Artificial neural network modelling of a large-scale wastewater treatment plant operation

Güçlü, Dünyamin; Dursun, Şükrü

doi:10.1007/s00449-010-0430-x

Cited by 81 publications

(35 citation statements)

References 20 publications

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“…While the results of this study in compared to other studies is better. In study of Guclu and Dursun, correlation coefficient was calculated as 0.85 for COD modeling [8]. They used 8 input parameters include flow rate, return activated sludge and waste activated sludge, DO, COD, SS, total kjeldahl nitrogen (TKN) and COD load in modeling process but in the present study four parameters were used.…”

Section: Resuls and Discussionmentioning

confidence: 99%

“…Moreover, the ease of regression models run is no secret for anyone. However in most studies, ANN results were better than another models in prediction of water quality parameters [3,5,8,10,14]. …”

Section: Resuls and Discussionmentioning

confidence: 99%

“…From the obtained results, an ANN model appears to be a useful tool for prediction of the BOD in Melen river. In study of Guclu and Dursun [8] three independent ANN models trained with back-propagation algorithm were developed to predict effluent COD, suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. Elmolla et al examined the implementation of ANN for the prediction and simulation of COD removal from antibiotic aqueous solution by the Fenton process and is very close to the experimental results with correlation coefficient (R 2 ) of 0.997 and mean square error (MSE) of 0.000376 [9].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Abyaneh

2014

J Environ Health Sci Engineer

168

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This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD.

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Section: Resuls and Discussionmentioning

confidence: 99%

Section: Resuls and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Abyaneh

2014

J Environ Health Sci Engineer

168

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“…Treatment process models are essential tools to assure proper operation and better control of the wastewater treatment plants [4]. Some deterministic models have been developed based on the fundamental biokinetics, such as activated sludge model number one (ASM1) [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models

Bagheri

Mirbagheri

Kamarkhani

et al. 2016

Desalination and Water Treatment

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2015): Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models, Desalination and Water Treatment, A B S T R A C TThis research was an effort to develop hybrid multilayer perceptron and radial basis function artificial neural network-genetic algorithm (MLPANN-GA and RBFANN-GA) models to accurately predict effluent biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in a submerged membrane bioreactor. The input variables of the networks were influent BOD, influent COD, influent TN or influent TP, sludge retention time (SRT), mixed liquor suspended solid, membrane permeability, and transmembrane pressure. Training procedures of all effluent quality parameters were successful for both the MLPANN-GA and RBFANN-GA models. The training and testing models showed an almost perfect match between the experimental and predicted values. Based upon the statistical analysis, results indicated that there is a very little difference between predicted and experimental values of the effluent BOD, COD, TN, and TP. The predicted and experimental values of the effluent concentrations gave a very low root mean squared error and a high coefficient of determination very close to one demonstrated high accuracy of these models to predict output variables. It became clear that the models based on the genetic algorithm (GA) were much better than those models without GA from the viewpoint of the achievement of an accurate prediction of the effluent BOD, COD, TN, and TP. The results indicated that the accuracy of all models increased when GA was applied to neural networks. The mean average error for the hybrid models varied from 3 to 8%.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Sanayei

Chaibakhsh

et al. 2014

International Journal of Chemical Engineering

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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 = 0.44. 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 2 > 0.99 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.

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Artificial neural network modelling of a large-scale wastewater treatment plant operation

Cited by 81 publications

References 20 publications

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models

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

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