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

Marco Cotrina, National University of Trujillo, Department of Mining Engineering, PeruFollow
Jairo Marquina, National University of Trujillo, Department of Mining Engineering, Peru
Jose Mamani, National University of the Altiplano, Faculty of Chemical Engineering, Peru
Solio Arango, National University of Trujillo, Department of Mining Engineering, Peru
Eusebio Antonio, National University of Trujillo, Department of Mining Engineering, Peru
Eduardo Noriega, National University of Trujillo, Department of Mining Engineering, Peru
Teofilo Donaires, National University of the Altiplano, Faculty of Chemical Engineering, Peru
Dominga Cano, National University of Juliaca, Faculty of Industrial Process Engineering, Peru

Author ORCID Identifier

Marco Antonio Cotrina Teatino: 0000-0003-3801-0370

Jairo Jhonatan Marquina Araujo: 0000-0002-5880-8227

Jose Mamani Quispe: 0000-0001-7803-7936

Solio Arango-Retamozo: 0000-0003-3594-0329

Eusebio Antonio-Araujo: 0000-0001-5072-5411

Eduardo Manuel Noriega-Vidal: 0000-0001-7674-7125

Teofilo Donaires-Flores: 0000-0002-2983-1874

Dominga Cano-Ccoa: 0000-0003-2146-3955

Abstract

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.

Recommended Citation

Cotrina, Marco; Marquina, Jairo; Mamani, Jose; Arango, Solio; Antonio, Eusebio; Noriega, Eduardo; Donaires, Teofilo; and Cano, Dominga (2025) "Machine learning techniques for optimizing the efficiency and costs of drill steel in sandstone and granodiorite: a case study in Peru," Journal of Sustainable Mining: Vol. 24 : Iss. 3 , Article 8.
Available at: https://doi.org/10.46873/2300-3960.1466