Aeration of biological reactors in wastewater treatment plants is important to obtain a high removal of soluble organic matter as well as for nitrification but requires a significant use of energy. It is hence of importance to control the aeration rate, for example, by ammonium feedback control. The goal of this report is to model the dynamics from the set point of an existing dissolved oxygen controller to effluent ammonium using two types of system identification methods for a Hammerstein model, including a newly developed recursive variant. The models are estimated and evaluated using noise corrupted data from a complex mechanistic model (Activated Sludge Model no.1). The performances of the estimated nonlinear models are compared with an estimated linear model and it is shown that the nonlinear models give a significantly better fit to the data. The resulting models may be used for adaptive control (using the recursive Hammerstein variant), gain-scheduling control, L2 stability analysis, and model based fault detection.
A nitrifying activated sludge process with ammonium feedback and inflow feedforward control is studied. The feedback and feedforward measurements are subject to time delay and the control loop includes a saturation. A Hammerstein-based model is therefore identified, including process delays and the nonlinearity. A Monod-type nonlinearity is used, as motivated by the oxygen injection efficiency reduction, with increasing dissolved oxygen concentration. The proposed feedback control strategy includes tuning of a linear lag compensator that has a limited low-frequency gain which allows global stability to be established with the Popov criterion. The feedforward controller attenuates the disturbance and provides an overall improvement of the control strategy. The performance of the combined feedback and feedforward aeration controller is evaluated with a benchmark model.
Ammonium feedback is commonly used for controlling the aeration in wastewater treatment plants having biological nitrogen removal. The paper proposes the use of a PI controller tuning method based on a simplified identified model with Hammerstein structure. The Hammerstein model accounts for the delay of the process caused by the settler, the multiple bioreactors dynamics and the main nonlinear process effects. The controller tuning method exploits the Popov inequality for a pre-computation of (a subset of) the stability region of the closed loop system as a function of the PI-controller parameters, quantified in terms of the maximum loop delay of the system. The controller parameters are selected in the computed stability region. In the numerical study, the plant is identified as a Hammerstein model using a recently published method, here extended to identify multiple bioreactor tank dynamics. The identification data is obtained from a high fidelity simulator, which is also used for evaluation of the proposed controller tuning method. The results show that the proposed procedure results in a PI-controller tuning with predictable stability properties and a good performance.