Fouling Prediction of Heat Exchanger Based on Genetic Optimal SVM Algorithm

The fouling of heat exchanger is an unsolved difficult problem in all over the world. The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. The application of Support Vector Machine (SVM) based on Statistical Learning Theory to predict heat exchanger fouling was introduced, and the Genetic Algorithm (GA) was applied for optimizing the parameters of the support vector machine. One of the experiment databases of Heat exchanger fouling was used for prediction; the choosing of the parameters was also discussed. The simulations show that the precision of the GA-SVM is better than the standard SVM in certain experiment condition. The prediction model based on GA-SVM offers another method for the research of heat exchanger fouling.