文献类型：期刊文献

英文题名：Peripheric sensors-based leaking source tracking in a chemical industrial park with complex obstacles

作者：Chen, Shikuan[1];Du, Wenli[1];Peng, Xin[1];Cao, Chenxi[1];Wang, Xinjie[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

年份：2022

卷号：78

外文期刊名：JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES

收录：;EI(收录号：20222712313762);WOS:【SCI-EXPANDED(收录号:WOS:000827267700001)】；

基金：This work is supported by National Natural Science Fund for Distinguished Young Scholars (61725301) , National Natural Science Foundation of China (62136003, 62173145) , the Fundamental Research Funds for the Central Universities (222202217006) and Shanghai AI Lab.

语种：英文

外文关键词：Chemical industrial park; Peripheric sensor; FLACS; Source tracking; Convolutional neural network

摘要：Hazardous gas leakage can cause irreversible damage to the environment and human health. When it happens, it's necessary to find the accurate position of the leaking source efficiently and take effective measures to reduce or prevent more irreversible losses. However, source tracking in the scenario with complex obstacles faces the challenge caused by turbulent wind flow. In this paper, ethane leak scenarios with different leaking sources and environmental conditions are simulated using the Flame acceleration simulator (FLACS). Considering that sensors are often deployed at the boundaries of industrial parks for the detection of hazardous gas leakage, the concentration information of these peripheric sensors is mapped to images, which serve as inputs to a convolutional neural network (CNN) to determine the location of the leaking source and wind direction in a chemical industrial park with complex obstacles. The results show the effectiveness of the proposed method. In addition, fixed failure rates of the sensor along with additional meteorological conditions are considered to evaluate the performance of generalization.