This paper presents a methodology to develop multivariate loss functions to measure the operational loss of process facilities. The proposed methodology uses loss functions to provide a model for operational loss due to deviation of key process characteristics from their target values. Having estimated the marginal loss functions for each monitored process variable, copula functions are then used to link the univariate margins and develop the multivariate loss function. The maximum likelihood evaluation method is used to estimate the copula parameters. Akaike's information criterion (AIC) is then applied to rank the copula models and choose the best fitting copula. A simulation study is provided to demonstrate the efficiency of the copula estimation procedure. The flexibility of the proposed approach in using any form of the symmetrical and asymmetrical loss functions and the practical application of the methodology are illustrated using a separation column case study.

Item Type:	Refereed Article
Keywords:	Akaike's information criterion (AIC), copula function, distillation column, multivariate model, process risk assessment, process safety, distillation columns, maximum likelihood, maximum likelihood estimation, Akaike's information criterions
Research Division:	Engineering
Research Group:	Chemical Engineering
Research Field:	Process Control and Simulation
Objective Division:	Expanding Knowledge
Objective Group:	Expanding Knowledge
Objective Field:	Expanding Knowledge in Engineering
Author:	Khan, FI (Professor Faisal Khan)
ID Code:	120630
Year Published:	2015
Web of Science® Times Cited:	2
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2017-08-29
Last Modified:	2017-11-03
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