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Algorithmic Optimization of an Underground Witwatersrand-Type Gold Mine Plan

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Abstract

In the mining environment, mine planning is complicated by the presence of unfavorable environmental conditions, limited knowledge of the shape and size of the deposit, ore body characteristics, and volatile market conditions. In this paper, we propose a top-down algorithmic approach to strategically optimize the cutoff grade and net present value (NPV), and implement its solutions at the operation level, while simultaneously mitigating operation risks, to maximize the life of an ultra-deep gold mine from the Witwatersrand Basin, South Africa. To date, the Witwatersrand Basin has contributed about 28% of the world’s total gold supply from a series of Mesoarchaean quartz pebble conglomerate units (referred to as reefs). Through a quantitative analysis using algebraic and stochastic methods, we ranked mining variables in terms of their margin sensitivity and impact/adjustability efficacy. The results of this study showed the following. By using our proposed approach, an underground mine plan can be optimized by focusing on few key variables. Strategic mining of combinations of high-grade panels with low-grade panels and counter-balancing their risk profiles can yield optimal executable mine plan results (i.e., higher NPV, ideal profit margin, and lower risk) without sterilizing a given mineral resource for underground mining operations.

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References

  • Ajak, A. D., Lilford, E., & Topal, E. (2018). Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty. Resources Policy, 55, 62–79.

    Article  Google Scholar 

  • Alford, C., Brazil, M., & Lee, D. H. (2007). Optimisation in underground mining. Springer, pp. 561–577.

  • Aristizabal, R. J. (2012). Estimating the parameters of the three-parameter lognormal distribution. M.Sc. thesis, Florida International University, Florida, United States of America.

  • Campeau, L.-P., & Gamache, M. (2019). Short-term planning optimization model for underground mines. Computers & Operations Research. https://doi.org/10.1016/j.cor.2019.02.005.

    Article  Google Scholar 

  • Carlyle, W. M., & Eaves, B. C. (2001). Underground planning at Stillwater mining company. Interfaces, 31, 50–60.

    Article  Google Scholar 

  • Collard J (2013). Strategic planning of an underground mine with variable cut-off grades. Les Cahiers du GERAD G201386.

  • Del Castillo, M. F., & Dimitrakopoulos, R. (2019). Dynamically optimizing the strategic plan of mining complexes under supply uncertainty. Resources Policy, 60, 83–93.

    Article  Google Scholar 

  • Deloitte (2014). Tracking the trends 2014. The top 10 issues mining companies will face in the coming year. Accessed on 11 May 2020 at https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Energy-and-Resources/dttl-er-Tracking-the-trends-2014_EN_final.pdf.

  • Frimmel, H. E. (2014). A giant Mesoarchaean crustal gold–enrichment episode: possible causes and consequences for exploration. Society of Economic Geologists Special Publications, 18, 209–234.

    Google Scholar 

  • Frimmel, H. E. (2018). Episodic concentration of gold to ore grade through Earth’s history. Earth-Science Reviews, 180, 148–158.

    Article  Google Scholar 

  • Frimmel, H. E., Groves, D. I., Kirk, J., Ruiz, J., Chesley, J., & Minter, W. E. L. (2005). The formation and preservation of the Witwatersrand goldfields, the largest gold province in the world. In J. W. Hedenquist, J. F. H. Thomson, & R. J. Goldfarb (Eds.), Economic geology one hundredth anniversary volume (pp. 769–797). Littleton, CO: Society of Economic Geologists.

    Google Scholar 

  • Frimmel, H. E., & Hennigh, Q. (2015). First whiffs of atmospheric oxygen triggered onset of crustal gold cycle. Mineralium Deposita, 50, 5–23.

    Article  Google Scholar 

  • Frimmel, H. E., & Nwaila, G. T. (2020). Geologic evidence of syngenetic gold in the Witwatersrand Goldfields, South Africa. In: Sillitoe, T., Goldfarb, R., Robert, F., & Simmons, S. (Eds.), Geology of the major gold deposits and provinces of the world. Soc. Econ. Geol., Littleton, Special Publ. 23, in press.

  • Heinrich, C. (2015). Witwatersrand gold deposits formed by volcanic rain, anoxic rivers and Archaean life. Nature Geoscience, 8, 206–209. https://doi.org/10.1038/ngeo2344.

    Article  Google Scholar 

  • Jory, S., Benamraoui, A., Roshan, B. D., & Madichie, N. O. (2016). Net present value analysis and the wealth creation process: A case illustration. The Accounting Educators Journal, 26, 85–99.

    Google Scholar 

  • Kositcin, N., & Krapež, B. (2004). SHRIMP U-Pb detrital zircon geochronology of the Late Archaean Witwatersrand Basin of South Africa: relation between zircon provenance age spectra and basin evolution. Precambrian Research, 129, 141–168.

    Article  Google Scholar 

  • Lane, K. F. (1964). Choosing the optimum cut-off grade. Colorado School of Mines Quarterly, 59, 485–492.

    Google Scholar 

  • Lane, K. F. (1988). The economic definition of ore: Cut-off grade in theory and practice. London: Mining Journal Books Limited. ISBN: 978-0-9941852-7-3.

    Google Scholar 

  • Little, J., Knights, P., & Topal, E. (2013). Integrated optimization of underground mine design and scheduling. Journal of the Southern African Institute of Mining and Metallurgy, 113, 775–785.

