文献类型：期刊文献

英文题名：A Novel Multi-Sensor Data-Driven Approach to Source Term Estimation of Hazardous Gas Leakages in the Chemical Industry

作者：Lang, Ziqiang[1,2];Wang, Bing[2];Wang, Yiting[2];Cao, Chenxi[2];Peng, Xin[2];Du, Wenli[2];Qian, Feng[2]

机构：[1]Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, England;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：10

期号：8

外文期刊名：PROCESSES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000845954700001)】；

基金：This work was supported by National Natural Science Foundation of China Basic Science Centre Program Grant: 61988101, Key Program Grant: 62136003, and Grant 22178103.

语种：英文

外文关键词：source term estimation; multi-sensor data-driven; real-time experimental observations and implementation; unsupervised multi-sensor data clustering and analysis; independent hazardous-gas-leakage scenarios (IHGLSs)

摘要：Source term estimation (STE) is crucial for understanding and addressing hazardous gas leakages in the chemical industry. Most existing methods basically use an atmospheric transport and dispersion (ATD) model to predict the concentrations of hazardous gas leakages from different possible sources, compare the predicted results with multi-sensor data, and use the deviations to search and derive information on the real sources of leakages. Although performing well in principle, complicated computations and the associated computer time often make these methods difficult to apply in real time. Recently, many machine learning methods have also been proposed for the purpose of STE. The idea is to build offline a machine-learning-based STE model using data generated with a high-fidelity ATD model and then apply the machine learning model to multi-sensor data to perform STE in real time. The key to the success of a machine-learning-based STE is that the machine-learning-based STE model has to cover all possible scenarios of concern, which is often difficult in practice because of unpredictable environmental conditions and the inherent robust problems with many supervised machine learning methods. In order to address challenges with the existing STE methods, in the present study, a novel multi-sensor data-driven approach to STE of hazardous gas leakages is proposed. The basic idea is to establish a multi-sensor data-driven STE model from historical multi-sensor observations that cover the situations known as the independent hazardous-gas-leakage scenarios (IHGLSs) in a chemical industry park of concern. Then the established STE model is applied to online process multi-sensor data and perform STE for the chemical industry park in real time. The new approach is based on a rigorous analysis of the relationship between multi-sensor data and sources of hazardous gas leakages and derived using advanced data science, including unsupervised multi-sensor data clustering and analysis. As an example of demonstration, the proposed approach is applied to perform STE for hazardous gas-leakage scenarios wherein a Gaussian plume model can be used to describe the atmospheric transport and dispersion. Because of no need of ATD-model-based online optimization and supervised machine learning, the new approach can potentially overcome many problems with existing methods and enable STE to be literally applied in engineering practice.