Natural catastrophic events affecting technological systems may lead to the release of hazardous materials, giving rise to so-called “NaTech” events. The increasing number of NaTech events observed over recent years, possibly related to climate change, has reinforced interest in and the need to investigate the risk of exposure to natural hazards affecting the process industry. In the literature, most attention has been devoted to accidents triggered by floods and earthquakes, whereas no specific analysis protocols aimed at assessing the risk posed by tornadoes and severe wind gusts on production facilities are currently available. Consequently, the objective of this paper is to fill a gap in NaTech risk assessment by proposing a qualitative methodology for assessing the risk related to tornadoes and severe wind gusts. The proposed approach provides a basis for an objective, although simplified, comparison of both the risk posed by different plants potentially exposed to extreme wind events and the identification of the most critical equipment within a single plant. The analysis, partly based on existing qualitative methods, requires limited resources and data and relies on index-based evaluations.
A screening phase is first carried out by assessing the statistical incidence of the natural event using a European database, followed by the application of a qualitative method for evaluating equipment vulnerability and the consequences of their failure. Although the methodology was initially developed using data from the Italian territory, it can be applied to other countries, as demonstrated by the location sensitivity analysis performed at the end of the case study. The application of the methodology to a real case study showed that vertically developed and exposed assets, such as flare stacks, process columns, and gasoline storage tanks, represent the most critical equipment, consistently associated with medium to high risk levels. The sensitivity analysis, performed by relocating the same plant into different geographical areas, confirmed the robustness of the approach, as highly vulnerable assets remained critical across all locations, while less vulnerable equipment (e.g., pumps and phase separators) exhibited risk levels strongly dependent on local wind hazard conditions. These results demonstrate the capability of the methodology to effectively discriminate among territorial risk levels and to support the identification of installations requiring more detailed quantitative analyses.
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