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2 Journal of Sustainable Mining

2 2025

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Prediction of unit haulage cost in an underground mine using machine learning techniques

Cotrina Marco, Marquina Jairo, Polo Junior

Journal of Sustainable Mining

2025

Vol. 24, iss. 2

250--266

The primary objective of the research was to apply machine learning techniques to forecast the unit costs of ore hauling in an underground mine. The methodology employed was quantitative, with a nonexperimental and descriptive design. Haulage data were collected over a 12-month period. Furthermore, an exploratory data analysis (EDA) was conducted using various models, including ANN-MLP (Artificial Neural Network – Multilayer Perceptron), Random Forests, Extreme Gradient Boosting, Support Vector Regression, Decision Tree, KNN, and Bayesian Regression, to handle the data’s complexity. The data were split into 80% for training, 10% for testing, and 10% for validation. The results indicated that the ANN-MLP achieved an R2 of 0.94 and an MSE of 8.77, the Random Forests showed an R2 of 0.97 with an MSE of 3.78, XGBoost achieved an R2 of 0.99 with an MSE of 2.03, SVR yielded an R2 of 0.96 with an MSE of 5.05, KNN obtained an R2 of 0.90 with an MSE of 13.57, and the Bayesian Regression model achieved an R2 of 0.88 with an MSE of 16.15. Ultimately, it was concluded that the XGBoost model exhibited the best performance in forecasting the unit costs of ore haulage in an underground mine.

Machine learning techniques for optimizing the efficiency and costs of drill steel in sandstone and granodiorite: a case study in Peru

Cotrina Marco, Marquina Jairo, Mamani Jose, Arango Solio, Antonio Eusebio, Noriega Eduardo, Donaires Teofilo, Cano Dominga

Journal of Sustainable Mining

2025

Vol. 24, iss. 3

393--407

This research aims to optimize the efficiency and costs of drilling steel in sandstone and granodiorite rocks using machine learning techniques in a Peruvian mine. Predictive models, including random forest (RF), XGBoost (XGB), decision trees (DT), and artificial neural networks (ANN), were applied, along with optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and simulated annealing (SA). A dataset of 705 entries was analyzed, focusing on drill bit wear, percussion and rotation pressures, and cost per meter drilled. Model performance was evaluated using R2, RMSE, MAE, and MAPE. The ANN model demonstrated the highest accuracy, with an R2 of 0.64, RMSE of 0.05 and MAE of 0.04, for efficiency, and an R2 of 0.96, RMSE of 0.03 and MAE of 0.03 for costs. Optimization with PSO offered the best performance, increasing the efficiency of drill steel by 360% in sandstone and 423.33% in granodiorite, with a slight cost increase of 8.82% in sandstone and a cost reduction of 32.71% in granodiorite. In conclusion, machine learning techniques proved effective in optimizing the efficiency and drilling cost in different types of rocks in an underground mine.