A GPRFNN-based control system for wastewater treatment process

As a nonlinear and time-varying complex dynamic system, wastewater treatment process (WWTP) is difficult to be controlled. In this paper, to control the dissolved oxygen (DO) concentration in a WWTP, a growing and pruning recurrent fuzzy neural network (GPRFNN)-based control system is proposed which contains RFNN controllers and RFNN identifier. The identifier is used to model the WWTP with an adaptive algorithm to afford model information for the controllers, while the controllers are designed to adjust the control variables to make the WWTP run smoothly. Furthermore, the structure of the RFNN is self-organized to keep the output steady in structural adjustment phase, which is also theoretically proved. Finally, the control performance of the proposed system is shown by simulation results.