Abstract
This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important process engineering problems. The method incorporates hybrid support vector regression and differential evolution technique (SVR-DE) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
M. Agarwal, A.M. Jade, V.K. Jayaraman and B.D. Kulkarni, Chem. Eng. Prog., 57 (2003).
B.V. Babu and K.K.N. Sastry, Comp. Chem. Eng., 23, 327 (1999).
C. Burges, Data Mining and Knowledge Discovery, 2(2), 1 (1998).
H. M. Cartwright and R.A. Long, Ind. Eng. Chem. Res., 32, 2706 (1993).
V. Cherkassky and F. Mulier, Learning from data: Concepts theory and methods, John Wiley & Sons (1998).
V. Cherkassky and Y. Ma, Practical selection of SVM parameters and noise estimation for SVM regression, Neurocomputing (special issue on SVM) (2002).
P. Doron, D. Granica and D. Barnea, Int. J. of Multiphase Flow, 13, 535 (1987).
T. F. Edgar and D. M. Himmelblau, Optimization of chemical processes, McGraw-Hill, Singapore (1989).
A. Garrard and E. S. Fraga, Comput. Chem. Eng., 22, 1837 (1988).
K. C. Ghanta, Studies on rhelogical and transport characteristic of solid liquid suspension in pipeline, PhD Thesis, IIT Kharagpur (1996).
R.G. Gillies, K. B. Hill, M. J. Mckibben and C.A. Shook, Powder Technology, 104, 269 (1999).
R.G. Gillies and C.A. Shook The Can. J. Chem. Eng., 78, 709 (2000).
R.G. Gillies, C.A. Shook and K.C. Wilson, The Canadian Journal of Chemical Engineering, 69, 173 (1991).
G.W. Govier and K. Aziz, The flow of complex mixtures in pipes, Krieger Publication, Malabar, FL (1982).
T. Hastie, R. Tibshirani and J. Friedman, The elements of statistical learning data mining inference and prediction, Springer (2001).
L. B. Jack and A. K. Nandi, Mech. Sys. Sig. Proc., 16, 372 (2002).
D.R. Kaushal and Tomita Yuji, Int. J. Multiphase Flow, 28, 1697 (2002).
D. Mattera and S. Haykin, Support vector machines for dynamic reconstruction of a chaotic system, Advances in Kernel Methods, Support Vector Machine, MIT Press (1999).
D.M. Newitt, J. F. Richardson, M. Abbott and R. B. Turtle, Trans. Inst. Chem. Eng., 33, 93 (1955).
K. Price and R. Storn, Differential evolution, Dr. Dobb’s J., 18–24 (1997).
.C. Roco and C.A. Shook, Powder Technology, 39, 159 (1984).
. C. Roco and C. A. Shook, J. Fluids Eng., 107, 224 (1985).
K.K.N. Sastry, L. Behra and I. J. Nagrath, Differential evolution based fuzzy logic controller for nonlinear process control, Fundamenta Informaticae, Special Issue on Soft Comput. (1998).
B. Scholkopf, J. Burges and A. Smola, Advances in kernel methods: Support vector machine, MIT Press (1999).
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola and R. C. Williamson, Neural Comput., 13, 1443 (2001).
A. Smola, N. Murata, B. Schölkopf and K. Muller, Asymptotically optimal choice of epsilon-loss for support vector machines, Proc. ICANN (1998).
R.M. Turian and T. F. Yuan, AIChE J., 23, 232 (1977).
V. Vapnik, S. Golowich and A. Smola, Adv. in Neural Inform. Proces. Syst., 9, 281 (1996).
V. Vapnik, The nature of statistical learning theory, Springer Verlag, New York (1995).
V. Vapnik, Statistical learning theory, John Wiley, New York (1998).
E. J. Wasp, T.C. Aude, J. P. Kenny, R.H. Seiter and R. B. Jacques, Deposition velocities transition velocities and spatial distribution of solids in slurry pipelines, Proc. Hydro transport 1, BHRA Fluid Engineering, Coventry, UK, paper H42 53–76 (1970).
K.C. Wilson, A unified physically-based analysis of solid-liquid pipeline flow, Proceedings of the 4th International Conference on Hydraulic Transport of Solids, BHRA Fluid Engineering, Cranfield UK Paper A2, 1–16 (1976).
K.C. Wilson and F. J. Pugh, Can. J. Chem. Eng, 66, 721 (1988).
I. Zandi and G. Govatos, Proc. ACSE, J. Hydraul. Div., 93(HY3), 145 (1967).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lahiri, S.K., Ghanta, K.C. Support vector regression with parameter tuning assisted by differential evolution technique: Study on pressure drop of slurry flow in pipeline. Korean J. Chem. Eng. 26, 1175–1185 (2009). https://doi.org/10.1007/s11814-009-0195-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11814-009-0195-6