A Screening Methodology for the Identification of Critical Units in Major-Hazard Facilities Under Seismic Loading

The complexity of process industry and the consequences that Na-Tech events could produce in terms of damage to equipment, release of dangerous substances (flammable, toxic, or explosive), and environmental consequences have prompted the scientific community to focus on the development of efficient methodologies for Quantitative Seismic Risk Analysis (QsRA) of process plants. Several analytical and numerical methods have been proposed and validated through representative case studies. Nevertheless, the complexity of this matter makes their applicability difficult, especially when a rapid identification of the critical components of a plant is required, which may induce hazardous material release and thus severe consequences for the environment and the community. Accordingly, in this paper, a screening methodology is proposed for rapid identification of the most critical components of a major-hazard plant under seismic loading. It is based on a closed-form assessment of the probability of damage for all components, derived by using analytical representations of the seismic hazard curve and the fragility functions of the equipment involved. For this purpose, fragility curves currently available in the literature or derived by using low-fidelity models could be used for simplicity, whereas the parameters of the seismic hazard curve are estimated based on the regional seismicity. The representative damage states (DS) for each equipment typology are selected based on specific damage states/loss of containment (DS/LOC) matrices, which are used to individuate the most probable LOC events. The risk is then assessed based on the potential consequences of a LOC event, using a classical consequence analysis, typically adopted in risk analysis of hazardous plants. For this purpose, specific probability classes will be used. Finally, by associating the Probability Class Index (PI) with Consequence Index (CI), a Global Risk Index (GRI) is derived, which provides the severity of the scenario. This allows us to build a ranking of the most hazardous components of a process plant by using a proper risk matrix. The applicability of the method is shown through a representative case study.

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