HAZOP (Hazard and Operability Study) should be conducted prior to the construction and operation of offshore platforms to prevent critical safety risks. However, the conventional methodology faces challenges in knowledge management due to inefficient analysis processes and excessive workload requirements. This study proposes an ontology-based approach integrated with HAZOP to enable systematic knowledge reuse, sharing, inheritance, and expansion. Specifically, we developed a formal HAZOP ontology system that facilitates structured preservation and utilization of historical case data. Furthermore, a set of natural language processing-driven reasoning rules was established to codify and extend domain experts' tacit knowledge. The proposed framework was validated through a comprehensive case study involving offshore oil and gas processing systems, demonstrating its technical feasibility and operational effectiveness.

Yuan Liu, Aijun Ling. Research on Ontology Based Intelligent Method for HAZOP of Ocean Platform. Academic Journal of Engineering and Technology Science(2025), Vol. 8, Issue 2: 103-110. https://doi.org/10.25236/AJETS.2025.080214.

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