Climate Change Risk Assessment of Process Safety using Bayesian Network Model

Abstract

Climate change has introduced significant challenges to process safety in industrial operations, primarily due to the increasing severity and frequency of extreme weather events such as hurricanes, floods, and heatwaves. These evolving risks often exhibit complex interdependencies that exceed the capabilities of conventional risk assessment frameworks. This study addresses the need for a more robust approach by employing a Bayesian network model that integrates climate-related variables with traditional process safety elements. A comprehensive literature review was conducted to identify key climate parameters that influence industrial safety, which were then incorporated into the Bayesian framework to simulate diverse risk scenarios. The model was validated using two real-world case studies which is the Arkema plant explosion and incidents at the PrefChem facility. The model demonstrates how the methodology can capture climate-induced hazards and evaluate their impact on industrial processes. These simulations assess the probability of various risk outcomes under changing climate conditions. Based on these findings, targeted mitigation measures are proposed to enhance the resilience and safety of industrial operations in the face of climate variability. This research contributes to the development of a more comprehensive understanding of climate-driven risks and offers a practical decision-support tool for anticipating and managing their effects, thereby supporting safer and more sustainable industrial practices.