Process Safety Assessment Considering Multivariate Non-linear Dependence Among Process Variables

Nonlinear dependencies among highly correlated variables of a multifaceted process system pose significant challenges for process safety assessment. The copula function is a flexible statistical tool to capture complex dependencies and interactions among process variables in the causation of process...

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Bibliographic Details
Published in:Process safety and environmental protection 2020-03, Vol.135, p.70-80
Main Authors: Ghosh, Arko, Ahmed, Salim, Khan, Faisal, Rusli, Risza
Format: Article
Language:English
Subjects:
Bayesian analysis
Causation
copula function
Data collection
Failure analysis
Mathematical models
Multivariate analysis
multivariate process system
nonlinear dependency
Process safety analysis
Process variables
Risk management
Safety
Stochastic processes
Stochasticity
Time dependence
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Summary:Nonlinear dependencies among highly correlated variables of a multifaceted process system pose significant challenges for process safety assessment. The copula function is a flexible statistical tool to capture complex dependencies and interactions among process variables in the causation of process faults. An integration of the copula function with the Bayesian network provides a framework to deal with such complex dependence. This study attempts to compare the performance of the copula-based Bayesian network with that of the traditional Bayesian network in predicting failure of a multivariate time dependent process system. Normal and abnormal process data from a small-scale pilot unit were collected to test and verify performances of failure models. Results from analysis of the collected data establish that the performance of copula-based Bayesian network is robust and superior to the performance of traditional Bayesian network. The structural flexibility, consideration of non-linear dependence among variables, uncertainty and stochastic nature of the process model provide the copula-based Bayesian network distinct advantages. This approach can be further tested and implemented as an online process monitoring and risk management tool.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2019.12.006