文献类型：期刊文献

英文题名：Data-driven source term estimation of hazardous gas leakages in complex chemical industrial parks

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 Automatic Control & Syst Engn, Sheffield S1 3JD, England

年份：2026

卷号：206

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20255119741310);WOS:【SCI-EXPANDED(收录号:WOS:001645991300001)】；

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

语种：英文

外文关键词：Chemical industrial park; High-fidelity computational fluid dynamics; model; Incremental linear response matrix; Linear independence analysis; Source term estimation; Surrogate machine learning model

摘要：Hazardous gas leakage in chemical industrial parks (CIPs) can cause irreversible damage to the environment and human health. When this happens, it is crucial to perform source term estimation (STE) timely and accurately and take effective measures to reduce or prevent possible losses. To achieve real-time STE, machine learning (ML)-based STE methods have recently been developed, aiming to build a ML model to represent the relationship between sensor measurements and STE outcome to facilitate real-time applications. However, the problem with these methods is that they often cannot handle cases when sensor measurements are beyond the scope of the training dataset. To address this limitation, in the present study, a novel approach is developed in which ML is used to generate a surrogate representation of complex atmospheric transport and dispersion processes by utilizing data from a high-fidelity computational fluid dynamics (CFD) model. This surrogate ML model captures the forward relationship between hazardous gas leakage locations and rates and the resulting sensor observations, enabling efficient nonlinear optimisation for off-line STE. In addition, the study, for the first time, introduces the concept of the incremental linear response matrix to address issues with potential system nonlinearities. These approaches are evaluated on a pseudo-real concentration dataset generated by CFD simulated ethane leakage scenarios in a CIP with complex obstacles. The findings validate the effectiveness of the proposed approaches and demonstrate their superiority over existing ML-based STE methods, particularly in scenarios that extend beyond the training data.