文献类型：期刊文献

英文题名：Optimizing Sensor Placement for Enhanced Source Term Estimation in Chemical Plants

作者：Tian, Hao[1];Lang, Ziqiang[1,2];Cao, Chenxi[1];Wang, Bing[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：13

期号：3

外文期刊名：PROCESSES

收录：;EI(收录号：20251318133946);WOS:【SCI-EXPANDED(收录号:WOS:001452174800001)】；

基金：This work was supported by the National Natural Science Foundation of China (62394343, 62394345, 62103151), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Fundamental Research Funds for the Central Universities (222202517006).

语种：英文

外文关键词：unexpected gas leak; sensor placement optimization; source term estimation; Bayesian inference; adjoint equation; simulated annealing

摘要：The leakage of hazardous chemical gases in chemical plants can lead to severe consequences. Source term estimation (STE) algorithms are effective in locating the leak source. The layout of the sensor network significantly affects the performance of the STE algorithm, yet the underlying mechanism remains unclear. In this study, we first applied computational fluid dynamics (CFD) to simulate 160 hazardous chemical gas leakage scenarios under multi-directional wind conditions in two hypothetic scenes with a natural convection environment, creating an accident dataset. Subsequently, a mathematical model for sensor placement optimization was developed and applied to the dataset to generate a series of sensor layout solutions. Based on these layouts, 12,216 STE cases were calculated. By analyzing the error distribution of these cases, the relationship between sensor placement and STE performance was systematically investigated, and the most effective sensor layout optimization strategies were discussed. This study found that in scenarios with complex obstacles, increasing the average measured concentration of the sensor network can significantly reduce the errors in the STE algorithm.