Fault Detection in a Fluid Catalytic Cracking Process using Bayesian Recurrent Neural Network

Process safety is still an issue in modern chemical industries. Accidents in chemical processes are still frequent and cause great losses for chemical industries. In this context, there is a demand for the development of intelligent fault detection and diagnosis (FDD) methods that can help operators manage chemical process faults. Since a large amount of process data has become available for monitoring systems as a result of the huge deployment of computer systems and information technologies in chemical industries, the study of data-based FDD methods has become the focus of this research area. Therefore, this work proposes to investigate the performance of a promising Bayesian recurrent neural network-based method in the detection of faults in a real chemical process. The case study is related to the detection of a specific type of fault in a real fluid catalytic cracking process. The method presented satisfactory performance during testing experiments, with a good accuracy detection and a very small number of false-negative cases.