泄漏
泄漏(经济)
灵活性(工程)
计算机科学
人工神经网络
卷积神经网络
检漏
管道运输
网络模型
数据挖掘
人工智能
机器学习
工程类
环境工程
统计
宏观经济学
经济
数学
作者
Alibek Kopbayev,Faisal Nadeem Khan,Ming Yang,Syeda Zohra Halim
标识
DOI:10.1016/j.psep.2022.03.002
摘要
Natural gas leakage can impose significant danger on a facility and its surrounding communities. Methods for early detection and diagnosis of such leakages have been developed and widely used for gas pipelines and storage tanks. Most techniques include inspection of sensor-aided mathematical models. Application of machine learning techniques to gas leakage detection has been rarely explored. In the present work, convolutional network (to model spatial likelihood of leak) is combined with bi-directional long short-term memory layer network, or BiLSTM (to model temporal dependence of leak likelihood) to perform leak detection and diagnosis. The developed model was trained and tested using sequence of concentration profiles generated using open-source simulated data. The model learned successfully to predict gas leakage and classify its size. The study also explores the flexibility of this network to perform quick detection and diagnose with the limited data. While the networks did not require parameter adjustments to achieve high prediction accuracy, further optimization is possible through data selection and pre-processing. The model needs to be further tested for wide range of leak scenarios. At its present condition, the combined application of convolutional network and BiLSTM shows promising results for early and accurate leak detection in natural gas facilities. Experimental results are needed to confirm the effectiveness of the model and data uncertainty.
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