Predicting Open Pit Mine Production using Machine Learning Techniques: A Case Study in Peru

Cotrina Teatino, Marco Antonio; Marquina Araujo, Jairo Jhonatan; Noriega Vidal, Eduardo Manuel; Mamani Quispe, Jose Nestor; Ccatamayo Barrios, Johnny Henrry; Gonzalez Vasquez, Joe Alexis; Arango Retamozo, Solio Marino

doi:10.22044/jme.2024.14416.2703

Document Type : Original Research Paper

Authors

¹ Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

² Department of Mining Engineering, University of Chile, Santiago, Chile

³ Department of Mining Engineering, National University of San Cristóbal de Huamanga, Ayacucho, Peru

⁴ Department of Industrial Engineering, National University of Trujillo, Trujillo, Peru

https://doi.org/10.22044/jme.2024.14416.2703

Abstract

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.

Keywords

Main Subjects

Surface Mining

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Predicting Open Pit Mine Production using Machine Learning Techniques: A Case Study in Peru

References

References

Volume 15, Issue 4October 2024Pages 1345-1355

Volume 15, Issue 4
October 2024
Pages 1345-1355