Performance evaluation and modelling of an integrated municipal wastewater treatment system using neural networks

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.