AI-aided chemical process risk assessment

Industrial accidents result in severe human and environmental consequences each year, highlighting the critical need for advanced risk assessment methodologies. Traditional risk management approaches, while structured and widely adopted, often struggle to capture the dynamic and nonlinear nature of industrial processes, limiting their effectiveness in modern safety-critical applications. In response to these challenges, Machine Learning (ML) has emerged as a promising tool for risk analysis, offering the ability to model complex interactions and predict hazardous scenarios with greater accuracy. This research investigates the application of ML to enhance chemical process risk assessment, with a particular focus on predicting the consequences of industrial accidents, including injuries and fatalities. The study is structured into three main phases: (i) an analysis and preprocessing of the Hydrogen Incident and Accident Database (HIAD), identifying data sparsity issues and developing an imputation model to address missing values; (ii) the implementation of predictive models to estimate accident severity, leveraging both pre- and post-incident data; and (iii) validation of the proposed methodology on a second dataset, the Major Hazard Incident Data Service (MHIDAS), to assess its generalizability. The findings demonstrate that integrating ML techniques with conventional risk analysis frameworks can enhance predictive capabilities and facilitate a more proactive approach to industrial safety management. However, challenges related to data quality and model interpretability remain critical considerations, underscoring the need for robust validation strategies to ensure the alignment of ML-driven risk assessment tools with overarching safety objectives. This study contributes to the ongoing discourse on dynamic risk assessment, presenting a case study that highlights the potential of data-driven methodologies to improve safety in chemical process industries.