In the context of domino effects, vulnerability analysis of chemical and process plants aims to identify and protect installations which are relatively more susceptible to damage and thus contribute more to the initiation or propagation of domino effects. In the present study, we have developed a methodology based on graph theory for domino vulnerability analysis of hazardous installations within process plants, where owning to the large number of installations or complex interdependencies, the application of sophisticated reasoning approaches such as Bayesian network is limited. We have taken advantage of a hypothetical chemical storage plant to develop the methodology and validated the results using a dynamic Bayesian network approach. The efficacy and out-performance of the developed methodology have been demonstrated via a real-life complex case study.

Item Type:	Refereed Article
Keywords:	domino effect, dynamic Bayesian network, graph metrics, process plant
Research Division:	Engineering
Research Group:	Manufacturing engineering
Research Field:	CAD/CAM systems
Objective Division:	Energy
Objective Group:	Energy exploration
Objective Field:	Coal exploration
UTAS Author:	Abbassi, R (Dr Rouzbeh Abbassi)
UTAS Author:	Khan, F (Professor Faisal Khan)
ID Code:	109595
Year Published:	2016
Web of Science® Times Cited:	51
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2016-06-22
Last Modified:	2017-11-03
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