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Dynamic Risk Assessment of a Nonlinear Non‐Gaussian System Using a Particle Filter and Detailed Consequence Analysis
Author(s) -
Zadakbar Omid,
Khan Faisal,
Imtiaz Syed
Publication year - 2015
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22212
Subject(s) - particle filter , fault detection and isolation , nonlinear system , process (computing) , computer science , filter (signal processing) , fault (geology) , sampling (signal processing) , reliability engineering , importance sampling , gaussian , data mining , control theory (sociology) , engineering , mathematics , statistics , monte carlo method , artificial intelligence , physics , control (management) , quantum mechanics , seismology , actuator , computer vision , geology , operating system
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.