk mitigation strategies for managing the normal and hot blocks, including the type of explosives product and accessories and hot-hole management tools to be used.
• Develop focused risk mitigation strategies for managing holes of different temperature on the same block, including the types and quantities of explosives products and accessories and hot-hole management tools to be used.
• Measure the initial temperature of the holes on a block using the appropriate temperature-measuring device.
• Record the measured temperatures on a data sheet and on hole identifiers, such as plant markers, tags, or flags.
• Record other information, including the hole depth and hole conditions, such as the venting (and the colour), smoking (and the colour), presence of water, and the smell released from the hole.
• Classify the holes on a block according to their temperature into categories such as cool/normal, hot, or very/extremely hot.
• Mark the holes with an identifier, such as a tag or flag, that is visible to all workers on the block.
• Perform hot-hole treatment where applicable and as per the risk assessment of the mine.
• Seal off those hot holes that will not be charged as per the risk assessment of the mine.
• Re-measure the temperature of the holes on the block just prior to charging
• Record the measured temperature on a data sheet and on the tag or flag.
• Assess the temperature measurement to identify whether it still falls within the correct temperature class.
• If the temperature of the hole has increased and it now falls within a new class, re-classify the hole and blast block accordingly.
• Perform hot-hole treatment where applicable and as per the risk assessment of the mine.
• In all the holes classified as hot holes, insert the appropriate temperaturemonitoring device to monitor temperature change of the hot-hole-specific explosives product.
• Load the holes with hot-hole-specific explosives product as per the suppliers’ recommendations.
• Monitor the hot -hole monitoring device for any alarms that may be triggered due to an increase in the temperature of the hot-hole explosive product.
• Monitor the hot holes for any signs of smoke or fumes, as these indicate that the explosives product is heating up.
Development of Best Practice Guideline for the management of hot holes in surface coal mines
associated charging and blasting practices. A summary of the main contents of the BPG for the management of hot holes is shown in Table III.
Drilling and blasting activities of the mining cycle should be preceded by a pre-emptive risk assessment to identify the risks associated with hot holes and devise associated mitigating measures. Such an assessment should result in the classification of mining blocks according to the varying risk; for example, mining blocks located over old underground workings would carry a higher risk of hot holes than other mining blocks. This would enable the selection of appropriate site-specific hot-hole management tools, explosives products, and any other relevant charging and blasting accessories ahead of time. The selection of explosives products and accessories for use in hot-hole environments should be informed by the mine site conditions (Figure 5) and the associated risk assessments. Additionally, large-scale tests on the explosive products should be conducted at the mining sites. These tests assist decision makers in identifying the full spectrum of risk associated with blasting in such environments by utilizing the actual conditions (hole depth, hole diameter, in-hole temperature, etc.).
When the products have been selected, the mine site risk assessments, which would have classified the risk associated with the hot holes according to in-hole temperatures, should be followed in conjunction with the relevant technical data sheets from the manufacturer. Blasting accessories, such as initiating systems, used in hot-hole environments should be compatible with the selected hot-hole-specific explosive product.
After holes have been drilled on the mining blocks, it is important that the temperature is measured using the appropriate temperature-measuring device. Such a device should accurately measure the in-hole rock surface temperature and/or in-hole ambient temperature, enable multi-point temperature detection or temperature measurement along the hole length, and be durable so that it can function in rugged environments containing dust, water, and mud. Furthermore, the use of the temperature-measuring device should not decrease the productivity of workers. Thus, the time to set it up and record the temperature in a hole should be less than two minutes, considering that a block may contain over 100 holes. The measured hole temperatures should be recorded on data sheets and on identifiers, such as tags or flags, that would be placed on the crest of a hole. Ideally, the temperature measurements would
be digitally captured and communicated to the relevant authorities in real- or near real-time for quick updates to the risk assessments and blast designs.
Unexpected operational constraints may deem it impossible to drill, charge, and blast on the same day, leading to changes (increase or decrease) in the temperature of the holes on a block. Therefore, further temperature measurements should be taken on the day of charging and blasting so that the blast design and risk assessment are accordingly updated. For example, an increase in the temperature of a hole may result in re-classification of the hole from a normal hole to a hot hole, which would affect how it is managed or charged. The risk associated with hot holes is high during the charging process; therefore, all activities that occur during this process should be completed safely and promptly. In addition to remeasuring the temperature of the holes before charging commences, other activities that should be performed include monitoring the temperature of hot-hole explosives products and continuously assessing the holes to identify any developing hazards. Moreover, a loading sequence that enables a quick but safe charging process should be adopted to enable workers on the block to safely exit the block in the event of an emergency.
The treatment of hot holes involves the use of various materials and procedures to reduce their temperature to below 40°C, after which the holes can be charged as normal holes. However, there was inadequate information available on chemicals such as cooling agents to make recommendations on their usage in reducing the temperature of hot holes. Available research revealed that cooling agents are used in dousing surface fires, but their effectiveness is reduced by their inability to access the source of the heat or fire (Eroglu, 2003; Phillips et al., 2011).The use of water as a cooling medium for hot holes is not recommended by mining and explosives stakeholders. In addition, Phillips et al. (2011) suggested that using water for cooling in hot holes above 80°C leads to steaming and/or the production of water gas. Water gas consists of two flammable gases, carbon dioxide and hydrogen, which are sources of ignition and a hazard to personnel working in that environment. Other ways of treating hot holes or managing their risk involve the use of foam expander plugs, gas bags, and PVC sleeves. Foam expander plugs (Figure 6) can be used to seal off venting holes, holes with temperatures that pose a high risk
Figure 5—Various mine site conditions for the selection of explosives products (AEISG, 2020; Tose, 2022)
Development of Best Practice Guideline for the management of hot holes in surface coal mines
to workers (holes with temperatures above the threshold), and breakthrough (or bhoboza) holes. Engagements with mining personnel revealed that, at high temperatures (approximately above 200°C), the solidified plug may catch heat and burn, releasing some white smoke. Therefore, the use of foam plugs to seal off hot holes at high temperatures should be investigated and incorporated into the risk assessment.
