This cutting-edge research explores the potential of large language models (LLMs) to fully automate HAZOP studies without human intervention. The paper reviews recent advances in natural language processing and machine learning, including BERT embeddings, BiLSTM networks, and attention mechanisms for scenario severity classification. The study examines whether generative AI can replicate the expert judgment traditionally required in HAZOP analysis. This is highly relevant to PSM as it addresses the persistent challenge of HAZOP being resource-intensive and dependent on expert availability. The research discusses the evolution from rule-based expert systems to data-driven ML approaches and finally to LLMs, representing a potential paradigm shift in how hazard identification is conducted. The findings have significant implications for small and medium-sized chemical facilities that may lack access to experienced HAZOP facilitators.
