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

Abstract

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.

Key words: support vector regression, genetic algorithm, slurry pressure drop

摘要： 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.

关键词: support vector regression, genetic algorithm, slurry pressure drop

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.

References

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