ANN Modeling to Predict the COD and Efficiency of Waste Pollutant Removal from Municipal Wastewater Treatment Plants
Saeed Pakrou1 * , Naser Mehrdadi2 and Akbar Baghvand3
1
Civil Engineering – Environmental,
Aras International Campus,
Iran
2
Civil Engineering – Environmental,
University of Tehran,
Iran
3
Civil Engineering, Environmental,
University of Tehran,
Iran
http://dx.doi.org/10.12944/CWE.10.Special-Issue1.106
The system in this study is modeled by neural network and studies conducted in simulating the presumptive developed sewage treatment plant with the single activated sludge process and SSSP software along with the system’s experiences. The results obtained by the developed neural network model are analyzed for the presumptive treatment plant. The maximum correlation coefficient is 0.98 for modeling the presumptive waste treatment plant. Using real data from the Tabriz waste treatment plant, the best and most appropriate neural network model is obtained as R equals to 0.898, the maximum removal efficiency of the treatment plant relating to the TSS pollutant is equal to 94 percent, and the minimum removal efficiency related to TS is equal to 38 percent. Likewise, the removal efficiency of mentioned pollutants is equal to 95 and 37 percent estimated by the neural network, respectively, which indicates a relatively high accuracy considering the error percentage existing in the input data.
Copy the following to cite this article:
Pakrou S, Mehrdadi N, Baghvand A. ANN Modeling to Predict the COD and Efficiency of Waste Pollutant Removal from Municipal Wastewater Treatment Plants. Special Issue of Curr World Environ 2015;10(Special Issue May 2015). DOI:http://dx.doi.org/10.12944/CWE.10.Special-Issue1.106
Copy the following to cite this URL:
Pakrou S, Mehrdadi N, Baghvand A. ANN Modeling to Predict the COD and Efficiency of Waste Pollutant Removal from Municipal Wastewater Treatment Plants. Special Issue of Curr World Environ 2015;10(Special Issue May 2015).
Available from: http://cwejournal.org?p=702/
