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dc.contributor.authorBriš, Radim
dc.contributor.authorMedonos, Sava
dc.contributor.authorWilkins, Chris
dc.contributor.authorZdráhala, Adam
dc.date.accessioned2014-05-21T07:33:48Z
dc.date.available2014-05-21T07:33:48Z
dc.date.issued2014
dc.identifier.citationReliability Engineering & System Safety. 2014, vol. 125, p. 54-66.cs
dc.identifier.issn0951-8320
dc.identifier.issn1879-0836
dc.identifier.urihttp://hdl.handle.net/10084/101866
dc.description.abstractRisk to safety of personnel in process industries is normally modeled in the application of event trees, where the risk is defined as a product of event frequency and its consequences. This method is steady state whilst the actual event is time dependent, comprising, for a gas release, as for example, the size of gas cloud being released, probabilities of ignition, fire or explosion, fatality, escalation to new releases and fire and/or explosion, and the probability of fatality, all varying with time. This Paper presents a method whereby the time-dependent events and the time-dependent probability of fatality are modeled by means of the direct Monte Carlo simulation method. Different accident scenarios are modeled and discussed resulting from event trees analysis. Using the simulation method the modeled scenarios change with relevant probabilities at defined times to configurations with appropriate probabilities of fatalities. Simple sensitivity analysis in dependency on most relevant events is also performed. The Paper uses a realistic example from the offshore industry for which the probability of fatality is quantified by the use of simulation method.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesReliability Engineering & System Safetycs
dc.relation.urihttp://dx.doi.org/10.1016/j.ress.2013.05.010cs
dc.rightsCopyright © 2013 Elsevier Ltd. All rights reserved.cs
dc.subjectoffshore industrycs
dc.subjecttime-dependent risk modelingcs
dc.subjecthydrocarbon-related fire riskcs
dc.subjectMonte Carlo methodcs
dc.titleTime-dependent risk modeling of accidental events and responses in process industriescs
dc.typearticlecs
dc.identifier.doi10.1016/j.ress.2013.05.010
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume125cs
dc.description.lastpage66cs
dc.description.firstpage54cs
dc.identifier.wos000333790600007


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