Model Predictive Control for the Activated Sludge Process

Gaoming Feng; Yujie Zhang

doi:10.54691/4sfeg461

Authors

Gaoming Feng
Yujie Zhang

DOI:

https://doi.org/10.54691/4sfeg461

Keywords:

Water Pollution; Wastewater Treatment; Model Predictive Control; Disturbance Observer.

Abstract

To address the issues of large fluctuations in influent load, high aeration energy consumption caused by conservative traditional on-off control strategies, unstable dissolved oxygen (DO) concentration, and poor sensor reliability in the activated sludge wastewater treatment process, a Model Predictive Control (MPC) method integrating data quality monitoring and a feedforward disturbance mechanism is proposed. A multi-input multi-output prediction framework based on an incremental Auto-Regressive eXogenous (ARX) model is constructed. The influent disturbance observer (BOD) load is introduced into the model as a feedforward variable. An MPC method based on a feedforward disturbance mechanism is designed to solve the hysteresis problem of feedback control. For the discrete characteristics of the actuators, PWM modulation is used to realize continuous control. Long-term actual engineering verification was carried out in a wastewater treatment plant in Jiaozuo, Henan. The results show that compared with the traditional PLC on-off control, this system reduces the average aeration energy consumption by more than 25%. The treatment efficiency is increased by 25%. The energy consumption per unit BOD removal stably reaches 0.8 kWh/kg BOD, which is an excellent industry level. The annual CO₂ emission is reduced by about 260 tons. The effluent quality compliance rate is not affected. This system can effectively suppress the invalid overshoot of DO concentration. It solves the control problems caused by multi-variable strong coupling and sensor failures. On the premise of ensuring absolute safety of effluent quality, significant energy saving, economic and environmental benefits are realized.

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