Rapid Prediction of Leakage Dispersion at Natural Gas Distribution Stations: A Prototype Development Using Computational Fluid Dynamics and Machine Learning

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.