In the context of addressing dangerous gas leakage on offshore platforms, the prompt identification of leakage sources is paramount for implementing targeted emergency measures. This paper introduces a rapid leakage source location method leveraging a preexisting database on gas leakage and dispersion consequences, coupled with a deep learning approach. Initially, latin hypercube sampling (LHS) and computational fluid dynamics (CFD) are employed to simulate the consequences of gas leakage and dispersion on offshore platforms. Subsequently, a neural network model, specifically a long short-term memory (LSTM) model, is trained using concentration data. This trained model facilitates the rapid localization of the leakage source based on real-time detector data. Furthermore, optimization of the model is conducted through an analysis of various hyperparameters. Finally, the efficacy of the proposed methodology is demonstrated through a comparison between predicted results and actual operating conditions. This approach enables swift and precise identification of leakage sources during incidents, thereby facilitating a more efficient emergency response.
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