Layers of Protection Analysis Using Possibility Theory

Abstract

An important issue faced by risk analysts is how to deal with uncertainties associated with accident scenarios. In industry, one often uses single values derived fro m historical data or literature to estimate events probability or their frequency. However, both dynamic environments of systems and the need to consider rare component failures may make unrealistic this kind of data. In this paper, uncertainty encountered in Layers Of Protection Analysis (LOPA) is considered in the framework of possibility theory. Data provided by reliability databases and/or experts judgments are represented by fuzzy quantities (possibilit ies). The fu zzy outcome frequency is calculated by extended mu ltip licat ion using αcuts method. The fu zzy outcome is co mpared to a scenario risk tolerance criteria and the required reduction is obtained by resolving a possibilistic decision-making problem under necessity constraint. In order to validate the proposed model, a case study concerning the protection layers of an operational heater is carried out.

Hazard Identification and Risk Assessment in Two Gas Refinery Units

“…These results indicated that the maximum protection layers were performed for this scenario and an acceptable level of safety was observed. It is encouraging to compare this figure with that of Ouazraoui et al (2012), who found that higher safety in-tegrity levels could be achieved by application of required IPLs, which led to reduction of severity and probability of accidents (3). The applications of proposed IPLs to less flow of amines to sweetening towers scenario in node 3 of both refineries could not reduce the severity of this scenario enough for acceptable risks.…”

“…Safety instrumented systems (SISs) are necessary to keep the risks at acceptable levels in these industries (1,2). Many flammable and toxic materials are used in process industries, which may have adverse effects on workers' health (3). Technical and organizational requirements can be used to reduce the risks to tolerable levels (4).…”

Multi-criterion analysis based on integrated process-risk optimization

“…As presented in Ouazraoui et al (2012), Zerrouki and Tamrabet (2015) this process allows for better recovery of liquid hydrocarbons, starting with the pre-separation of the raw gas from the wells and its compression on the booster station at a pressure of 117 kg/cm 2 and a temperature of 62°C. In the high-pressure separation section, the recovered liquid hydrocarbons are separated as petroleum gas and condense in the C102 deethanizer of the fractional section.…”