Research on the Fouling Prediction of Heat Exchanger Based on Support Vector Machine
Lingfang Sun, Yingying Zhang, Xinpeng Zheng, Shanrang Yang, Yukun Qin · 2008
The development of prediction researching on heat exchanger fouling in recent years is reviewed. The application of Support Vector Machine based on Statistical Learning Theory to predict heat exchanger fouling is reported in this paper. We construct a six-inputs and one-output network according to the fouling monitor principle and parameters, the modeling of the SVM programmed with MATLAB, and trained with V-SVR 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 0.3 percent, and better than the RBF. SVM can be used to predict heat exchanger fouling, and has perfect prediction precision. The prediction model based on SVM offers anther method for the research of heat exchanger fouling.