A PINN-LSTM Model for Real-Time Gas Concentration Prediction in Mine Conveyor Belt Fires

Accurate prediction of toxic gas concentrations during conveyor-belt fires is essential for ensuring mine safety, yet the nonlinear, time-varying, and turbulent characteristics of underground environments pose significant challenges for real-time forecasting. This study proposes a Physics-Informed Neural Network–Long Short-Term Memory (PINN-LSTM) hybrid model that integrates the one-dimensional convection–diffusion equation as a physical constraint with the sequential learning capability of an LSTM. Full-scale mine tunnel combustion experiments and Fire Dynamics Simulator (FDS) numerical simulations under multiple wind speeds and distances were conducted for model training and validation. The results indicate that the proposed PINN-LSTM achieves the lowest error metrics under all test conditions. The model reduced MSE and RMSE by 70–78% and 65–73%, respectively, compared with traditional LSTM models, and by 8–12% compared with the PINN-TCN variant. The proposed PINN-LSTM achieves the lowest error under all conditions. The PINN-LSTM model has strong prediction accuracy, physical interpretability, and real-time reasoning ability, providing a reliable and physically consistent solution for intelligent gas monitoring and early warning systems in underground fire scenarios.