Wastewater treatment plants (WWTPs) play an important role in protecting the quality of water sources. The optimum operation of WWTPs in response to continuous changes in the characteristics of the influent of the WWTP is very important, and it can improve the quality of the effluent of the WWTP. In this study, an approach for optimal operation of the WWTP has been presented considering the quantitative and qualitative variables of influent. In the proposed method, first, the simulation model of WWTP is developed and calibrated using the recorded data of its influent and effluent characteristics as well as operation conditions. Then, the influent is classified into clusters quantitatively and qualitatively k-means clustering method. In the final step, after determining the effective operation parameters, the AMOEA-MAP optimization algorithm is used to determine the optimal values of operation parameters for each cluster of influents based on its quantitative and qualitative characteristics including flow rate, COD, ammonium, and temperature. The proposed approach was implemented on a WWTP in the South of Tehran, the capital of Iran. Dissolved oxygen (DO) in the aeration tank, waste-activated sludge flow rate (QWAS) and the ratio of the supernatant flow rate of the sludge dewatering unit to the effluent flow rate (Qd/Qe) were considered as operation parameters affecting the performance of the system in removing pollutants and their optimal values were obtained as DO, 0.25-1.7 mg/l, QWAS, 875-2000 m3/day, and Qd/Qe, 10-14%. Using this method, i.e., changing system operation conditions based on influent characteristics, has improved the performance of a system in reducing COD, ammonium, and nitrate in the effluent by 11-41, 17-20 and 15-34, respectively.
Keywords: Activated sludge; Clustering; Model calibration; Operation optimization; Simulation; Wastewater treatment plant modeling.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.