Dynamic safety assessment: Scenario identification via a possibilistic clustering approach

Abstract

Dynamic safety and reliability methodologies aim at quantitatively describing the integrated dynamic response of the systems/components/operating crew during an accident by combining the models of the underlying process dynamics and human operator actions with the stochastic processes governing the failure, repair and state transitions of components and systems. The amount of information produced by such dynamic analyses, in terms of scenarios and probability distributions of the occurring events, is very broad and usually calls for a significant effort in the post-processing phase. In an attempt to retrieve and organize this information, the present paper presents an approach for identifying and grouping the scenarios resulting from a dynamic system safety analysis. The aim is to single out the principal patterns of system evolution with respect not only to the final system states but also to the time of events and to the process evolution. Due to the burden of the analysis, this is often overlooked in dynamic safety analyses, which mainly focus on the system states at the end of the scenarios with little consideration given to the actual evolution of the system towards these states. Monte Carlo simulation is exploited for generating stochastic scenarios that are then grouped by combining information from the end state, the sequence of events and the physical behavior of the process variables. The grouping is based on possibilistic clustering classification. The approach has been tested on scenarios produced by dynamic simulation of a chemical batch reactor of literature in which a highly exothermic process is worked out.