# 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 prime 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/m\({}^{3}\), 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. blasting; fragmentation prediction; GA-LSSVM; cost; multi-objective; parameter optimization +
Footnote †: journal: Applied Sciences  
In recent years, improved intelligent algorithms have become increasingly popular in blasting prediction research [17; 18; 19; 20; 21]. For instance, [PERSON] et al. [22] used the jellyfish search optimization (JSO) algorithm to optimize the hyperparameters of the Light-GBM model, establishing a hybrid model to predict blast fragmentation. [PERSON] et al. [23] employed multiple meta-heuristic algorithms to optimize the hyperparameters of LSSVM, demonstrating that the LSSVM-BFO model has the best prediction efficiency. [PERSON] et al. [24] developed four improved artificial intelligence models and determined that the cascade forward neural network (CFNN) model optimized by the Levenberg-Marquardt algorithm (LMA) was the best, using root mean square error (RMSE) as the statistical indicator. This also indicates that established hybrid models can predict blast fragmentation more accurately. However, these predictions mainly serve as reference data for field personnel and do not provide practical blasting design parameters to directly guide on-site production work. Therefore, many researchers have conducted in-depth studies on the optimization of blasting parameters [25; 26; 27]. Given the nonlinear and multi-factor nature of blasting operations, multi-objective intelligent optimization algorithms have been widely used. [PERSON] et al. [28] utilized a particle swarm optimization algorithm to establish a multi-objective optimization model for cemented backfill, incorporating strength, regional stress, and filling cost and used multiple regression to establish surrogate models for each objective, achieving the optimal design parameters. [PERSON] et al. [29] developed a stochastic multi-objective planning model to optimize multiple blasting outcomes in open-pit metal mines, ultimately determining the optimal adjustable parameters for the best blasting scheme. Moreover, multi-objective particle swarm optimization (MOPSO) can optimize multiple indices in blasting operations [30]. It is noteworthy that the feasibility of the prediction and optimization results in production also needs to consider cost factors, as costs limit the realization of optimization effects.