Prediction Model of Pressure Distribution under Floating Strips

Shuai Hou; Yu Jia Liu; Fuan Hua; Xin Yue Wang; Zhao Dong Wang; Jian Guo Li; Jia Dong Li; Yong Li; Guo Dong Wang

doi:10.4028/www.scientific.net/MSF.789.560

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HomeMaterials Science ForumMaterials Science Forum Vol. 789Prediction Model of Pressure Distribution under...

Prediction Model of Pressure Distribution under Floating Strips

Abstract:

The accuracy prediction of the strip's upper pressure and lower pressure is very important to the accuracy control of air flotation oven, therefore the aluminum can get high surface quality and improving the product quality. And the pressure prediction is very important factor in air flotation oven. In this paper, pso-lssvm pressure prediction model was established. The experiment was carried out in an experimental air flotation oven and pressure data was collected, then the pso-lssvm model was trained based on the training data. The pso-lssvm pressure model' result was compared with the experimental value. The experimental result shows that the pso-lssvm model can get higher accuracy and is suited to predict pressure distribution.

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Periodical:

Materials Science Forum (Volume 789)

Pages:

560-565

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.789.560

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Online since:

April 2014

Authors:

Shuai Hou*, Yu Jia Liu, Fuan Hua, Xin Yue Wang, Zhao Dong Wang, Jian Guo Li, Jia Dong Li, Yong Li, Guo Dong Wang

Keywords:

Floating Strip, Lssvm, Particle Swarm Optimization (PSO), Pressure Distribution

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References

[1] SU Xiao-li, WANG Ye-ke. Domestic largest air cushion furnace in continuous solution heat treatment line for aluminum alloy strip. Light Alloy Fabrication Technology.vol(39), no 7(2011), pp.29-32.

Google Scholar

[2] Hyun-Ki Cho. Flow-Induced Vibration Of a Web Floating Over a Pressure-Pad Air Bar [D]. Oklahoma State University(2005), pp.62-64.

Google Scholar

[3] Hyunkicho. An Alytical And Computational Study Of The Asymmetry OF Webs Pass Gover. Konkuk University. (1997), pp.1-30.

Google Scholar

[4] Liu Li-xia, Zhuang Yi-qi, Liu Xue-yong. Tax forecasting theory and model based on SVM optimized by PSO. Expert Systems with Applications(2011), pp.116-120.

DOI: 10.1016/j.eswa.2010.06.022

Google Scholar

[5] Liao, R, H. Zheng. Particle swarm optimization-least squares support vector regression based forecasting model On dissolved gases in oil-filled power transformers. Electric Power Systems Research 81(12)(2011), pp.2074-2080.

DOI: 10.1016/j.epsr.2011.07.020

Google Scholar

[6] Cherkassky, V,Y.Ma, Practical selection of SVM parameters and noise estimation for SVM regression .Neural Networks 17(1). (2004), pp.113-126.

DOI: 10.1016/s0893-6080(03)00169-2

Google Scholar

[7] Li-xia, L, Z. Yi-qi, et al. Tax forecasting theory and model based on SVM optimized by PSO. Expert Systems with Applications 38(1)(2011), pp.116-120.

DOI: 10.1016/j.eswa.2010.06.022

Google Scholar