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Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor

Yoo, Chang Kyoo; Villez, Kris; Lee, In-Beum; Rosén, Christian LU and Vanrolleghem, Peter A. (2007) In Biotechnology and Bioengineering 96(4). p.687-701
Abstract
Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and soon. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearty; (2) multiple models with a;posterior probability for modeling different operating regions; (3) local batch monitoring by the T-2- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM)... (More)
Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and soon. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearty; (2) multiple models with a;posterior probability for modeling different operating regions; (3) local batch monitoring by the T-2- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then; these local regions can be supervised separately; leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
sequencing batch reactor, probabilistic modeling, operational modes, multiple, batch monitoring and supervision, biological system, (SBR), wastewater treatment
in
Biotechnology and Bioengineering
volume
96
issue
4
pages
687 - 701
publisher
John Wiley & Sons
external identifiers
  • wos:000244287200007
  • scopus:33947154689
ISSN
1097-0290
DOI
10.1002/bit.21220
language
English
LU publication?
yes
id
65534906-02b1-43f5-83d8-ca73b47f1a65 (old id 674319)
date added to LUP
2007-12-19 09:44:51
date last changed
2017-10-08 03:44:54
@article{65534906-02b1-43f5-83d8-ca73b47f1a65,
  abstract     = {Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and soon. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearty; (2) multiple models with a;posterior probability for modeling different operating regions; (3) local batch monitoring by the T-2- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then; these local regions can be supervised separately; leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.},
  author       = {Yoo, Chang Kyoo and Villez, Kris and Lee, In-Beum and Rosén, Christian and Vanrolleghem, Peter A.},
  issn         = {1097-0290},
  keyword      = {sequencing batch reactor,probabilistic modeling,operational modes,multiple,batch monitoring and supervision,biological system,(SBR),wastewater treatment},
  language     = {eng},
  number       = {4},
  pages        = {687--701},
  publisher    = {John Wiley & Sons},
  series       = {Biotechnology and Bioengineering},
  title        = {Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor},
  url          = {http://dx.doi.org/10.1002/bit.21220},
  volume       = {96},
  year         = {2007},
}