Document Type : Original Research Paper
Author
Department of Mining Engineering, Tarbiat Modares University, Tehran, Iran
10.22044/jme.2025.16002.3080
Abstract
Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these challenges by directly integrating critical geometallurgical parameters, specifically rock hardness (SPI index) and clay content, into the short-term production planning process. The simultaneous integration of these key geometallurgical feed quality attributes within an operational MILP model distinguishes this work from previous approaches and effectively bridges geological data analytics with operational decision-making, aligning economic objectives with enhanced metallurgical performance. Utilizing real operational data from the Sarcheshmeh Copper Mine, the framework was validated over a 186-day period. It achieved optimal production conditions on 137 days (73.6% of the duration), realizing a maximum Net Present Value (NPV) of $132,000. Key outcomes included a significant 21% reduction in concentrate grade variability and a 15% decrease in flotation reagent consumption, achieved through the simultaneous control of SPI and clay content. Advanced statistical methods were employed to identify critical relationships. While the model demonstrates scalability for porphyry copper mines globally, its successful implementation depends on careful parameter customization and alignment with existing infrastructure. This research work underscores the substantial value of data-driven, integrated optimization techniques in enhancing both profitability and process stability within mineral processing circuits.
Keywords
Main Subjects
[1]. Blom, M., Pearce, A. R., & Stuckey, P. J. (2019). Short-term planning for open pit mines: a review. International Journal of Mining, Reclamation and Environment, 33(5), 318-339.
[2]. Chen, X., & Peng, Y. (2018). Managing clay minerals in froth flotation—A critical review. Mineral Processing and Extractive Metallurgy Review, 39(5), 289-307.
[3]. Scott, A., Kanchibotla, S., & Morrell, S. (1999, January). Blasting for mine to mill optimisation. In Proceedings of the Explo (Vol. 99, pp. 3-8).
[4]. Saldana, M., Gallegos, S., Arias, D., Salazar, I., Castillo, J., Salinas-Rodríguez, E., ... & Cisternas, L. A. (2024). Applications of Kuz–Ram Models in Mine-to-Mill Integration and Optimization—A Review. Minerals, 14(11), 1162.
[5]. Quigley, M., & Dimitrakopoulos, R. (2020). Incorporating geological and equipment performance uncertainty while optimising short-term mine production schedules. International Journal of Mining, Reclamation and Environment, 34(5), 362-383.
Nelis, G., Morales, N., & Jelvez, E. (2023). Optimal mining cut definition and short-term open pit production scheduling under geological uncertainty. Resources Policy, 81, 103340.
[7]. Chatterjee, S., & Dimitrakopoulos, R. (2020). Production scheduling under uncertainty of an open-pit mine using Lagrangian relaxation and branch-and-cut algorithm. International Journal of Mining, Reclamation and Environment, 34(5), 343-361.
[8]. Mirzehi, M., Rezakhah, M., Mousavi, A., & Nabavi, Z. (2023). New MIP model for short-term planning in open-pit mines considering loading machine performance: a case study in Iran. International Journal of Mining and Mineral Engineering, 14(4), 341-364.
[9]. Fu, Z., Asad, M. W. A., & Topal, E. (2019). A new model for open-pit production and waste-dump scheduling. Engineering Optimization, 51(4), 718-732.
[10]. Upadhyay, S. P., Doucette, J., & Askari-Nasab, H. (2021). Short-term production scheduling in open pit mines with shovel allocations over continuous time frames. International Journal of Mining and Mineral Engineering, 12(4), 292-308.
[11]. Upadhyay, S. P., & Askari-Nasab, H. (2019). Dynamic shovel allocation approach to short-term production planning in open-pit mines. International Journal of Mining, Reclamation and Environment, 33(1), 1-20.
[12]. Blom, M., Pearce, A. R., & Stuckey, P. J. (2017). Short-term scheduling of an open-pit mine with multiple objectives. Engineering Optimization, 49(5), 777-795.
[13]. Blom, M., Pearce, A. R., & Stuckey, P. J. (2018). Multi-objective short-term production scheduling for open-pit mines: a hierarchical decomposition-based algorithm. Engineering Optimization, 50(12), 2143-2160.
