화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.29, No.5, 636-643, May, 2012
DOI10.1007/s11814-011-0228-9  Export Citation
Modeling of a paper-making wastewater treatment process using a fuzzy neural network
Mingzhi Huang1, 2, Jinquan Wan3, 4, †, Yan Wang3, 4, Yongwen Ma3, 4, Huiping Zhang5, Hongbin Liu2, Zhanzhan Hu6, and ChangKyoo Yoo2, †
  • 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
E-mail:,
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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