Safeguards identification in computer aided HAZOP study by means of multilevel flow modelling

Jing Wu, Mengchu Song, Xinxin Zhang and Morten Lind

Journal of Risk and Reliability, 2023, vol. 237, issue 5, 922-946

Abstract: Multilevel Flow Modelling (MFM) was proposed as a knowledge representation method for Hazard and Operability Studies (HAZOPs). MFM reasoning software can facilitate the cause-consequence analysis during the HAZOP analysis of whole life cycle of the plant. Recent studies have further confirmed that MFM offers the opportunity to redeploy the insights achieved by the HAZOP team to assist an operator dealing with an abnormal event. However, past studies into MFM-based HAZOP have been lacking in the specification of the principle. This principle makes MFM possible to determine safeguards for studied hypothetical events. This paper proposes such principle, which further increases the application of computer aids in HAZOP studies. The paper provides an analysis and classification of different types of safeguards on the functional level and introduces the safeguards into MFM methodology. It further presents an MFM-specific barrier function and its reasoning rules. The safeguard representation and reasoning transparency explicitly the relationship between suitable safeguards and hypothetical events given knowledge about the complex interdependencies between process design, equipment design, safety barriers and instrumentation. Based on the principles developed, an existing MFM model of a typical oil and gas process module is extended with explicit safety functions and used as an example for utilizing the specified principle for identification of safeguards. Potential safeguards for the process module are produced as the results.

Keywords: Artificial intelligence; knowledge representation; knowledge-based reasoning; safety functions; multilevel flow modelling (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:237:y:2023:i:5:p:922-946

DOI: 10.1177/1748006X221086341

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