Conference paper · Book chapter
Using MFM methodology to generate and define major accident scenarios for quantitative risk assessment studies
China University of Mining and Technology1
Department of Electrical Engineering, Technical University of Denmark2
Automation and Control, Department of Electrical Engineering, Technical University of Denmark3
Center for Electric Power and Energy, Centers, Technical University of Denmark4
Energy System Management, Center for Electric Power and Energy, Centers, Technical University of Denmark5
Department of Chemical and Biochemical Engineering, Technical University of Denmark6
CAPEC-PROCESS, Department of Chemical and Biochemical Engineering, Technical University of Denmark7
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.
Language: | English |
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Publisher: | Elsevier |
Year: | 2017 |
Edition: | 1 |
Pages: | 589-594 |
Proceedings: | 27th European Symposium on Computer Aided Process Engineering |
Series: | Computer Aided Chemical Engineering |
ISBN: | 0444639659 , 0444639705 , 9780444639653 and 9780444639707 |
ISSN: | 15707946 |
Types: | Conference paper and Book chapter |
DOI: | 10.1016/B978-0-444-63965-3.50100-8 |
ORCIDs: | Lind, Morten , Wu, Jing , Zhang, Xinxin and Sin, Gürkan |