[14]. Both, C., & Dimitrakopoulos, R. (2020). Joint stochastic short-term production scheduling and fleet management optimization for mining complexes. Optimization and Engineering, 21(4), 1717-1743.
[15]. Blom, M., Pearce, A. R., & Stuckey, P. J. (2019). Short-term planning for open pit mines: a review. International Journal of Mining, Reclamation and Environment, 33(5), 318-339.
[16]. Kambala Malundamene, M., Habib, N. A., Soulaimani, S., Abdessamad, K., & Askari-Nasab, H. (2025). State-of-the-art optimization methods for short-term mine planning. F1000Research, 13, 1107.
[17]. Rezakhah, M., & Moreno, E. (2019, November). Open pit mine scheduling model considering blending and stockpiling. In International Symposium on Mine Planning & Equipment Selection (pp. 75-82). Cham: Springer International Publishing.
[18]. Moreno, E., Ferreira, F., Goycoolea, M., Espinoza, D., Newman, A., & Rezakhah, M. (2015). Linear programming approximations for modeling instant-mixing stockpiles. In Application of computers and operations research in the mineral industry-proceedings of the 37th international symposium, APCOM (Vol. 2009, pp. 582-587).
[19]. Rezakhah, M., Moreno, E., & Newman, A. (2020). Practical performance of an open pit mine scheduling model considering blending and stockpiling. Computers & Operations Research, 115, 104638.
[20]. Moreno, E., Rezakhah, M., Newman, A., & Ferreira, F. (2017). Linear models for stockpiling in open-pit mine production scheduling problems. European Journal of Operational Research, 260(1), 212-221.
[21]. Kumar, A., & Chatterjee, S. (2017). Open-pit coal mine production sequencing incorporating grade blending and stockpiling options: An application from an Indian mine. Engineering optimization, 49(5), 762-776.
[22]. Habib, N. A., Ben-Awuah, E., & Askari-Nasab, H. (2024). Short-term planning of open pit mines with Semi-Mobile IPCC: a shovel allocation model. International Journal of Mining, Reclamation and Environment, 38(3), 236-266.
[23]. Mousavi, A., Kozan, E., & Liu, S. Q. (2016). Open-pit block sequencing optimization: A mathematical model and solution technique. Engineering Optimization, 48(11), 1932-1950.
[24]. Eivazy, H., & Askari-Nasab, H. (2012). A hierarchical open-pit mine production scheduling optimisation model. International Journal of Mining and Mineral Engineering, 4(2), 89-115.
[25]. Khan, A., & Asad, M. W. A. (2020). A mathematical programming model for optimal cut-off grade policy in open pit mining operations with multiple processing streams. International Journal of Mining, Reclamation and Environment, 34(3), 149-158.
[26]. Kazemi, M. M. K., Nabavi, Z., Rezakhah, M., & Masoudi, A. (2023). Application of XGB-based metaheuristic techniques for prediction time-to-failure of mining machinery. Systems and Soft Computing, 5, 200061.
[27]. Khajevand, S., Rezakhah, M., Monjezi, M., & Manríquez León, F. A. (2025). Enhancing Transportation Fleet Efficiency in Open-Pit Mining via Simulation: a Case Study. Journal of Mining and Environment, 16(3), 997-1007.
[28]. Mohtasham, M., Mirzaei-Nasirabad, H., Askari-Nasab, H., & Alizadeh, B. (2021). A multi-objective model for fleet allocation schedule in open-pit mines considering the impact of prioritising objectives on transportation system performance. International Journal of Mining, Reclamation and Environment, 35(10), 709-727.
[29]. Kumral, M. (2012). Production planning of mines: Optimisation of block sequencing and destination. International Journal of Mining, Reclamation and Environment, 26(2), 93-103.
[30]. Huang, S., Li, G., Ben-Awuah, E., Afum, B. O., & Hu, N. (2020). A robust mixed integer linear programming framework for underground cut-and-fill mining production scheduling. International Journal of Mining, Reclamation and Environment, 34(6), 397-414.
[31]. Pourrahimian, Y., Askari–Nasab, H., & Tannant, D. (2012). Mixed–Integer Linear Programming formulation for block–cave sequence optimisation. International Journal of Mining and Mineral Engineering, 4(1), 26-49.
