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.

Recommended Citation

Lahiri, Sandip K. and Ghanta, Kartik Chandra (2009) "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.
DOI: 10.2202/1934-2659.1329
Available at: http://www.bepress.com/cppm/vol4/iss1/4