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Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents

The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of...

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Detalles Bibliográficos
Autores principales: Lu, Yunmeng, Wang, Tiantian, Liu, Tiezhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432183/
https://www.ncbi.nlm.nih.gov/pubmed/32722457
http://dx.doi.org/10.3390/ijerph17155364
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author Lu, Yunmeng
Wang, Tiantian
Liu, Tiezhong
author_facet Lu, Yunmeng
Wang, Tiantian
Liu, Tiezhong
author_sort Lu, Yunmeng
collection PubMed
description The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of the accident. To systematically excavate and analyze the underlying causes of accidents, this paper first integrates emergency elements in the frame of orbit intersection theory and proposes 14 nodes to represent the evolution path of the accident. Then, combined with historical data and expert experience, a Bayesian network (BN) model of CPEAs was established. Through scenario analysis and sensitivity analysis, the interaction between factors and the impact of the factors on accident consequences was evaluated. It is found that the direct factors have the most obvious influence on the accident consequences, and the unsafe conditions contribute more than the unsafe behaviors. Furthermore, considering the factor chain, the management factors, especially safety education and training, are the key link of the accident that affects unsafe behaviors and unsafe conditions. Moreover, effective government emergency response has played a more prominent role in controlling environmental pollution. In addition, the complex network relationship between elements is presented in a sensitivity index matrix, and we extracted three important risk transmission paths from it. The research provides support for enterprises to formulate comprehensive safety production management strategies and control key factors in the risk transmission path to reduce CPEA risks.
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spelling pubmed-74321832020-08-24 Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents Lu, Yunmeng Wang, Tiantian Liu, Tiezhong Int J Environ Res Public Health Article The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of the accident. To systematically excavate and analyze the underlying causes of accidents, this paper first integrates emergency elements in the frame of orbit intersection theory and proposes 14 nodes to represent the evolution path of the accident. Then, combined with historical data and expert experience, a Bayesian network (BN) model of CPEAs was established. Through scenario analysis and sensitivity analysis, the interaction between factors and the impact of the factors on accident consequences was evaluated. It is found that the direct factors have the most obvious influence on the accident consequences, and the unsafe conditions contribute more than the unsafe behaviors. Furthermore, considering the factor chain, the management factors, especially safety education and training, are the key link of the accident that affects unsafe behaviors and unsafe conditions. Moreover, effective government emergency response has played a more prominent role in controlling environmental pollution. In addition, the complex network relationship between elements is presented in a sensitivity index matrix, and we extracted three important risk transmission paths from it. The research provides support for enterprises to formulate comprehensive safety production management strategies and control key factors in the risk transmission path to reduce CPEA risks. MDPI 2020-07-25 2020-08 /pmc/articles/PMC7432183/ /pubmed/32722457 http://dx.doi.org/10.3390/ijerph17155364 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lu, Yunmeng
Wang, Tiantian
Liu, Tiezhong
Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents
title Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents
title_full Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents
title_fullStr Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents
title_full_unstemmed Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents
title_short Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents
title_sort bayesian network-based risk analysis of chemical plant explosion accidents
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432183/
https://www.ncbi.nlm.nih.gov/pubmed/32722457
http://dx.doi.org/10.3390/ijerph17155364
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