Development and implementation of an intelligent early warning system for preventing environmental pollution from coal spontaneous combustion

Coal spontaneous combustion fires threaten personal safety, increase carbon emissions, release toxic and harmful gases, and cause serious environmental pollution. The study of intelligent early warnings for coal spontaneous combustion can advance fire prevention and control measures, making a meaningful contribution to the ecological and environmental protection in mining areas. To address the limitations in selecting characteristic index gases for coal spontaneous combustion and the low accuracy of traditional temperature prediction and discrimination models, an intelligent identification system was developed. The system integrates laboratory research and analysis, intelligent algorithm optimization, index rationality verification, and field measurement and application, all based on characteristic index gases. By constructing a dynamic discriminant model of coal self-gas temperature, the composite index of coal spontaneous combustion characteristics is further optimized and verified. Model performance was evaluated using root mean square error (RMSE), decision coefficient ( R 2 ), mean absolute error (MAE), and mean absolute percentage error (MAPE). The prediction results for three, four, and five parameters were obtained. The results indicate that the R 2 value was 0.9975 under the conditions of O 2 , CO, C 2 H 4 , and CH 4 /C 2 H 6 , demonstrating the best model performance. The MAE was 1.9272, the RMSE was 2.5114, and the MAPE was 2.0830%. These findings enable optimal selection of self-ignition warning indicators for coal. A comparative analysis of the improved whale optimization (MSWOA-BP), gray wolf optimization (GWO-BP), standard whale optimization (WOA-BP), and particle swarm optimization (PSO-BP) models was performed to verify the universality of preferred feature indicators and the accuracy of prediction models. A comparative analysis between on-site measured temperatures and model-predicted temperatures demonstrated that the model exhibited high accuracy. This research provides a valuable reference for developing on-site coal spontaneous combustion warning systems, enabling efficient prediction and early warning, which are crucial for coal resource safety, efficient mining, and fire prevention.