Research on the fouling prediction of heat exchanger based on Support Vector Machine optimized by Particle Swarm Optimization algorithm

The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. Heat exchanger fouling prediction was introduced based on Support Vector Machine (SVM), and the Particle Swarm Optimization (PSO) 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 PSO-SVM is better than the standard SVM in certain experiment condition and mean relative error is 0.5971%. The prediction model based on PSO-SVM offers another method for the prediction research of heat exchanger fouling.

[1]  Chai Hai-di.  The Methods Study of Fouling Prediction Using Radial Basis Function network , 2006 .

[2]  Xu Zhi.  ANALYTICAL FOULING MODEL BASED ON PROBABILISTIC APPROACH , 2003 .

[3]  Fan Shaosheng.  Gray theory based prediction for condenser fouling , 2007 .

[4]  B. A. Garrett-Price,et al.  Fouling of Heat Exchangers: Characteristics, Costs, Prevention, Control, and Removal , 1985 .

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Yang Shan.  AUTOMATIC ON-LINE MONITOR OF FOULING THERMAL RESISTANCE AND ITS APPLICATION , 2001 .

[7]  Zhou Ze-kui.  Study of periodic fouling prediction method based on fuzzy neural network , 2004 .

[8]  Yingying Zhang,et al.  Research on the fouling prediction of heat exchanger based on wavelet neural network , 2008, 2008 IEEE Conference on Cybernetics and Intelligent Systems.

[9]  T. R. Bott.  Fouling of Heat Exchangers , 1995 .

[10]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[11]  Fan Shao-sheng.  Prediction of Fouling in Condenser Based on Multi-Model Combination , 2005 .

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Stéphane Lecoeuche,et al.  Online fouling detection in electrical circulation heaters using neural networks , 2003 .

[14]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[15]  Inmaculada Arauzo,et al.  Monitoring and prediction of fouling in coal-fired utility boilers using neural networks , 2005 .

[16]  Wu Li-feng.  Application of Grey Theory to Forecasting of Fouling Thermal Resistance , 2004 .

[17]  C Riverol,et al.  Estimation of fouling in a plate heat exchanger through the application of neural networks , 2005 .

[18]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[19]  Fan Shao-sheng.  The Prediction of Fouling in Condenser Based on T-S Model , 2005 .

[20]  Johan A. K. Suykens,et al.  Sparse least squares Support Vector Machine classifiers , 2000, ESANN.

[21]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[22]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[23]  Marappagounder Ramasamy,et al.  Heat exchanger fouling model and preventive maintenance scheduling tool , 2007 .