Total phosphorus and total Kjeldahl nitrogen removal using an aerobic granular sludge process. Case studies ANN and RSM modeling

Abstract

Removing nutrients from wastewater is essential because high concentrations in aquatic systems lead to severe eutrophication problems, the most common impairment of surface waters such as lakes and oceans. Total phosphorus (TP) and total Kjeldahl nitrogen (TKN) were removed from mixed wastewater using an aerobic granular sludge process in a sequencing batch reactor (AGS-SBR). An artificial neural network (ANN) and response surface methodology (RSM) were applied to evaluate the main parameters of the process. For TKN removal, only cycle time (CT) (0.0475) was a significant variable, achieving removal efficiencies of up to 81%. In TP case removal, two parameters, VER and AR, were substantial for this process, completing elimination efficiencies of around 40%. On comparing the models with statistical indices, ANN coupled with the moth-flame optimization algorithm (ANN-MFO) demonstrated higher performance with an adjusted R2 (0.9866) for the case of TP removal and (0.9519) for TKN removal.