Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system

Authors:
Jinquan Wan;Mingzhi Huang;Yongwen Ma;Wenjie Guo;Yan Wang;Huiping Zhang;Weijiang Li;Xiaofei Sun
Affiliations:
State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China and School of Chemistry and Chemical Engineering, South China U ...;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China;School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model's architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. The minimum mean absolute percentage errors of 1.003% and 0.5161% for COD"e"f"f and SS"e"f"f could be achieved using ANFIS. The maximum correlation coefficient values for COD"e"f"f and SS"e"f"f were 0.9912 and 0.9882, respectively. The minimum mean square errors of 1.2883 and 0.0342, and the minimum RMSEs of 1.135 and 0.1849 for COD"e"f"f and SS"e"f"f could also be achieved.