Operational risk assessment: A case of the Bhopal disaster

Ming Yang, Faisal Khan, Paul Amyotte

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile. This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.

Original languageEnglish
Pages (from-to)70-79
Number of pages10
JournalProcess Safety and Environmental Protection
Volume97
DOIs
Publication statusPublished - Sep 1 2015
Externally publishedYes

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Keywords

  • Accident precursor
  • Bayesian networks
  • Bhopal disaster
  • Dynamic risk assessment
  • Loss functions
  • Operational risk assessment

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality

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