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 | ||
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