Multivariate control based on recurrent wavelet neural network for wastewater treatment process

Abstract

The wastewater treatment process (WWTP), including multiple biochemical reactions, is a coupled and dynamic process. Thus, it is a challenge to achieve precise control of the WWTP. In order to address this issue, the self-organizing recurrent wavelet neural network controllers combined with the input mechanism (ISRWNNs) are proposed. First, the joint input mechanism is established for the coupling of dissolved oxygen (DO) and nitrate nitrogen (NO). Unlike the common multi-controller, the input of the proposed method takes into account both DO and NO errors to solve the coupling problem. Then, the self-organization algorithm of controller is proposed to automatically adjust the structure of the controllers for the dynamicity of WWTP. Furthermore, the stability of ISRWNN is analyzed through the Lyapunov stability theorem. Finally, the experimental results show that the proposed ISRWNN can obtain good control results of WWTP.