文献类型：期刊文献

英文题名：Source term estimation of hazardous gas leakages under turbulent atmospheric transport dispersion scenarios

作者：Ni, Chuantao[1];Lang, Ziqiang[1,2];Wang, Bing[1];Li, Ang[1];Cao, Chenxi[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, England

年份：2025

卷号：88

起止页码：222

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20255019699826);WOS:【SCI-EXPANDED(收录号:WOS:001639977200001)】；

基金：The work was supported by the National Natural Science Foundation of China (Basic Science Center Program 61988101, 62303186, 62203173) .

语种：英文

外文关键词：Computation fluid dynamics; Hazardous gas leakage; Optimization; Source term estimation; Turbulent flow

摘要：Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving safety and reliability in engineering practice. To achieve real-time STE, least squares-based STE methods have recently been developed. However, these methods require the number and locations of potential hazardous gas leakage sources are known as a priori, which is difficult in many practical scenarios. To address this limitation, we propose a new data-driven STE approach, which enables the STE to be implemented in real time and applicable to complicated turbulent dispersion scenarios. The linear independent analysis in data science is applied to historically collected concentration data of a hazardous gas of concern from a network of sensors to extract the sensor data which represent independent hazardous gas leakage scenarios (IHGLSs). An appropriate Gaussian model approximation to a high-fidelity computational fluid dynamics (CFD) model that must be used to represent the hazardous gas leakage scenarios of concern is built, and the off-line STE of IHGLSs using the approximating Gaussian model is then performed to build the data-driven STE model. The performance of the proposed approach is evaluated by using data that are generated by simulating ethane leakage scenarios in a CIP using a CFD model. Results indicate that the leakage localization accuracy is 100% and the mean relative estimation error for the leakage strength is 6.76%. Moreover, the proposed approach is validated with real data in Prairie Grass field dispersion experiments, demonstrating the practical applicability of the proposed approach. (c) 2025 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.