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International Journal of Chemical Engineering
Volume 2014 (2014), Article ID 248450, 7 pages
Research Article

Long-Term Prediction of Biological Wastewater Treatment Process Behavior via Wiener-Laguerre Network Model

1Environmental Technology Division, School of Industrial Technology, Universiti Sains Malaysia, Penang 11800, Malaysia
2Faculty of Science, University of Guilan, Rasht, Guilan 41938-33697, Iran
3Department of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan 41996-13769, Iran
4The Academic Centre for Education, Culture and Research (ACECR), Institute for Environmental Research, Rasht, Guilan 41365-3114, Iran

Received 21 August 2013; Accepted 26 November 2013; Published 22 January 2014

Academic Editor: Dmitry Murzin

Copyright © 2014 Yasaman Sanayei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A Wiener-Laguerre model with artificial neural network (ANN) as its nonlinear static part was employed to describe the dynamic behavior of a sequencing batch reactor (SBR) used for the treatment of dye-containing wastewater. The model was developed based on the experimental data obtained from the treatment of an effluent containing a reactive textile azo dye, Cibacron yellow FN-2R, by Sphingomonas paucimobilis bacterium. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was . In order to adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics.