Document Type : Original Research Paper

Authors

• Marco Antonio Cotrina Teatino ¹
• Jairo Jhonatan Marquina Araujo ¹
• Eduardo Manuel Noriega Vidal ²
• Jose Nestor Mamani Quispe ³
• Johnny Henrry Ccatamayo Barrios ⁴
• Joe Alexis Gonzalez Vasquez ⁵
• Solio Marino Arango Retamozo ⁶

¹ Departamento de Ingeniería de Minas, Facultad de Ingeniería, Universidad Nacional de Trujillo

² Departamento de Ingeniería de Minas, Universidad Nacional de Trujillo, Trujillo, Peru

³ Departamento de Ingeniería de Minas, Universidad de Chile, Santiago, Chile.

⁴ Departamento de Ingeniería de Minas, Universidad Nacional San Cristóbal de Huamanga, Ayacucho, Perú.

⁵ Departamento de Ingeniería Industrial, Universidad Nacional de Trujillo, Trujillo, Perú.

⁶ Departamento de Ingeniería de minas, Universidad Nacional de Trujillo, Trujillo, Perú

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

• Machine learning
• Open Pit Mine Production
• Bayesian Regression
• Predictive Modeling in Mining

Main Subjects

• Surface Mining