Layer of protection analysis (LOPA) is an efficient tool used for evaluating the risk associated with different industries that face significant threats with severe consequences. LOPA offers a semi-quantitative outcome, leveraging information from process hazard analysis such as the frequency of initiating events, the severity of consequences, and the probability of failure upon demand. By disregarding less severe or infrequent consequences, LOPA becomes a practical and costeffective solution suitable for real-time applications.Bayesian-LOPA methodology, an enhanced version of LOPA based on Bayes' theorem, has been recently developed. Bayesian logic utilizes prior event knowledge to predict future events, aiming to reduce uncertainty in the failure data of independent protection layers (IPLs) or events within a plant. The posterior value obtained through Bayesian estimation incorporates both historical data from prior events and real-time data from the plant, resulting in more reliable failure data for assessing risk and ensuring the safety of the plant. In this particular study, Bayesian-LOPA was applied to assess the risk and mitigate accident scenarios in a Sodium hypochlorite plant by implementing Independent Protection Layers. The obtained risk value can be compared against risk criteria defined by the plant or government to determine if any accident scenario fails to meet the set criteria. If necessary, additional IPLs may be suggested to reduce the risk to an acceptable level. Comparatively, Bayesian-LOPA proves to be a more dependable risk assessment tool than the traditional LOPA approach. It aids in prioritizing various scenarios for maintenance and safety enhancement efforts, thereby improving the overall safety of the plant

Keywords : Risk assessment, Bayes’ theorem, Bayesian logic, LOPA, protection layers.