relation: https://khub.utp.edu.my/scholars/13416/
title: Process Safety Assessment Considering Multivariate Non-linear Dependence Among Process Variables
creator: Ghosh, A.
creator: Ahmed, S.
creator: Khan, F.
creator: Rusli, R.
description: 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. © 2019 Institution of Chemical Engineers
publisher: Institution of Chemical Engineers
date: 2020
type: Article
type: PeerReviewed
identifier:   Ghosh, A. and Ahmed, S. and Khan, F. and Rusli, R.  (2020) Process Safety Assessment Considering Multivariate Non-linear Dependence Among Process Variables.  Process Safety and Environmental Protection, 135.  pp. 70-80.  ISSN 09575820     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077430655&doi=10.1016%2fj.psep.2019.12.006&partnerID=40&md5=cee2482fe1be76a644a1297c81db3a96
relation: 10.1016/j.psep.2019.12.006
identifier: 10.1016/j.psep.2019.12.006