Gas bags can be used to seal off venting holes on a block. However, tests showed that sealing off hot holes with gas bags over long periods may cause them to burst or deflate, which would enable the escape of smoke from within the hole. It is recommended that, as part of a risk assessment, tests on gas bags should be conducted on different sized holes of varying temperature to enable the identification of the optimal conditions for the use of gas bags to seal off hot holes. PVC sleeves are recommended for use as a form of physical separation between the surface of the hole and the hothole emulsion and as a container for the emulsion in breakthrough holes, cracked holes, and pillar holes. The sleeve works by retaining the water (approximately 17%–20%) inside the sleeve, which protects the explosive; only when this water has boiled off does the explosive rapidly increase in temperature until it reaches detonation temperature.
Conclusions and recommendations
A mixed methods approach consisting of qualitative and quantitative research was used to review SOPs and hot-hole temperature-measuring and -monitoring devices, to monitor and observe current hot-hole procedures at selected mines, to assess the effectiveness of various hot-hole management accessories, such as gas bags and PVC sleeves, and to collect and analyze temperature data. The results indicated that the management of hot holes requires a focus on pre-emptive risk assessment of mining blocks, the identification of hot holes using the correct temperature-measuring devices, and the continuous monitoring of hot holes from the time of drilling until just before blasting. Hothole management accessories, such as PVC sleeves, were found
to be effective in insulating the hot-hole emulsion from the rock mass temperature, thus preventing the potential for premature detonation.
The following are recommended for incorporation into the SOPs for different mines to manage the risk of hot holes:
➤ Definitions – clear definitions of hot holes that align with the current DMRE regulations, and the associated temperature classifications or ranges.
➤ Hot-hole measurement and monitoring – clarity on what instruments should be used and by whom, how the instruments should be used to avoid incorrect readings, when and how often the instruments should be used and calibrated. Information on what is being measured (the in-hole air temperature or the in-hole rock temperature) should also be clearly specified.
It is further recommended that mines should consider the following in the selection and use of temperature-measurement and -monitoring devices:
➤ Mines to advise manufacturers to consider further technological design changes on both temperaturemeasurement and -monitoring devices to ensure that they provide accurate results in the presence of water, dust, and smoke. These devices should also be user-friendly to avoid their abuse and incorrect use, which would defeat the purpose of enhancing safety.
➤ Mines should consider using temperature-measurement devices that measure the in-hole rock temperature and/or (depending on practicality and affordability) the in-hole air temperature to gain better separate understandings of the rock and air temperatures. For instance, it is the rock that comes into direct contact with the explosives and hot-hole management accessories. Knowing its temperature would assist in determining the limitations of the products.
➤ Temperature-measuring devices selected should ideally produce a profile of temperatures along the depth of the hole.
Figure 6—Foam expander plug after sealing off a hole
Development of Best Practice Guideline for the management of hot holes in surface coal mines
This way, the production team will get a clear indication of the position of the heat source in instances where it is not at the bottom of the hole.
➤ When the heat source is near the top of a hole, it is far more dangerous than when the heat source is at the bottom of a hole because heat rises and there is less explosive to heat at the top of a hole before it gets to critical temperature.
➤ Temperature-monitoring devices selected should be audible in the presence of explosives trucks during charging and have multiple measuring points along the explosive column. Regarding the charging and blasting of hot holes, it is recommended that mines select explosives products and accessories for use in hot-hole environments based on the mine site conditions and the associated risk assessments. Furthermore, it is encouraged that large-scale tests on the explosive products should be conducted at the mining sites to enable identification of the full spectrum of risks associated with blasting in such environments.
References
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Anthony, D., Grobbelaar, J. 2009. Hot hole monitoring at Klenkopje Mine, Witbank: Coaltech Research Association (Unpublished). Australasian Explosives Industry Safety Group. 2021. AEISG Codes of Practice. [Online]Available at: https://www.aeisg.org.au/ wp-content/uploads/ELEVATED-TEMPERATURE-ANDREACTIVE-GROUND-COP-EDITION-5-APRIL-2020-3.pdf[ Accessed 2 November 2021].
Coaltech Research Association. 2022. Coaltech Research Association. [Online] Available at: https://coaltech.co.za/wp-content/uploads/2022/07/M2020-1Management-of-hot-hole-blasting-Best-Practice-Guideline-forthe-Management-of-Hot-Holes-in-Opencast-Coal-Mines.pdf [Accessed 1 September 2023].
Cresswell, J., Cresswell, J. 2018. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 5th edn. Upper Saddle River, New Jersery: Pearson Education. Department of Mineral Resources. 2015. Mine Health and Safety Act, 1996 (Act No. 29 of 1996), Johannesburg: Department of Mineral Resources and Energy.
