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

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

Identyfikatory

DOI

10.46873/2300-3960.1466

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

machine learning; drilling efficiency; steel drill bit; costs; optimization techniques

PL

uczenie maszynowe; wydajność wiercenia; wiertło stalowe; koszty; techniki optymalizacji

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2025
• Tom: Vol. 24, iss. 3
• Strony: 393--407
• Opis fizyczny: Bibliogr. 50 poz.

Twórcy

autor

Cotrina Marco

• National University of Trujillo, Department of Mining Engineering, Peru

• https://orcid.org/000-0003-3801-0370

autor

Marquina Jairo

• National University of Trujillo, Department of Mining Engineering, Peru

• https://orcid.org/0000-0002-5880-8227

autor

Mamani Jose

• National University of the Altiplano, Faculty of Chemical Engineering, Peru

• https://orcid.org/0000-0001-7803-7936

autor

Arango Solio

• National University of Trujillo, Department of Mining Engineering, Peru

• https://orcid.org/0000-0003-3594-0329

autor

Antonio Eusebio

• National University of Trujillo, Department of Mining Engineering, Peru

• https://orcid.org/0000-0001-5072-5411

autor

Noriega Eduardo

• National University of the Altiplano, Faculty of Chemical Engineering, Peru

• https://orcid.org/0000-0001-7674-7125

autor

Donaires Teofilo

• National University of Juliaca, Faculty of Industrial Process Engineering, Peru

• https://orcid.org/0000-0002-2983-1874

autor

Cano Dominga

• National University of Juliaca, Faculty of Industrial Process Engineering, Peru

• https://orcid.org/0000-0003-2146-3955

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