文献类型：期刊文献

英文题名：Data-driven detection and assessment of abnormal hydrogen leakages in indoor industrial and community settings

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, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Sheffield, Sch Elect & Elect Engn, Sheffield S1 3JD, England

年份：2026

卷号：213

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20262620979188);WOS:【SCI-EXPANDED(收录号:WOS:001808382300001)】；

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. The data for the real-world experimental study is provided by Professor Li Xuefang from Shandong University.

语种：英文

外文关键词：Hydrogen leakage score; Independent hydrogen leakage scenarios; Linear independence analysis; Abnormal hydrogen leakage; Data-driven detection and quantitative assessment

摘要：Abnormal hydrogen leakages (AHLs) can pose significant risks to industrial operations and public safety. Detection and assessment of AHLs are therefore essential for issuing necessary warnings and conducting accident analysis. To address this, numerous multivariate statistical process monitoring and machine learning-based methods have been developed. However, these approaches are primarily limited to qualitatively identifying the presence of excessive leakage and are unable to quantitatively assess the overall leakage conditions, which is required for accurate alarm generation and comprehensive post-accident analysis. To address this challenge, this study proposes a novel data-driven approach for the detection and quantitative assessment of AHLs in settings where minor hydrogen leakages commonly occur under normal operating conditions-situations frequently observed in indoor hydrogen-related industrial and community systems. The key idea is to apply linear independence analysis to historical hydrogen concentration data collected from a sensor network, to identify data corresponding to independent hydrogen leakage scenarios (IHLSs). An IHLS-based "hydrogen leakage score" is then developed to enable both detection and quantitative assessment of AHLs. The performance of the proposed approach is evaluated using data from computational fluid dynamics simulations as well as real-world experiments and is compared with three state-of-the-art methods. The results demonstrate the effectiveness and robustness of the proposed approach, highlighting its advantages and potential for detecting and quantitatively assessing AHLs in indoor hydrogen-related industrial and community systems.