Model identification of water purification system using RBF neural network

The RBF (radial basis function) neural network was studied for the model identification of an ozonation/BAC system. The optimal ozone's dosage and the remain time in carbon tower were analyzed to build the neural network model by which the expected outflow COD_M can be acquired under the inflow COD_M condition. The improved self-organized learning algorithm can assign the centers into appropriate places, and the RBF network's outputs at the sample points fit the experimental data very well. The model of ozonation/BAC system based on the RBF network can describe the relationship among various factors correctly, and a new promising approach to the water purification process is provided.