A novel risk analysis approach for FPSO single point mooring system using Bayesian Network and interval type-2 fuzzy sets. (15th December 2022)

• Record Type: Journal Article
• Authors: Yu, Jianxing; Ding, Hongyu; Yu, Yang; Wu, Shibo; Zeng, Qingze; Ma, Wentao
• Is Part Of: Ocean engineering. Volume 266(2022)Part 5
• Publication Date: 2022-12-15
• Subjects: Bayesian network -- Interval type-2 fuzzy sets -- Risk analysis -- FPSO single Point mooring system; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.113144
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Floating Production, Storage and Offtake (FPSO) is one of the key components of offshore oil development and the single point mooring system is the key technology affecting the safety of the FPSO. Therefore, risk analysis is useful in preventing the failure of the FPSO single point mooring system. However, traditional techniques fall short in terms of quantitative assessment, dynamic control, and handling uncertainty. This study provides a risk analysis approach that can be used to assess the FPSO single point mooring system safety risk. The Bayesian Network and interval type-2 fuzzy sets are used to overcome the difficulties in gathering and evaluating the data required for the calculation of the FPSO single point mooring system safety hazards. The proposed approach allows for the identification of the primary underlying causes of these occurrences. The effectiveness of the proposed methods for risk analysis of FPSO single point mooring systems is proved using a case study. According to the findings, it is believed that this study could be useful in providing a thorough and all-encompassing evaluation of the safety risk in multi-process systems and in suggesting a strategic strategy for reducing the FPSO single point mooring system safety hazards. Highlights: Bayesian Network and interval type-2 fuzzy sets methods are proposed for the FPSO single point mooring system. The PA operator is introduced in Fuzzy Bayesian Network analysis to aggregate the opinions of domain experts. An improved probability conversion method is proposed for expert elicitation. The results show the outperformance of proposed method in providing more detailed and realistic results.