Research on Optimized RBF Neural Network Based on GA for Sewage Treatment

During the sewage treatment process, the biological reaction pool is able to obtain enough dissolved oxygen by aeration. Micro-organisms in the biological reaction pool rely on dissolved oxygen to decompose organic matter in sewage into inorganic matter, so that the sewage is purified. The control of dissolved oxygen concentration is a complex nonlinear process and it is difficult to establish the mathematical model. This paper is about to build a RBF neural network model to forecast DO concentration with the incoming water quality parameters as the model input. Furthermore, this model is optimized using genetic algorithms. With simulation analysis, the optimize RBF neural network model Based on GA has a better effect than the traditional RBF neural network model. This control strategy can control dissolved oxygen concentration stably, improve processing efficiency, save energy under the premise of the drainage water quality up to standard.