Journal
APPLIED SOFT COMPUTING
Volume 11, Issue 3, Pages 3238-3246Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2010.12.026
Keywords
Adaptive network-based fuzzy inference system; Wastewater treatment; Prediction; Principal component analysis
Categories
Funding
- Guangdong Provincial Department of Science [2008A080800003]
- Technology Research Project [2003A30406]
- Science and Technology Foundation of Guangzhou city [2004Z3-D027]
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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 CODeff and SSeff could be achieved using ANFIS. The maximum correlation coefficient values for CODeff and SSeff 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 CODeff and SSeff could also be achieved. Crown Copyright (C) 2010 Published by Elsevier B. V. All rights reserved.
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