Application of fuzzy partial least squares (FPLS) modeling nonlinear biological processes

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.