Domino effects are low-probability high-consequence accidents causing severe damage to humans, process plants, and the environment. Because domino effects affect large areas and are difficult to control, preventive safety measures have been given priority over mitigative measures. As a result, safety distances and safety inventories have been used as preventive safety measures to reduce the escalation probability of domino effects. However, these safety measures are usually designed considering static accident scenarios. In this study, we show that compared to a static worst-case accident analysis, a dynamic consequence analysis provides a more rational approach for risk assessment and management of domino effects. This study also presents the application of Bayesian networks and conflict analysis to risk-based allocation of chemical inventories to minimize the consequences and thus to reduce the escalation probability. It emphasizes the risk management of chemical inventories as an inherent safety measure, particularly in existing process plants where the applicability of other safety measures such as safety distances is limited. © 2013 Society for Risk Analysis.

Item Type:	Refereed Article
Keywords:	Bayesian network; Conflict analysis; Domino effect; Inherent safety; Risk management
Research Division:	Engineering
Research Group:	Engineering practice and education
Research Field:	Engineering practice and education not elsewhere classified
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in engineering
UTAS Author:	Khan, F (Professor Faisal Khan)
ID Code:	100320
Year Published:	2014
Web of Science® Times Cited:	50
Deposited By:	Research Division
Deposited On:	2015-05-11
Last Modified:	2017-10-30
Downloads:	0