Abnormal situation management: Challenges and opportunities in the big data era. (4th August 2016)

• Record Type: Journal Article
• Authors: Shu, Yidan; Ming, Liang; Cheng, Feifan; Zhang, Zhanpeng; Zhao, Jinsong
• Is Part Of: Computers & chemical engineering. Volume 91(2016)
• Page Start: 104
• Page End: 113
• Publication Date: 2016-08-04
• Subjects: Abnormal situation management -- Process safety -- Fault diagnosis -- Big data -- Artificial immune system -- Cloud computing; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.04.011
• Languages: English
• ISSNs: 0098-1354

Abstract

Graphical abstract: Highlights: Fault detection and diagnosis (FDD) techniques have not been mature since it was proposed 40 years ago. Developments of chemical process FDD are reviewed from a perspective gained through a personal involvement in the research area. The reason why process FDD has not been widely and successfully implemented in the chemical process industry is discussed. A new framework is proposed based on big data from similar chemical processes for addressing the challenging issues in ASM. Abstract: Although modern chemical processes are highly automatic, abnormal situation management (ASM) still heavily relies on human operators. Process fault detection and diagnosis (FDD) are one of the most important issues of ASM but few FDD systems have been satisfactorily applied in real chemical processes since the concept of FDD was proposed about 40 years ago. In this paper, developments of chemical process FDD are briefly reviewed. The reason why FDD has not been widely implemented in the chemical process industry is discussed. One of the insights gained is that some basic problems in FDD such as how to define faults and how many faults to diagnose have not even been addressed well while researchers tirelessly try to invent new methods to diagnose fault. A new framework is proposed based on the big data in a cloud computing environment of a big chemical corporation for addressing the challenging issues in ASM.