An integrated approach for dynamic economic risk assessment of process systems

Sunday A. Adedigba, Faisal Khan, Ming Yang

Research output: Contribution to journalArticleScientificpeer-review

14 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
Pages (from-to)312-323
Number of pages12
JournalProcess Safety and Environmental Protection
Volume116
DOIs
Publication statusPublished - May 2018
Externally publishedYes

Keywords

  • Dynamic failure prediction
  • Economic consequences
  • Loss functions
  • Process safety
  • Structure learning of Bayesian network from data and risk analysis

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