Predication of sludge recycling system using PCA-WNN

In order to achieve an effective prediction of process performance and accuracy on-line steering of wastewater treatment plants, principal components analysis-Wavelet neural network(PCA-WNN) control model for predicting wastewater treatment plant the sludge recycling flowrate is established based on the theory and methodology of PCA-WNN. Firstly, the paper utilizes kernel principal component analysis method to realize reduce the dimension of the input vectors and orthogonalize the components of the input vectors. Then effluent quality predictive model is built using wavelet neural networks. 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 PCAWNN network application in industry.