Comparative Analysis of Machine Learning Regression Methods for Geometallurgical Modeling in the Sungun Copper Porphyry Deposit | ||
| Journal of Mining and Environment | ||
| مقاله 6، دوره 16، شماره 6، آذر و دی 2025، صفحه 1941-1956 اصل مقاله (5.55 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15088.2885 | ||
| نویسندگان | ||
| Meysam Nikfarjam1؛ Ardeshir Hezarkhani* 2؛ Farhad Azizafshari3؛ Hamidreza Golchin3 | ||
| 1Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran. | ||
| 2Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 3National Iranian Copper Industries Co. (NICICO), Sungun Copper Mine, East-Azerbaija, Iran. | ||
| چکیده | ||
| Geometallurgical modeling (GM) plays a crucial role in the mining industry, enabling a comprehensive understanding of the complex relationship between geological and metallurgical factors. This study focuses on evaluating metallurgical varibles at the Sungun Copper mine in Iran by measuring and predicting process properties, including semi-autogenous power index (SPI), recovery (Re), and concentration grade. To overcome the additivity limitations of geostatistical methods, we utilized machine learning algorithms for enhanced predictive modeling, aiming to improve decision-making and optimize mining operations in geometallurgy. The research incorporates crucial data inputs such as sample coordinates, grades, lithology, mineralization zones, and alteration to assess the accuracy and reliability of different machine learning regression methods. The Relative Standard Deviation (RSD) is highlighted as a significant metric for comparing the accuracy of predicted process properties. Evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) further confirm the superiority of specific modeling methods in certain scenarios. The K-Nearest Neighbors (KNN) method exhibits superior accuracy, lower error metrics (RMSE and MAE), and a higher R2 for modeling the SPI test. For modeling Cu grade in concentrate, Support Vector Regression (SVR) proves to be effective and reliable, outperforming the Multilayer Perceptron (MLP) method. Despite MLP's high R2, its higher RSD suggests increased uncertainty and variability in the predictions. Therefore, SVR is considered more suitable for modeling Cu grade in concentrate. Findings optimize operations at Sungun Copper mine, improving decision-making, efficiency, and profitability. | ||
| کلیدواژهها | ||
| Geometallurgical modeling؛ process properties؛ semi-autogenous power index (SPI)؛ machine learning algorithms؛ Sungun Copper mine | ||
| مراجع | ||
|
[1]. Deutsch, C. (2013). Geostatistical modelling of geometallurgical variables—Problems and solutions. Paper presented at the Proceedings of the International Geometallurgy Conference, Brisbane, Australia.
[2]. Gordon, H. J. J. (2019). A mineralogical approach to quantifying ore variability within a polymetallic Cu-Pb-Zn Broken Hill-type deposit and its implications for geometallurgy. Stellenbosch: Stellenbosch University.
[3]. Lishchuk, V. (2016). Geometallurgical programs–critical evaluation of applied methods and techniques.
[4]. Sepúlveda Escobedo, E. M. (2018). Quantification of uncertainty of geometallurgical variables for mine planning optimisation.
[5]. Vann, J., Jackson, J., Coward, S., & Dunham, S. (2011). The geomet curve–a model for implementation of geometallurgy. First AusIMM International Geometallurgy, 1-10.
[6]. Walters, S., & Kojovic, T. (2006). Geometallurgical mapping and mine modelling (GEMIII)-the way of the future.
[7]. Williams, S. R., & Richardson, J. (2004). Geometallurgical Mapping: A new approach that reduces technical risk. Paper presented at the Proceedings 36th Annual Meeting of the Canadian Mineral Processors.
[8]. Lishchuk, V., Lamberg, P., & Lund, C. (2015). Classification of geometallurgical programs based on approach and purpose. Paper presented at the SGA 2015: 24/08/2015-27/08/2015.
[9]. Dominy, S., Murphy, B., & Gray, A. (2011). Characterisation of gravity amenable gold ores—Sample representivity and determination methods. Paper presented at the Proceedings International Geometallurgy Conference; The Australasian Institute of Mining and Metallurgy: Melbourne, Australia.
