Research on the fouling prediction of heat exchanger based on wavelet neural network

The application of wavelet neural network based on Levenberg-Marquardt Optimization to predict heat exchanger fouling is reported in this paper. We construct a 6-6-1 network according to the fouling monitor principle and parameters, the modeling of the wavelet neural network programmed with MATLAB, and trained with Levenberg-Marquarde Optimization algorithm, all training data came from the Automatic Dynamic Simulator of Fouling and input the network after normalized processing and reclassification. Simulations show that the relative error of fouling prediction is less than 0.71 percent, and wavelet neural network can be used to predict heat exchanger fouling, and has the rapid convergence rate and perfect prediction precision, the Levenberg-Marquarde Optimization algorithm can also improve convergence rate of wavelet neural network to a certain extent.