Eroglu, N., Moolman, C. 2003. Develop methods to prevent and control spontaneous combustion associated with mining and subsidence. Johannesburg: Coaltech Research Association. Retrieved from https://coaltech.co.za/wp-content/ uploads/2019/10/Task-3.4.1-The-Prevention-and-Controlof-Spontaneous-Combustion-associated-with-mining-andsubsidence-2003.pdf
Genc, B., Cook, A., 2015. Spontaneous combustion risk in South African Coalfields. Journal of the Southern African Institute of Mining and Metallurgy, vol. 115, pp. 563–568.
Mine Health and Safety Council. 2019. Explosives Regulations Ammendment Note - 2019, Johannesburg: Mine Health and Safety Council.
Muswellbrook Coal Company Limited. 2010. Spontaneous Combustion Management Plan, Muswellbrook: Muswellbrook Coal Company Limited.
Ngwenyama, P., de Graaf, W. 2021. Risks and challenges affecting opencast pillar mining in previously mined underground bord and pillar workings. Journal of the Southern African Institute of Mining and Metallurgy, vol. 121, pp. 623–633.
Oageng, O. 2016. Buffer blasting as applicable to spontaneous combustion in coal surface mines, Johannesburg: University of Johannesburg.
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Phillips, H., Uludag, S., Chabedi, K. 2011. Prevention and Control of Spontaneous Combustion Best Practice Guidelines for Surface Coal Mines in South Africa, Johannesburg: Coaltech Research Association.
Rorke, A., Conradie, M. 2018. Temperature Sensitivity of INNOVEX 100 and INNOVEX 206 Based on Laboratory Tests, Johannesburg: BME (Unpublished).
Sharma, P. D. 2009. Blasting of Hot Holes in Opencast Coal Mines Retrieved from Mining and Blasting: https://miningandblasting. wordpress.com/2009/07/07/blasting-of-hot-holes-in-opencastcoal-mines/.
Sloss, L.L. 2015. Assessing and Managing Spontaneous Combustion of Coal, London, United Kingdom: IEA Clean Coal Centre.
Tose, S. 2018. Update - Extreme Blasting , Johannesburg: AEL.
Tose, S. 2022. Specialised Blasting Techniques - Hot holes Johannesburg, Unpublished.
Uludag, S. 2007. A visit to the research on Wits-Ehac index and its relationship to inherent coal properties for Witbank Coalfield. Journal of the Southern African Institute of Mining and Metallurgy, vol. 107, pp. 671–679.
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Affiliation:
Universidade Federal de Alfenas, Brazil
Correspondence to: E. Rodovalho
Email: edmo.rodovalho@unifal-mg.edu.br
Dates:
Received: 28 Jul. 2021
Revised: 18 Feb. 2022
Accepted: 21 Feb. 2024
Published: August 2024
How to cite: Rodovalho, E., El Hajj, T.M., Teodoro, G.C., de Tomi, G., and Tenório, J.A.S. 2024. Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method. Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 8. pp. 483–490
DOI ID:
http://dx.doi.org/10.17159/24119717/1696/2024
ORCID:
T.M. El Hajj
http://orcid.org/0000-0001-7678-3759
G.C. Teodoro
http://orcid.org/0000-0003-3728-2921
G. de Tomi
http://orcid.org/0000-0002-7836-1389
J.A.S. Tenório
http://orcid.org/0000-0002-7849-7470
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method
by E. Rodovalho, T.M. El Hajj, G.C. Teodoro, G. de Tomi, and J.A.S. Tenório
Abstract
The selection of an underground mining method stems from a multivariate analysis that considers geomechanical, geological, economic, and operational parameters. Even after identifying the most compatible method, there is no assurance that the mining company will achieve the best performance. The geological complexity of some deposits requires adaptations of methods described in the literature to obtain more selectivity and reduce mining waste. There are some studies on geometry of underground structures, but a methodology that describes an adaptation of a room-and-pillar mining method for ore bodies with down-dip varying from 20° to 25° is novel. The present work aimed to reduce dilution by adapting the traditional room-and-pillar mining method (TRP) to inclined ore bodies. This new method is entitled short-hole room-and-pillar (SHRP). The equations that measure the dilution are defined according to the geometry of stopes and openings. The results comprise comparative analyses of the operational and planned dilutions to measure the performance of the SHRP method. The average operating dilution of the SHRP method was more than five times lower than the planned dilution according to the TRP method. Low operational dilution indicates high selectivity of the method and its potential to reduce underground mining tailings.
The main reason for evaluating new underground mining projects is the market value of the mineral or products obtained from these operations. Only minerals or materials with high value can make this kind of industrial structure viable, because these are mined together with waste to create a sufficiently high stoping height. Gold mineralizations are examples, but their geometry, shape, and ore grade distribution are diversified. In addition, host rocks have a great diversity of geomechanical, geological, and structural conditions. Based on this information, the adjustment of a certain mining method seeks to maximize the productivity and safety of operations and openings (Emad et al., 2014; Iphar and Alpay, 2018). In both works, the authors discuss the state-of-the-art of underground mining methods to propose a design capable of increasing operational efficiency and safety. In addition to these objectives, it is necessary to reduce losses and waste generation through a multivariate analysis that identifies a mining method or customizes an existing one. According to Costa et al. (2017), many of the world's leading underground mines operate more than one mining method at the same mine. This reality places the customization and development of new mining methods as a fundamental practice to achieve higher levels of economic, operational, and environmental efficiency in mining. Furthermore, it is possible to generate an index of sustainable development of mining activities by analyzing these factors (Amirshenava and Osanloo, 2019). This practice is strongly recommended in order to promote responsible mining and cleaner production.
