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Appraising economic uncertainty in open-pit mining based on fixed and variable metallurgical recovery

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Given that a source is located underground and detected by sounds that cannot be completely known or predicted, every stage of the operation from grade changes to product sales exhibits uncertainties. Parameters and constraints used in mining optimizations (sales price, costs, efficiency, etc.) comprise uncertainties. In this research, chrome open-pit resource optimization activities were performed in the province of Adana, Turkey. Metallurgical recovery, which is considered a constant as an optimization parameter in mining software, has been optimized as a variable based on fixed and variable values related to the waste material grade of processing. Based on scenario number 7, which yields the highest net present value in both optimizations, this difference corresponds with an additional $1.4 million, i.e., 7% minimum. When the number of products sold were compared, a difference of 25,977 tons of concentrate production was noted (Optimization II produces less than Optimization I). In summary, concentrated efficiency and economic findings show that using variable metallurgical recovery parameters in NPV estimation improves optimization success by reducing the level of uncertainty.
Rocznik
Strony
699--713
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Çukurova University, Department of Mining Engineering, Adana, Turkey
autor
  • Çukurova University, Department of Mining Engineering, Adana, Turkey
Bibliografia
  • [1] Z. Levinson, R. Dimitrakopoulos, Adaptive simultaneous stochastic optimization of a gold mining complex: A case study. The South Afr. Insitute. Min. Metall. 120, 221-232 (2015). DOI: http://dx.doi.org/10.17159/2411-9717/829/2020.
  • [2] G. Bardossy, J. Fodor, Evaluation of Uncertainties and Risks in Geology. Springer-Verlag, Berlin Heidelberg. (2004).
  • [3] B. Tutmez, An uncertainty oriented fuzzy methodology for grade estimation. Comput. Geosci. 33, 280-288 (2007). DOI: https://doi.org/10.1016/j.cageo.2006.09.001.
  • [4] B. Groeneveld, E. Lame, Flexible open-pit enamel design under uncertainty. J. Min. Sci. 47, 212-226 (2011). DOI: https://doi.org/10.1134/S1062739147020080.
  • [5] C. Montoya, X. Emery, E. Rubio, J. Wiertz, Multivariate resource modelling for assessing uncertainty in mine design and mine planning. J. South. Afr. Inst. Min. Metall. 112, 353-363 (2012).
  • [6] R.C. Goodfellow, R. Dimitrakopoulos, Global optimization of open-pit mining complexes with uncertainty. Appl. Soft. Comput. 40, 292-304 (2016). DOI: https://doi.org/10.1016/j.asoc.2015.11.038.
  • [7] J.B. Boisvert, M.E. Rossi, K. Ehrig, C.V. Deutsch, Geometallurgical modeling at Olympic dam mine. South Aus tralia. Math. Geosci. 45, 901-925 (2013). DOI: https://doi.org/10.1007/s11004-013-9462-5.
  • [8] M. Garrido, J.M. Ortiz, F. Villaseca, W. Kracht, B. Townley, R. Miranda, Change of support using non-additive variables with Gibbs Sampler: application to metallurgical recovery of sulphide ores. Comput. Geosci. 122, 68-76 (2019). DOI: https://doi.org/10.1016/j.cageo.2018.10.002.
  • [9] L.A. Freire, J.Y.P. Leite, D.N. da Silva, B.G. da Silva, J.C.S. Oliveira, Behavior of the chromite tailings in a centrifugal concentrator (Falcon). REM Int. Eng. J., Ouro. Preto. 72, 147-152 (2019). DOI: http://doi.org/10.1590/0370-44672018720016.
  • [10] R.G. Dimitrakopoulos, S.A.A. Sabour, Evaluating mine plans under uncertainty: can the real options make a difference? Resour. Policy. 32, 116-125 (2007). DOI: https://doi.org/10.1016/j.resourpol.2007.06.003.
  • [11] G.L. Smith, S.N. Surujhlal, K.T. Manyuchi, Strategic mine planning - communicating uncertainty with scenarios. J. South Afr. Inst. Min. Metall. 108, 725-732 (2008).
  • [12] M. Brunette, Incorporating geo-metallurgical information into mine production scheduling. J Oper. Res. Soc. 62, 60-68 (2011). DOI: https://doi.org/10.1057/jors.2009.174.
  • [13] M. Brunette, Grade control in multi-variable ore deposits as a quality management problem under uncertainty. Int. J. Qual. Reliab. Managing. 32, 334-345 (2015). https://doi.org/10.1108/IJQRM-08-2013-0134.
  • [14] E. Moosavi, J. Gholamnejad, Optimal extraction sequence modeling for open pit mining operation considering the dynamic cutoff grade. J. Min. Sci. 52, 956-964 (2016). DOI: https://doi.org/10.1134/S1062739116041465.
  • [15] L. Montiel, R. Dimitrakopoulos, A heuristic approach to the stochastic optimization of mine production schedules. J. Heuristics. 23, 397-415 (2017). DOI: https://doi.org/10.1007/s10732-017-9349-6.
  • [16] N. Morales, S. Seguel, A. Cáceres, E. Jélvez, M. Alarcón, Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning. Minerals. 9, 1-26 (2019). DOI: https://doi.org/10.3390/min9020108.
  • [17] D. Kržanović, V. Conić, D. Bugarin, I. Jovanović, D. Božić, Maximizing economic performance in the mining industry by applying bioleaching technology for extraction of polymetallic mineral deposits. Minerals 9, 1-14 (2019). DOI: https://doi.org/10.3390/min9070400.
  • [18] D.S. Rao, Minerals and Coal Process Calculations. CRC Press, London (2016).
  • [19] Metso, Basics in Minerals Processing, 11th edn, Metso Corporation, (2018).
  • [20] N. Morales, E. Jélvez, P. Nancel-Penard, A. Marinho, O. Guimaraes, A comparison of conventional and direct block scheduling methods for open-pit mine production scheduling. In: Proceedings of the 37th APCOM conference, Fairbanks, AK, USA, 23-27 May 2015; Bandopadhyay, Fairbanks, Society for Mining, Metallurgy & Exploration, 1040-1051 (2015).
  • [21] L. Lerchs, F. Grossman, Optimum design of open-pit mines. Can. Min. Metall. Bull. 58, 17-24 (1965).
  • [22] M. Smith, Educational material [Online]. https://drive.google.com/file/d/0ByH6SOBdx3WcMDJrLUl3aC1MbFU/ view. Accessed 15 March 2016 (2014).
  • [23] MiningMath, Tutorial SimSched direct block scheduler beta. https://knowledge.miningmath.com/start-here/ economic-values. Accessed 15 December 2021, (2015).
  • [24] F.R. Souza, H.R. Burgarelli, A.S. Nader, C.E.A. Ortiz, L.S. Chaves, L.A. Carvalho, V.F.N. Torres, T.R. Câmara, R. Galery, Direct block scheduling technology: analysis of Avidity. REM Int. Eng. J. 71, 97-104 (2018). DOI: http://doi.org/10.1590/0370-44672017710129.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aaab23bf-e126-4562-bdb9-186e2c4c013e
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