Automated Construction of Bayesian Networks of Chemical Process for Dynamic Risk Assessment

Chemical processes are characterized by high complexity and inherent hazards, necessitating systematic and comprehensive risk assessment methodologies. However, traditional risk assessment approaches are often limited to static analyses and small-scale models, resulting in insufficient coverage and a lack of detailed, unit-level insights. This paper proposes an automated Bayesian network–based dynamic risk assessment (DRA) framework for full-process risk analysis in chemical plants. The proposed method automatically extracts knowledge from established risk analysis documents, such as HAZOP reports, and integrates it with dynamic process monitoring data, expert knowledge, and reliability databases. By effectively leveraging multi-source heterogeneous data, an equipment-level causal inference network is constructed automatically, addressing the high labor demand and limited scalability of conventional DRA approaches. The proposed framework is validated through an industrial FCC case study at a large petrochemical enterprise in Zhejiang, China. The results demonstrate that the method can effectively construct Bayesian causal networks for chemical processes and quantitatively evaluate both the likelihood and severity of accident consequences under various failure scenarios, thereby enabling systematic and dynamic risk assessment of chemical processes.