Explainable deep learning for spatiotemporal high-temperature evolution and predictive modeling in coal seam enhanced combustion

Xiao Cui, Baisheng Nie, Hengyi He, Peng Liu, Kaidan Bai, Haowen Zhou, Jingtao Yang,
Explainable deep learning for spatiotemporal high-temperature evolution and predictive modeling in coal seam enhanced combustion,
Process Safety and Environmental Protection,
Volume 204,
2025,
108084,
ISSN 0957-5820,
https://doi.org/10.1016/j.psep.2025.108084.
(https://www.sciencedirect.com/science/article/pii/S0957582025013515)
Abstract: Temperature monitoring during deep coal seam combustion is essential for optimizing in-situ heat extraction and ensuring operational safety. Accurate prediction of temperature variations under enhanced combustion thus becomes a critical tool for maintaining both safety and efficiency. In this study, continuous-ventilation coal combustion experiments were performed to examine the spatiotemporal evolution of temperature, gas emissions, and mass variation. The results showed that the high-temperature zone migrated along the airflow direction, accompanied by pronounced spatiotemporal fluctuations in gas concentrations. Using 13 input features—Coal Weight, Cross Section, Coal Quality Loss Rate, CO/CO2, CO/H2, C3H8, C3H6, C2H4, C2H6, CH4, H2, CO2, and CO—predictive models for enhanced combustion temperature were developed with multiple machine learning methods. Eleven models were assessed, including LSTM, CNN-LSTM, CNN-LSTM with Attention, BP, RNN, CNN, GRU, Transformer, RF, RBF, and XGBoost. Among them, the CNN-LSTM-Attention model achieved the best performance, with an R2 of 0.987, MAE of 0.41, RMSE of 0.59, and MAPE of 0.47—substantially outperforming the other ten models. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that CO2 concentration had the strongest impact on prediction (mean SHAP value: 0.0555), followed by the CO/CO2 ratio (0.0346) and Cross Section (0.024). Overall, this study proposes a robust and interpretable approach for high-precision temperature prediction during underground coal combustion, offering important guidance for thermal monitoring in in-situ heat extraction systems.
Keywords: Enhanced coal combustion; High-temperature migration; Convolutional neural network (CNN); Long short-term memory (LSTM); Attention mechanism; SHAP interpretability