Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gao, Han, Yang, Yunkai, Shi, Hongxing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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
_version_ 1785125571634659328
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
work_keys_str_mv AT gaohan dominoindexarapidquantificationtoolforthedominoeffectinchemicalplants
AT yangyunkai dominoindexarapidquantificationtoolforthedominoeffectinchemicalplants
AT shihongxing dominoindexarapidquantificationtoolforthedominoeffectinchemicalplants