Modelo geometalúrgico del índice de trabajo para un depósito pórfido cuprífero mediante aprendizaje automático

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Nelson Jesús Ramos-Armijos
https://orcid.org/0000-0001-9188-6422
Marilú Calderón-Celis
https://orcid.org/0000-0002-1374-9307

Resumen

El desarrollo de proyectos mineros en etapas de exploración y prefactibilidad conlleva desafíos relacionados a la heterogeneidad e incertidumbre geológica y de procesamiento debido a la variabilidad en sus atributos primarios y de respuesta. Por lo tanto, el objetivo de esta investigación radica en elaborar el modelo geometalúrgico del índice de trabajo de Bond. Para esto se desarrollaron modelos de regresión lineal en Jupyter Notebook considerando 790 muestras de resistencia a la compresión uniaxial de la roca (UCS), litologías, zonas minerales e índice de trabajo de Bond (BWi) en un depósito pórfido cuprífero. Los resultados indican relación directamente proporcional de tipo lineal entre el BWi y UCS, generando modelamientos con desempeños aceptables de R2 entre 0.76 a 0.90. Además, las litologías y zonas mineralizadas en el depósito estudiado son características relevantes relacionadas a la conminución. Finalmente, de acuerdo a la competencia de la roca, esta se clasifica como tipo media a dura, dura y muy dura en las zonas de óxidos, mixtos y sulfuros primarios respectivamente.

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Ramos-Armijos, N. J., & Calderón-Celis, M. (2024). Modelo geometalúrgico del índice de trabajo para un depósito pórfido cuprífero mediante aprendizaje automático. FIGEMPA: Investigación Y Desarrollo, 18(2), 42–60. https://doi.org/10.29166/revfig.v18i2.6700
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Biografía del autor/a

Nelson Jesús Ramos-Armijos, Universidad Nacional Mayor de San Marcos. Lima, Perú

Unidad de Posgrado. Facultad de Ingeniería Geológica, Minera, Metalúrgica y Geográfica. Ciudadela Universitaria, Av. Venezuela. 15081

Marilú Calderón-Celis, Universidad Nacional Mayor de San Marcos. Lima, Perú

Unidad de Posgrado. Facultad de Ingeniería Geológica, Minera, Metalúrgica y Geográfica. Ciudadela Universitaria, Av. Venezuela. 15081, Perú

Citas

Aljadani, A., Alharthi, B., Farsi, M. A., Balaha, H. M., Badawy, M. and Elhosseini, M. A. (2023) "Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach", Mathematics, 11(19), p. 4055. https://doi.org/10.3390/math11194055

Aras, A., Özşen, H. and Dursun, A.E. (2020) "Using Artificial Neural Networks for the Prediction of Bond Work Index from Rock Mechanics Properties", Mineral Processing and Extractive Metallurgy Review, 41(3), pp. 145–152. https://doi.org/10.1080/08827508.2019.1575216

Bangdiwala, S.I. (2018) "Regression: simple linear", International Journal of Injury Control and Safety Promotion, pp. 113–115. https://doi.org/10.1080/17457300.2018.1426702

Bilal, D. (2017). Geometallurgical estimation of comminution indices for porphyry copper deposit applying mineralogical approach. Master’s Thesis. Availabe at: https://ltu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149944&dswid=1083

Chicco, D., Warrens, M.J. and Jurman, G. (2021) "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", PeerJ Computer Science, 7, pp. 1–24. https://doi.org/10.7717/PEERJ-CS.623

Dehaine, Q., Michaux, S. P., Pokki, J., Kivinen, M. and Butcher, A. R. (2020) "Battery minerals from Finland: Improving the supply chain for the EU battery industry using a geometallurgical approach", European Geologist Journal, 49, pp. 5–11. https://doi.org/10.5281/zenodo.3938855

Deutsch, C. V. (2023) "The Place of Geostatistical Simulation through the Life Cycle of a Mineral Deposit", Minerals, 13(11), p. 1400. https://doi.org/10.3390/min13111400

Dominy, S., O’connor, L., Parbhakar-Fox, A., Glass, H. and Purevgerel, S. (2018) "Geometallurgy—A Route to More Resilient Mine Operations", Minerals, 8(12), p. 560. https://doi.org/10.3390/min8120560

