Robust Multivariable Control for Municipal Wastewater Denitrification Process

Abstract

The control of internal flow and external carbon is crucial for the municipal wastewater denitrification process. However, due to the disturbance and interactions in the process, it is difficult to achieve suitable control performance. To solve this problem, a robust multivariable control (RMC) scheme is proposed to improve the process control efficiency. First, a mechanism-based control method is designed to provide an explicit control signal that mitigates the effect of load changes. Second, a robust control method, using a fuzzy neural network sliding mode controller, is developed to improve the tracking accuracy. Third, an adaptive learning algorithm is proposed to tune the parameters of RMC so that the closed-loop system is stable in the term of Lyapunov stability theory. Finally, the benchmark simulations of municipal wastewater denitrification process demonstrate that, compared with other control strategies, the proposed method yields a stable control performance with an obvious energy saving effect.