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The purpose of this paper is to validate a fuzzy risk graph model through a case study results carried out on a safety instrumented system (SIS).

The proposed model is based on an inference fuzzy system and deals with uncertainty data used as inputs of the conventional risk graph method. The coherence and redundancy of the developed fuzzy rules base are first verified in the case study. A new fuzzy model is suggested for a multi-criteria characterization of the avoidance possibility parameter. The fuzzy safety integrity level (SIL) is determined for two potential accident scenarios.

The applicability of the proposed fuzzy model on SIS shows the importance and pertinence of the proposed fuzzy model as decision-making tools in preventing industrial hazards while taking into consideration uncertain aspects of the data used on the conventional risk graph method. The obtained results show that the use of continuous fuzzy scales solves the problem of interpreting results and provides a more flexible structure to combine risk graph parameters. Therefore, a decision is taken on the basis of precise integrity level values and protective actions in the real world are suggested.

Fuzzy logic-based safety integrity assessment allows assessment of the SIL in a more realistic way by using the notion of the linguistic variable for representing information that is qualitative and imprecise and, therefore, ensures better decision making on risk prevention.

The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels.

The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed.

In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process.

The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately.

In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.