It is important to emphasize that the aim of the modification or combination of classical mining methods, first and foremost, is ensuring the stability of underground excavations. Suitable conditions for safety allow more effective control of economic, operational, and environmental parameters. According to Ghasemi and Shahriar (2012), the design of supporting pillars is the most critical geometric aspect of the room-and-pillar method. These authors developed an experimental application of a coal mine where a direct relationship between the pillar design and an increase of recovery was identified. The authors also indicated that the increase in recovery can generate a reduction in the dilution. In this same context, Mark (2016) used empirical techniques applied to rock mechanics that represent another viable alternative
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method
for the design of pillars. The authors present a practical and innovative methodology to measure the quality of rock mass. In addition, Zhu et al. (2018) associated the geometry of the pillars with safety of the operations; however, the methodology is based on the analysis of pillar failure applied only to flat and tabular ore bodies. It is well known that excavation stability and safety of operations are fundamental aspects in the application, modification, or combination of underground mining methods. This scenario motivates the application of different geometric simulation methods for underground structures. Even results obtained from small-scale tests are meaningful to guide future actions. As an example, Zhang et al. (2018) developed a laboratory simulation method to increase ore recovery. Following this practice, the present work also relies on geometric simulations to evaluate the compatibility of a stope’s geometry with the mining equipment employed.
Considering current active mines, stability remains a major challenge for the mining industry. Heidarzadeh et al. (2018) stated that stress-induced failure is among the main causes of underground mine stoppages in Canada. The authors emphasize that the main variable capable of controlling the instability effects is the geometry of the openings. Dilution is an aspect among the main reasons for closing underground mines around the world. Jang et al. (2015) stated that high dilution is the main cause of mine closures and that the combination of different mathematical modeling methods can control this variable.
As dilution is strongly related to waste generation in mining, it is important to highlight some important points. An et al. (2018) stated that operations in veins and narrow ore bodies usually present high rates of overbreak and underbreak. The present research is applied to narrow ore bodies where the geometry of the openings is directly related to dilution. Additionally, the authors propose a method capable of reducing these losses and waste, generated by modifying variables related to the geometry of the openings. Recently, other studies have also presented methods capable of reducing losses and mining waste generation, such as ore losses (Jang et al., 2015; Rodovalho and Cabral, 2014). The application of these techniques is even more important with mine aging, because these operations tend to generate more waste and tailings (Lagos et al., 2018). Even in small-scale operations, reduction of waste generation is a necessity. Seccatore et al. (2014) described adaptations of more efficient industrial techniques for small-scale underground mining. Regarding small-scale gold mines, waste management is even more deficient due to adopting outdated technologies (Astrand et al., 2018; Seccatore and Theije, 2017).
There are several case studies and methods capable of reducing dilution, but there is little availability of a geometry or operational arrangement for the room-and-pillar mining method applied to ore bodies with 20° to 25° of dip. Oosthuizen (2005) described a mining method suited to the shape of these ore bodies, known as the T-cut mining method. The author showed relevant achievements after comparative analysis carried out in a South African platinum mine. Traditionally, this method is applied to horizontal or sub-horizontal bodies. The present work sought to adapt the room-and-pillar mining method for gold ore bodies with dip varying from 20° to 25°, aiming to reduce the operational dilution. To illustrate the application of the method, a gold mine located in Brazil was used as the experimental application. The mining method, called shorthole room-and-pillar (SHRP), is developed through simulation and geometric analysis. This is an unprecedented application in underground mining in Brazil. The main result of this development is the reduction of the operational dilution in relation to the
planned dilution. In the experimental application, it is possible to identify operational dilutions up to 20% of the planned dilution, indicating a high reduction of dilution generated by the mining operations. Operational dilution represents the greatest challenge in the development of underground mining projects. Control of this variable represents a greater capacity for reduction of residues of mining, because a smaller volume of gangue minerals will be sent to minerals processing. This reduces the demand for new tailing dams, increases the operational efficiency of the project, and improves environmental performance.
Methodology
The methodology of the present work begins with evaluation of the development structures and typical accesses of an underground mine. These data are the result of geometric simulation, based on a geological and geomechanical model using mine design software. However, it is necessary that these excavations are compatible with the mineralization to be evaluated in the experimental application. The research scope is to evaluate the selectivity: the operational variable related to this factor is the dilution. This was practically evaluated in a gold mine in Brazil. The main alternatives for calculating the dilution are discussed and selected for application to the experimental site as a part of the strategic mine plan. Figure 1 presents a flowchart that summarizes the methodology applied during this work.
Proposed mining layout
The SHRP mining method, applied by the studied mine, has a height limitation of 2.4 m. The cycle of operations starts with drilling by fan drill. After blasting, the material is removed by low-profile loaders and hauled by low-profile trucks that climb up the main ramp to the crusher. The height of all equipment cannot exceed 2.4 m. The most important access excavation to mining panels is the main ramp. This structure allows access to the main drift through haulage crosscuts. Figure 2 shows the layout of these structures applied to stope development. The main drifts are represented in red: they are always horizontal and follow the strike of the ore body. Along the main drift, there are accesses to the secondary ramps that represent the connection with rooms and short-hole stopes. The spans are highlighted in Figure 2. Figure 3 shows details of the rooms and short-hole stopes through a vertical section. In this layout, it is possible to identify the positions of the mining panels in relation to the development structures and pillars.
