Predicting Open Pit Mine Production using Machine Learning Techniques: A Case Study in Peru | ||
| Journal of Mining and Environment | ||
| مقاله 10، دوره 15، شماره 4، دی 2024، صفحه 1345-1355 اصل مقاله (2.52 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.14416.2703 | ||
| نویسندگان | ||
| Marco Antonio Cotrina Teatino* 1؛ Jairo Jhonatan Marquina Araujo1؛ Eduardo Manuel Noriega Vidal1؛ Jose Nestor Mamani Quispe2؛ Johnny Henrry Ccatamayo Barrios3؛ Joe Alexis Gonzalez Vasquez4؛ Solio Marino Arango Retamozo4 | ||
| 1Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru | ||
| 2Department of Mining Engineering, University of Chile, Santiago, Chile | ||
| 3Department of Mining Engineering, National University of San Cristóbal de Huamanga, Ayacucho, Peru | ||
| 4Department of Industrial Engineering, National University of Trujillo, Trujillo, Peru | ||
| چکیده | ||
| The primary objective of this research was to apply machine learning techniques to predict the production of an open pit mine in Peru. Four advanced techniques were employed: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (RB). The methodology included the collection of 90 datasets over a three-month period, encompassing variables such as operational delays, operating hours, equipment utilization, the number of dump trucks used, and daily production. The data were allocated 70% for training and 30% for testing. The models were evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Variance Accounted For (VAF), and the Coefficient of Determination (R2). The results indicated that the Bayesian Regression model was the most effective in predicting production in the open pit mine. The RMSE, MAPE, VAF, and R2 for the models were 3686.60, 3581.82, 4576.61, and 3352.87; 12.65, 11.09, 15.31, and 11.90; 36.82, 40.72, 1.85, and 47.32; 0.37, 0.41, 0.41, and 0.47 for RF, XGBoost, KNN, and RB, respectively. This research highlights the efficacy of machine learning techniques in predicting mine production and recommends adjusting each model's parameters to further enhance outcomes, significantly contributing to strategic and operational management in the mining industry. | ||
| کلیدواژهها | ||
| Machine learning؛ Open Pit Mine Production؛ Bayesian Regression؛ Predictive Modeling in Mining | ||
| مراجع | ||
|
[1]. Alarie, S. and Gamache, M. (2002). Overview of solution strategies used in truck dispatching systems for open pit mines. Int J Surf Min Reclamat Environ, 16(1), 59-76.
[2]. Arteaga, F., Nehring, M. and Knights, P. (2018). The equipment utilization versus mining rate trade-off in open pit mining. Int J Min Reclamat Environ, 32(7), 495-518.
[3]. Santelices, G., Pascual, R., Luer, A., Mac Cawley, A. and Galar, D. (2017). Integrating mining loading and hauling equipment selection and replacement decisions using stochastic linear programming. Int J Min Reclamat Environ, 31(1), 52-65.
[4]. Edwards, D., Holt, G. and Harris, F. (2002). Predicting downtime costs of tracked hydraulic excavators operating in the UK opencast mining industry. Construct Manag Econ, 20(7), 581-591.
[5]. Fisonga, M. and Mutambo, V. (2017). Optimization of the fleet per shovel productivity in surface mining: case study of Chilanga Cement, Lusaka Zambia. Cogent Eng, 4(1), 1386852.
[6]. Ozdemir, B. and Kumral, M. (2018). Appraising production targets through agent-based Petri net simulation of material handling systems in open pit mines. Simulat Model Pract Theor, 87, 138-154.
[7]. Lanke, A. Hoseinie, S. and Ghodrati, B. (2016). Mine production index (MPI)-extension of OEE for bottleneck detection in mining. Int J Min Sci Technol, 26(5), 753-760.
[8]. Soofastaei, A. Karimpour, E. Knights, P. and Kizil, M. (2018). Energy-efficient loading and hauling operations. Green Energy Technol, 121-146.
[9]. Kaba, F. Temeng, V. and Eshun, P. (2016). Application of Discrete event simulation in mine production forecast. Ghana Min J, 16(1).
[10]. Jung, D. and Choi, Y. (2021). Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation. Minerals, 11(2), 148.
[11]. Michalski, R. Carbonell, J. and Mitchell, T. (2013). Machine Learning: An Artificial Intelligence Approach. Springer Science & Business Media: Berlin/Heidelberg.
[12]. Malhotra, R. (2015). A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing, 27, 504-518.