    Google Scholar 

  • McDonald, J. H. (2014). Handbook of biological statistics (3rd ed., pp. 140–144). Baltimore, MD: Sparky House Publishing.

    Google Scholar 

  • Müller, J., & Frimmel, H. E. (2010). Numerical analysis of historic gold production cycles and implications for future sub-cycles. The Open Geology Journal, 4, 29–34.

    Article  Google Scholar 

  • Murphy, M. M., Ellenberger, J. L., Esterhuizen, G. S., & Miller, T. (2016). Analysis of roof and pillar failure associated with weak floor at a limestone mine. International Journal of Mining Science and Technology, 26, 471–476.

    Article  Google Scholar 

  • Musingwini, C. (2016). Presidential address: Optimization in underground mine planning- developments and opportunities. Journal of the Southern African Institute of Mining and Metallurgy, 116, 809–820.

    Article  Google Scholar 

  • Nehring, M., Topal, E., Kizil, M., & Knights, P. (2012). Integrated short-and medium-term underground mine production scheduling. Journal of the Southern African Institute of Mining and Metallurgy, 112, 365–378.

    Google Scholar 

  • Neingo, P. N., & Tholana, T. (2016). Trends in productivity in the South African gold mining industry. Journal of the Southern African Institute of Mining and Metallurgy, 116, 283–290.

    Article  Google Scholar 

  • Newman, C., Newman, D., & Dupuy, R. (2020). Development of a multiple level underground limestone mine from geology through mine planning. International Journal of Mining Science and Technology, 30, 63–67.

    Article  Google Scholar 

  • Nwaila, G. T., Manzi, M. S. D., Kirk, J., Maselela, H. K., Durrheim, R. J., Rose, D. H., et al. (2019). Recycling of palaeoplacer gold through mechanical and post-depositional mobilisation in the Neoarchaean Black Reef Formation, South Africa. The Journal of Geology, 127, 137–166.

    Article  Google Scholar 

  • Nwaila, G. T., Zhang, S. E., Frimmel, H. E., et al. (2020). Local and target exploration of conglomerate-hosted gold deposits using machine learning algorithms: a case study of the Witwatersrand gold ores, South Africa. Natural Resources Research, 29, 135–159.

    Article  Google Scholar 

  • O’Sullivan, D., & Newman, A. (2015). Optimization-based heuristics for underground mine scheduling. The European Journal of Operational Research, 241, 248–259.

    Article  Google Scholar 

  • Schofield, N., Moore, J., & Carswell, J. (2013). Mine to mill reconciliation. Australasian Institute of Mining and Metallurgy Bulletin, pp. 38–42.

  • Tholana, T., Musingwini, C., & Njowa, G. (2013). An algorithm to construct industry cost curves used in analysing cash cost performance of operations for selected minerals in South Africa. Journal of the Southern African Institute of Mining and Metallurgy, 113, 473–484.

    Google Scholar 

  • Tucker, R. F., Viljoen, R. P., & Viljoen, M. J. (2016). A review of the Witwatersrand Basin—the world’s greatest goldfield. Episodes, 39, 105–133.

    Article  Google Scholar 

  • Underground Mining Solutions LLC. (2020). Maximizing value, one mine at a time. Accessed on 11 May 2020 at http://www.ugmsolutions.com/software.

  • WGC (World Gold Council). (2020). Global gold production. Accessed on 11 May 2020 at https://www.gold.org/.

  • Yaylacı, E. D., & Düzgün, H. S. (2017). Evaluating the mine plan alternatives with respect to bottom-up and top-down sustainability criteria. Journal of Cleaner Production, 167, 837–849.

    Article  Google Scholar 

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Acknowledgments

Glen Nwaila acknowledges funding from National Research Foundation (NRF) Thuthuka Grant and DSI-NRF CIMERA. We thank Mark Burnett for his insightful discussions, industry knowledge and pre-review of the 1st draft manuscript. Julie E. Bourdeau is acknowledged for her pre-reviews of the draft manuscript. We thank the Editor-in-Chief (John Carranza) and two anonymous reviewers for their constructive comments, which helped us to improve the manuscript.

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Authors and Affiliations

  1. School of Geosciences, University of the Witwatersrand, Private Bag 3, Wits, 2050, South Africa

    G. T. Nwaila

  2. PG Techno Wox, 43 Patrys Avenue, Helikon Park, Randfontein, 1759, South Africa

    S. E. Zhang

  3. Tolmay Enterprises, 150 Galena Avenue, Kloofendal, 1703, South Africa

    L. C. K. Tolmay

  4. Bavarian Georesources Centre, Department of Geodynamics and Geomaterials Research, Institute of Geography and Geology, University of Würzburg, Am Hubland, 97074, Würzburg, Germany

    H. E. Frimmel

  5. Department of Geological Sciences, University of Cape Town, Rondebosch, 7700, South Africa

    H. E. Frimmel

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  1. G. T. Nwaila
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  2. S. E. Zhang
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  3. L. C. K. Tolmay
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  4. H. E. Frimmel
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Correspondence to G. T. Nwaila.

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Nwaila, G.T., Zhang, S.E., Tolmay, L.C.K. et al. Algorithmic Optimization of an Underground Witwatersrand-Type Gold Mine Plan. Nat Resour Res 30, 1175–1197 (2021). https://doi.org/10.1007/s11053-020-09772-7

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  • DOI: https://doi.org/10.1007/s11053-020-09772-7

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