Fuzzy principal component regression (FPCR) is proposed to model the non-linear process of sewage treatment plant (STP) data matrix. The dimension reduction of voluminous data was done by principal component analysis (PCA). The PCA score values were partitioned by fuzzy-c-means (FCM) clustering, and a Takagi–Sugeno–Kang (TSK) fuzzy model was built based on the FCM functions. The FPCR approach was used to predict the reduction in chemical oxygen demand (COD) and biological oxygen demand (BOD) of treated wastewater of Vidyaranyapuram STP with respect to the relations modeled between fuzzy partitioned PCA scores and target output. The designed FPCR model showed the ability to capture the behavior of non-linear processes of STP. The predicted values of reduction in COD and BOD were analyzed by performing the linear regression analysis. The predicted values for COD and BOD reduction showed positive correlation with the observed data.
Non-linear modeling using fuzzy principal component regression for Vidyaranyapuram sewage treatment plant, Mysore – India
Ayesha Sulthana, K. C. Latha, Mohammad Imran, Ramya Rathan, R. Sridhar, S. Balasubramanian; Non-linear modeling using fuzzy principal component regression for Vidyaranyapuram sewage treatment plant, Mysore – India. Water Sci Technol 1 September 2014; 70 (6): 1040–1047. doi: https://doi.org/10.2166/wst.2014.333
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