Using treated wastewater could be a realistic solution for the scarcity of fresh water in Iran; therefore, effluent quality evaluation is an essential task. In the present study, the effluent quality index (EQI) for south of Tehran municipal wastewater treatment plant was found considering eight quality parameters and sub-index formulae method. Further, with the help of an artificial neural network (ANN), design and applicability of feed-forward, three-layer perceptron neural network model for computing the EQI has been assessed. In this study, ANN modeling is done by ANN toolbox in MATLAB 2013. The modeling efforts showed that the optimal network architecture was 8–7–1 and that the best EQI predictions were associated with the Levenberg–Marquardt back propagation training algorithm and Tansig transfer function. The EQI predictions of this model had signiﬁcant and very high correlation (R = 0.96, MSE = 0.1) with the measured EQI values. The ANN approach which is used in this article suggested powerful tool to EQI computation and prediction, since it reduced lengthy computations and using various sub-index formula for each value.
Artiﬁcial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant
Maliheh Falah Nezhad, Naser Mehrdadi, Ali Torabian, Sadegh Behboudian; Artiﬁcial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant. Journal of Water Supply: Research and Technology-Aqua 16 February 2016; 65 (1): 18–27. doi: https://doi.org/10.2166/aqua.2015.030
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