Optimization of Fragmentation and Operational Costs of Drilling and Blasting using Hybrid Machine Learning Models in an Open-Pit Mine in Peru

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 (X۵۰) 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 ۱۰۰ blasts, was split into ۷۰% for training and ۳۰% for testing. The SVR+PSO model achieved the highest accuracy for fragmentation prediction, with an RMSE of ۰.۲۷, MAE of ۰.۲۱, and R۲ of ۰.۹۲. The RF+GA model was most effective for cost prediction, with an RMSE of ۴۱۴.۵۸, MAE of ۳۵۴.۱۴, and R۲ of ۰.۹۹. Optimization scenarios were implemented by reducing burden (۴.۳ m to ۳.۸ m) and spacing (۵.۰ m to ۴.۵ m), achieving a ۵.۷% reduction in X۵۰ (۱۷.۶ cm to ۱۶.۶ cm) and a ۹.۵% cost decrease (۶۳,۰۰۰ USD to ۵۷,۰۰۰ USD per blast). Predictions for ۳۰ future blasts using the RF + GA model estimated a total cost of ۱.۷ MUSD, averaging ۵۵,۱۸۰ 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.

کلیدواژه ها:

Fragmentation ، hybrid models ، Machine learning ، Particle Swarm Optimization ، cost of extraction