A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.
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Research Article|
May 01 2008
Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes
Kris Villez;
1BIOMATH, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium E-mail: kris.villez@biomath.ugent.be; peter.vanrolleghem@gci.ulaval.ca
E-mail: kris.villez@biomath.ugent.be
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Magda Ruiz;
Magda Ruiz
2eXiT, Department of Electronics, Computer Science and Automatic Control, University of Girona, Campus Montilivi CP 17071 Building PIV, Girona, Spain E-mail: mlruizo@silver.udg.es
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Gürkan Sin;
Gürkan Sin
1BIOMATH, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium E-mail: kris.villez@biomath.ugent.be; peter.vanrolleghem@gci.ulaval.ca
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Joan Colomer;
Joan Colomer
2eXiT, Department of Electronics, Computer Science and Automatic Control, University of Girona, Campus Montilivi CP 17071 Building PIV, Girona, Spain E-mail: mlruizo@silver.udg.es
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Christian Rosén;
Christian Rosén
3IEA, Lund University, Box 118, SE-221 00, Lund, Sweden E-mail: christian.rosen@iea.lth.se
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Peter A. Vanrolleghem
Peter A. Vanrolleghem
1BIOMATH, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium E-mail: kris.villez@biomath.ugent.be; peter.vanrolleghem@gci.ulaval.ca
4modelEAU, Dépt. génie civil, Pavillon Pouliot, Université Laval, Québec, QC , G1K 7P4, Canada E-mail: peter.vanrolleghem@gci.ulaval.ca
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Water Sci Technol (2008) 57 (10): 1659–1666.
Citation
Kris Villez, Magda Ruiz, Gürkan Sin, Joan Colomer, Christian Rosén, Peter A. Vanrolleghem; Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes. Water Sci Technol 1 May 2008; 57 (10): 1659–1666. doi: https://doi.org/10.2166/wst.2008.143
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