Articles & Issues
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
-
Received April 18, 2008
Accepted February 1, 2009
-
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © KIChE. All rights reserved.
All issues
Support vector regression with parameter tuning assisted by differential evolution technique: Study on pressure drop of slurry flow in pipeline
Department of Chemical Engineering, NIT, Durgapur, West Bengal, India
sk_lahiri@hotmail.com
Korean Journal of Chemical Engineering, September 2009, 26(5), 1175-1185(11), 10.1007/s11814-009-0195-6
Download PDF
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_x000D_
conditions, physical properties, and pipe diameters.
References
Agarwal M, Jade AM, Jayaraman VK, Kulkarni BD, Chem. Eng. Prog., 57 (2003)
Babu BV, Sastry KKN, Comput. Chem. Eng., 23(3), 327 (1999)
Burges C, Data Mining and Knowledge Discovery, 2, 1 (1998)
Cartwright HM, Long RA, Ind. Eng. Chem. Res., 32, 2706 (1993)
Cherkassky V, Mulier F, Learning from data: Concepts theory and methods, John Wiley & Sons (1998)
Cherkassky V, Ma Y, Practical selection of SVM parameters and noise estimation for SVM regression, Neurocomputing (special issue on SVM) (2002)
Doron P, Granica D, Barnea D, Int. J. of Multiphase Flow, 13, 535 (1987)
Edgar TF, Himmelblau DM, Optimization of chemical processes, McGraw- Hill, Singapore (1989)
Garrard A, Fraga ES, Comput. Chem. Eng., 22, 1837 (1988)
Ghanta KC, Studies on rhelogical and transport characteristic of solid liquid suspension in pipeline, PhD Thesis, IIT Kharagpur (1996)
Gillies RG, Hill KB, McKibben MJ, Shook CA, Powder Technol., 104(3), 269 (1999)
Gillies RG, Shook CA, Can. J. Chem. Eng., 78(4), 709 (2000)
Gillies RG, Shook CA, Wilson KC, The Canadian Journal of Chemical Engineering, 69, 173 (1991)
Govier GW, Aziz K, The flow of complex mixtures in pipes, Krieger Publication, Malabar, FL (1982)
Hastie T, Tibshirani R, Friedman J, The elements of statistical learning data mining inference and prediction, Springer (2001)
Jack LB, Nandi AK, Mech. Sys. Sig. Proc., 16, 372 (2002)
Kaushal DR, Yuji T, Int. J. Multiphase Flow, 28, 1697 (2002)
Mattera D, Haykin S, Support vector machines for dynamic reconstruction of a chaotic system, Advances in Kernel Methods, Support Vector Machine, MIT Press (1999)
Newitt DM, Richardson JF, Abbott M, Turtle RB, Trans. Inst. Chem. Eng., 33, 93 (1955)
Price K, Storn R, Differential evolution, Dr. Dobb’s J., 18-24 (1997)
Roco MC, Shook CA, Powder Technology, 39, 159 (1984)
Roco MC, Shook CA, J. Fluids Eng., 107, 224 (1985)
Sastry KKN, Behra L, Nagrath IJ, Differential evolution based fuzzy logic controller for nonlinear process control, Fundamenta Informaticae, Special Issue on Soft Comput. (1998)
Scholkopf B, Burges J, Smola A, Advances in kernel methods: Support vector machine, MIT Press (1999)
Scholkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC, Neural Comput., 13, 1443 (2001)
Smola A, Murata N, Scholkopf B, Muller K, Asymptotically optimal choice of epsilon-loss for support vector machines, Proc. ICANN (1998)
Turian RM, Yuan TF, AIChE J., 23, 232 (1977)
Vapnik V, Golowich S, Smola A, Adv. in Neural Inform. Proces. Syst., 9, 281 (1996)
Vapnik V, The nature of statistical learning theory, Springer Verlag, New York (1995)
