The Support Vector Regression with the parameter tuning assisted by a differential evolution technique: Study of the critical velocity of a slurry flow in a pipeline | Zendy

Sandip Kumar Lahiri | Zendy; Kartik Chandra Ghanta | Zendy

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The Support Vector Regression with the parameter tuning assisted by a differential evolution technique: Study of the critical velocity of a slurry flow in a pipeline

Author(s) -

Sandip Kumar Lahiri,

Kartik Chandra Ghanta

Publication year - 2008

Publication title -

chemical industry and chemical engineering quarterly

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.189

H-Index - 26

eISSN - 2217-7434

pISSN - 1451-9372

DOI - 10.2298/ciceq0803191l

Subject(s) - support vector machine , differential evolution , pipeline (software) , differential (mechanical device) , slurry , range (aeronautics) , flow (mathematics) , process (computing) , velocity vector , critical ionization velocity , regression , computer science , mathematics , engineering , algorithm , artificial intelligence , mechanics , statistics , mechanical engineering , physics , geometry , environmental engineering , aerospace engineering , operating system

This paper describes a robust Support Vector regression (SVR) methodology, which can offer a superior performance for important process engineering pro- blems. The method incorporates hybrid support vector regression and a diffe- rential evolution technique (SVR-DE) for the efficient tuning of SVR meta para- meters. The algorithm has been applied for the prediction of critical velocity of the solid-liquid slurry flow. A comparison with selected correlations in the lite- rature showed that the developed SVR correlation noticeably improved the prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.

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