Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants
SCIE
SCOPUS
- Title
- Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants
- Authors
- Yoo, CK; Vanrolleghem, PA; Lee, IB
- Date Issued
- 2003-10-09
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- A new approach to nonlinear modeling and adaptive monitoring using fuzzy principal component regression (FPCR) is proposed and then applied to a real wastewater treatment plant (WWTP) data set. First, principal component analysis (PCA) is used to reduce the dimensionality of data and to remove collinearity. Second, the adaptive credibilistic fuzzy-c-means method is used to appropriately monitor diverse operating conditions based on the PCA score values. Then a new adaptive discrimination monitoring method is proposed to distinguish between a large process change and a simple fault. Third, a FPCR method is proposed, where the Takagi-Sugeno-Kang (TSK) fuzzy model is employed to model the relation between the PCA score values and the target output to avoid the over-fitting problem with original variables. Here, the rule bases, the centers and the widths of TSK fuzzy model are found by heuristic methods. The proposed FPCR method is applied to predict the output variable, the reduction of chemical oxygen demand in the full-scale WWTP. The result shows that it has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions and discriminate between a sustained fault and a simple fault (or abnormalities) occurring within the process data. (C) 2003 Elsevier B.V. All rights reserved.
- Keywords
- adaptive credibilistic fuzzy-c-means (ADFCM); fuzzy-c-means (FCM) clustering; fuzzy principal component regression (FPCR); multivariate statistical analysis; principal component analysis (PCA); wastewater treatment process (WWTP); ACTIVATED-SLUDGE PROCESS; FAULT-DETECTION; NEURAL-NETWORK; SYSTEM
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/18304
- DOI
- 10.1016/S0168-1656(0
- ISSN
- 0168-1656
- Article Type
- Article
- Citation
- JOURNAL OF BIOTECHNOLOGY, vol. 105, no. 1-2, page. 135 - 163, 2003-10-09
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.