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

DOI: 10.1155/2014/248450

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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. 1. Introduction The reactive dye-containing effluents from dye manufacturing and application industries can cause serious environment pollution due to the toxicity and slow degradation of dyes [1]. In addition, the presence of dyes in water is highly visible and affects water transparency and aesthetics even in low concentrations. Therefore, the effluents must be treated before being released into the environment. In recent years, researchers have shown interests in biological treatment of wastewaters with high concentrations of dyes [2, 3]. Treatment of these polluted wastewaters requires high effectiveness and low cost dye removal processes [4]. Sequencing batch reactor (SBR) is a promising biological system for treating dye-containing wastewaters [5, 6]. This system is cost efficient and flexible to handle different feed characteristics. Furthermore, its operation is easier than other biological methods [1]. However, the SBR process, like other biological processes, is highly nonlinear, time varying, and subject to significant disturbances [7]. Modeling the treatment process can provide better understanding, design, operation, and control of the process [8]. The ability of artificial neural networks (ANNs) in black-box modeling of nonlinear systems with complicated structure has made them the most popular tool for modeling of biological processes [9]. In recent years, recurrent neural networks are


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