Modeling and Experimental Verification of Solid-Liquid Two-Phase Flow Long-Distance Pipeline Friction Drag Loss Based on LS-SVM

Abstract:

The mechanism model needs to assume a lot of prerequisites, but it is lack of these conditions in the solid-liquid two-phase flow in long-distance pipeline, therefore there will be a deviation between the friction drag loss of mechanism model and the actual value. This paper adopted the least square support vector machine (LS-SVM) to fix the value of the mechanism model, and increase the prediction accuracy. Meanwhile, for improving real-time online LS-SVM performance, introducing the local LS-SVM. The experiment result shows, LS-SVM and local LS-SVM greatly improved the forecast accuracy, compared with the mechanism model correction.

• Periodical: Advanced Materials Research (Volume 485)
• Pages: 548-553
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.485.548
• Online since: February 2012
• Authors: Jian De Wu, Xu Yi Yuan, Yu Gang Fan, Xiao Dong Wang
• Keywords: Adjustment Model, Friction Drag, Least Squares Support Vector Machines (Ls-SVM), Long-Distance Pipeline, Solid-Liquid Two-Phase Flow

References

[1] Junxiang Fei. Slurry and granular material transport hydraulics, Beijing: Tsinghua University Press(1994).

[2] Wasp E, J, etc. Slurry pipeline transportation of solid materials, translation of the Yellow River Conservancy Commission, Beijing: Water Conservancy and Electric Power Press(1980).

[3] Defang Shu, Xihuan Sun, Yongye Li, etc. Research Situation of Slurry Pipeline Pressure Loss[J]. Technology and Application, 1: 38-40, (2011).

[4] Xiangji Deng, Fusheng Ni, Rongmin Luo. Two kinds of calculation model of resistance losses in pipeline transport sand [J]. Hehai University Changzhou Campus Journal, 19(1): 54-59, (2005).

[5] Guangwen Chen, Desheng Gu. Resistance Loss Calculation of Slurry Horizontal Pipeline[J]. Guizhou University Journal (Natural Science Edition), 25 (2): 162-166, (1994).

[6] Guangwen Chen. Slurry Pipeline flow characteristics and resistance characteristics analysis [J]. Non-ferrous Metals Journal, 46 (1): 15-19, (1994).

[7] Lijuan Zhao, Fusheng Ni. Wasp method of Fine sediment pipeline pressure loss calculation[J]. Hehai University Changzhou Campus Journal, 20 (3) : 62-64, (2006).

[8] J.A.K. Suykens Vandewalle J. Chaos control using least-squares support vector machines[J]. International Journal of circuit theory and applications, 27: 605-615, (1999).

DOI: 10.1002/(sici)1097-007x(199911/12)27:6<605::aid-cta86>3.0.co;2-z

[9] J.A.K. Suykens, J. De Brabanter, L. Lukas, et al. Weighted least squares support vector machines: robustness and sparse approximation[J]. Neurocomputing , 48 : 85–105, (2002).

DOI: 10.1016/s0925-2312(01)00644-0

[10] Yan Weiwu, Zhu Hongdong, Shao Huihe. Soft Sensor Modeling Based on Support Vector Machines[J]. Journal of System Simulation (China), 15(10): 1494-1496, (2003).

[11] Liu Han, Liu Ding, Zheng Gang, Liang Yanming, Song Nianlong. Natural Gas Load Forecasting Based on Least Squares Support Vector Machine[J]. Journal of Chmical Industry and Engineering (China), 55(5): 828-832, (2004).

DOI: 10.1109/icmlc.2004.1378571