Online-Growing Neural Network Control for Dissolved Oxygen Concentration

Abstract

The activated sludge method is one of the most commonly used methods for wastewater treatment process (WWTP). Among them, the dissolved oxygen (DO) concentration is a key factor affecting microbial metabolism and wastewater treatment effectiveness. However, due to the nonlinearity and dynamicity of WWTP, it is difficult to precisely control the DO concentration by traditional control methods. To solve this problem, the online-growing pipelined recurrent wavelet neural network (OG-PRWNN) control is proposed to improve the DO control accuracy. First, the online growing mechanism is designed to adjust the number of modules of the controller by measuring the control performance. Then, the structure of the controller is automatically determined to meet the different operating conditions of the WWTP. Second, the online algorithm of parameters incorporating adaptive learning rates is designed to train the OG-PRWNN to meet the control requirements. In addition, the stability of the OG-PRWNN controller is analyzed by the Lyapunov stability theorem. Finally, the performance of the controller is verified by the benchmark simulation model of WWTP. Simulation results show that the OG-PRWNN controller can obtain better control accuracy.