Emmanuel, B., Ajayi, J.A. and Makhatha, E. (2019) "Investigation of copper recovery rate from copper oxide ore occurring as coarse grains locked in a porphyritic fine grain alumina and silica", Energy Procedia, 157, pp. 972–976. https://doi.org/10.1016/j.egypro.2018.11.264

García, G. G., Oliva, J., Guasch, E., Anticoi, H., Coello-Velázquez, A. L. and Menéndez-Aguado, J. M. (2021) "Variability study of bond work index and grindability index on various critical metal ores", Metals, 11(6), p. 970. https://doi.org/10.3390/met11060970

Garrido, M. et al. (2019) "An overview of good practices in the use of geometallurgy to support mining reserves in copper sulfides deposits", In Procemin-Geomet 2019. https://doi.org/10.6084/m9.figshare.11290538

Gholami, A., Asgari, K., Khoshdast, H. and Hassanzadeh, A. (2022) "A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine", Physicochemical Problems of Mineral Processing, 58(3), p. 147841. https://doi.org/10.37190/ppmp/147841

Godoy, D.R., Álvarez, V., Mena, R., Viveros, P. and Kristjanpoller, F. (2024) "Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions", Machines, 12(1), p. 60. https://doi.org/10.3390/machines12010060

Haffez, G. (2012) "Correlation Between Work Index and Mechanical Properties of some Saudi Ores", Journal of Engineering Sciences, 40(1), pp. 271–280. https://doi.org/10.21608/jesaun.2012.112727

Harbort, G., Lam, K. and Sola, C. (2013) The use of Geometallurgy to Estimate Comminution Parameters within Porphyry Copper Deposits. In The Second AusIMM International Geometallurgy Conference 2013. Brisbane, pp. 217–230. https://www.ausimm.com/publications/conference-proceedings/the-second-ausimm-international-geometallurgy-conference-2013/the-use-of-geometallurgy-to-estimate-comminution-parameters-within-porphyry-copper-deposits/

Harbort, G., Manfrino, A. and Wright, J. (2011) Development of the Zafranal Geometallurgical Model. In First AusIMM International Geometallurgy Conference (GeoMet) 2011. Brisbane, pp. 1–12. https://www.ausimm.com/publications/conference-proceedings/first-ausimm-international-geometallurgy-conference-geomet-2011/development-of-the-zafranal-geometallurgical-model/

Hunt, J.A. and Berry, R.F. (2017) ‘Economic geology models #3. Geological contributions to geometallurgy: A review’, Geoscience Canada, 44(3), pp. 103–118. https://doi.org/10.12789/geocanj.2017.44.121

Kalichini, M., Corin, K. C., O’Connor, C. T. and Simukanga, S. (2017) "The role of pulp potential and the sulphidization technique in the recovery of sulphide and oxide copper minerals from a complex ore", Journal of the Southern African Institute of Mining and Metallurgy, 117(8), pp. 803–810. https://doi.org/10.17159/2411-9717/2017/v117n8a11

Kalota, F. (2024) "A Primer on Generative Artificial Intelligence", Education Sciences., 14(2), p. 172. https://doi.org/10.3390/educsci14020172

Kim, S.J., Bae, S.J. and Jang, M.W. (2022) "Linear Regression Machine Learning Algorithms for Estimating Reference Evapotranspiration Using Limited Climate Data", Sustainability, 14(18), p. 11674. https://doi.org/10.3390/su141811674

Lishchuk, V., Koch, P. H., Ghorbani, Y. and Butcher, A. R. (2020) "Towards integrated geometallurgical approach: Critical review of current practices and future trends", Minerals Engineering, pp. 1–16. https://doi.org/10.1016/j.mineng.2019.106072

Lishchuk, V., Lund, C. and Ghorbani, Y. (2019) "Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy", Minerals Engineering, 134, pp. 156–165. https://doi.org/10.1016/j.mineng.2019.01.032

Lishchuk, V. and Pettersson, M. (2021) "The mechanisms of decision-making when applying geometallurgical approach to the mining industry", Mineral Economics, 34(1), pp. 71–80. https://doi.org/10.1007/s13563-020-00220-9

