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Knowledge-Based Multiclass Support Vector Machines Applied to Vertical Two-Phase Flow

  • Olutayo O. Oladunni
  • Theodore B. Trafalis
  • Dimitrios V. Papavassiliou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)

Abstract

We present a knowledge-based linear multi-classification model for vertical two-phase flow regimes in pipes with the transition equations of McQuillan & Whalley [1] used as prior knowledge. Using published experimental data for gas-liquid vertical two-phase flows, and expert domain knowledge of the two-phase flow regime transitions, the goal of the model is to identify the transition region between different flow regimes. The prior knowledge is in the form of polyhedral sets belonging to one or more classes. The resulting formulation leads to a Tikhonov regularization problem that can be solved using matrix or iterative methods.

Keywords

Support Vector Machine Flow Regime Bubble Flow Annular Flow Liquid Holdup 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Olutayo O. Oladunni
    • 1
  • Theodore B. Trafalis
    • 1
  • Dimitrios V. Papavassiliou
    • 2
  1. 1.School of Industrial EngineeringThe University of OklahomaNormanUSA
  2. 2.School of Chemical, Biological, and Materials EngineeringThe University of OklahomaNormanUSA

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