Simulation of an industrial wastewater treatment plant using artificial neural networks

Gontarski, C.A.; Rodrigues, Paulo Rogério Pinto; Mori, Milton; Prenem, L.F.

doi:10.1016/s0098-1354(00)00449-x

Cited by 89 publications

(33 citation statements)

References 3 publications

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“…Based on the mechanism of human nervous systems, the ANN models can be classified into three major groups; 1) feedforward network, 2) recurrent network and 3) unsupervised network (Fig 3). The most usual model is feedforward network which has frequently been used in anaerobic treatment studies (Zhu et al, 1998;Gontarski et al, 2000;Strik et al, 2005;Ozkaya et al, 2007;Parthiban et al, 2007). Although, recurrent and unsupervised networks are powerful models developed to map the variables of anaerobic processes non-linearly, a few studies have applied them in on-line or control applications (Kecman, 2001).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…A few years later, Gontarski et al also proved the successful application of neural networks in the simulation of industrial anaerobic treatment plant in Brazil. They showed that the liquid flow rate and pH of the inlet stream were the major variables in controlling the plant and the neural network presented desirable results in minimizing the plant fluctuations (Gontarski et al, 2000). In a different study, a hybrid technique providing principal component analysis (PCA) together with neural networks was used for optimal control of a wastewater treatment process (Choi & Park, 2001).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

Tabatabaei¹,

Sulaiman²,

Nikbakht³

et al. 2011

Alternative Fuel

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

Tabatabaei¹,

Sulaiman²,

Nikbakht³

et al. 2011

Alternative Fuel

View full text Add to dashboard Cite

“…Accordingly, a computational neural network consists of simple processing units called neurons [25][26][27]. In general, a neural net (multilayered perceptron), is a parallel interconnected structure consisting of: (i) an input layer of neurons (independent variables), (ii) a number of hidden layers, and (iii) an output layer (dependent variables).…”

Section: Neural Network Modelingmentioning

confidence: 99%

Bioremediation of Malachite Green from Contaminated Water by Three Microalgae: Neural Network Modeling

Khataee¹,

Zarei

Pourhassan

2010

CLEAN Soil Air Water

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Biological decolorization of the triphenylmethane dye, Malachite Green (MG), by three microalgae, Chlorella, Cosmarium and Euglena species was investigated. Results indicated that the decolorization was dependent on reaction time, initial dye concentration, algal concentration, pH and temperature. The reusability and efficiency of the algae in long term repetitive operations were also examined. Since all of the algae had reasonable reusability in repetitive decolorization operations, the process seems to be biodegradation. An artificial neural network (ANN) model was developed to predict the biological decolorization of MG solution. The findings indicated that the ANN provided reasonable predictive performance (R 2 = 0.98). The influence of each parameter on the variable studied was assessed, and reaction time and initial pH were found to be the most significant factors, followed by temperature, initial dye concentration and amount of algae. Simulations based on the developed ANN model can estimate the behavior of the biological decolorization process under different conditions.

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“…Under this situation, the effluent quality cannot be predicted appropriately using some numerical models, especially mechanism models. Some soft computation techniques, such as artificial neural network (ANN), in which the mechanism's reactions can be ignored are available presently and applied to biological wastewater treatment process [7][8][9][10][11][12][13]. In our previous work, online monitoring data from simple and cheap online meters such as DO or pH meters were used to train ANN for effluent prediction [12].…”

Section: Introductionmentioning

confidence: 99%

Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach

Pai

Wang

Chiang

et al. 2009

Bioprocess Biosyst Eng

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Three types of adaptive network-based fuzzy inference system (ANFIS) in which the online monitoring parameters served as the input variable were employed to predict suspended solids (SS(eff)), chemical oxygen demand (COD(eff)), and pH(eff) in the effluent from a biological wastewater treatment plant in industrial park. Artificial neural network (ANN) was also used for comparison. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. When predicting, the minimum mean absolute percentage errors of 2.90, 2.54 and 0.36% for SS(eff), COD(eff) and pH(eff) could be achieved using ANFIS. The maximum values of correlation coefficient for SS(eff), COD(eff), and pH(eff) were 0.97, 0.95, and 0.98, respectively. The minimum mean square errors of 0.21, 1.41 and 0.00, and the minimum root mean square errors of 0.46, 1.19 and 0.04 for SS(eff), COD(eff), and pH(eff) could also be achieved.

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Simulation of an industrial wastewater treatment plant using artificial neural networks

Cited by 89 publications

References 3 publications

Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

Influential Parameters on Biomethane Generation in Anaerobic Wastewater Treatment Plants

Bioremediation of Malachite Green from Contaminated Water by Three Microalgae: Neural Network Modeling

Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach

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