Manakitsa, N., Maraslidis, G. S., Moysis, L. and Fragulis, G. F. (2024) "A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision", Technologies, 12(2), p. 15. https://doi.org/10.3390/technologies12020015

Mohammadi, S., Rezai, B., Abdollahzadeh, A. and Mortazavi, S. (2021) "Evaluation of the geometallurgical indices for comminution properties at Sarcheshmeh porphyry copper mine, Iran", Iranian Journal of Earth Sciences, 13(1), pp. 41–49. https://sanad.iau.ir/journal/ijes/Article/678955?jid=678955

Morales, N., Seguel, S., Cáceres, A., Jélvez, E. and Alarcón, M. (2019) "Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning", Minerals, 9(2), p. 108. https://doi.org/10.3390/min9020108

Mwanga, A., Rosenkranz, J. and Lamberg, P. (2015) "Testing of ore comminution behavior in the geometallurgical context—A review", Minerals, 5(2), pp. 276–297. https://doi.org/10.3390/min5020276

Mu, Y. and Salas, J.C. (2023) "Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit", Processes, 11(6), p. 1775. https://doi.org/10.3390/pr11061775

Nghipulile, T., Moongo, T. E., Dzinomwa, G., Maweja, K., Mapani, B., Kurasha, J. and Amwaama, M. (2023) "Effect of mineralogy on grindability - A case study of copper ores", Journal of the Southern African Institute of Mining and Metallurgy, 123(3), pp. 133–144. https://doi.org/10.17159/2411-9717/1714/2023

Özer, Ü. and Çabuk, E. (2007) "Relationship Between Bond Work Index and Rock Parameters", Istanbul Earth Sciences Review, 20(1), pp. 43–49. https://dergipark.org.tr/en/pub/iuyerbilim/issue/18564/196066

Phetla, T.P. and Muzenda, E. (2010) "A Multistage sulphidisation flotation procedure for a low grade malachite copper ore", World Academy of Science, Engineering and Technology, 70, pp. 255–261. https://doi.org/10.5281/zenodo.1077972

Radhoush, S., Whitaker, B.M. and Nehrir, H. (2023) "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks", Energies, 16(16), p. 5972. https://doi.org/10.3390/en16165972

Ram Chandar, K., Deo, S. and Baliga, A. (2016) "Prediction of Bond’s work index from field measurable rock properties", International Journal of Mineral Processing, 157, pp. 134–144. https://doi.org/10.1016/j.minpro.2016.10.006

Ranjbar, A., Mousavi, A. and Asghari, O. (2021) "Using Rock Geomechanical Characteristics to Estimate Bond Work Index for Mining Production Blocks", Mining, Metallurgy & Exploration, 38(6), pp. 2569–2583. https://doi.org/10.1007/s42461-021-00498-5

Rossi, M. and Deutsch, C. (2014) Mineral Resource Estimation. Dordrecht: Springer Dordrecht. https://doi.org/10.1007/978-1-4020-5717-5

Saldaña, M., Gálvez, E., Navarra, A., Toro, N., and Cisternas, L. A. (2023) "Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry", Materials, 16(8), p. 3220. https://doi.org/10.3390/ma16083220

Sepúlveda, E. (2018). Quantification of uncertainty of geometallurgical variables for mine planning optimisation. Doctoral Thesis. Available at: https://hdl.handle.net/2440/114242

Schlesinger, M. E., King, M. J., Sole, K. C. and Davenport, W. G. (2011) "Production of Cu Concentrate from Finely Ground Cu Ore", In Extractive Metallurgy of Copper. Elsevier, pp. 51–71. https://doi.org/10.1016/b978-0-08-096789-9.10004-6

Taye, M.M. (2023) "Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions", Computers, 12(5), p. 91. https://doi.org/10.3390/computers12050091

Todorovic, D., Trumic, M., Andric, L., Milosevic, V. and Trumic, M. (2017) "A quick method for bond work index approximate value determination", Physicochemical Problems of Mineral Processing, 53(1), pp. 321–332. https://www.journalssystem.com/ppmp/A-quick-method-for-Bond-work-index-approximate-value-determination,64630,0,2.html

Zhang, Z., Xiao, Y. and Niu, H. (2022) "DEA and Machine Learning for Performance Prediction", Mathematics, 10(10), p. 1776. https://doi.org/10.3390/math10101776