Productivity Evaluation in Open Pit Mining Using Machine Learning Methods | ||
| Journal of Mining and Environment | ||
| مقاله 16، دوره 17، شماره 3، مرداد و شهریور 2026، صفحه 1065-1076 اصل مقاله (2.29 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15692.3017 | ||
| نویسندگان | ||
| Heydar Bagloo* 1؛ Mohsen Soleiman Dehkordi2 | ||
| 1Research and Development Unit, Dispatching center, Chadormalu Mining Complex, Yazd, Iran | ||
| 2Management Department of Chadormalu Mining Complex, Yazd, Iran | ||
| چکیده | ||
| Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing methods. However, evaluating the effectiveness of these optimization techniques, particularly in short-term mining activities under varying operational conditions, remains essential. Additionally, understanding how changes in operational conditions impact productivity is important for addressing production fluctuations in daily mining operations. To tackle these challenges, this study uniquely applies advanced machine learning techniques to short-term mining planning, resulting in the development of a real-time Productivity Evaluation Model (PEM) based on supervised learning methods for optimizing truck-shovel operations in open-pit mining. The model, developed and tested using data from a large-scale mining operation in Iran, demonstrated that the Decision Tree was the most effective, achieving an R² value of 0.96. This was closely followed by Random Forest and Gradient Boosting, both with R² values of 0.95. However, the choice of the most suitable learning method may vary depending on the specific dataset and context. The model determines the most appropriate learning method for each dataset within specific mining operations. | ||
| کلیدواژهها | ||
| Open-pit mining؛ Truck؛ shovel؛ Machine learning؛ Supervised learning | ||
| مراجع | ||
|
[1]. Ali, S., et al. (2017). Mineral supply for sustainable development requires resource governance. Nature, 543(7645), 367-372.
[2]. Humphreys, D. (2020). Mining productivity and the fourth industrial revolution. Mineral Economics, 33(1), 115-125.
[3]. Kinnunen, P.H.M., & Kaksonen, A.H. (2019). Towards circular economy in mining: Opportunities and bottlenecks for tailings valorization. Journal of Cleaner Production, 228, 153-160.
[4]. Blom, M., Pearce, A., & Stuckey, P. (2018). Short-Term Planning for Open Pit Mines: A Review. International Journal of Mining, Reclamation and Environment, 33.
[5]. Moradi, A., & Askari Nasab, H. (2015). Open-Pit Mine Production Optimization: A Review of Models and Algorithms. Engineering, 251436017.
[6]. Gamache, M., & Alarie, S. (2002). Overview of Solution Strategies Used in Truck Dispatching Systems for Open Pit Mines. International Journal of Surface Mining, Reclamation and Environment, 16, 59-76.
[7]. Elijah, K., et al. (2021). Optimisation of shovel-truck haulage system in an open pit using queuing approach. Arabian Journal of Geosciences, 14(11), 973.
[8]. Cerna, G.P., & Obredor-Baldovino, T. (2024). A Mixed Integer Programming optimization model for mining truck dispatch policies using traffic constraints: Case of a copper mine in northern Chile. Procedia Computer Science, 231, 660-665.
[9]. Aguayo, I.A., Nehring, M., & Ullah, G.M.W. (2021). Optimising Productivity and Safety of the Open Pit Loading and Haulage System with a Surge Loader. Mining, 1, 167-179.
[10]. Kuznetsov, D., & Kosolapov, A. (2022). Dynamic of performance of open-pit dump trucks in ore mining in severe climatic environment. Transportation Research Procedia, 63, 1042-1048.
[11]. Bakhtavar, E., & Mahmoudi, H. (2020). Development of a scenario-based robust model for the optimal truck-shovel allocation in open-pit mining. Computers & Operations Research, 115, 104539.
[12]. Bajany, D.M., Xia, X., & Zhang, L. (2017). A MILP Model for Truck-shovel Scheduling to Minimize Fuel Consumption. Energy Procedia, 105, 2739-2745.
[13]. Makarova, I., et al. (2023). Improving the Environmental Friendliness of the Mining Complex Through Alternative Fuel for Mine Dump Trucks. Transportation Research Procedia, 68, 755-760.
[14]. Munirathinam, M., & Yingling, J.C. (1994). A review of computer-based truck dispatching strategies for surface mining operations. International Journal of Surface Mining, Reclamation and Environment, 8(1), 1-15.
[15]. Souza, M.J.F., et al. (2010). A hybrid heuristic algorithm for the open-pit-mining operational planning problem. European Journal of Operational Research, 207(2), 1041-1051.
[16]. Ta, C.H., Ingolfsson, A., & Doucette, J. (2013). A linear model for surface mining haul truck allocation incorporating shovel idle probabilities. European Journal of Operational Research, 231(3), 770-778.
[17]. Zhang, L. & Xia, X. (2015). An Integer Programming Approach for Truck-Shovel Dispatching Problem in Open-Pit Mines. Energy Procedia, 75, 1779-1784.
[18]. Mohtasham, M., Mirzaei Nasirabad, H., & Mahmoodi Markid, A. (2017). Development of a goal programming model for optimization of truck allocation in open pit mines. Journal of Mining and Environment, 8(3), 359-371.
[19]. Patterson, S.R., Kozan, E., & Hyland, P. (2017). Energy efficient scheduling of open-pit coal mine trucks. European Journal of Operational Research, 262(2), 759-770.
[20]. Moradi Afrapoli, A., Tabesh, M., & Askari-Nasab, H. (2019). A multiple objective transportation problem approach to dynamic truck dispatching in surface mines. European Journal of Operational Research, 276(1), 331-342.
[21]. Ghaziania, H.h., et al. (2021). Design of Loading and Transportation Fleet in Open-Pit Mines using Simulation Approach and Metaheuristic Algorithms. Journal of Mining and Environment, 12(4), 1177-1188.
[22]. Mirzaei-Nasirabad, H., et al. (2023). An optimization model for the real-time truck dispatching problem in open-pit mining operations. Optimization and Engineering, 24(4), 2449-2473. | ||
|
آمار تعداد مشاهده مقاله: 399 تعداد دریافت فایل اصل مقاله: 41 |
||