A Data-Driven Hybrid Intelligent Optimization Framework for Sustainable Mineral Resource Extraction

Accurate prediction of mean fragment size is a fundamental requirement for enhancing operational efficiency, reducing ecological disturbances, and fostering the sustainable use of mineral resources. However, traditional empirical and statistical approaches often struggle with high-dimensional variables, limited computational speed, and the challenge of modeling small or sparse datasets. This study proposes a hybrid machine learning optimization framework that integrates Random Forest (RF), Whale Optimization Algorithm (WOA), and Extreme Gradient Boosting (XGBoost). Based on high-dimensional and small-sample data collected from historical blasting operations in open-pit mines, the framework employs a data-driven approach to construct a prediction model for mean fragment size, with the aim of enhancing the sustainability of mineral resource extraction through optimized blast design. The raw blasting fragmentation dataset was first preprocessed using a multi-step procedure to improve data quality. RF was then employed to assess and select 19 input features for dimensionality reduction, while WOA was utilized to optimize the hyperparameters of the predictive model. Finally, XGBoost was applied to model the small-sample blasting fragmentation dataset. Comparative experiments demonstrated that the proposed model achieved superior predictive performance with a coefficient of determination (R2) of 0.93. In addition, the cosine amplitude method was used to analyze the sensitivity of different variables affecting the mean fragment size (MFS), and the SHAP method was applied to quantitatively reveal the marginal contribution of each input variable to the prediction.