Using MFM methodology to generate and define major accident scenarios for quantitative risk assessment studies

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2017Researchpeer-review

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Generating and defining Major Accident Scenarios (MAS) are commonly agreed as the key step for quantitative risk assessment (QRA). The aim of the study is to explore the feasibility of using Multilevel Flow Modeling (MFM) methodology to formulating MAS. Traditionally this is usually done based on historical incidents or the outcome of HAZOP/HAZID. This paper suggests using MFM to model the plant, and then performs systematic reasoning based on the model to produce casual paths of plant failure scenarios. The cause trees generated by MFM are transformed into fault trees, which are then used to calculate likelihood of each MAS. Combining the likelihood of each scenario with a qualitative risk matrix, each major accident scenario is thereby ranked for consideration for detailed consequence analysis. The methodology is successfully highlighted using part of BMA-process for production of hydrogen cyanide as case study.
Original languageEnglish
Title of host publicationProceedings of the 27th European Symposium on Computer Aided Process Engineering (ESCAPE 27)
EditorsAntonio Espuña, Moisès Graells, Luis Puigjaner
Volume40
PublisherElsevier
Publication date2017
Edition1
Pages589-594
ISBN (Print)9780444639653
ISBN (Electronic)9780444639707
DOIs
Publication statusPublished - 2017
Event27th European Symposium on Computer Aided Process Engineering - Barcelona, Spain
Duration: 1 Oct 20175 Oct 2017
Conference number: 27
https://www.elsevier.com/books/27th-european-symposium-on-computer-aided-process-engineering/espuna/978-0-444-63965-3

Conference

Conference27th European Symposium on Computer Aided Process Engineering
Number27
CountrySpain
CityBarcelona
Period01/10/201705/10/2017
Internet address
SeriesComputer Aided Chemical Engineering
Volume40
ISSN1570-7946
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Major Accident Scenarios, Multilevel Flow Modeling, QRA

ID: 133323750