Vapnik V, Statistical learning theory, John Wiley, New York (1998)
Wasp EJ, Aude TC, Kenny JP, Seiter RH, Jacques RB, 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)
Wilson KC, 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)
Wilson KC, Pugh FJ, Can. J. Chem. Eng, 66, 721 (1988)
Zandi I, Govatos G, Proc. ACSE, J. Hydraul. Div., 93, 145 (1967)
Babu BV, Sastry KKN, Comput. Chem. Eng., 23(3), 327 (1999)
Burges C, Data Mining and Knowledge Discovery, 2, 1 (1998)
Cartwright HM, Long RA, Ind. Eng. Chem. Res., 32, 2706 (1993)
Cherkassky V, Mulier F, Learning from data: Concepts theory and methods, John Wiley & Sons (1998)
Cherkassky V, Ma Y, Practical selection of SVM parameters and noise estimation for SVM regression, Neurocomputing (special issue on SVM) (2002)
Doron P, Granica D, Barnea D, Int. J. of Multiphase Flow, 13, 535 (1987)
Edgar TF, Himmelblau DM, Optimization of chemical processes, McGraw- Hill, Singapore (1989)
Garrard A, Fraga ES, Comput. Chem. Eng., 22, 1837 (1988)
Ghanta KC, Studies on rhelogical and transport characteristic of solid liquid suspension in pipeline, PhD Thesis, IIT Kharagpur (1996)
Gillies RG, Hill KB, McKibben MJ, Shook CA, Powder Technol., 104(3), 269 (1999)
Gillies RG, Shook CA, Can. J. Chem. Eng., 78(4), 709 (2000)
Gillies RG, Shook CA, Wilson KC, The Canadian Journal of Chemical Engineering, 69, 173 (1991)
Govier GW, Aziz K, The flow of complex mixtures in pipes, Krieger Publication, Malabar, FL (1982)
Hastie T, Tibshirani R, Friedman J, The elements of statistical learning data mining inference and prediction, Springer (2001)
Jack LB, Nandi AK, Mech. Sys. Sig. Proc., 16, 372 (2002)
Kaushal DR, Yuji T, Int. J. Multiphase Flow, 28, 1697 (2002)
Mattera D, Haykin S, Support vector machines for dynamic reconstruction of a chaotic system, Advances in Kernel Methods, Support Vector Machine, MIT Press (1999)
Newitt DM, Richardson JF, Abbott M, Turtle RB, Trans. Inst. Chem. Eng., 33, 93 (1955)
Price K, Storn R, Differential evolution, Dr. Dobb’s J., 18-24 (1997)
Roco MC, Shook CA, Powder Technology, 39, 159 (1984)
Roco MC, Shook CA, J. Fluids Eng., 107, 224 (1985)
Sastry KKN, Behra L, Nagrath IJ, Differential evolution based fuzzy logic controller for nonlinear process control, Fundamenta Informaticae, Special Issue on Soft Comput. (1998)
Scholkopf B, Burges J, Smola A, Advances in kernel methods: Support vector machine, MIT Press (1999)
Scholkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC, Neural Comput., 13, 1443 (2001)
Smola A, Murata N, Scholkopf B, Muller K, Asymptotically optimal choice of epsilon-loss for support vector machines, Proc. ICANN (1998)
Turian RM, Yuan TF, AIChE J., 23, 232 (1977)
Vapnik V, Golowich S, Smola A, Adv. in Neural Inform. Proces. Syst., 9, 281 (1996)
Vapnik V, The nature of statistical learning theory, Springer Verlag, New York (1995)
Vapnik V, Statistical learning theory, John Wiley, New York (1998)
Wasp EJ, Aude TC, Kenny JP, Seiter RH, Jacques RB, 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)
Wilson KC, 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)
Wilson KC, Pugh FJ, Can. J. Chem. Eng, 66, 721 (1988)
Zandi I, Govatos G, Proc. ACSE, J. Hydraul. Div., 93, 145 (1967)
