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Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling** Supported by the Korea Research Foundation Grant Funded by the Korean Government (MOEHRD) (KRF-2007-331-D00089) and Funded by Seoul Development Institute (CS070160).

Authors
Journal
Chinese Journal of Chemical Engineering
1004-9541
Publisher
Elsevier
Publication Date
Volume
16
Issue
1
Identifiers
DOI: 10.1016/s1004-9541(08)60035-x
Keywords
  • Selected Papers From The 4 Th International Symposium On Design
  • Operation And Control Of Chemical Processes
Disciplines
  • Biology
  • Medicine

Abstract

Abstract A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

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