Knowledge-Based multiclass support vector machines applied to vertical two-phase flow

Authors:
Olutayo O. Oladunni;Theodore B. Trafalis;Dimitrios V. Papavassiliou
Affiliations:
School of Industrial Engineering, The University of Oklahoma, Norman, OK;School of Industrial Engineering, The University of Oklahoma, Norman, OK;School of Chemical, Biological, and Materials Engineering, The University of Oklahoma, Norman, OK
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 2
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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.