[32]. Mohammadi, S., Rezai, B., Abdollazadeh, A., & Mortazavi, S. M. (2021). Evaluation of the geometallurgical indices for comminution properties at Sarcheshmeh porphyry copper mine, Iran. Iranian Journal of Earth Sciences, 13(1), 41-49.
[33]. Garcia, G. G., Coello-Velazquez, A. L., Perez, B. F., & Menendez-Aguado, J. M. (2021). Variability of the ball mill bond’s standard test in a ta ore due to the lack of standardization. Metals, 11(10), 1606.
[34]. Morales, Nelson, Sebastián Seguel, Alejandro Cáceres, Enrique Jélvez, and Maximiliano Alarcón. "Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning." Minerals 9, no. 2 (2019): 108.
[35]. Kumral, M. (2011). Incorporating geo-metallurgical information into mine production scheduling. Journal of the Operational Research Society, 62(1), 60-68.
[36]. Both, C., & Dimitrakopoulos, R. (2023). Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes. International Journal of Mining Science and Technology, 33(2), 185-199.
[37]. Both, C., & Dimitrakopoulos, R. (2021). Applied machine learning for geometallurgical throughput prediction a case study using production data at the tropicana gold mining complex. Minerals, 11(11), 1257.
[38]. Bhuiyan, M., Esmaieli, K., & Ordóñez-Calderón, J. C. (2019). Application of data analytics techniques to establish geometallurgical relationships to bond work index at the Paracutu Mine, Minas Gerais, Brazil. Minerals, 9(5), 302.
[39]. Lishchuk, V., Koch, P. H., Ghorbani, Y., & Butcher, A. R. (2020). Towards integrated geometallurgical approach: Critical review of current practices and future trends. Minerals Engineering, 145, 106072.
[40]. Hunt, J. A., & Berry, R. F. (2017). Economic geology models 3. Geological contributions to geometallurgy: A review. Geoscience Canada, 44(3), 103-118.
[41]. Butcher, A. R., Dehaine, Q., Menzies, A. H., & Michaux, S. P. (2023). Characterisation of ore properties for geometallurgy. Elements, 19(6), 352-358.
[42]. Frenzel, M., Baumgartner, R., Tolosana-Delgado, R., & Gutzmer, J. (2023). Geometallurgy: present and future. Elements, 19(6), 345-351.
[43]. 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.
[44]. Lishchuk, V., Koch, P. H., Ghorbani, Y., & Butcher, A. R. (2020). Towards integrated geometallurgical approach: Critical review of current practices and future trends. Minerals Engineering, 145, 106072.
[45]. Behnamfard, A., Roudi, D. N., & Veglio, F. (2020). The performance improvement of a full-scale autogenous mill by setting the feed ore properties. Journal of Cleaner Production, 271, 122554.
[46]. Jahani, M., Noaparast, M., Farzanegan, A., & Langarizadeh, G. (2012). Application of SPI for Modeling energy consumption in Sarcheshmeh SAG and ball mills. Journal of Mining and Environment, 2(1).
[47]. Gandy, C. J., & Younger, P. L. (2003). Effect of a Clay Cap on oxidation of Pyrite withinMine Spoil. Quarterly Journal of Engineering Geology and Hydrogeology, 36(3), 207-215.
[48]. Garcia, G. G., Coello-Velazquez, A. L., Perez, B. F., & Menéndez-Aguado, J. M. (2021). Variability of the ball mill bond’s standard test in a ta ore due to the lack of standardization. Metals, 11(10), 1606.
[49]. Gregory, D. D., Large, R. R., Halpin, J. A., Baturina, E. L., Lyons, T. W., Wu, S., ... & Bull, S. W. (2015). Trace element content of sedimentary pyrite in black shales. Economic Geology, 110(6), 1389-1410.
[50]. Morales, N., Seguel, S., Cáceres, A., Jélvez, E., & Alarcón, M. (2019). Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning. Minerals, 9(2), 108.
[51]. Mu, Y., & Salas, J. C. (2023). Data-driven synthesis of a geometallurgical model for a copper deposit. Processes, 11(6), 1775.
[52]. Eivazy, H., & Askari-Nasab, H. (2012). A mixed integer linear programming model for short-term open pit mine production scheduling. Mining Technology, 121(2), 97-108.
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