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A decision-making framework for protecting process plants from flooding based on fault tree analysis

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  • Hauptmanns, Ulrich

Abstract

The protection of process plants from external events is mandatory in the Seveso Directive. Among these events figures the possibility of inundation of a plant, which may cause a hazard by disabling technical components and obviating operator interventions. A methodological framework for dealing with hazards from potential flooding events is presented. It combines an extension of the fault tree method with generic properties of flooding events in rivers and of dikes, which should be adapted to site-specific characteristics in a concrete case. Thus, a rational basis for deciding whether upgrading is required or not and which of the components should be upgraded is provided. Both the deterministic and the probabilistic approaches are compared. Preference is given to the probabilistic one. The conclusions drawn naturally depend on the scope and detail of the model calculations and the decision criterion adopted. The latter has to be supplied from outside the analysis, e.g. by the analyst himself, the plant operator or the competent authority. It turns out that decision-making is only viable if the boundary conditions for both the procedure of analysis and the decision criterion are clear.

Suggested Citation

  • Hauptmanns, Ulrich, 2010. "A decision-making framework for protecting process plants from flooding based on fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 970-980.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:9:p:970-980
    DOI: 10.1016/j.ress.2010.04.008
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    Cited by:

    1. Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), 2018. "The Road to a Digitalized Supply Chain Management: Smart and Digital Solutions for Supply Chain Management," Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management, volume 25, number 25.
    2. Lu, Lu & Zhou, Rong & de Souza, Robert, 2018. "Enhanced FMEA for supply chain risk identification," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), The Road to a Digitalized Supply Chain Management: Smart and Digital Solutions for Supply Chain Management. Proceedings of the Hamburg International C, volume 25, pages 311-330, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Yılmaz, Emre & German, Brian J. & Pritchett, Amy R., 2023. "Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Khakzad, Nima & Van Gelder, Pieter, 2018. "Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 403-411.

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