Application of loss functions in process economic risk assessment

Faisal Khan, Hangzhou Wang, Ming Yang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Loss functions describe the economical consequences of the deviations from the target values. In recent years they have been used in wide range of application including process safety assessment. This paper provides a novel analysis to assess potential loss due to process deviation. The assessed losses help to better estimate process economic risk, which in turn assist in effective process system design and operational decision-making. The analysis is presented in four different development stages: (i) loss functions focusing on simple functions; (ii) loss functions with estimated maximum loss; (iii) loss functions focusing on probability distributions; and (iv) loss functions in which both distributions of variables and their dependencies are considered (i.e., hierarchical Bayesian based loss functions). Details discussion on development stages three and four are presented with case studies. First case study demonstrates application of inverted probability distribution and while second case study provides application of the hierarchical Bayesian loss functions. Advantage and disadvantages of different types of loss functions are also discussed. Finally, future research directions have been proposed.

Original languageEnglish
Pages (from-to)371-386
Number of pages16
JournalChemical Engineering Research and Design
Volume111
DOIs
Publication statusPublished - 2016
Externally publishedYes

Fingerprint

Risk assessment
Economics
Probability distributions
Decision making
Systems analysis

Keywords

  • Hierarchical Bayesian loss function
  • Inverted Beta loss function
  • Inverted normal loss functions
  • Loss functions
  • Markov Chain Monte Carlo

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)

Cite this

Application of loss functions in process economic risk assessment. / Khan, Faisal; Wang, Hangzhou; Yang, Ming.

In: Chemical Engineering Research and Design, Vol. 111, 2016, p. 371-386.

Research output: Contribution to journalArticle

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