[13]. Handelman, G. Kuan, H. Chandra, R. Razavi, A. Huang, S. Brooks, M. Asadi, H. (2019). Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods. Am. J. Roentgenol, 212(1), 38-43.
[14]. Spasic, I. and Nenadic, G. (2020). Clinical Text Data in Machine Learning: Systematic Review. JMIR Medical Informatics, 8(3).
[15]. Bellinger, C. Mohomed, M. Zaiane, O. and Osornio, A. (2017). A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17(907).
[16]. Senders, J. Staples, P. Karhade, A. Zaki, M. Gormley, W. Broekman, M. and Arnaout, O. (2018). Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review. World Neurosurgery, 109, 476-786.
[17]. Mosavi, A. Salimi, M. Faizollahzadeh, S. Rabczuk, T. Shamshirband, S. and Varkonyi, A. (2019). State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies, 12(7), 1301.
[18]. Jenis, J. Ondriga, J. Hrcek, S. Brumercik, F. Cuchor, M. and Sadovsky, E. (2023). Engineering Applications of Artificial Intelligence in Mechanical Design and Optimization. Machines, 11(6), 577.
[19]. Whitehall, B. and Lu, S. (1991). Machine learning in engineering automation —The present and the future. Computers in Industry, 17(2-3), 91-100.
[20]. Portugal, I. Alencar, P. and Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 97, 205-227.
[21]. Mendoza, J. (2021). Optimización del valor presente neto aplicando secuenciamiento de fases direccionadas en el diseño del pit del proyecto Cotabambas-Panoro Minerals. Arequipa.
[22]. Huang G, G. Y. (2023). Application of Machine Learning in Material Synthesis and Property Prediction. Materials (Basel), 16(17), 5977.
[23]. Sarker, I. H. (2021). Machine Learning: Algorithms, Real World Applications and Research Directions. SN Computer Science, 2, 160.
[24]. Baek, J. and Choi, Y. (2019). Deep neural network for ore production and crusher utilization prediction of truck haulage system in underground mine. Appl Sci, 9(19), 4180.
[25]. Baek, J. and Choi, Y. (2020). Deep neural network for predicting ore production by truck-haulage systems in open-pit mines. Appl Sci, 10(5), 1657.
[26]. Choi, Y. Nguyen, H. Bui, X. Nguyen-Thoi, T. and Park, S. (2021). Estimating ore production in open-pit mines using various machine learning algorithms based on a truck-haulage system and support of internet of things. Nat Resour Res, 30, 1141-1173.
[27]. Nartey, F. Kwasi, A. Nkrumah, M. and Kweku, C. (2024). Predicting open-pit mine production using machine learning techniques. Journal of Sustainable Mining, 23(2).
[28]. Fenández-Delgado, M. Cernadas, E. Barro, S. and Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res, 15, 3133-3181.
[29]. Ho, T. (1998). The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell, 20(8), 832-844.
[30]. Breiman, L. (1996). Bagging predictors. Mach. Learn, 24(2), 123-140.
[31]. Friedman, J. (2001). Greedy boosting approximation: A gradient boosting machine. Annal. Stat, 29(5), 1189-1232.
[32]. Nabavi, Z. Mirzehi, M. Dehghani, H. and Ashtari, P. (2023). A Hybrid Model for Back-Break Prediction using XGBoost Machine learning and Metaheuristic Algorithms in Chadormalu Iron Mine. Journal of Mining and Environment, 14(2), 689-712.
[33]. Breiman, L. Friedman, J. and Olshen, R. (1984). Classification and regression trees. wadsworth int, Group, 37(15), 237-251.
[34]. Emrah, U. Dagasan, Y. and Topal, E. (2021). Mineral grade estimation using gradient boosting regression trees. International Journal of Mining, Reclamation and Environment, 35(10), 728-742.
[35]. Yu, K. and Zhang, X. (2002). Kernel Nearest Neighbor Algorithm. Neural Processing Letters, 15, 147-156.
[36]. Bárcena, M. J. Garín , M. A. and Matrín, A. (2017). Un simulador para asistir en la enseñanza del teorema de Bayes.
[37]. Patel, A. Chatterjee, S. and Gorai, A. (2019). Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades. Earth Sci Inform, 12, 197-210.
[38]. Prasad, K. Gorai, A. and Goyal, P. (2016). Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmos Environ, 128, 246-262. | ||
|
آمار تعداد مشاهده مقاله: 172 تعداد دریافت فایل اصل مقاله: 123 |
||