Leakage incidents at natural gas distribution stations (NGDSs) present severe fire and explosion risks, demanding immediate, data-driven emergency responses. While crucial for minimizing hazard impacts, real-time prediction of gas dispersion ranges remains a significant operational challenge. To partially address this critical safety need, this study introduces a rapid-response prediction framework prototype integrating computational fluid dynamics (CFD) with machine learning (ML). Specifically, a comprehensive database of 500 experimentally validated CFD leakage scenarios at 60 s was developed first, specifically focusing on mapping gas concentration contours within the critical 5–15% flammability range. To identify the most effective real-time predictive tool, three ML algorithms, including a backpropagation neural network (BPNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were evaluated. The BPNN initially outperformed the sequence models, with a coefficient of determination (R2) of 0.96, a mean squared error (MSE) of 1.35, a mean absolute error (MAE) of 0.77, a maximum absolute error (MaxAE) of 4.94 and an average training time of 4.23 s per epoch. To further meet the stringent speed and precision demands of emergency scenarios, the model was enhanced via particle swarm optimization (PSO-BPNN). This optimized framework achieved exceptional accuracy (R2 = 0.99, MSE = 0.34, and MAE = 0.38) while reducing the training time to just 1.42 s per epoch under the current computational configuration. The developed CFD-ML prototype provides a practical, highly efficient tool for NGDS operators and emergency responders, enabling them to instantly visualize hazard zones, optimize evacuation protocols, and safely mitigate leakage incidents before ignition occurs.
Mi et al. (Sun,) studied this question.