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

Publication history: Received July 1, 2011
Accepted August 30, 2011

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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Modeling of a paper-making wastewater treatment process using a fuzzy neural network

Mingzhi Huang^{1 2} Jinquan Wan^{3 4†} Yan Wang^{3 4} Yongwen Ma^{3 4} Huiping Zhang⁵ Hongbin Liu² Zhanzhan Hu⁶ ChangKyoo Yoo^2†

¹State Key Laboratory of Pulp and Paper Engineering, School of Chemistry and Chemical Engineering, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, Guangzhou 510006, China ²Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea ³College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, China ⁴The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, China ⁵School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China ⁶School of Business Administration, South China University of Technology, Guangzhou 510640, China

ppjqwan@scut.edu.cn

Korean Journal of Chemical Engineering, May 2012, 29(5), 636-643(8), 10.1007/s11814-011-0228-9

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Abstract

An intelligent system that includes a predictive model and a control was developed to predict and control the performance of a wastewater treatment plant. The predictive model was based on fuzzy C-means clustering, fuzzy inference and neural networks. Fuzzy C-means clustering was used to identify model’s architecture, extract and optimize fuzzy rule. When predicting, MAPE was 4.7582% and R was 0.8535. The simulative results indicate that the learning ability and generalization of the model was good, and it can achieve a good predication of effluent COD. The control model was based on a fuzzy neural network model, taking into account the difference between the predicted value of COD and the setpoint. When simulating, R was 0.9164, MAPE was 5.273%, and RMSE was 0.0808, which showed that the FNN control model can effectively change the additive dosages. The control of a paper-making wastewater treatment process in the laboratory using the developed predictive control model and MCGS (monitor and control generated system) software shows the dosage was computed accurately to make the effluent COD remained at the setpoint, when the influent COD value or inflow flowrate was changed. The results indicate that reasonable forecasting and control performances were achieved through the developed system; the maximum error was only 3.67%, and the average relative error was 2%.

Keywords

Fuzzy Neural Network Industrial Wastewater Treatment Predictive Control Fuzzy C-means Clustering Hybrid Algorithm

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