Risk assessment of worldwide refinery accidents using advanced classification methods

Abstract

A global dataset of refinery accidents for the years 1990-2016 was analyzed to evaluate the capacity of 16 attributes to differentiate between accidents that cause or not fatalities. For this purpose a Dominance-based Rough Set Approach (DRSA) analysis was carried out. The quality of approximation and accuracy measures confirmed that the established information table is able to distinguish outcome levels in terms of fatalities. Furthermore, the suitability of the extracted rules to describe hidden relationships in the accident dataset was demonstrated. Although, the predictive capacity of the decision rules was not satisfactory, the rules still proved to be useful to identify the attributes that contribute most to assign an accident to the correct outcome class. In summary, this study provided a number of new and substantial insights on worldwide refinery accidents, which complement and extend previous findings for accident frequencies and associated trends as well as different types of consequences. period (Otway and Pahner, 1976). In 1975, the US Reactor Safety Study (WASH-1400) marked a major milestone in probabilistic risk assessment (US NRC, 1975), and subsequently spread to other disciplines and countries (Rasmussen, 1981). The need for systematic and consistent, comparative risk assessment of energy technologies has been recognized since the 1980s (Fritzsche, 1989, Inhaber, 1979). Since then, it became a central element both in the comprehensive evaluation of the risk performance of energy technologies (Burgherr and Hirschberg, 2014), and in the broader context of sustainability assessment (Cinelli et al., 2014, Hirschberg and Burgherr, 2015, Santoyo-Castelazo and Azapagic, 2014). Recently, an increased interest can be observed to assess the frequencies and consequences of accidents overtime , and to compare them among energy chains, chain stages, activities and

Dynamics of severe accidents in the oil & gas energy sector derived from the authoritative ENergy-related severe accident database

“…Here, we explore the chains-of-events leading to severe accidents in the oil & gas industry using a data-driven approach based on ENSAD, which has been described as the most authoritative resource for comparative risk analysis of accidents in the energy sector [ 11 , 13 , 19 ]. This is the first study to apply graph theory and catastrophe dynamics modelling [ 25 – 28 ] to ENSAD, allowing us to describe the general topological properties of severe accidents at refineries [ 29 ], oil tankers [ 30 ] and gas networks [ 31 ], based on a rich database of more than a thousand events, spanning from 1970 to 2016, that includes information on the chains-of-events that led to these accidents. By describing severe accidents at critical infrastructures (CIs) with dependency risk graphs, we can first identify their main sources, catalysts, and sinks.…”