Figure 1—Methodological summary detailing the sequence of activities aimed to reduce dilution
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method
Main Drift
Secondary Ramp
Chamber
Short Hole Stope
All short-hole stopes have pillars between them, represented by spaces in Figure 2. After the mining stope, there is no circulation through these openings as a link between one room and another. The method relies on the barrier pillars to strengthen the support of the openings in the mining and development areas. Figure 4 highlights these structures that are provided along the secondary ramps and main drifts. The initial stages of mining are indicated in orange, where the lower rooms are located. Development starts with the lowest elevation rooms and proceeds upwards.
The advance system of the SHRP method is based on retreating from lower rooms up to the higher rooms. The geometry shown in Figure 4 is a plan view, where the stopes of the lower part of the figure are at a lower elevation. Following this premise, the orange stopes represent the initial phase of mining and then the operations advance to the upper stopes. As the ore body presents down-dip ranging from 20° to 25°, it is not possible to use the traditional room-and-pillar mining method (TRP) where the openings follow the design floor-to-floor and roof-to-roof. For ore bodies with this inclination, only the concept of short-hole and long-hole openings remains. This denomination is applied in the traditional method and is associated with the length of the drilling column, which varies from 1 to 1.3 m. These dimensions are typical features of narrow vein mineralizations and make the method highly selective. Van Dorssen (2002) provided a description of the development of a long-hole stoping system suitable for narrow platinum reefs and discussed the difficulty of mechanized mining operations to this ore body geometry.
Figure 5 presents the results and dimensions of the geometric analysis for typical sections of short-hole and long-hole bords. The main procedure of this analysis is the computational simulation
that considers the equipment dimensions, geotechnical variables, and ore body geometry in order to minimize dilution. Most of the mineralized body has down-dip up to 23°, which implies application of the SHRP method. With the aim of increasing the selectivity, several geometric configurations were tested using computational tools for mine design. The distance between rooms that most favoured selectivity was 6 m, as shown in Figure 4. Considering that the horizontal width of the pillars follows the standard of 3 m, according to geomechanical constraints, the total area of the pillar
Figure 2—Sketch of stope development during room-and-pillar mining (plan view)
Figure 3—Vertical section indicating positions of support structures and development structures (rooms and short holes)
Figure 4—Positions of main support structures and initial stages of development (plan view)
Figure 5—Comparison of designs of long-hole and short-hole rooms
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method
is 18 m2. Each short hole has a maximum length of 8 m, limiting an area of only 48 m² of blasting to be executed on the ore body. The efficiency evaluation of the mining method proposed in this work is made based on short-hole openings due to their predominance in the studied mine. Considering a given room, the roof and floor do not correspond with the roof and floor of the adjacent room. The brow between the short- and long-hole excavations was 8 m. After blasting, the brow is removed by low-profile dozers. This is the main difference between the SHRP and TRP methods, as shown in Figure 5. If the TRP method was adopted in the studied mine, the design of a room considers the same roof and floor of the adjacent room. This conceptual divergence implies different dilution indices that are evaluated in this study.
At some spots in the studied ore body, the dip is greater than 23°. In these places, long-hole geometry is more adequate and the distances between the mining galleries are up to 17 m, according to Figure 5. In this case, the horizontal spacing of the long hole is 8 m, where the sections of the pillars are 3 m × 17 m. Considering these dimensions, the openings have an area of 132 m2, which indicates the need to install rock bolts to support the roof. This stabilization is an important aspect as it reduces the possibility of dilution with material coming from the roof. After waste removal, the ore is blasted and removed by low-profile loader.
Dilution
The main variables to evaluate the efficiency of a mining method are the dilution and loss of ore. Considering underground mining following the SHRP method, the ore loss is denominated as underbreak and dilution as overbreak. Jang et al. (2015) stated that overbreak measures the contamination of ore with host rocks. The principle of this measure is the relationship between the mass of waste that contaminates the ore during development. Figure 6 shows a cross-section between two rooms following the TRP method to illustrate the concept of dilution. Considering the shape of the ore body, the limit of the planned geometry will be defined. However, during development, deviations may occur in relation to the mining plan. The main causes are instability, geomechanical factors, rock mass characteristics, or operational deviations. The presence of these factors defines the operational geometry.
Considering that the scope of the present work includes evaluation of the SHRP method compared with the TRP method, the dilution evaluation is a key step to measure differences between the methods. For this, the planned dilution was calculated following the TRP method parameters, as shown in Figure 6. As the studied mine applied the SHRP method, the operational geometry was compared with the short-hole geometry that allows calculation of the operational dilution. Equation [1] measures the planned dilution
(PD), while Equation [2] measures the operational dilution (OD). The PD parameter is calculated according to the geometry described in Figure 6, following the TRP method. The variables used in the equations are described in Table I, which also informs the units of measurement. The product PV x gmat represents the planned production (PP) according to the opening dimensions in the SHRP method described in Figure 4.
