Wastewater treatment control method based on adaptive recurrent fuzzy neural network
HAN Gai-tang
QIAO Jun-fei
HAN Hong-gui
Abstract：Due to the nonlinear and highly time-varying issues of wastewater treatment processes, a wastewater treat-ment control method based on adaptive recurrent fuzzy neural network (RFNN) is proposed. Firstly, the adaptive RFNN identifier is used to establish the nonlinear dynamic model of wastewater treatment process. The model can afford the state variable information of wastewater treatment process to RFNN controller, which can ensure the accuracy of manipulated variable is adjusted by controller. Secondly, RFNN identifier and RFNN controller are learning through gradient descent algorithm with an adaptive learning rate, which guarantee the convergence of learning process of RFNN, and a function is constructed by lyapunov theory to prove the convergence of this algorithm. Finally, the simulation experiment carried out based on BSM1 platform. Compared with PID, model predictive control and forward neural network control techniques, the simulation results show that the proposed method can improve obviously the control accuracy of wastewater treatment.
Keywords：wastewater treatmentrecurrent fuzzy neural networkadaptive learning ratebenchmark simulation model 1 (BSM1)
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1252-1258 )