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

Author(s)

Taira, Gustavo R. ; Park, Song W. ; Zanin, Antonio C. ; Porfirio, Carlos R.

• Document type: Journal article
• Source: IFAC PAPERSONLINE; v. 55, n. 7, p. 6-pg., 2022-08-05.

Abstract

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. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-ne-nd/4.0/) (AU)

• FAPESP's process: 19/08280-9 - Application of bayesian deep learning for Smart City monitoring and chemical industry processes operation
• Grantee: Gustavo Ryuji Taira
• Support Opportunities: Scholarships in Brazil - Master