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Hybrid Support Vector Regression and Genetic Algorithm Technique – A Novel Approach in Process Modeling
Chemical Product and Process Modeling (2009)
  • sandip k lahiri
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 critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.
Keywords
  • support vector regression,
  • genetic algorithm,
  • slurry critical velocity
Disciplines
Publication Date
March 28, 2009
Citation Information
sandip k lahiri. "Hybrid Support Vector Regression and Genetic Algorithm Technique – A Novel Approach in Process Modeling" Chemical Product and Process Modeling Vol. 4 Iss. 1, Article-4 (2009)
Available at: http://works.bepress.com/sandip_lahiri/16/