%T Process Safety Assessment Considering Multivariate Non-linear Dependence Among Process Variables
%V 135
%I Institution of Chemical Engineers
%A A. Ghosh
%A S. Ahmed
%A F. Khan
%A R. Rusli
%P 70-80
%X 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
%K Bayesian networks; Process monitoring; Risk management; Safety engineering; Statistical mechanics; Stochastic models; Stochastic systems, Copula functions; Multivariate process; Nonlinear dependencies; On-line process monitoring; Process safety; Stochastic nature; Structural flexibilities; Time-dependent process, Complex networks
%D 2020
%R 10.1016/j.psep.2019.12.006
%O cited By 20
%L scholars13416
%J Process Safety and Environmental Protection