SSA-LSTM-Multi-Head Attention Modelling Approach for Prediction of Coal Dust Maximum Explosion Pressure Based on the Synergistic Effect of Particle Size and Concentration

Coal dust explosions are severe safety accidents in coal mine production, posing significant threats to life and property. Predicting the maximum explosion pressure ( ${\text{P}}_{\text{m}}$) of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions. In this study, a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations ( ${\text{C}}_{\text{dust}}$), resulting in a dataset of 70 experimental groups. Through Spearman correlation analysis and random forest feature selection methods, particle size ( ${\text{D}}_{10}$, ${\text{D}}_{20}$, ${\text{D}}_{50}$) and mass concentration ( ${\text{C}}_{\text{dust}}$) were identified as critical feature parameters from the ten initial parameters of the coal dust samples. Based on this, a hybrid Long Short-Term Memory (LSTM) network model incorporating a Multi-Head Attention Mechanism and the Sparrow Search Algorithm (SSA) was proposed to predict the maximum explosion pressure of coal dust. The results demonstrate that the SSA-LSTM-Multi-Head Attention model excels in predicting the maximum explosion pressure of coal dust. The four evaluation metrics indicate that the model achieved a coefficient of determination (R²), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) of 0.9841, 0.0030, 0.0074, and 0.0049, respectively, in the training set. In the testing set, these values were 0.9743, 0.0087, 0.0108, and 0.0069, respectively. Compared to artificial neural networks (ANN), random forest (RF), support vector machines (SVM), particle swarm optimized-SVM (PSO-SVM) neural networks, and the traditional single-model LSTM, the SSA-LSTM-Multi-Head Attention model demonstrated superior generalization capability and prediction accuracy. The findings of this study not only advance the application of deep learning in coal dust explosion prediction but also provide robust technical support for the prevention and risk assessment of coal dust explosions.