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Article: Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries

TitleCellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries
Authors
KeywordsCellular automata
Accident
Modeling
Forecasting
Diffusion
Process industry
Advection
Issue Date2006
Citation
Journal of Hazardous Materials, 2006, v. 137, n. 1, p. 8-30 How to Cite?
AbstractThe strategies to prevent accidents from occurring in a process industry, or to minimize the harm if an accident does take place, always revolve around forecasting the likely accidents and their impacts. Based on the likely frequency and severity of the accidents, resources are committed towards preventing the accidents. Nearly all techniques of ranking hazardous units, be it the hazard and operability studies, fault tree analysis, hazard indice, etc. - qualitative as well as quantitative - depend essentially on the assessment of the likely frequency and the likely harm accidents in different units may cause. This fact makes it exceedingly important that the forecasting the accidents and their likely impact is done as accurately as possible. In the present study we introduce a new approach to accident forecasting based on the discrete modeling paradigm of cellular automata. In this treatment an accident is modeled as a self-evolving phenomena, the impact of which is strongly influenced by the size, nature, and position of the environmental components which lie in the vicinity of the accident site. The outward propagation of the mass, energy and momentum from the accident epicenter is modeled as a fast diffusion process occurring in discrete space-time coordinates. The quantum of energy and material that would flow into each discrete space element (cell) due to the accidental release is evaluated and the degree of vulnerability posed to the receptors if present in the cell is measured at the end of each time element. This approach is able to effectively take into account the modifications in the flux of energy and material which occur as a result of the heterogeneous environment prevailing between the accident epicenter and the receptor. Consequently, more realistic accident scenarios are generated than possible with the prevailing techniques. The efficacy of the approach has been illustrated with case studies. © 2006 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/206272
ISSN
2015 Impact Factor: 4.836
2015 SCImago Journal Rankings: 1.692
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSarkar, Chinmoy R.-
dc.contributor.authorAbbasi, Shahid Abbas-
dc.date.accessioned2014-10-22T01:25:33Z-
dc.date.available2014-10-22T01:25:33Z-
dc.date.issued2006-
dc.identifier.citationJournal of Hazardous Materials, 2006, v. 137, n. 1, p. 8-30-
dc.identifier.issn0304-3894-
dc.identifier.urihttp://hdl.handle.net/10722/206272-
dc.description.abstractThe strategies to prevent accidents from occurring in a process industry, or to minimize the harm if an accident does take place, always revolve around forecasting the likely accidents and their impacts. Based on the likely frequency and severity of the accidents, resources are committed towards preventing the accidents. Nearly all techniques of ranking hazardous units, be it the hazard and operability studies, fault tree analysis, hazard indice, etc. - qualitative as well as quantitative - depend essentially on the assessment of the likely frequency and the likely harm accidents in different units may cause. This fact makes it exceedingly important that the forecasting the accidents and their likely impact is done as accurately as possible. In the present study we introduce a new approach to accident forecasting based on the discrete modeling paradigm of cellular automata. In this treatment an accident is modeled as a self-evolving phenomena, the impact of which is strongly influenced by the size, nature, and position of the environmental components which lie in the vicinity of the accident site. The outward propagation of the mass, energy and momentum from the accident epicenter is modeled as a fast diffusion process occurring in discrete space-time coordinates. The quantum of energy and material that would flow into each discrete space element (cell) due to the accidental release is evaluated and the degree of vulnerability posed to the receptors if present in the cell is measured at the end of each time element. This approach is able to effectively take into account the modifications in the flux of energy and material which occur as a result of the heterogeneous environment prevailing between the accident epicenter and the receptor. Consequently, more realistic accident scenarios are generated than possible with the prevailing techniques. The efficacy of the approach has been illustrated with case studies. © 2006 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofJournal of Hazardous Materials-
dc.subjectCellular automata-
dc.subjectAccident-
dc.subjectModeling-
dc.subjectForecasting-
dc.subjectDiffusion-
dc.subjectProcess industry-
dc.subjectAdvection-
dc.titleCellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jhazmat.2006.01.081-
dc.identifier.pmid16713088-
dc.identifier.scopuseid_2-s2.0-33747767609-
dc.identifier.volume137-
dc.identifier.issue1-
dc.identifier.spage8-
dc.identifier.epage30-
dc.identifier.isiWOS:000240581300002-

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