Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model

摘要/Abstract

摘要： This paper describes a robust support vector regression(SVR) methodology,which can offer superior performance for important process engineering problems.The method incorporates hybrid support vector regression and genetic algorithm technique(SVR-GA) for efficient tuning of SVR meta-parameters.The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow.Acomparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions,physical properties,and pipe diameters.

关键词: support vector regression, genetic algorithm, slurry pressure drop

Abstract: This paper describes a robust support vector regression(SVR) methodology,which can offer superior performance for important process engineering problems.The method incorporates hybrid support vector regression and genetic algorithm technique(SVR-GA) for efficient tuning of SVR meta-parameters.The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow.Acomparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions,physical properties,and pipe diameters.

Key words: support vector regression, genetic algorithm, slurry pressure drop

S. K. Lahiri, K. C. Ghanta. Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model[J]. , 2008, 16(6): 841-848.

参考文献

1 Vapnik, V., Golowich, S., Smola, A., “Support vector method for function approximation regression estimation and signal processing”, Adv. Neural Inform. Proces. Syst., 9, 281-287(1996).
2 Vapnik, V., The Nature of Statistical Learning Theory, Springer Verlag, New York(1995).
3 Vapnik, V., Statistical Learning Theory, John Wiley, New York(1998).
4 Jack, L.B., Nandi, A.K., “Fault detection using support vector machines and artificial neural networks augmented by genetic algorithms”, Mech. Sys. Sig. Proc., 16, 372-390(2002).
5 Agarwal, M., Jade, A.M., Jayaraman, V.K., Kulkarni, B.D., “Support vector machines:A useful tool for process engineering applications”, Chem. Eng. Prog, 23, 57-62(2003).
6 Edgar, T.F., Himmelblau, D.M., Optimization of Chemical Processes, McGraw-Hill, Singapore(1989).
7 Cartwright, H.M., Long, R.A., “Simultaneous optimization of chemical flowshop sequencing and topology using genetic algorithms”, Ind. Eng. Chem. Res., 32, 2706-2711(1993).
8 Garrard, A., Fraga, E.S., “Mass exchange network synthesis using genetic algorithms”, Comput. Chem. Eng., 22, 1837-1839(1988).
9 Goldberg, D.E., Genetic Algorithms in Search Optimization and Machine Learning, Addison–Wesley, New York(1989).
10 Hanagandi, V., Ploehn H., Nikolaou, M., “Solution of the self consistent field model for polymer adsorption by genetic algorithms”, Chem. Eng. Sci., 51, 1071-1077(1996).
11 Polifke, W., Geng, W., Dobbeling, K., “Optimization of rate coefficients for simplified reaction mechanisms with genetic algorithms”, Combust. Flame, 113, 119-121(1988).
12 Cherkassky, V., Mulier, F., Learning from Data:Concepts Theory and Methods, John Wiley & Sons(1998).
13 Cherkassky, V., Shao, X., Mulier, F., Vapnik, V., “Model complexity control for regression using VC generalization bounds”, IEEE Transaction on Neural Networks, 10(5), 1075-1089(1999)
14 Babu, B.V., Sastry, K.K.N., “Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation”, Comput. Chem. Eng., 23, 327-339(1999).
15 Wilson, W.E., “Mechanics of flow with non collo dal inert solids”, Trans. ASCE, 107, 1576-1594(1942).
16 Wasp, E.J, Kenny, J.P., Gandhi, R.L., Solid Liquid Flow-Slurry Pipeline Transportation, Trans. Tech. Publ., Rockport, Mass., USA(1977).
17 Hsu, F.L., “Flow of non-colloidal slurries in pipelines:Experiments and numerical simulations”, Ph. D. Thesis, University of Illinois, Chaicago(1987).
18 Ghanta, K.C., “Studies on rhelogical and transport characteristic of solid liquid suspension in pipeline”, Ph. D. Thesis, IIT, Kharagpur, India(1996).
19 Gillies, R.G., Hill, K.B., Mckibben, M.J., Shook, C.A., “Solids transport by laminar Newtonian flows”, Powder Technol., 104, 269–277(1999).
20 Durand, R., Condolios, E., “Experimental investigation of the transport of solids in pipes”, Journal of Hydrauliques Computation, 10, 29-55(1952).(in France)
21 Newitt, D.M., Richardson, J.F., Abbott, M., Turtle, R.B., "Hydraulic conveying of solids in horizontal pipes”, Trans. Instn Chem. Engrs, 33, 93-113(1955).
22 Zandi, I., Govatos, G., “Heterogeneous flow of solids in pipelines”, In:Proc. ACSE J. Hydraul. Div., 93(HY3), 145-159(1967).
23 Shook, C.A., Daniel, S.M., Scott, J.A., Holgate, J.P., “ Flow of suspensions in pipelines, two mechanism of particle suspension”, Can J. Chem. Eng., 46, 238-244(1968).
24 Schriek, W., Smith, L.G., Haas, D.B., Husband, W.H.W., “Experimental studies on the hydraulic transport of coal”, Report E73-17, Saskatchewan business magazine, Saskatchewan Research Council, Saskatoon, Canada(1973).
25 Scarlett, B., Grimley, A., “Particle velocity and concentration profiles during hydraulic transport in a circular pipe”, In:Proc. Hydrotransport 3, BHRA-Fluid Engineering, Cranfield,UK., paper D3, 23(1974).
26 Turian, R.M., Yuan, T.F., “Flow of slurries in pipelines”, AIChE J., 23, 232-243(1977).
27 Govier, G.W., Aziz, K., The Flow of Complex Mixtures in Pipes, Krieger Publication, Malabar, FL(1982).
28 Gillies, R., Husband, W.H.W., Small, M., Shook, C.A., “Concentration distribution effects in a fine particle slurry”, In:Proc. 8th Technical Conference, Slurry Transport Association, 131-142(1983).
29 Roco, M.C., Shook, C.A., “Computational methods for coal slurry pipeline with heterogeneous size distribution”, Powder Technol., 39, 159-176(1984).
30 Roco, M.C., Shook C.A., “Turbulent flow of incompressible mixtures”, J. Fluids Eng., 107, 224-231(1985).
31 Ma, T.W., “Stability, rheology and flow in pipes, bends, fittings, valves and venturimeters of concentrated non-Newtonian suspensions”, Ph. D. Thesis, University of Illinois, Chaicago(1987).
32 Doron, P., Granica, D., Barnea, D., “Slurry flow in horizontal pipes-experimental and modeling”, Int. J. Multiphase Flow, 13, 535-547(1987).
33 Schaan, J., Robert, J.S., Gillies, R.G., Shook, C.A., “The effect of particle shape on pipeline friction for Newtonian slurries of fine particles”, Can. J. Chem. Eng., 78, 717-725(2000).
34 Kaushal, D.R., Tomita, Y., “Solids concentration profiles and critical velocity in pipeline flow of multisized particulate slurries”, Int. J. Multiphase Flow, 28, 1697-1717(2002).

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