Abstract
Nowadays, Constructed wetland (CW) technologies present an advantageous alternative system for wastewater treatment. However, there is not an effective model admitted to providing an implement for forecasting their performances. In this study, the neural network model was applied to predict the effluent physicochemical parameters and total coliforms and fecal streptococci in a hybrid constructed wetland plant (HCW) processing domestic wastewater. The Tidili treatment plant was made up of three parallel vertical flow beds (VF), followed by two horizontal flow beds (HF) working in parallel, with Phragmites australis as the vegetation. The Tidili treatment plant was controlled every 15 days for 2 years. Sampling was taken at the tank inlet, and at both the VF and HF outlets. The Sigmoidal activation functions with a Feed-Forward Back-Propagation were used to foretell the removal rates of pollutants from domestic wastewater. The main removal percentages of physicochemical parameters were 94% of TSS, 92% of BOD5, 90% of COD, 66% of TN and 63% of TP. HCWs showed a high capacity to eliminate coliforms (4.44 Log units total coliforms, 4.30 Log units fecal coliforms) and fecal streptococci (4.08 Log units). Artificial neural networks (ANNs) was calibrated and validated based on the physicochemical parameters (TSS, BOD5, COD) and microbiological parameters (TC, FS). The model indicated that the simulated values of physicochemical parameters and microbiological parameters were in close coordination with their target values. Thus, ANN model was found to be a useful implement to forecast the examined performances using HCWs.
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This work was supported by the National Centre for Studies and Research on Water and Energy (CNEREE), University of Cadi Ayyad.
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Elfanssi, S., Mabrouki, J., Oumlouki, K.E., Ghizlane Fattah, Mandi, L. (2023). Modeling and Simulation of Phytoremediation Technology by Artificial Neural Network. In: Mabrouki, J., Mourade, A., Irshad , A., Chaudhry, S. (eds) Advanced Technology for Smart Environment and Energy. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-25662-2_7
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