Intelligent Control of an Aeration Tank Using Model Predictive Control and a Digital Twin
In the context of water scarcity and tightening environmental requirements, improving the energy efficiency of biological wastewater treatment processes has become particularly important. The aeration tank is one of the most energy-intensive and dynamically complex units, strongly affected by the variability in influent flow and composition. Conventional PID controllers do not provide predictive disturbance compensation and often result in excessive aeration and increased energy consumption. The study proposes an intelligent control approach based on a digital twin, neural network-based influent flow forecasting, and model predictive control (MPC). The digital twin represents a dynamic model of the biological process incorporating key state variables, including substrate, activated sludge, and dissolved oxygen concentrations. The LSTM neural network model is employed to predict the hydraulic load based on historical plant operation data, as well as to compensate for residual nonlinear dynamics that are not represented by the linearized MPC model. The predicted influent flow values are incorporated into the MPC framework as measured disturbances, enabling the generation of anticipatory control actions for the aeration system. The adequacy of the digital twin was validated using operational data from the wastewater treatment facilities of Semey city and was characterized by RMSE = 0.14 mg/L, MAE = 0.09 mg/L, R2 = 0.94, and MAPE = 6.3%. The simulation results demonstrated that, compared with the fuzzy PID controller, the application of MPC reduced the RMSE by 57.1%, decreased the overshoot from 24% to 8%, reduced the integral absolute error (IAE) by 60.9%, and lowered the energy consumption of the aeration system by 21.1%, while maintaining the dissolved oxygen concentration within the permissible operating range. The proposed Advisory MPC architecture is compatible with existing PLC–SCADA systems and can serve as a basis for the gradual digital modernization of wastewater treatment facilities without modifying the existing automation loops.