Modelling and optimizing hydraulic retention time in the biological aeration unit: Application of artificial neural network and particle swarm optimization

The biological aeration unit (BAU) is essential for removing organic and inorganic matter in the wastewater. Microorganisms present in the wastewater are able to remove organic and inorganic matter. The challenge is that the BAU consumes large quantities of energy during the process, due to the constant supply of air for the respiration of microorganisms. The standard hydraulic retention time (HRT) in the BAU is between 4 and 12 h per treatment cycle which is extensive, hence the high levels of energy consumption. The purpose of this study is to optimize the HRT in the BAU, by reducing the operation time of the air pumps/blowers, resulting in less energy consumption per treatment cycle. Particle swarm optimization (PSO) algorithm optimizes the HRT in the BAU. Variables in the optimization model are verified using the sensitivity analysis method. The results of the study show a reduction in HRT from 4 to 2.4954 h, which is a 37.6 % energy saving in the BAU. The biggest drivers in the HRT optimization model are energy consumption (61.3 %), airflow rate (36.8 %), and temperature (1.2 %). Therefore, decreasing airflow rate and increasing wastewater temperature reduces HRT in BAU.