An aeration requirements calculating method based on BOD5 soft measurement model using deep learning and improved coati optimization algorithm

Abstract

Conventionally, the supply-side is over-aerated to meet the oxygen demand of biological wastewater treatment in wastewater treatment plants (WWTPs). Such systems do not consider the various influent loadings and actual oxygen consumption. It cannot respond effectively to fluctuating influent, resulting in unnecessary aeration and energy wastage. In this study, we developed a novel approach to calculate aeration requirements in real time. Firstly, the oxygen demand formula for biological nitrogen and phosphorus removal is modified by considering realistic factors (temperature, oxygen partial pressure and so on) to construct the aeration demand formula. Then, to address the problem that BOD5 in aeration formula is difficult to measure in real time, a novel hybrid model integrating Fuch mapping, reverse learning strategy, Coati Optimization Algorithm (COA), and Backpropagation Artificial Neural Network (BP-ANN) is proposed in this study. Finally, aeration requirements are calculated based on real-time water quality data. Experimental results show that the ICOA-BP model with 9 neurons in hidden layer predicted the optimum BOD5 concentration by achieving a 25.146 mean absolute error and a 0.735 coefficient of determination (all-R2) in all datasets. The optimization led to a 15.164 % aeration savings and 38.942 % energy savings. The proposed method of aeration calculation is automated and intelligent, responding to fluctuating changes in influent quality and reducing unnecessary aeration and energy consumption while meeting the requirements of effluent quality.