Monte Carlo Simulations for probabilistic validation of consequence reasoning from Multilevel Flow Modelling

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Abstract

Multilevel Flow Modelling can be used to identify causes or consequences of anomalies in process systems. The models can be used to identify numerous possible propagations of causes or effects but cannot distinguish between likely and unlikely causes or effects. We present a method for identifying likely and unlikely effect propagations in a given process window from Monte Carlo Simulations. We show that the joint probability of effects can be used to determine the probability of individual propagation paths. The analysis allows to identify subsets of the process window where certain effect propagations are more likely. The method enables prompt identification of likely propagations of effects from process anomalies.
Original languageEnglish
Title of host publicationProceedings of 25th IEEE International Conference on Emerging Technologies and Factory Automation
Volume1
PublisherIEEE
Publication date2020
Pages1351-1354
ISBN (Print)9781728189567
DOIs
Publication statusPublished - 2020
Event25th IEEE International Conference on Emerging Technologies and Factory Automation - Virtual event, Vienna, Austria
Duration: 8 Sep 202011 Sep 2020
http://www.ieee-etfa.org/2020/

Conference

Conference25th IEEE International Conference on Emerging Technologies and Factory Automation
LocationVirtual event
CountryAustria
CityVienna
Period08/09/202011/09/2020
Internet address
SeriesEmerging Technologies and Factory Automation (etfa), International Conference on
ISSN1946-0759

Keywords

  • Functional Modelling
  • Multilevel Flow Modelling
  • Consequence prediction
  • Monte Carlo Simulations

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