基于SVR-GA模型的浆态管流压力差的预测(英文)

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.