邓陈辉, 张纪涵. 基于LSTM的海上LNG转驳系统泄漏事故预测方法研究. 力学与实践, xxxx, x(x): 1-10. doi: 10.6052/1000-0879-23-466
引用本文: 邓陈辉, 张纪涵. 基于LSTM的海上LNG转驳系统泄漏事故预测方法研究. 力学与实践, xxxx, x(x): 1-10. doi: 10.6052/1000-0879-23-466
Deng Chenhui, Zhang Jihan. Research on leakage accident prediction method of offshore lng transfer system based on lstm. Mechanics in Engineering, xxxx, x(x): 1-10. doi: 10.6052/1000-0879-23-466
Citation: Deng Chenhui, Zhang Jihan. Research on leakage accident prediction method of offshore lng transfer system based on lstm. Mechanics in Engineering, xxxx, x(x): 1-10. doi: 10.6052/1000-0879-23-466

基于LSTM的海上LNG转驳系统泄漏事故预测方法研究

RESEARCH ON LEAKAGE ACCIDENT PREDICTION METHOD OF OFFSHORE LNG TRANSFER SYSTEM BASED ON LSTM

  • 摘要: 在海上液化天然气(LNG)转驳系统中,一旦发生泄漏事故,其后果将极其严重,可能引发火灾、爆炸、中毒等危害。液化天然气泄漏事故发生速度迅猛,因此如何快速进行泄漏扩散的预测对于应对突发事件下的人员疏散和设备保护至关重要。本研究构建了一种基于长短期记忆神经网络(LSTM)的海上液化天然气转驳系统泄漏扩散预测模型,利用流体动力学仿真计算,获取了大量的基础数据集,然后通过训练,能够有效地对气体扩散浓度进行准确预测,所得结果的均方差和平均绝对误差均低于门控循环单元神经网络模型和反向传播神经网络模型。

     

    Abstract: 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.

     

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