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

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 ۹۰ 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 ۷۰% for training and ۳۰% 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 (R۲). 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 R۲ for the models were ۳۶۸۶.۶۰, ۳۵۸۱.۸۲, ۴۵۷۶.۶۱, and ۳۳۵۲.۸۷; ۱۲.۶۵, ۱۱.۰۹, ۱۵.۳۱, and ۱۱.۹۰; ۳۶.۸۲, ۴۰.۷۲, ۱.۸۵, and ۴۷.۳۲; ۰.۳۷, ۰.۴۱, ۰.۴۱, and ۰.۴۷ 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.

کلیدواژه ها:

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