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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.

chemical industry and chemical engineering quarterly

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