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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received May 23, 2003
Accepted July 10, 2004

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Application of Fuzzy Partial Least Squares (FPLS) Modeling Nonlinear Biological Processes

Chang Kyoo Yoo^† Yoon Ho Bang¹ In-Beum Lee² Peter A. Vanrolleghem and Christian Ros´en³

Yoo CK Bang YH Lee IB Vanrolleghem PA Rosen C

BIOMATH: Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, B-9000 Gent, Belgium ¹LG Environmental Strategy Insitute (LGESI), Yonsei Univ., 134 Shinchon-dong, Seoul 120-749, Korea ²LG Environmental Strategy Insitute (LGESI), Pohang University of Science and Technology, San 31 Hyoja Dong, Pohang 790-784, Korea ³IEA: Department of Industrial Electrical Engineering and Automation, Lund University, LTH, Box 118, SE-221 00, Lund, Sweden

Korean Journal of Chemical Engineering, November 2004, 21(6), 1087-1097(11), 10.1007/BF02719479

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Abstract

We applied a nonlinear fuzzy partial least squares (FPLS) algorithm for modeling a biological wastewater treatment plant. FPLS embeds the Takagi-Sugeno-Kang (TSK) fuzzy model into the regression framework of the partial least squares (PLS) method, in which FPLS utilizes a TSK fuzzy model for nonlinear characteristics of the PLS inner regression. Using this approach, the interpretability of the TSK fuzzy model overcomes some of the handicaps of previous nonlinear PLS (NLPLS) algorithms. As a result, the FPLS model gives a more favorable modeling environment in which the knowledge of experts can be easily applied. Results from applications show that FPLS has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions in a simulation benchmark of biological process as well as in a full-scale wastewater treatment process. The result shows that it has the ability to model the nonlinear process and handle multiple operating conditions and is able to predict the key components of nonlinear biological processes.

Keywords

Fuzzy Partial Least Squares (FPLS) Multivariate Statistical Analysis Nonlinear Modeling Nonlinear PLS(NLPLS) Partial Least Squares (PLS) Wastewater Treatment Process (WWTP)

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