(562bb) Semi-pilot plant for tertiary treatment of domestic wastewater using algal photo-bioreactor, with artificial intelligence

This study attempted to investigate the removal of biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonia-nitrogen (NH4-N), and total phosphorus (TP) from secondary treated domestic wastewater using algal photo-bioreactor. A semi-pilot plant was constructed and operated for 112 days under continuous flow conditions at Zenin wastewater treatment plant, Giza, Egypt (WWTP) which consists of an algal photo-bioreactor with an effective volume of 188 litters and a lamella settler. The removal of the studied parameters was studied at different hydraulic retention times (HRTs) and mixed liquor suspended solids (MLSS) concentrations. A continuous illumination of the photo-bioreactor was maintained using sunlight in the morning and incandescent lamps at night. The best overall organic and nutrients removal efficiency was recorded at HRT of 16.1 hours. Artificial neural network (ANN) with a structure of 3-10-1 was used to predict the BOD, COD, TSS, NH4-N, and TP removal efficiencies. It was revealed that the ANN model adequately predicted the studied parameters removal efficiencies with r2 greater than 90%. © 2019 American Institute of Chemical Engineers. All rights reserved.