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

Abstract

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.

Prediction of sludge settleability through artificial neural networks with optimized input variables

“…Compared with MLPANN, LSTM was more suitable for dealing with time series data. If there were time lag input variables, the accuracy of the model will improve (Mokhtari et al, 2020). However, the input variables in this study were no related to time series; thus, the performance of MLPANN was better.…”

“…MLPANN has been successfully applied in biological wastewater treatment processes (Abba & Elkiran, 2017; Mokhtari et al, 2020). Therefore, conventional MLPANN was used to predict the performance of municipal WWTP.…”

“…MLPANN has been successfully applied in biological wastewater treatment processes (Abba & Elkiran, 2017;Mokhtari et al, 2020).…”

“…Compared with MLPANN, LSTM was more suitable for dealing with time series data. If there were time lag input variables, the accuracy of the model will improve (Mokhtari et al, 2020).…”

Pragmatic analysis of wastewater treatment methods from a statistical perspective

“…Thus, a wide variety of inter-process variations are required, which directly depend upon the type of application for which the water is being treated. Machine learning has been utilized in a few approaches to counter the inter-process variations and analyze the effect of treatment on resulting water quality (Mokhtari et al 2020).…”