Document Type : Original Research Paper

Authors

• Marco Antonio Cotrina Teatino ¹
• Jairo Jhonatan Marquina Araujo ¹
• Jose Nestor Mamani Quispe ²
• Solio Marino Arango-Retamozo ³
• Johnny Henrry Ccatamayo-Barrios ⁴
• Joe Alexis Gonzalez-Vasquez ⁵
• Teofilo Donaires-Flores ⁶
• Maxgabriel Alexis Calla-Huayapa ⁷

¹ Universidad Nacional de Trujillo

² Department of Chemical Engineering, Faculty of Chemical Engineering, National University of the Altiplano, Puno, Peru.

³ Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

⁴ Department of Mining Engineering, Universidad Nacional San Cristobal de Huamanga, Ayacucho, Peru

⁵ Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

⁶ Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru

⁷ Faculty of Industrial Process Engineering, National University of Juliaca, Juliaca, Peru

Abstract

Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation efficiency. This work aims to optimize fragmentation (X₅₀) and drilling and blasting costs using hybrid machine learning models, an innovative approach that improves predictive accuracy and economic feasibility. Six models were developed: Artificial Neural Networks (ANNs), Decision Trees (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The dataset, comprising 100 blasts, was split into 70% for training and 30% for testing. The SVR+PSO model achieved the highest accuracy for fragmentation prediction, with an RMSE of 0.27, MAE of 0.21, and R² of 0.92. The RF+GA model was most effective for cost prediction, with an RMSE of 414.58, MAE of 354.14, and R² of 0.99. Optimization scenarios were implemented by reducing burden (4.3 m to 3.8 m) and spacing (5.0 m to 4.5 m), achieving a 5.7% reduction in X₅₀ (17.6 cm to 16.6 cm) and a 9.5% cost decrease (63,000 USD to 57,000 USD per blast). Predictions for 30 future blasts using the RF + GA model estimated a total cost of 1.7 MUSD, averaging 55,180 USD per blast. These findings confirm the effectiveness of machine learning in cost optimization and improving blasting efficiency, presenting a robust data-driven approach to optimizing mining operations.

Keywords

• Fragmentation
• hybrid models
• machine learning
• particle swarm optimization
• cost of extraction

Main Subjects

• Drilling & Blasting