RSM and ANN Comparative Modelling with a Granulation Treatment in Mixed Waters

Abstract

A Box-Behnken design was used for the analysis using a gray wolf optimizer (GWO)-coupled artificial neural network (ANN) model and response surface methodology (RSM) to analyze the effect of three operating parameters (volumetric exchange ratio [VER], aeration rate [AR], and cycle time [CT]) manipulated during an aerobic granular sludge process (AGS) sequencing batch reactor on modeling the removal of chemical oxygen demand (COD) in mixed wastewater. The most efficient architecture for COD showed the highest efficiency for modeling the AGS. The RSM model and plot results indicate that the CT and AR were the most influential on COD removal efficiency. When compared with models with statistical indices, GWO-ANN demonstrated higher performance compared to RSM.