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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
McQuillan, K.W., Whalley, P.B.: Flow Patterns in Vertical Two-Phase Flow. Int. J. Multiphase Flow 11, 161–175 (1985)
Mandhane, J.M., Gregory, G.A., Aziz, K.: A Flow Pattern Map for Gas-Liquid Flow in Horizontal Pipes. Int. J. Multiphase Flow 1, 537–553 (1974)
Taitel, Y., Bornea, D., Dukler, A.E.: Modeling Flow Pattern Transitions for Steady Upward Gas-Liquid Flow in Vertical Tubes. AIChE J. 26, 345 (1980)
Taitel, Y., Dukler, A.E.: A Model for Predicting Flow Regime Transitions in Horizontal and Near Horizontal Gas-Liquid Flow. AIChE J. 22, 47 (1976)
Petalas, N., Aziz, K.: A Mechanistic Model for Multiphase Flow in Pipes. In: CIM98-39, Proceedings, 49th Annual Technical Meeting of the Petroleum Society of the CIM, Calgary, Alberta, Canada, June 8-10 (1998)
Osman, E.A.: Artificial Neural Networks Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow. SPE 68219 (March 2000)
Mi, Y., Ishii, M., Tsoukalas, L.H.: Flow Regime Identification Methodology with Neural Networks and Two-Phase Flow Models. Nuclear Engineering and Design 204, 87–100 (2001)
Ternyik, J., Bilgesu, H.I., Mohaghegh, S.: Virtual Measurement in Pipes, Part 2: Liquid Holdup and Flow Pattern Correlations. SPE 30976 (September 1995)
Trafalis, T.B., Oladunni, O., Papavassiliou, D.V.: Two-Phase Flow Regime Identification with a Multi-Classification SVM Model. Industrial & Engineering Chemistry Research 44, 4414–4426 (2005)
Mangasarian, O.L., Shavlik, J.W., Wild, E.W.: Knowledge-Based kernel approximation. Journal of Machine Learning Research 5, 1127–1141 (2004)
Fung, G., Mangasarian, O.L., Shavlik, J.W.: Knowledge-Based support vector machine classifiers. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Neural Information Processing Systems 15 (NIPS 2002), Vancouver, BC, December 10-12, pp. 521–528. MIT Press, Cambridge (2003)
Tikhonov, A.N., Arsenin, V.Y.: Solution of Ill-Posed Problems. Winston, Washington D.C (1977)
Pelckmans, K., Suykens, J.A.K., De Moor, B.: Morozov, Ivanov and Tikhonov regularization based LS-SVMs. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316. Springer, Heidelberg (2004)
Oladunni, O., Trafalis, T.B.: Linear Multi-classification Tikhonov Regularization Knowledge-based Support Vector Machine (L M T R KSVM), Technical Report, School of Industrial Engineering, University of Oklahoma, Norman, Oklahoma (2005)
Santosa, B., Conway, T., Trafalis, T.B.: Knowledge Based-Clustering and Application of Multi-Class SVM for Genes Expression Analysis. Intelligent Engineering Systems through Artificial Neural Networks 12, 391–395 (2002)
Hsu, C.-W., Lin, C.-J.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Lewis, J.M., Lakshmivarahan, S., Dhall, S.: Dynamic Data Assimilation. Cambridge University Press, Cambridge (2006)
MATLAB User’s Guide. The Math-Works, Inc., Natwick, MA 01760 (1994-2003), http://www.mathworks.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oladunni, O.O., Trafalis, T.B., Papavassiliou, D.V. (2006). Knowledge-Based Multiclass Support Vector Machines Applied to Vertical Two-Phase Flow. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_29
Download citation
DOI: https://doi.org/10.1007/11758501_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
Online ISBN: 978-3-540-34380-6
eBook Packages: Computer ScienceComputer Science (R0)