Advertisement

An Application of Neural Networks for Image Reconstruction in Electrical Capacitance Tomography Applied to Oil Industry

  • Norberto Flores
  • Ángel Kuri-Morales
  • Carlos Gamio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

The article presents a possible solution to a typical tomographic images generation problem from data of an industrial process located in a pipeline or vessel. These data are capacitance measurements obtained non-invasively according to the well known ECT technique (Electrical Capacitance Tomography). Every 313 pixels image frame is derived from 66 capacitance measurements sampled from the real time process. The neural nets have been trained using the backpropagation algorithm where training samples have been created synthetically from a computational model of the real ECT sensor. To create the image 313 neuronal nets, each with 66 inputs and one output, are used in parallel. The resulting image is finally filtered and displayed. The different ECT system stages along with the different tests performed with synthetic and real data are reported. We show that the image resulting from our method is a faster and more precise practical alternative to previously reported ones.

Keywords

Image Reconstruction Algorithm Electrical Capacitance Tomography Guard Electrode Permittivity Distribution Electrical Capacitance Tomography System 
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

  1. 1.
    Beck, M., Byars, M., Dyakowski, T., Waterfall, R., He, R., Wang, S.J., Yang, W.Q.: Principles and industrial applications of electrical capacitance tomography. Measurement + Control 30, 197–200 (1997)Google Scholar
  2. 2.
    Dyakowski, T., Jeanmeure, L.F., Jaworski, A.J.: Applications of electrical tomography for gas-solids and liquid-solids flows – A review. Powder technol 112, 174–192 (2000)CrossRefGoogle Scholar
  3. 3.
    Gamio, J.C., Ortiz-Alemán, C.: An interpretation of the Linear Back-Projection Algorithm Used in Capacitance Tomography. In: 3rd World Congress on Industrial Process Tomography. Bannf, pp. 427–432 (2003)Google Scholar
  4. 4.
    Hammer, E.A., Johansen, G.: A Process tomography in the oil industry – State of the art and future possibilities. Measurement + Control 3, 11–14 (1997)Google Scholar
  5. 5.
    Hecht-Nielsen, R.: Kolmogorov’s mapping neural network existence theorem. In: First IEEE International Conference on Neural Networks, vol. 30, pp. 212–216 (1987)Google Scholar
  6. 6.
    Huang, S.M., Xie, C.G., Thorn, R., Snowen, D., Beck, M.S.: Design of sensor electronics for electrical capacitance tomography. IEE Proc. G. 139, 83–88 (1992)Google Scholar
  7. 7.
    Khan, S.H., y Abdullah, F.: Finite element modeling of multielectrode capacitive systems for flow imaging. IEE Proceedings-G 140(3), 216–222 (1993)Google Scholar
  8. 8.
    Nooralahiyan, A.Y., Hoyle, B., Bailey, N.: Neural network for pattern association in electrical capacitance tomography. IEE Proc.-Circuits Devices Syst. 141(6), 517–521 (1994)CrossRefGoogle Scholar
  9. 9.
    Sun, T.D., Mudde, R., Schouten, J.C., Scarlett, B., van den Bleek, C.M.: Image reconstruction of an electrical capacitance tomography system using an artificial neural network. In: 3rd World Congress on Industrial Process Tomography, Buxton, pp. 174–180 (1999)Google Scholar
  10. 10.
    Yang, W.Q., Peng, L.: Image reconstruction algorithms for electrical capacitance tomography – Review Article. Measurement Science and Technology 13, R1–R13 (2003)CrossRefGoogle Scholar
  11. 11.
    Warsito, W., Fan, L.-S.: Neural network based multi-criterion optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography. Measurement Science and Technology 12, 2198–2210 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Norberto Flores
    • 1
  • Ángel Kuri-Morales
    • 2
  • Carlos Gamio
    • 3
  1. 1.Instituto Mexicano del PetróleoSan Bartolo Atepehuacan, Distrito FederalMéxico
  2. 2.Instituto Tecnológico Autónomo de MéxicoProgreso Tizapán, Distrito FederalMéxico
  3. 3.Glasgow Caledonian UniversityGlasgowUnited Kingdom

Personalised recommendations