Prediction of wastewater sludge recycle performance using Radial Basis Function Neural Network

Dynamic modelling and simulation is increasingly being employed as an aid in the design and operation of wastewater treatment plants (WWTPs). 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, the 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 COST 624 Simulation Benchmark data is 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 water industry.