Experimental application
The methodology was created and applied at a Brazilian gold mine. The studied mine is dedicated to underground mining of narrow bodies with a mean thickness of 1.8 m and down-dip ranging between 20° and 25°. The application of the SHRP method is unprecedented in Brazil and the method to evaluate performance based on the TRP method is a meaningful discussion. The most relevant operational difference between the methods is the free access between rooms in the TRP method. In contrast, the SHRP method does not have this type of access. The procedure selected to evaluate the efficiency of the SHRP method is the comparative analysis of dilution indicators.
Selection of mining areas
Figure 7 represents a plan view of the rooms and pillars of the mine. The figure shows the secondary ramp RS604, one of three secondary ramps selected for the study, especially rooms C5, C6, C7, and C14. The areas bordered in purple represent the short holes selected for study in this area. The area that separates them laterally represents the pillars that support the roof of the mine. In addition, the traffic of equipment in one room is isolated from the other rooms. The present study considered a total of 12 rooms that comprised 105 short holes.
Regarding drilling and blasting operations, it is important to highlight some aspects. As shown in Figure 7, each short hole has a length ranging from 2 to 8 m. Considering that drilling equipment has drill rods of 3.2 m in length, short holes with higher lengths require additional drilling in the adjacent room. After drilling, blasting is performed on both sides. This is standard procedure; however, there may be variations that require specific blasting projects that are not addressed in the present study.
Table I
List of variables applied to the dilution calculation
Planned volume is the multiplication of height, width, and advance (SHRP method)
Figure 6—Cross-section of a mining plan according to the traditional roomand-pillar method, detailing the planned and operational dilution
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method
Equipment selection
The equipment selected to perform the mining operations was the same for both SHRP and TRP. The equipment used to provide safe conditions into the openings were the scaling machine and rockbolting rigs. Scaling is the operation of knocking loose rocks off the back. The studied mine uses scaling machines to take loose rocks off the back before any other operations, to avoid unsafe conditions. Roof bolting is the operation devoted to stabilizing the back and walls by installing rock bolts and cable bolts. The equipment selected for roof bolting was a rock-bolting rig, which was applied to installation of both cable bolts and rock bolts.
After the stabilization and safety operations, the mining operations comprised drilling, blasting, loading, and haulage start. The studied mine adopts low-profile face-drilling rigs for drilling operations. This equipment fits well in current opening dimensions and provides good operating conditions. A low-profile loader was selected for loading operations due to the reduced opening dimensions, considering the minimum stoping height of 2.4 m. Haulage was performed by low-profile dump trucks that hauled the materials to the destination.
Results and Discussion
The comparative analysis between PD and OD indicators for each selected short hole represents the main parameter of comparison between the SHRP and TRP methods. These indicators are defined by Equations [1] and [2], respectively. The results for the rooms of secondary ramp RS604, according to Equations [1] and [2], are available in Figure 8. After analyzing the PD and OD results, only one (SH C04) of the 28 short holes evaluated obtained an operational dilution higher than the planned dilution: the other 27 stopes indicated that the operational dilution following the SHRP method was lower than the planned dilution that followed the TRP methodology. These results indicate that the SHRP methodology allows more selective mining, where the ore is mined with lower
levels of waste contamination. Minor ore contamination implies lower generation of tailings in minerals processing.
Figure 9 shows the mean PD and OD values for all three secondary accesses evaluated in the present study. These results also indicate significant reduction of the dilution for the other secondary accesses after adoption of the SHRP method. Another important aspect for evaluation is the position of the short hole according to the elevation. As the ore body has a down-dip of up to 25°, the mining direction of some short holes is upwards or downwards in the same room. Both configurations can be comparatively evaluated between the SHRP and TRP methods. Figure 10 shows the results of OD and PD for upward and downward short holes. As in other comparisons, the SHRP method had the lowest dilution in any mining direction.
The evaluation of mining geometries in the mining industry is often performed with the use of computer graphic design tools. Some mining methods allow optimization techniques and mathematical modelling to be applied in order to define, among several possible alternatives, the most suitable mining geometry. This assumption is not valid for the present study due to the operational characteristics of the studied method.
The analysis of mining geometries is a summary of the fit between the dimensions of the openings and the ore body shape. The dimensions of the openings are minimal and compatible with mining equipment. Therefore, the methodology of the present work was based on adjusting the openings with the help of computational tools of graphic design. In addition, the dilution calculation considered classical concepts widely published in literature. Currently, some bibliographical references describe dilution calculation methods that are more accurate and adapted to the geometry of the mineralized body. Vokhmin et al. (2017) described a method that considers irregular contact zones. These authors developed equations where the dilution is calculated considering the irregularity of the geological contacts. Considering that the experimental application refers to gold mineralization of narrow
Figure 7—Plan view of some rooms connected to secondary ramp RS604 that were selected for comparative analysis
Reducing
mining tailings and operational dilution: a new application of the room-and-pillar mining method
veins with microstructures, it is possible to identify more precise dilution values. However, the algorithm developed by Vokhmin et al. (2017) is not yet available on an industrial scale and was not applied in the present study.
Conclusions
The mining industry, especially underground operations, are under severe pressure from society to reduce waste and environmental impacts. Other industrial segments have already implemented reduced waste and environmental impacts in their value chains, applying concepts of circular economy and green technologies.
