An Integrated Approach for Dynamic Economic Risk Assessment of Process Systems

Sunday Adedigba, Faisal Khan, Ming Yang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper proposes a dynamic economic risk analysis methodology for process systems. The Bayesian Tree Augmented Naïve Bayes (TAN) algorithm is applied to model the precise and concise probabilistic dependencies that exist among key operational process variables to detect faults and predict the time dependent probability of system deviation. The modified inverted normal loss function is used to define system economic losses as a function of process deviation. The time dependent probability of system deviation owing to an abnormal event is constantly updated based on the present state of the relevant process variables. The integration of real time probability of system deviation with potential losses provides the risk profile of the system at any instant. This risk profile can be used as the basis for operational decision making and also to activate the emergency safety system. The proposed methodology is tested and verified using the Richmond refinery accident.
Original languageEnglish
JournalProcess Safety and Environmental Protection
Publication statusPublished - Feb 2018

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integrated approach
Risk assessment
risk assessment
Economics
economics
methodology
economic system
Risk analysis
Security systems
accident
Accidents
Decision making
decision making
loss

Keywords

  • Dynamic failure prediction
  • Loss functions
  • economic consequences
  • Process safety
  • Structure learning of Bayesian network from data
  • Risk analysis

Cite this

An Integrated Approach for Dynamic Economic Risk Assessment of Process Systems. / Adedigba, Sunday; Khan, Faisal ; Yang, Ming.

In: Process Safety and Environmental Protection, 02.2018.

Research output: Contribution to journalArticle

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AB - This paper proposes a dynamic economic risk analysis methodology for process systems. The Bayesian Tree Augmented Naïve Bayes (TAN) algorithm is applied to model the precise and concise probabilistic dependencies that exist among key operational process variables to detect faults and predict the time dependent probability of system deviation. The modified inverted normal loss function is used to define system economic losses as a function of process deviation. The time dependent probability of system deviation owing to an abnormal event is constantly updated based on the present state of the relevant process variables. The integration of real time probability of system deviation with potential losses provides the risk profile of the system at any instant. This risk profile can be used as the basis for operational decision making and also to activate the emergency safety system. The proposed methodology is tested and verified using the Richmond refinery accident.

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