Application of Artificial Neural Network in Process Safety Assessment

Quantitative risk assessment is a crucial step in safety analysis of process systems. Advancement of modern technologies has resulted in availability of large volume of process data. This tendency urges the need of developing new risk assessment approaches. Fault tree (FT), a conventional risk analysis method, is found to be ineffective in dynamic risk analysis and data analytics due to its static nature and reliance on experts‟ judgment in developing stage. The use of artificial neural network (ANN) in risk assessment of process systems is not a new concept. ANN is a structured model that is built upon data samples and learning algorithms to process complex input/output data in the way that it is trained. The application of ANN can help to overcome some of the limitations of FT. The dynamic and data-driven nature, independency on prior information on events relationships, and less reliance on experts‟ judgement are the advantages of ANN over FT. However, there is limited work on the development of ANN-based risk assessment models using the conventional methods such as FT as an informative base. This study proposes a methodology of mapping FT into ANN to support convenient and effective application of ANN in risk assessment. The proposed method is demonstrated through its application to failure analysis of one of the causes of Tesoro Anacortes Refinery accident. The results of network‟s accident modelling performance have shown that the ANN model (mapped from the FT) is an effective risk assessment technique in terms of application for estimation of the TE failure probability.