Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park
The production and storage of major hazard installations (MHIs) bring potential risks to chemical industrial park (CIP). In the production system of MHIs, its dangerous degree is mainly determined by key parameters, and abnormal key parameters often lead to accidents. To predict the real-time risk v...
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2019-01-01
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Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2019/6250483 |
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doaj-10f45140f83a4e968c230c331c3f54e02020-11-25T00:52:58ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/62504836250483Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial ParkYaguang Kong0Chenfeng Xie1Song Zheng2Peng Jiang3Meng Guan4Fang Wang5Hangzhou Dianzi University, ChinaHangzhou Dianzi University, ChinaHangzhou Dianzi University, ChinaHangzhou Dianzi University, ChinaHangzhou Dianzi University, ChinaHangzhou Dianzi University, ChinaThe production and storage of major hazard installations (MHIs) bring potential risks to chemical industrial park (CIP). In the production system of MHIs, its dangerous degree is mainly determined by key parameters, and abnormal key parameters often lead to accidents. To predict the real-time risk values of MHIs and improve accident prevention ability of CIP, we need a method that can combine dynamic prediction and assessment. Quantitative risk assessment (QRA) is not capable of modelling risk variations during the operation of a process. Therefore, this paper adopts the data-driven approach. Inspired by visual qualitative analysis and quantitative analysis, a dynamic early warning method is proposed for MHIs. We can get the future trend of these key parameters by using strongly correlation variables to predict key parameters. Fuzzy evaluation analysis is performed on the risk levels of key parameters, and the dynamic evaluation index of these MHIs is obtained. This method can be applied to the dynamic evaluation of MHIs system in CIP. It can contribute to the safety of CIP in some aspects.http://dx.doi.org/10.1155/2019/6250483 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yaguang Kong Chenfeng Xie Song Zheng Peng Jiang Meng Guan Fang Wang |
spellingShingle |
Yaguang Kong Chenfeng Xie Song Zheng Peng Jiang Meng Guan Fang Wang Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park Complexity |
author_facet |
Yaguang Kong Chenfeng Xie Song Zheng Peng Jiang Meng Guan Fang Wang |
author_sort |
Yaguang Kong |
title |
Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park |
title_short |
Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park |
title_full |
Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park |
title_fullStr |
Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park |
title_full_unstemmed |
Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park |
title_sort |
dynamic early warning method for major hazard installation systems in chemical industrial park |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1076-2787 1099-0526 |
publishDate |
2019-01-01 |
description |
The production and storage of major hazard installations (MHIs) bring potential risks to chemical industrial park (CIP). In the production system of MHIs, its dangerous degree is mainly determined by key parameters, and abnormal key parameters often lead to accidents. To predict the real-time risk values of MHIs and improve accident prevention ability of CIP, we need a method that can combine dynamic prediction and assessment. Quantitative risk assessment (QRA) is not capable of modelling risk variations during the operation of a process. Therefore, this paper adopts the data-driven approach. Inspired by visual qualitative analysis and quantitative analysis, a dynamic early warning method is proposed for MHIs. We can get the future trend of these key parameters by using strongly correlation variables to predict key parameters. Fuzzy evaluation analysis is performed on the risk levels of key parameters, and the dynamic evaluation index of these MHIs is obtained. This method can be applied to the dynamic evaluation of MHIs system in CIP. It can contribute to the safety of CIP in some aspects. |
url |
http://dx.doi.org/10.1155/2019/6250483 |
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