• Yoo, Chang-Kyoo (School of Environmental Science and Engineering/Department of Chemical Engineering, Pohang University of Science and Technology) ;
  • Vanrolleghem, Peter A. (BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University) ;
  • Lee, In-Beum (School of Environmental Science and Engineering/Department of Chemical Engineering, Pohang University of Science and Technology)
  • Published : 2006.04.30


Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.


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