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

This paper describes a robust Support Vector regression (SVR) methodology, which can offer a superior performance for important process engineering problems. The method incorporates hybrid support vector regression and a differential evolution technique (SVR-DE) for the efficient tuning of SVR meta parameters. The algorithm has been applied for the prediction of critical velocity of the solid-liquid slurry flow. A comparison with selected correlations in the literature 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.

[1]  Henry A Babcock,et al.  PAPER 8 – HETEROGENEOUS FLOW OF HETEROGENEOUS SOLIDS , 1971 .

[2]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[3]  Robert A. Lordo,et al.  Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.

[4]  Yuji Tomita,et al.  Solids concentration profiles and pressure drop in pipeline flow of multisized particulate slurries , 2002 .

[5]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[6]  Raffi M. Turian,et al.  Flow of slurries in pipelines , 1977 .

[7]  Raffi M. Turian,et al.  The critical velocity in pipeline flow of slurries , 1980 .

[8]  David G. Thomas.  Transport characteristics of suspensions: Part VI. Minimum transport velocity for large particle size suspensions in round horizontal pipes , 1962 .

[9]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[10]  D. Himmelblau,et al.  Optimization of Chemical Processes , 1987 .

[11]  Clifton A. Shook,et al.  Solids transport by laminar Newtonian flows , 1999 .

[12]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[13]  B. Babu,et al.  Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation , 1999 .

[14]  Edward J. Wasp,et al.  Solid Liquid Flow Slurry Pipeline Transportation , 1977 .

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[17]  Vladimir Cherkassky,et al.  Learning from Data: Concepts, Theory, and Methods , 1998 .