This paper discusses the evolution of the Barrier and Operational Risk Analysis (BORA) methodology into a more flexible tool by integrating fuzzy logic with a Bayesian Network (BN) framework to improve safety risk assessments in industrial processes. While BORA is traditionally used to assess the performance of safety barriers, it has limitations, particularly in dynamic risk assessment, handling dependencies, and managing uncertainties. To address these issues, fuzzy logic is applied to transform generic data into fuzzy sets, using the cumulative inverse method to derive crisp values using screened OREDA, ICSI, and SINTEF datasets supplemented by calibrated expert triplets to address data gaps and imprecision. This approach enables a more accurate representation of frequency and failure probability values. By incorporating a BN, the framework yields a versatile model capable of probabilistic reasoning. This enhancement enables real-time updates of risk levels by considering the interdependencies of safety barriers while incorporating the latest available data. The suggested approach involves transforming BORA into a network of probabilistic variables, enhancing predictive accuracy and decision-making processes. The importance of this approach is underscored through uncertainty and sensitivity analyses. A case study in the CP2K Unit Reactor showcases the practical benefits of using the fuzzy BORA-BN in industrial processes. The proposed method reduced the predicted overall accident frequency from 1.16 × 10−4 yr−1 to 3.03 × 10−7 yr−1, demonstrating improved uncertainty management.
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