Modeling of wastewater treatment process using recurrent neural network

Wastewater treatment process (WWTP) is a highly nonlinear dynamic process. It is difficult for modeling key parameters of WWTP. In order to measure the parameters, a new recurrent neural network (RNN) with novel topology is proposed in this paper. The proposed RNN is a class of locally recurrent globally feed-forward neural network which consists of static nonlinear and dynamic linear subsystems, and its dynamic properties are realized using neurons with internal feedback. This proposed RNN can be stated that if all neurons in the networks are stable which is guaranteed. Finally, compared with the normal feed forward networks, the experiment results show that this proposed RNN is more efficient in modeling the wastewater treatment system.