However, the mining industry has adapted its operations slowly. In the case of Brazilian mining, the wasteless concept does not yet have widespread application, as traditional methods of mining continue to dominate. The present work describes the first industrial application of the SHRP method in a gold mine in Brazil. Dilution is directly related to the generation of tailings in mining, as it increases the presence of gangue minerals in minerals processing. The application of this methodology in other mines can reduce the demand for new tailing dams. Therefore, the present work describes an important step of the mining industry in the application of wasteless practices.
Figure 8—Planned and operational dilution for all short holes of secondary ramp RS604
Figure 9 —Comparative analysis: dilution rate stratified by all three secondary ramps
Figure 10—Analysis of dilution rate regarding the mining direction for all secondary ramps
Reducing mining tailings and operational dilution: a new application of the room-and-pillar mining method
This study fulfilled the objective of developing a method capable of reducing operational dilution over traditional methods (TRP). The results indicate that the reduction in operational dilution is steady and significant. The mean operating dilution in the 105 short holes evaluated was 5.3 times lower than the planned dilution in the TRP method. The results indicate that this proportion extends to the individual short holes, because more than 96% of the short holes of the secondary ramp RS604 achieved lower operational dilution than planned. When comparing upward and downward mining areas, operating dilution remained significantly lower. Even after shift in direction along the ore body, the operational dilutions of upward and downward mining were 7 times and 2.4 times lower, respectively. These results confirm that the SHRP method is more efficient because it reduces the generation of mining waste and promotes better economic exploitation of the deposit. The SHRP method was successfully implemented. Operation dilution control is the main achievement after SHRP operational implementation. The results obtained in this research may be significantly improved with further investigations of two aspects. The first is to develop adaptations of other mining methods to reduce losses and waste generation. The second is the implementation of algorithms devoted to the calculation of dilution as applied to narrow bodies. Vokhmin et al. (2017) developed applications for contacts for which the shape approaches a sinusoidal and sawtooth profile. New algorithms can function as a plug-in of mine design software and support the adjustment of mining geometries to narrow bodies with different shapes. Further studies may verify the rock mechanics aspects relating to excavation and pillar stability.
Acknowledgements
The authors would like to thank Brio Gold Inc. for supporting this research. The present work was carried out with the support of CNPq, National Council of Scientific and Technological Development - Brazil (Process number: 155140/2018-3). English editing of this manuscript was carried out by Kathryn C. Sole (PhD).
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Contributions
E. da Cunha Rodovalho - First lead author (carried out the study and wrote the paper). T.M. El Hajj carried out the operational studies and literature review. G. Calegari Teodoro carried out the operational studies. G. de Tomi supervised the study. J.A. Soares Tenório proofread the manuscript. u
Abstract Submission Deadline – 05 August 2024
OBJECTIVES
The conference is centered on improving safety, health and environmental practices within the mining and metallurgical industry. It seeks to create a platform for knowledge-sharing and idea exchange among various stakeholders, including mining companies, Department of Mineral Resources and Energy (DMRE), Minerals Council South Africa, labour unions, and health and safety practitioners at all levels within the industry. The main objectives of the conference are as follows:
Promoting Learning: The conference aims to facilitate a space where attendees can learn from each other’s experiences and best practices. This will help enhance overall safety and environmental standards in the mining and metallurgical sector.
Addressing Safety, Health, and Environment: The conference will focus on discussions related to safety and health issues within the industry, including the well-being of employees, contractors, and local communities. It will also emphasize the importance of reducing the environmental impact of mining and metallurgical processes.
Enhancing Relationships with Local Communities: Recognizing the significance of local communities, the conference will address the issues surrounding their relationship with mining companies. This can include concerns about environmental effects, community engagement, and socioeconomic impacts.
Zero Harm Approach: The conference will highlight the importance of adopting a ‘zero harm’ approach to health and safety in the mining and metallurgical sector. This means striving for an injury-free and accidentfree workplace.
Value-Based Approach: A value-based approach to health and safety implies that these aspects are not just compliance-driven but are deeply ingrained in the organizational values and culture. This conference aims to encourage discussions and strategies to promote such a value-based approach.
Addressing Key Challenges: The conference will tackle major challenges in the mining industry, such as logistics, energy usage, and safety concerns related to employees, contractors, and communities. By bringing together diverse stakeholders and sharing their expertise and experiences, this conference hopes to foster a safer and more sustainable mining and metallurgical industry. It emphasizes the importance of collaboration and collective efforts to address the complex challenges faced by the sector.
PARTNERSHIP OPPORTUNITIES
Sponsorship opportunities are available. Companies wishing To partner on this event should contact the Conference Coordinator.
