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Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model


Hashemi, SJ and Khan, FI and Ahmed, S, Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model, Computers and Chemical Engineering, 93 pp. 128-142. ISSN 0098-1354 (2016) [Refereed Article]

Copyright Statement

Copyright 2016 Elsevier Ltd.

DOI: doi:10.1016/j.compchemeng.2016.06.011


Integrated safety analysis of hazardous process facilities calls for an understanding of both stochastic and topological dependencies, going beyond traditional Bayesian Network (BN) analysis to study cause-effect relationships among major risk factors. This paper presents a novel model based on the Copula Bayesian Network (CBN) for multivariate safety analysis of process systems. The innovation of the proposed CBN model is in integrating the advantage of copula functions in modelling complex dependence structures with the cause-effect relationship reasoning of process variables using BNs. This offers a great flexibility in probabilistic analysis of individual risk factors while considering their uncertainty and stochastic dependence. Methods based on maximum likelihood evaluation and information theory are presented to learn the structure of CBN models. The superior performance of the CBN model and its advantages compared to traditional BN models are demonstrated by application to an offshore managed pressure drilling case study.

Item Details

Item Type:Refereed Article
Keywords:Akaike's information criterion, correlation, dependence structure, multivariate probabilistic model. Bayesian networks, complex networks, correlation methods, factor analysis, information theory, ingestion (engines), maximum likelihood
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:120364
Year Published:2016
Web of Science® Times Cited:34
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-08-23
Last Modified:2017-11-06

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