Due to the nonlinear and highly time-varying issues of wastewater treatment processes

a kind of multi-variable control method based on the recurrent fuzzy neural network (RFNN) is proposed. The proposed RFNN can obtain self-adaptive control accuracy of operating variables. The controller uses the learning rate on the basis of conventional BP learning algorithm on adaptive learning algorithm and the introduction of momentum to train network parameters

can avoid falling into local optimum network

which improved network control of the system accuracy. Finally

based on the benchmark simulation model (BSM1)

experiments validate the effectiveness of the method that control the dissolved oxygen concentration in the fifth partition and nitrate nitrogen concentration in the second partition. Compared to PID

forward neural network and conventional recurrent neural network

the experimental results show that this control method can improve the adaptive control precision of the system.