Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor.
In recent years, multiscale monitoring approaches, which combine principal component analysis (PCA) and multi-resolution analysis (MRA), have received considerable attention. These approaches are potentially very efficient for detecting and analyzing diverse ranges of faults and disturbances in chemical and biochemical processes. In this work, multiscale PCA is proposed for fault detection and diagnosis of batch processes. Using MRA, measurement data are decomposed into approximation and details at different scales. Adaptive multiway PCA (MPCA) models are developed to update the covariance structure at each scale to deal with changing process conditions. Process monitoring by a unifying adaptive multiscale MPCA involves combining only those scales where significant disturbances are detected. This multiscale approach facilitates diagnosis of the detected fault as it hints to the time-scale under which the fault affects the process. The proposed adaptive multiscale method is successfully applied to a pilot-scale sequencing batch reactor for biological wastewater treatment.
Lee
Dae Sung
DS
Advanced Environmental Biotechnology Research Center (AEBRC), School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongbuk 790-784, Republic of Korea. dslee@postech.ac.kr
Park
Jong Moon
JM
Vanrolleghem
Peter A
PA
D000465:Algorithms; D019149:Bioreactors; D018929:Cell Culture Techniques / Q000379:methods*; D003198:Computer Simulation; D005246:Feedback; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000379:methods; D009862:Online Systems; D025341:Principal Component Analysis