Integration of MIMAH and Fuzzy Bayesian Networks for risk analysis in chemical tanker loading operations

This study provides a systematic risk assessment approach for chemical tanker loading operations, focusing on a high-risk scenario identified through operational data from a model vessel. To address the complexities of chemical transportation, a hybrid methodology combining the Methodology for the Identification of Major Accident Hazards (MIMAH) and Fuzzy Bayesian Network (FBN) analysis was developed. MIMAH's structured framework systematically identifies critical events using a Bow-Tie (BT) diagram, integrating Fault Tree (FT) and Event Tree (ET) providing a thorough breakdown of potential accident pathways. This BT structure was converted into a Bayesian Network (BN) to improve probability estimations by incorporating conditional dependencies and expert-driven fuzzy logic, particularly where historical data was limited. The study further employed a dual-method sensitivity analysis, integrating Fussell-Vesely (FV) importance measures and Improvement Index (II), to identify critical and improvement-prone basic events (BEs). Key findings highlight the dominance of human error in high-risk events, particularly manifold connection failures and incorrect valve operations, alongside mechanical vulnerabilities with significant improvement potential. This hybrid approach extends ARAMIS principles to maritime contexts, integrating reliability-based and fuzzy-based probability estimation methods in chemical tanker operations, and provides a detailed and adaptable framework that enhances safety and resilience in hazardous maritime transport.