Abstract
Mathematical models based on instant environmental inputs are increasingly applied to optimize the operation of wastewater treatment plants (WWTPs) for improving treatment efficiency. This study established a numerical model consisting of the activated sludge module ASM3 and EAWAG bio-P module, and calibrated the model using data from a full-scale experiment conducted in a WWTP in Nanjing, China. The calibrated model was combined with online sensors for water temperature, chemical oxygen demand, NH+ 4-N and PO3- 4-P to optimize and dynamically adjust the operation of the WWTP. The results showed that, compared to the original default operation mode, the effluent water quality was significantly improved after optimization even without supplementation of external carbon or alkalinity, and the required aeration rate in spring, summer, autumn, and winter was reduced by 15, 41, 33 and 11%, respectively. The study indicated that there was the potential for application of closed-loop automatic control to regulate operating parameters to improve wastewater treatment processes through the integration of data on influent characteristics and environmental conditions from sensors, and results from simulation models.
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