Determination of the relationship between sewage odour and BOD by neural networks

Önkal-Engin, Güleda; Demir, İbrahim; Engin, Seref Naci

doi:10.1016/j.envsoft.2004.04.012

Cited by 96 publications

(50 citation statements)

References 22 publications

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“…Similarly, Cigizoglu (2005b) concluded that the GRNN approach did not require an iterative training procedure, unlike the FFBP method and GRNN forecasting performance was found to be superior to the FFBP, statistical, and stochastic methods in terms of the selected performance criteria. Onkal-Engin et al (2005) used an ANN trained with a BP algorithm to determine the relationship between sewage sample odours and their related Biochemical Oxygen Demand (BOD) values. They concluded that ANNs could be successfully used to classify the sewage samples collected from different locations of a WWTP.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

Yetilmezsoy

Sapci-Zengin

2007

Stoch Environ Res Risk Assess

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A three-layer Artificial Neural Network (ANN) model (9:12:1) for the prediction of Chemical Oxygen Demand Removal Efficiency (CODRE) of Upflow Anaerobic Sludge Blanket (UASB) reactors treating real cotton textile wastewater diluted with domestic wastewater was presented. To validate the proposed method, an experimental study was carried out in three lab-scale UASB reactors to investigate the treatment efficiency on total COD reduction. The reactors were operated for 80 days at mesophilic conditions (36-37.5°C) in a temperature-controlled water bath with two hydraulic retention times (HRT) of 4.5 and 9.0 days and with organic loading rates (OLR) between 0.072 and 0.602 kg COD/m 3 /day. Five different dilution ratios of 15, 30, 40, 45 and 60% with domestic wastewater were employed to represent seasonal fluctuations, respectively. The study was undertaken in a pH range of 6.20-8.06 and an alkalinity range of 1,350-1,855 mg/l CaCO 3 . The concentrations of volatile fatty acids (VFA) and total suspended solids (TSS) were observed between 420 and 720 mg/l CH 3 COOH and 68-338 mg/l, respectively. In the study, a wide range of influent COD concentrations (COD i ) between 651 and 4,044 mg/l in feeding was carried out. CODRE of UASB reactors being output parameter of the conducted anaerobic treatment was estimated by nine input parameters such as HRT, pH, COD i concentration, operating temperature, alkalinity, VFA concentration, dilution ratio (DR), OLR, and TSS concentration. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model predicted CODRE values based on experimental data and all the predictions were proven to be satisfactory with a correlation coefficient of about 0.8245. In the ANN study, the Levenberg-Marquardt Algorithm (LMA) was found as the best of 11 BP algorithms. In addition to determination of the optimal ANN structure, a linear-nonlinear study was also employed to investigate the effects of input variables on CODRE values in this study. Both ANN outputs and linear-nonlinear study results were compared and advantages and further developments were evaluated.

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Section: Introductionmentioning

confidence: 99%

Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

Yetilmezsoy

Sapci-Zengin

2007

Stoch Environ Res Risk Assess

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“…In recent years, much research was done in wastewater treatment based on ANNs. These researches have been mainly focused on estimation of wastewater process parameters [7], simulation of wastewater treatment performance [8][9][10], monitoring [6], controlling [11], classification, [4,12] and software sensor design [13]. In the literature to date, a limited number of applications have been made to aerobic large scale wastewater treatment plants using ANN model for modelling of a plant operation [14].…”

Section: Introductionmentioning

confidence: 99%

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

Güçlü

Dursun

2010

Bioprocess Biosyst Eng

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Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

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“…Up to now, there are a number of applications of ANN models in environmental engineering (Strik et al, 2005). They have already been used to simulate the effect of climate change on discharge and the export of dissolved organic carbon and nitrogen from river basins (Clair and Ehrman, 1996), to forecast salinity (Maier and Dandy, 1996), to model air pollution (Abdul-Wahab and Al-Alawi, 2002;Nunnari et al, 2004), to simulate and forecast residual chlorine concentrations within urban water systems (Rodriguez and Sérodes, 1999), to determine the relationship between sewage odor and BOD (Onkal-Engin et al, 2005), and to determine the leachate amount from municipal solid waste landfill (Karaca and Ozkaya, 2006). Holubar et al (2002) demonstrated that the anaerobic digestion of surplus sludge can be effectively modeled by means of a hierarchical system of neural networks; such that a prediction of biogas production and composition could be made several time-steps in advance.…”

Section: Introductionmentioning

confidence: 99%

Neural network prediction of thermophilic (65°C) sulfidogenic fluidized‐bed reactor performance for the treatment of metal‐containing wastewater

Şahinkaya

Özkaya

Kaksonen

et al. 2007

Biotech & Bioengineering

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The performance of a fluidized-bed reactor (FBR) based sulfate reducing bioprocess was predicted using artificial neural network (ANN). The FBR was operated at high (65 degrees C) temperature and it was fed with iron (40-90 mg/L) and sulfate (1,000-1,500 mg/L) containing acidic (pH = 3.5-6) synthetic wastewater. Ethanol was supplemented as carbon and electron source for sulfate reducing bacteria (SRB). The wastewater pH of 4.3-4.4 was neutralized by the alkalinity produced in acetate oxidation and the average effluent pH was 7.8 +/- 0.8. The oxidation of acetate is the rate-limiting step in the sulfidogenic ethanol oxidation by thermophilic SRB, which resulted in acetate accumulation. Sulfate reduction and acetate oxidation rates showed variation depending on the operational conditions with the maximum rates of 1 g/L/d (0.2 g/g volatile solids (VS)/d) and 0.3 g/L/d (0.06 g/g VS/d), respectively. This study presents an ANN model predicting the performance of the reactor and determining the optimal architecture of this model; such as best back-propagation (BP) algorithm and neuron numbers. The Levenberg-Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 20. The developed ANN model predicted acetate (R=0.91), sulfate (R=0.95), sulfide (R=0.97), and alkalinity (R=0.94) in the FBR effluent. Hence, the ANN based model can be used to predict the FBR performance, to control the operational conditions for improved process performance.

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Determination of the relationship between sewage odour and BOD by neural networks

Cited by 96 publications

References 22 publications

Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

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

Neural network prediction of thermophilic (65°C) sulfidogenic fluidized‐bed reactor performance for the treatment of metal‐containing wastewater

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