[10]. Lishchuk, V., & Pettersson, M. (2021). The mechanisms of decision-making when applying geometallurgical approach to the mining industry. Mineral Economics, 34, 71-80.
[11]. Dominy, S. C., O’Connor, L., Parbhakar-Fox, A., Glass, H. J., & Purevgerel, S. (2018). Geometallurgy—A route to more resilient mine operations. Minerals, 8(12), 560.
[12]. Garrido, M., Sepúlveda, E., Ortiz, J. M., Navarro, F., & Townley, B. (2018). A methodology for the simulation of synthetic geometallurgical block models of porphyry ore bodies. Paper presented at the Procemin 14th International Mineral Processing Conference (PROCEMIN). 5th International Seminar on Geometallurgy (GEOMET). Santiago, Chile.
[13]. Hunt, J., Berry, R., Bradshaw, D., Triffett, B., & Walters, S. (2014). Development of recovery domains: Examples from the Prominent Hill IOCG deposit, Australia. Minerals Engineering, 64, 7-14.
[14]. Hunt, J., Kojovic, T., & Berry, R. (2013). Estimating comminution indices from ore mineralogy, chemistry and drill core logging.
[15]. Lamberg, P. (2011). Particles-the bridge between geology and metallurgy. Paper presented at the Konferens i mineralteknik 2011: 08/02/2011-09/02/2011.
[16]. Sepulveda, E., Dowd, P., Xu, C., & Addo, E. (2017). Multivariate modelling of geometallurgical variables by projection pursuit. Mathematical Geosciences, 49, 121-143.
[17]. Patnaik, S., Yang, X.-S., & Sethi, I. K. (2019). Advances in Machine Learning and Computational Intelligence Proceedings of ICMLCI 2019. Proceedings of ICMLCI, 1.
[18]. Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Integr. Ser. Inf. Syst, 36, 1-12.
[19]. Oliver, S., & Willingham, D. (2016). Maximise orebody value through the automation of resource model development using machine learning. Paper presented at the Proceedings of the Third AusIMM International Geometallurgy Conference, Perth, Australia.
[20]. Sagar, D., Cheng, Q., & Agterberg, F. (2018). Handbook of mathematical geosciences: fifty years of IAMG: Springer Nature.
[21]. Afzal, P., Farhadi, S., Boveiri Konari, M., Shamseddin Meigooni, M., & Daneshvar Saein, L. (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia, 12(1), 191-199.
[22]. Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., & Sadeghi, B. (2022). Combination of machine learning algorithms with concentration-area fractal method for soil geochemical anomaly detection in sediment-hosted Irankuh Pb-Zn deposit, Central Iran. Minerals, 12(6), 689.
[23]. Farhadi, S., Tatullo, S., Konari, M. B., & Afzal, P. (2024). Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping. Journal of Geochemical Exploration, 260, 107441.
[24]. Abbaszadeh, M., Hezarkhani, A., & Soltani-Mohammadi, S. (2013). An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Geochemistry, 73(4), 545-554.
[25]. Ebdali, M., & Hezarkhani, A. (2024). A comparative study of decision tree and support vector machine methods for gold prospectivity mapping. Mineralia Slovaca, 56(2).
[26]. Saremi, M., Maghsoudi, A., Hoseinzade, Z., & Mokhtari, A. R. (2024). Data-driven AHP: A novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran. Earth Science Informatics, 17(6), 5063-5078.
[27]. Saljoughi, B. S., & Hezarkhani, A. (2024). A Comparative Analysis of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) Data-driven Models for Prospecting Porphyry Cu Mineralization; Case Study of Shahr-e-Babak Area, Kerman Province, SE Iran. Journal of Mining and Environment, 15(2), 761-790.
[28]. Rajabinasab, B., & Asghari, O. (2019). Geometallurgical domaining by cluster analysis: Iron ore deposit case study. Natural Resources Research, 28, 665-684.
[29]. Lishchuk, V., Lund, C., & 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, 156-165.
[30]. Coward, S., Vann, J., Dunham, S., & Stewart, M. (2009). The primary-response framework for geometallurgical variables. Paper presented at the Seventh international mining geology conference.
