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8491957 
Journal Article 
Performance evaluation and modelling of an integrated municipal wastewater treatment system using neural networks 
Mokhtari, HA; Bagheri, M; Mirbagheri, SA; Akbari, Ali 
2020 
1 
Water and Environment Journal
ISSN: 1747-6585
EISSN: 1747-6593 
WILEY 
HOBOKEN 
34 
622-634 
WOS:000522759900001 
This study evaluates and models the impacts of employing biofilm carriers in sequencing batch reactors (SBR). A neural network (NN) was used to predict contaminants in the effluent and analyse the importance of operating parameters. With a hydraulic retention time of 7 h, the removal efficiency of chemical oxygen demand (COD), total phosphorous (TP), and total suspended solids (TSS) were 85, 82, and 98.9%, respectively. The removal efficiency of COD, TP, and TSS in our hybrid system was superior to regular single SBR systems. The training procedure of the NN model was successful and almost a perfect match was achieved between predicted values and experimental values. For all models predicting effluent COD, TP, and TSS, the correlation coefficient was higher than 0.99, and mean squared error approached zero. The analysis of input parameters demonstrated that influent concentration is a significant factor in the modelling of effluent characteristics. 
biofilm carriers; integrated system; neural network; performance; prediction; sequencing batch reactor