Application of a multi-plant QRA: A case study investigating the risk impact of the construction of a new plant on an existing chemical plant's risk levels

Development and application of an off-site maintenance system in the petrochemical industry

“…However, in the said industry, the frequency of accidents has significantly increased in the last decades (Fabiano and Curro, 2012). The petrochemical industry has been regarded as one of the major high-risk industries worldwide because of the high-risk characteristics of petrochemical installations , which include the following: (1) the operation involves complex processes with processed flammable and explosive substances; (2) losses would be unimaginable once there is a major unpredictable failure of an establishment (Guo et al, 2009); (3) the consequences can affect not only many people inside and outside these establishments, but also the surrounding environment (Papazoglou et al, 1999); (4) inherent congestion in process installations, whereby process equipment is often situated in close proximity to one another, increases the probability of catastrophic accidents and amplifies their potential consequences (Baesi et al, 2013).…”

Proximity risk assessment for two sensitive chemical plants based on the accident scenario consequence analysis

“…The accident frequency should not exceed a tolerable magnitude of 10 −5 per year before and after the new plant came under operation (and of ca 10 −8 per year if domino effects are also possible) . Evaluations include accident effects and consequence assessments for the selected domino effect scenarios Determination of the optimal operating capacity of the plant to keep the productivity within benefit limits but also minimizing the accident scenario consequences (including domino effects).…”

“…When the analysed plants are neighbours, the individual plant risk evaluation should be followed by evaluation of accident scenarios in which these plants might be involved because of the so-called domino effect occurrence (especially those generated by the plant fires). [19][20][21]53] According to Kadri et al, [20] the domino effect has three common features: (1) a primary accident scenario initiating the domino accident sequence (by thermal effects, overpressure, toxic releases and missiles), (2) propagation of the primary event, due to an escalation vector generated by the primary scenario, resulting in the damage of at least one secondary target, and (3) one or several secondary accident scenarios, involving the same or different plant units. To model such chains of accidents, the individual plant risk assessment should be accompanied by additional data (including history of accidents involving similar plants), such as [20,21,52] :…”

Information, decision and planning support for industrial risk management in India

“…The frequency of a scenario specifies the number of probable occurrences of that scenario in a period of time and are generally taken from failure frequency databases and literature prepared after analysing historical data of previous incidents (Uijt de Haag et al, 2005a). The probability of an intermediate event varies from 0 -1 indicating the possibility of occurrence of that event (Baesi et al, 2013).…”

“…Probabilistic QRA methods have been found to be practical for influencing long term planning decisions (Ale, 2002;Christou et al, 2011;Tixier et al, 2006). Different elements of the method have evolved over time and are considered to be based on sound scientific logic in the risk management practice (Baesi et al, 2013;Lees, 1996;Pasman et al, 2014).…”