Abstract:
Chemical Process Industries usually contains a diverse inventory of hazardous chemicals and complex systems required to perform process operations such as storage, separation, reaction, compression etc. The complex interactions between these equipment make them vulnerable to catastrophic accidents. Risk and failure assessment provide engineers with an intuitive tool for decision making in the operation of such plants. However traditional quantitative risk assessment methods are unable to update information during operational lifetime of a process. Abnormal events and near-misses occur regularly during the operational lifetime of a system. This Accident Sequence Precursors (ASP) can be used to demonstrate the real-time operating condition of a plant. Dynamic Failure Assessment (DFA) methodology proposed by Meel and Seider (2006) which is based on Bayesian statistical methods incorporates ASP data to revise the generic failure probabilities of the systems during its operational lifetime.
Herein the DFA methodology is applied on an ammonia storage unit in a specialized chemical industry. Ammonia is stored in cold storage tanks as liquefied gas at atmospheric pressure. These tanks are susceptible to failures due to various abnormal conditions arising due process failures.
Tank failure due to three such abnormal conditions is considered. The variation of the failure probability of the safety systems and that of occurrence probability of consequences are demonstrated. ASP data collected from plant specific sources and safety expert judgement has been used. The failure probabilities of some safety systems concerned show considerable deviation from the generic values. The method helps to locate the components which have undergone more degradation over the period and hence must be paid attention to. In addition a Bayesian predictive model has been used to predict the number of abnormal events in the next time interval.
