This paper presents a dynamic risk assessment using a comprehensive economic consequence methodology in combination with a multivariate model-based fault detection method. The proposed approach aims to calculate process risk dynamically at each sampling instant, and also to identify and screen the faults that are not hazardous. The approach relies on a particle filter combined with a comprehensive economic consequence methodology. The fault detection module uses a state space model of the process plant and a particle filter algorithm that calculates the probability of the fault. The output of this module is then combined with the consequence module, which uses loss functions to relate process deviations to economic losses. The consequence module identifies, quantifies, and integrates losses for a given scenario. Combining the two modules for risk assessment makes this approach more reliable in the analysis of realistic nonlinear process systems, and improves decision-making for process design and risk management.

Item Type:	Refereed Article
Keywords:	dynamic risk assessment, fault detection, loss functions, particle filtering, decision making, economic and social effects, fault detection, losses, Monte Carlo methods, risk management, risks, signal filtering and prediction, state space methods
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:	120661
Year Published:	2015
Web of Science® Times Cited:	14
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2017-08-30
Last Modified:	2017-11-06
Downloads:	0