储罐
计算机科学
算法
泄漏(经济)
人工神经网络
实时计算
人工智能
工程类
机械工程
宏观经济学
经济
作者
Yujian Wu,Gang Yang,Jiangang Sun,Lifu Cui,Mengzhu Wang
标识
DOI:10.1088/1361-6501/ad25e2
摘要
Abstract Temperature is the most important safety monitoring indicator for leakage diagnosis during the operation phase of liquefied natural gas (LNG) storage tanks. Timely monitoring and accurate identification of LNG leakage events are crucial for accident prevention, loss reduction, and facility safety maintenance. This study integrates artificial intelligence (AI) algorithms and temperature sensing data to achieve intelligent monitoring and diagnosis of leakage in LNG storage tanks. Firstly, a comprehensive temperature sensing network is constructed by combining numerical simulation of the temperature field and temperature sensing experiments using fiber Bragg grating sensors. Secondly, Python is used to perform linear grid interpolation and flattening on the sensing network, generating 2D temperature nephogram samples that are conducive to AI algorithm recognition. Finally, sample features are extracted using machine vision, and leakage location calculation, leakage diagnosis and leakage volume calculation are implemented with the help of machine learning algorithms, achieving satisfactory accuracy on the test set. In addition, the ConvLSTM framework is introduced for deep learning and recurrent neural network training, enabling spatiotemporal prediction of the leakage area.
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