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Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

Villez, Kris; Ruiz, Magda; Sin, Guerkan; Colomer, Joan; Rosén, Christian LU and Vanrolleghem, Peter A (2008) In Water Science and Technology 57(10). p.1659-1666
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
MPCA, LAMDA clustering, on-line monitoring, nutrient removal, SBR
in
Water Science and Technology
volume
57
issue
10
pages
1659 - 1666
publisher
IWA Publishing
external identifiers
  • wos:000256623900023
  • scopus:45549109188
ISSN
0273-1223
DOI
10.2166/wst.2008.143
language
English
LU publication?
yes
id
41157bd6-e763-4d3a-9942-ab399c015691 (old id 1191255)
date added to LUP
2008-09-08 12:50:44
date last changed
2017-11-19 03:47:06
@article{41157bd6-e763-4d3a-9942-ab399c015691,
  abstract     = {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.},
  author       = {Villez, Kris and Ruiz, Magda and Sin, Guerkan and Colomer, Joan and Rosén, Christian and Vanrolleghem, Peter A},
  issn         = {0273-1223},
  keyword      = {MPCA,LAMDA clustering,on-line monitoring,nutrient removal,SBR},
  language     = {eng},
  number       = {10},
  pages        = {1659--1666},
  publisher    = {IWA Publishing},
  series       = {Water Science and Technology},
  title        = {Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes},
  url          = {http://dx.doi.org/10.2166/wst.2008.143},
  volume       = {57},
  year         = {2008},
}