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

S. K. Lahiri; K. C. Ghanta

doi:10.1016/S1004-9541(09)60003-3

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

Updated：2026-01-06

- Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model
- Chinese Journal of Chemical Engineering Vol. 16, Issue 6, Pages: 841-848(2008)
- Affiliations：
  
  Department of Chemical Engineering, NIT, Durgapur, West Bengal,India
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(09)60003-3
  CLC：
- Published：2008
- Accepted：
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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.
DOI：

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. DOI： 10.1016/S1004-9541(09)60003-3.

摘要

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.

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