Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms

Abstract

Blasting prediction and parameter optimization can effectively improve blasting effectiveness and control production energy consumption. However, the presence of multiple factors and diverse effects in open-pit blasting increases the difficulty of effective prediction and optimization. Therefore, this study takes blasting fragmentation as the prediction indicator and proposes a hybrid intelligent model based on multiple parameters. The model employs a least squares support vector machine (LSSVM) optimized by a genetic algorithm (GA) for prediction. Additionally, the performance of GA-LSSVM was compared with LSSVM optimized by rime optimization algorithms (RIME-LSSVM) and by particle swarm optimization algorithms (PSO-LSSVM), unoptimized LSSVM, and the Kuz–Ram empirical model. Furthermore, considering both blasting fragmentation and blasting cost, a multi-objective particle swarm optimization (MOPSO) algorithm was used for blasting parameter optimization, followed by field validation. The results indicated that the GA-LSSVM model provided the best prediction of blasting fragmentation, achieving optimal evaluation metrics: a root mean square error (RMSE) of 1.947, a mean absolute error (MAE) of 1.688, and a correlation coefficient (r) of 0.962. Moreover, the MOPSO optimization model yielded the optimal blasting parameter combination: a burden of 5.5 m, spacing of 4.3 m, specific charge of 0.51 kg/m3, and subdrilling of 2.0 m. Field blasting tests confirmed the reliability of these parameters. This study can provide scientific recommendations for open-pit mine blasting design and cost control.