Process safety analysis using operational data and Bayesian network

Fault detection and diagnosis (FDD) methods have recently experienced significant advances. These methods provide valuable information from an abnormal situation management perspective. However, traditional FDD methods do not consider system failure analysis, which is required from the process safety perspective. This work seeks to overcome this barrier and presents a methodology to assess process system failure probability based on process operational data and system knowledge. The methodology is built using the principal component analysis (PCA) and a Bayesian network (BN). The PCA is used for FDD, while the BN determines the probability of system failure once a fault is detected. This portion of the network is based on the logical relationship of the data, operational thresholds, and system failure conditions. The proposed methodology is tested and verified on a level-controlled tank system and the real-life failure scenario of the 2010 Tesoro heat exchanger explosion. The results suggest that the proposed methodology helps to assess process system failure probability based on process operating conditions. The current work is expected to give a stimulus on digital process safety education.