文献类型：期刊文献

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

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

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[2]Department of Automatic Control and Systems Engineering,University of Sheffield,Sheffield,S13JD,United Kingdom

年份：2025

卷号：88

期号：12

起止页码：222

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报(英文版)

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

语种：英文

中文关键词：Computation fluiddynamics;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 datadriven 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 datadriven 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 is6.76%.Moreover,the proposed approach is validated with real data in Prairie Grass field dispersion experiments,demonstrating the practical applicability of the proposed approach.