Experimental study and optimization of a coal spontaneous combustion temperature distribution prediction model

With the increasing complexity of coal spontaneous combustion prediction scenarios and the growth of data volume, existing coal spontaneous combustion temperature prediction methods based on neural network models are unable to accurately capture key disaster-inducing precursors under specific scenarios, resulting in a significant decline in prediction accuracy and reliability in practical applications, which fails to meet the requirements of coal spontaneous combustion prediction under the development of intelligent coal mines. To address these issues, by investigating the variation patterns of multiple characteristic indicators of coal spontaneous combustion under ventilation influence, a high-temperature point prediction model for coal spontaneous combustion based on a Particle Swarm Optimization (PSO)-optimized BP neural network (PSO-BP) was proposed. Five factors, including the concentrations of O₂, CO, CO₂, CH₄, and the height from the air inlet, were selected as the input parameters of the model, and temperature values were taken as the prediction output. Experimental results showed that as coal temperature increased, the oxidation rate of coal samples accelerated, and the spontaneous combustion process of coal exhibited a three-stage evolution characteristic. The O₂ concentration showed a stage-wise decreasing trend, while the concentrations of CO, CO₂, and CH₄ exhibited exponential variations with ventilation time. The high-temperature zone dynamically migrated toward the air inlet side along the direction opposite to the airflow. After parameter optimization, the Mean Absolute Error (MAE) of the PSO-BP model was reduced by 1.2% compared with that before optimization, the Mean Bias Error (MBE) was reduced by 58.5%, and the Root Mean Square Error (RMSE) was reduced by 18.6%. Data from the goaf on the intake and return air sides of the No. 1 East coal seam in a mine of the Baojishan mining area of Gansu Jingmei Energy Company Ltd. were further selected to validate the model. The results showed that the PSO-BP model achieved the minimum error indices across different coal seams, and the fluctuation ranges of its error indices were smaller than those of the BP neural network model across different coal seams.