[31]. Carrasco, P., Chilès, J.-P., & Séguret, S. A. (2008). Additivity, metallurgical recovery, and grade. Paper presented at the 8th international Geostatistics Congress.
[32]. Keeney, L., & Walters, S. (2011). A methodology for geometallurgical mapping and orebody modelling. Paper presented at the GeoMet 2011-1st AusIMM International Geometallurgy Conference 2011.
[33]. Ameh.mxen, P. (2003). The application of the SAG POWER INDEX to ore body hardness characterization for the design and optimization of autogenous grinding circuits. (Master). McGiII University, Montreal.
[34]. Starkey, J., & Dobby, G. (1996). Application of the Minnovex SAG power index at five Canadian SAG plants. Proceeding Autogenous and Semi-Autogenous Grinding, 345-360.
[35]. Bahrami, A., Ghorbani, Y., Sharif, J. A., Kazemi, F., Abdollahi, M., Salahshur, A., & Danesh, A. (2021). A geometallurgical study of flotation performance in supergene and hypogene zones of Sungun copper deposit. Mineral Processing and Extractive Metallurgy, 130(2), 126-135.
[36]. Amankwaa-Kyeremeh, B., McCamley, C., Zanin, M., Greet, C., Ehrig, K., & Asamoah, R. K. (2023). Prediction and Optimisation of Copper Recovery in the Rougher Flotation Circuit. Minerals, 14(1), 36.
[37]. Boisvert, J. B., Rossi, M. E., Ehrig, K., & Deutsch, C. V. (2013). Geometallurgical modeling at Olympic dam mine, South Australia. Mathematical Geosciences, 45, 901-925.
[38]. Macmillan, E., Ehrig, K., Liebezeit, V., Kittler, P., Lower, C. (2011). Use of geometallurgy to predict tailings leach acid consumption at Olympic Dam. Paper presented at the The First AUSIMM International Geometallurgy Conference, Brisbane.
[39]. Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4): Springer.
[40]. Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2): Springer.
[41]. Meng, M., & Zhao, C. (2015). Application of support vector machines to a small-sample prediction. Advances in Petroleum Exploration and Development, 10(2), 72-75.
[42]. Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
[43]. Murphy, K. P. (2012). Machine learning: a probabilistic perspective: MIT press.
[44]. Hezarkhani, A., Williams-Jones, A., & Gammons, C. (1999). Factors controlling copper solubility and chalcopyrite deposition in the Sungun porphyry copper deposit, Iran. Mineralium deposita, 34, 770-783.
[45]. Hezarkhani, A. (1997). Physicochemical controls on alteration and copper mineralization in the Sungun porphyry copper deposit, Iran.
[46]. Calagari, A. A. (2003). Stable isotope (S, O, H and C) studies of the phyllic and potassic–phyllic alteration zones of the porphyry copper deposit at Sungun, East Azarbaidjan, Iran. Journal of Asian Earth Sciences, 21(7), 767-780.
[47]. Asghari, O., & Hezarkhani, A. (2008). Appling discriminant analysis to separate the alteration zones within the Sungun porphyry copper deposit. Journal of Applied Sciences, 24, 4472-4486.
[48]. Hezarkhani, A., & Williams-Jones, A. E. (1998). Controls of alteration and mineralization in the Sungun porphyry copper deposit, Iran; evidence from fluid inclusions and stable isotopes. Economic Geology, 93(5), 651-670.
[49]. Nikfarjam, M., Hezarkhani, A., & Aziz-Afshari, F. (2022). Geological domaining at Sungun porphyry copper deposit using cluster analysis. Global Journal of Computer Sciences: Theory and Research, 12, 78-92.
[50]. Mery, N., et al., Geostatistical modeling of the geological uncertainty in an iron ore deposit. Ore Geology Reviews, 2017. 88: p. 336-351.
[51]. David, D. (2013). Geometallurgical guidelines for miners, geologists and process engineers– discovery to design. Paper presented at the The Second AusIMM International Geometallurgy Conference, Brisbane, QLD, Australia.
[52]. Grubbs, F. E. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1-21. doi:10.1080/00401706.1969.10490657. | ||
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