Abstract
Gas leakages present significant explosion hazards in industrial settings, potentially leading to substantial human casualties, environmental damage, and economic losses in accident scenarios. Rapid detection and location of leak sources are crucial for timely corrective maintenance, mitigating the most hazardous situations. This study focuses on developing gated recurrent units (GRU) to identify hydrogen leaks in a chemical process module due to hydrogen release. Training and test databases were generated using 3D-CFD simulations for four leak scenarios and a non-leakage scenario. The inputs consisted of concentration profiles from eleven sensors, considering four leak sources, four wind speeds, and eight wind directions across various temporal lengths. The results demonstrated improved performance with higher input timesteps, achieving an accuracy of over 86.75% for unseen data. This indicates the strong generalisation of the model capabilities and potential for predicting leaks using easily obtainable inputs.
