Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks |
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| Affiliation: | 1. Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran;2. Department and Faculty of Basic Sciences, PUK University, Kermanshah, Iran;1. College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China;2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China;1. School of Information and Control Engineering, Liaoning Shihua University, Fushun, China;2. Departamento de Control Automatico, CINVESTAV-IPN, Mexico City, Mexico;1. School of Environmental Science and Technology, Hanoi University of Science and Technology, Hanoi, Vietnam;2. Energy and Environmental Department, National Institute of Applied Sciences of Lyon, 69621 Villeurbanne Cedex, France;3. Department of Civil Engineering, Regional Centre of Anna University, Tirunelveli, India;4. Graduate School of Water Resources, Sungkyunkwan University, South Korea;1. Department of Civil Engineering, Razi University, Kermanshah, Iran;2. Environmental Research Center, Razi University, Kermanshah, Iran;3. Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Muscat, Oman;4. Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada;5. School of Engineering, University of Guelph, Guelph, Ontario, NIG 2W1, Canada |
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| Abstract: | A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH4+-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m3/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH4+-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH4+-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH4+-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R2) and lower root mean squared errors (RMSE) values. |
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| Keywords: | Sequencing batch reactor Neural network modeling Multi-layer perceptron Radial basis function Municipal wastewater |
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