| 基于SVR-GA模型的浆态管流压力差的预测 |
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| 引用本文： | S.K. Lahiri,K.C. Ghanta. 基于SVR-GA模型的浆态管流压力差的预测[J]. 中国化学工程学报, 2008, 16(6): 841-848. DOI: 10.1016/S1004-9541(09)60003-3 |
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| 作者姓名： | S.K. Lahiri K.C. Ghanta |
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| 作者单位： | Department of Chemical Engineering, NIT, Durgapur, West Bengal, India |
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| 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. A comparison with selected correlations in the lit- erature 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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| 关 键 词： | support vector regression genetic algorithm slurry pressure drop |
| 收稿时间： | 2008-04-18 |
| 修稿时间： | 2008-4-18 |
Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model |
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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]. Chinese Journal of Chemical Engineering, 2008, 16(6): 841-848. DOI: 10.1016/S1004-9541(09)60003-3 |
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| Authors: | S.K. Lahiri K.C. Ghanta |
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| Affiliation: | Department of Chemical Engineering, NIT, Durgapur, West Bengal, India |
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| 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. A comparison 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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| Keywords: | support vector regression genetic algorithm slurry pressure drop
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