WHO SHOULD ATTEND
The conference should be of value to:
• Safety practitioners
• Mine management
• Mine health and safety officials
• Engineering managers
• Underground production supervisors
• Surface production supervisors
• Environmental scientists
• Minimizing of waste
• Operations manager
• Processing manager
• Contractors (mining)
• Including mining consultants, suppliers and manufacturers
• Education and training
• Energy solving projects
• Water solving projects
• Unions
• Academics and students
• DMRE
CALL FOR PAPERS
Call for papers on the topics of safety, health and environment Prospective authors are invited to submit titles and abstracts of their presentations in English and not longer than 500 words. Abstracts should be submitted to: Camielah Jardine, Head of Conferencing, E-mail: camielah@saimm.co.za
KEY DATES
Abstract submission – 5 August 2024
Acceptance of Abstracts – 19 August 2024
Extended Abstract submission - 23 September 2024
Camielah Jardine, Head of Conferences and Events FOR FURTHER INFORMATION, CONTACT: E-mail: camielah@saimm.co.za Tel:+27 11 530 0237 Web: www.saimm.co.za
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NATIONAL & INTERNATIONAL ACTIVITIES
16-17 September 2024 — The Control Conference Africa 2024
The following organizations have been admitted to the Institute as Company Affiliates
A and B Global Mining (Pty) Ltd
acQuire Technology Solutions
AECI Mining Chemicals, a division of AECI
Mining Ltd
African Pegmatite
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AMIRA International Africa (Pty) Ltd
Anglogold Ashanti Ltd
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Becker Mining (Pty) Ltd
Bluhm Burton Engineering Pty Ltd
Caledonia Mining South Africa
Castle Lead Works
DDP Specialty Products South Africa (Pty) Ltd
De-Tect Unit Inspection (Pty) Ltd
Digby Wells and Associates
EHL Consulting Engineers (Pty) Ltd
Elbroc Mining Products (Pty) Ltd
Epiroc South Africa (Pty) Ltd
Ex Mente Technologies (Pty) Ltd
Exxaro Resources Limited
FLSmidth Minerals (Pty) Ltd
G H H Mining Machines (Pty) Ltd
Geobrugg Southern Africa (Pty) Ltd
Glencore
Gravitas Minerals (Pty) Ltd
Hatch (Pty) Ltd
Herrenknecht AG
Impala Platinum Holdings Limited
IMS Engineering (Pty) Ltd
Ingwenya Mineral Processing
Ivanhoe Mines SA
Longyear South Africa (Pty) Ltd
Malvern Panalytical (Pty) Ltd
Maptek (Pty) Ltd
Micromine Africa (Pty) Ltd
Minearc South Africa (Pty) Ltd
Minerals Council of South Africa
MineRP Holding (Pty) Ltd
Mining Projection Concepts (Pty) Ltd
Mintek
MLB Investments CC
Modular Mining Systems Africa (Pty) Ltd
Murray & Roberts Cementation (Pty) Ltd
Optron (Pty) Ltd
Paterson & Cooke Consulting Engineers (Pty) Ltd
Redpath Mining (South Africa) (Pty) Ltd
Rosond (Pty) Ltd
Roytec Global (Pty) Ltd
Rustenburg Platinum Mines Limited - Union
Salene Mining (Pty) Ltd
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SENET (Pty) Ltd
Sibanye Gold Limited
Sound Mining Solution (Pty) Ltd
SRK Consulting SA (Pty) Ltd
Tomra (Pty) Ltd
Trans-Caledon Tunnel Authority
Ukwazi Mining Solutions (Pty) Ltd
VBKOM Consulting Engineers
Weir Minerals Africa
Zutari (Pty) Ltd
MINE CLOSURE CONFERENCE 2025
19-20 FEBRUARY 2025
MASLOW HOTEL, SANDTON, JOHANNESBURG
ABOUT THE CONFERENCE
During closure planning there are usually four parties involved, being the mining house, external stakeholders, consultants and the authorities who are responsible for closure plan and eventual relinquishment approval. There are subsequently numerous conflicting ideals between the parties during the evolution of mine planning to post-closure. This leads to unrealistic closure expectations and vague obligations that result in the lack of setting or accepting specific closure and relinquishment criteria. Without clear direction, achieving a closure certificate in South Africa remains uncertain. This leads industry to adopt very different positions around closure planning that ranges between best practice, compliance to legislation to minimal planning.
Successful relinquishment has been achieved internationally by creating a value chain for sustainable post-mining economies as early as possible. In South Africa relinquishment could possibly be achieved successfully by complying to the current legislative closure approach or alternatively by creating third party value by means of parallel economies. This could potentially be supplemented with a regional closure approach between mining houses. It is therefore imperative that the third party needs to be part of the value chain development and execution through meaningful community engagement to ensure the benefits of local knowledge and achieve social acceptance. Once the long-term value chain is in place it lays the foundation for closure- and relinquishment criteria and social integration. ESG compliance adds another layer to closure planning but can be very useful to add specific criteria and expedite closure actions. Regardless of the approach taken, engagement with regulators is required for overall alignment and changes to policies.
OBJECTIVES
To assist interested parties, industry and regulators to better understand the closure challenges within the current uncertain and fragmented closure space. This is underpinned by Environmental Social and Governance (ESG) compliance, reducing closure liabilities and risks, just transition and the ultimate goal of relinquishment. The conference will aim to present, workshop and identify the pressing focus areas for further research and engagement with the regulators.
TOPICS
• Current challenges towards relinquishment
• Alternative closure solutions
• Socio economics and just transitions
• ESG compliance and considerations to closure
CONFERENCE STRUCTURE
• Conference proceedings
• Conference panel discussion to identify focus areas, needs and future actions
• Planned breakaway after the conference for academics and after interested parties to discuss future research topics flowing from the conference
OUTCOMES
• Identify relevant challenges and research topics
• Identifying areas for future engagement with the regulator to influence policy and legislation to address the challenges
• To promote and understand the appetite of the industry to consider alternative closure solutions
FOR FURTHER INFORMATION, CONTACT:
Gugu Charlie,
E-mail: gugu@saimm.co.za
Tel: +27 11 530 0238
Web: www.saimm.co.za
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