A leakage accident in offshore liquefied natural gas(LNG)transfer systems can lead to severe consequences,including the risk of fire,explosions,and poisoning.These accidents occur rapidly,making it crucial to predict and respond swiftly,particularly for emergency evacuations and equipment protection.In this study,we propose a prediction model for LNG leak diffusion in offshore transfer systems,based on long short-term memory(LSTM)neural networks.Leveraging fluid dynamics simulations,we gather a substantial dataset.After rigorous training,our model effectively forecasts gas concentration diffusion.The mean square error and average absolute error are both lower than those of the gated recurrent unit(GRU)and backpropagation neural network models.
关键词
海上液化天然气转驳系统/泄漏事故/长短期记忆神经网络/门控循环单元/反向传播
Key words
offshore liquefied natural gas transfer system/leakage accident/long short-term memory/gated recurrent unit/backpropagation
维普
万方数据