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Dynamic risk assessment using failure assessment and Bayesian theory

Citation

Kalantarnia, M and Khan, F and Hawboldt, K, Dynamic risk assessment using failure assessment and Bayesian theory, Journal of Loss Prevention in the Process Industries, 22, (5) pp. 600-606. ISSN 0950-4230 (2009) [Refereed Article]

DOI: doi:10.1016/j.jlp.2009.04.006

Abstract

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit. © 2009 Elsevier Ltd. All rights reserved.

Item Details

Item Type:Refereed Article
Keywords:Accident precursor data; Bayesian theory; Dynamic failure assessment; Dynamic risk assessment; Failure probability; Accident precursor data; Accident precursors; Accident scenarios; Bayesian theory; Dynamic failure assessment; Dynamic failures
Research Division:Engineering
Research Group:Maritime Engineering
Research Field:Ocean Engineering
Objective Division:Mineral Resources (excl. Energy Resources)
Objective Group:Environmentally Sustainable Mineral Resource Activities
Objective Field:Environmentally Sustainable Mineral Resource Activities not elsewhere classified
Author:Khan, F (Professor Faisal Khan)
ID Code:94428
Year Published:2009
Web of Science® Times Cited:65
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2014-09-09
Last Modified:2014-09-09
Downloads:0

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