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Domino index: A rapid quantification tool for the domino effect in chemical plants
The severity of industrial accidents involving domino effects is widely acknowledged in chemical and process industries. The interdependence of installations and complexity of layouts pose significant challenges for the rapid quantitative assessment of domino effects in large chemical plants. In thi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598520/ https://www.ncbi.nlm.nih.gov/pubmed/37885735 http://dx.doi.org/10.1016/j.heliyon.2023.e21357 |
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author | Gao, Han Yang, Yunkai Shi, Hongxing |
author_facet | Gao, Han Yang, Yunkai Shi, Hongxing |
author_sort | Gao, Han |
collection | PubMed |
description | The severity of industrial accidents involving domino effects is widely acknowledged in chemical and process industries. The interdependence of installations and complexity of layouts pose significant challenges for the rapid quantitative assessment of domino effects in large chemical plants. In this study, a set of domino indices was introduced to measure the extent to which a given installation triggered and propagated domino effects, as well as to assess the overall domino effect in a specified area. An accelerated algorithm for domino accident modelling was developed based on Monte Carlo simulations to calculate the domino index. This algorithm can simulate all potential domino accident propagation pathways and the failure frequencies of installations. Two case studies, derived for a hypothetical chemical plant and actual oil-storage facilities, were examined to evaluate the applicability of the method. Furthermore, the method was validated using conditional probability calculations and vertex metrics. The results demonstrated that the proposed domino index is a useful tool for rapidly quantifying domino effects and that it can assist in identifying critical installations, designing plant layouts, and screening hazardous areas. The method and indices can provide guidance for the prevention of severe domino accidents. |
format | Online Article Text |
id | pubmed-10598520 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-105985202023-10-26 Domino index: A rapid quantification tool for the domino effect in chemical plants Gao, Han Yang, Yunkai Shi, Hongxing Heliyon Research Article The severity of industrial accidents involving domino effects is widely acknowledged in chemical and process industries. The interdependence of installations and complexity of layouts pose significant challenges for the rapid quantitative assessment of domino effects in large chemical plants. In this study, a set of domino indices was introduced to measure the extent to which a given installation triggered and propagated domino effects, as well as to assess the overall domino effect in a specified area. An accelerated algorithm for domino accident modelling was developed based on Monte Carlo simulations to calculate the domino index. This algorithm can simulate all potential domino accident propagation pathways and the failure frequencies of installations. Two case studies, derived for a hypothetical chemical plant and actual oil-storage facilities, were examined to evaluate the applicability of the method. Furthermore, the method was validated using conditional probability calculations and vertex metrics. The results demonstrated that the proposed domino index is a useful tool for rapidly quantifying domino effects and that it can assist in identifying critical installations, designing plant layouts, and screening hazardous areas. The method and indices can provide guidance for the prevention of severe domino accidents. Elsevier 2023-10-21 /pmc/articles/PMC10598520/ /pubmed/37885735 http://dx.doi.org/10.1016/j.heliyon.2023.e21357 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Gao, Han Yang, Yunkai Shi, Hongxing Domino index: A rapid quantification tool for the domino effect in chemical plants |
title | Domino index: A rapid quantification tool for the domino effect in chemical plants |
title_full | Domino index: A rapid quantification tool for the domino effect in chemical plants |
title_fullStr | Domino index: A rapid quantification tool for the domino effect in chemical plants |
title_full_unstemmed | Domino index: A rapid quantification tool for the domino effect in chemical plants |
title_short | Domino index: A rapid quantification tool for the domino effect in chemical plants |
title_sort | domino index: a rapid quantification tool for the domino effect in chemical plants |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598520/ https://www.ncbi.nlm.nih.gov/pubmed/37885735 http://dx.doi.org/10.1016/j.heliyon.2023.e21357 |
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