Multiphase flow monitoring in oil pipelines

  • Chris M. Bishop
Chapter

Abstract

Neural networks, and related statistical pattern recognition techniques, appear to be well suited to the solution of a wide range of monitoring and diagnostic problems. In many applications, it is difficult or impossible to perform first-principles modelling of the system under consideration. If, however, sufficiently large quantities of labelled training data can be made available, then a statistical approach becomes feasible.

Keywords

Multiphase Flow Phase Fraction Beam Line Multilayer Perceptron Hide Unit 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    CM Bishop and GD James (1993) Analysis of multiphase flows using dual-energy gamma densitometry and neural networks’, Nuclear Instruments and Methods in Physics Research A327 (1993) 580.ADSGoogle Scholar
  2. [2]
    MS Abouelwafa and EJM Kendall, J. Phys. E: Sci. Instrum., 13 (1980) 341.ADSCrossRefGoogle Scholar
  3. [3]
    K Rafa, T Tomoda and R Ridley Proc. Energy Sources Technology Conference and Exhibition, ASME (1989) 89-Pet-7.Google Scholar
  4. [4]
    M S Beck and A Plaskowski, Cross Correlation Flowmeters, (Adam Hilger, Bristol, 1987).Google Scholar
  5. [5]
    JS Watt, HW Zastawny, MD Rebgetz, PE Hartley and WK Ellis, Nuclear Techniques in the Exploration and Exploitation of Energy and Mineral Resources, (IAEA, Vienna, 1991) p. 481.Google Scholar
  6. [6]
    CM Bishop, invited review article: Neural Networks and their Applications to be published in Reviews of Scientific Instruments (1993).Google Scholar
  7. [7]
    CM Bishop, Neural Networks for Pattern Recognition, (Oxford University Press, 1994).Google Scholar
  8. [8]
    CM Bishop ‘Exact calculation of the Hessian matrix for the multilayer perceptron’ Neural Computation, 4 (1992) 494–501.CrossRefGoogle Scholar
  9. [9]
    CM Bishop ‘Improving the generalization properties of radial basis function neural networks’ Neural Computation 3 (1991) 579–588.CrossRefGoogle Scholar
  10. [10]
    C M Bishop ‘Curvature Driven Smoothing: A Learning Algorithm for Feedforward Networks’, to be published in IEEE Transactions on Neural Networks (1993).Google Scholar
  11. [11]
    A R Webb (1991) ‘Functional Approximation by Feedforward Networks: A Least-squares Approach to Generalisation’ RSRE Memorandum 4453, R.S.R.E., St. Andrews Road, Malvern, Worcs, WR14 3PS, U.K.Google Scholar
  12. [12]
    R O Duda and P Hart, Pattern Classification and Scene Analysis (John Wiley, New York, 1973).MATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Chris M. Bishop
    • 1
  1. 1.Neural Computing Research Group, Dept. of Computer Science and Applied MathematicsAston UniversityBirminghamUK

Personalised recommendations