Automation for HAZOP study: A state-of-the-art review and future research directions

Hazard and Operability Study is a structured and systematic metodology to identify and mitigate potential hazards and operational issues associated with a system, process, or facility. This methodology—dubbed as HAZOP—has been initially applied in the chemical industry and subsequently extended to other process industries. Despite its effectiveness, conventional HAZOP study is time consuming, labor-intensive, expensive,and heavily reliant on human judgement. To address these challenges, intelligent systems and different levels of automation have been developed, including knowledge-based approaches that use domain-specific rules, and expertise and data-driven models that identify potential hazards from historical data patterns. The existing AI HAZOP tools lack both full automation for generating HAZOP reports and a comprehensive knowledge base for detecting hazards and operational malfunctions. This paper provides a detailed literature review on the application of automated HAZOP methodologies across different industries. It summarizes the advancements and contributions made over the past decade, highlighting sophisticated technologies such as powerful knowledge representation formalisms and reasoning techniques. The benefits and shortcomings of existing technologies are discussed and future work directions are proposed.