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A consequences-based approach for the selection of relevant accident scenarios in emerging technologies

Abstract : The rapid progress of innovative biorefinery technologies raises the issue of emerging risk and major accident hazard in biorefineries, as process safety aspects of such technologies are not developing at the same pace with respect to their dissemination and scale-up. To address such problem, in the present work a consequence-based approach was integrated in the framework of a consolidated QRA methodology (ARAMIS), formerly developed in the context of Seveso Directive in the process industries (mainly oil&gas). Among all the possible scenarios identified using ARAMIS, Relevant Accident Scenarios (RAS) were selected by calculating hazard distances using threshold values for the evaluation of damages on human target derived from existing regulations on the control of major accident hazards. The method has been described and used to quantify the risk associated to a biorefinery process: the conversion of lignocellulosic biomass materials to levulinic acid and gamma-valerolactone, where the use of hazardous substances and severe process conditions are required. Using the proposed approach allowed to reduce the number of scenarios to be retained for further analysis. The RAS resulted 1/3 of the scenarios initially identified, simplifying the rest of the risk assessment procedure. The integrated methodology enabled to depict a risk figure for the plant analysed which is not over-conservative, being beneficial during the risk management phase, when risk reduction measures have to be selected and implemented in order to achieve risk tolerance.
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Contributor : Sébastien Leveneur Connect in order to contact the contributor
Submitted on : Sunday, June 9, 2019 - 8:54:41 AM
Last modification on : Wednesday, March 2, 2022 - 9:54:01 AM



Valeria Casson Moreno, Anna Laura Garbetti, Giacomo Antonioni, Sébastien Leveneur. A consequences-based approach for the selection of relevant accident scenarios in emerging technologies. Safety Science, Elsevier, 2019, 112 (3), pp.142-151. ⟨10.1016/j.ssci.2018.10.024⟩. ⟨hal-02151605⟩



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