| Abstrakt: |
Sludge recycle system is an important part of wastewater treatment plants(WWTP), which can ensure the required reactor sludge concentration, maintenance the dynamic balance between secondary sedimentation tanks and sludge reactor sludge concentration. This work proposes development of a Radial Basis Function (RBF) Neural Network model for prediction of the Sludge recycling flowrate, which ultimately affect the Sludge recycling process. Compared with the traditional constant sludge recycle ratio control, new idea is better in response to actual situation. According to analyzing and Evolutionary RBF Neural Network theory, a RBF Neural Network is designed. The data obtained from wastewater treatment were used to train and verify the model. Simulation shows good estimates for the sludge recycling flowrate. So the idea and model is a good way to the sludge recycle flow rate control. It is a meaningful Evolutionary Neural Network application in industry. [ABSTRACT FROM AUTHOR] |
