A novel Fuzzy Bayesian Network approach for safety analysis of process systems; An application of HFACS and SHIPP methodology. (20th January 2020) - Rostamabadi, Akbar; Jahangiri, Mehdi; Zarei, Esmaeil; Kamalinia, Mojtaba; Alimohammadlou, Moslem
Abstract: Chemical process industries (CPI) are inherently hazardous complex systems where large inventory of extremely flammable and explosive chemicals are processed and stored in a highly congested process area. A reliable safety analysis method plays a significant role to measure risks and to develop preventive strategies in process industries. This paper proposed a novel Fuzzy Bayesian Network for dynamic safety analysis of process systems by incorporating Bayesian network (BN) with Fuzzy Best Worst Method (Fuzzy-BWM). In the proposed approach a comprehensive and in-depth analysis of human and organizational factors (HOFs) involving in the accident scenario occurrence was also provided by integrating Human Factor Analysis and Classification System (HFACS) and System Hazard Identification, Prediction and Prevention (SHIPP) methodology into the model. An ethylene storage tank was selected to verify the applicability of the proposed approach and its application potential. The study also explained a comparison between the results of the proposed Fuzzy-BWM approach with the conventional BN approach and a quantitative risk assessment (QRA) conventional technique such as bow-tie (BT). The findings revealed the capability of the proposed Fuzzy-BWM approach to provide high reliable results and to detect risks that using the BT and BN approaches were not identified. Graphical abstract: Image 1 Highlights: A novel FBN approach was proposed for safety analysis of process systems. A BT diagram was applied for accident scenario modeling. HFACS and SHIPP model were integrated into the BT diagram to promote the model analytical properties. The model was converted into a BN to overcome the BT limitations in probability updating. Fuzzy-BWM was used to remedy uncertainties raised from application of crisp probabilities in BN.