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Process accident model considering dependency among contributory factors


Adedigba, SA and Khan, FI and Yang, M, Process accident model considering dependency among contributory factors, Process Safety and Environmental Protection, 102 pp. 633-647. ISSN 0957-5820 (2016) [Refereed Article]

Copyright Statement

2016 Institution of Chemical Engineers

DOI: doi:10.1016/j.psep.2016.05.004


With the increasing complexity of the hazardous process operation, potential accident modelling is becoming challenging. In process operation accidents, causation is a function of nonlinear interactions of various factors. Traditional accident models such as the fault tree represent cause and effect relationships without considering the dependency and nonlinear interaction of the causal factors. This paper presents a new non-sequential barrier-based process accident model. The model uses both fault and event tree analysis to study the cause-consequence relationship. The dependencies and nonlinear interaction among failure causes are modelled using a Bayesian network (BN) with various relaxation strategies. The proposed model considers six prevention barriers in the accident causation process: design error, operational failure, equipment failure, human failure and external factor prevention barriers. Each barrier is modelled using BN and the interactions within the barrier are also modelled using BN. The proposed model estimates the lower and upper bounds of prevention barriers failure probabilities, considering dependencies and non-linear interaction among causal factors. Based on these failure probabilities, the model predicts the lower and upper bounds of the process accident causation probability. The proposed accident model is tested on a real life case study.

Item Details

Item Type:Refereed Article
Keywords:accident modelling, accident prediction, Bayesian network analysis, prevention barrier dependency, probabilistic analysis, risk assessment; Bayesian networks, complex networks, fault tree analysis, nonlinear analysis, probability, risk analysis
Research Division:Engineering
Research Group:Engineering practice and education
Research Field:Risk engineering
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Khan, FI (Professor Faisal Khan)
ID Code:120383
Year Published:2016
Web of Science® Times Cited:32
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-08-23
Last Modified:2017-11-01

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