IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5285-d868093.html
   My bibliography  Save this article

Review of Selected Advances in Electrical Capacitance Volume Tomography for Multiphase Flow Monitoring

Author

Listed:
  • Rafiul K. Rasel

    (ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43212, USA)

  • Shah M. Chowdhury

    (ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43212, USA)

  • Qussai M. Marashdeh

    (Tech4Imaging LLC, Columbus, OH 43220, USA)

  • Fernando L. Teixeira

    (ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43212, USA)

Abstract

Electrical Capacitance Volume Tomography (ECVT) has emerged as an attractive technology for addressing instrumentation requirements in various energy-related multiphase flow systems. ECVT can monitor multiple flow conditions and reconstruct real-time 3D images from capacitance measurements using a large set of electrode plates placed around the processes column enclosing the sensed flow system. ECVT is non-intrusive and allows the measurement of changes in mutual capacitance between all possible plate pair combinations. The objective of this paper is to provide a comprehensive review of recent advances in ECVT, enabling robust monitoring of multiphase flows, especially water-containing multiphase flows.

Suggested Citation

  • Rafiul K. Rasel & Shah M. Chowdhury & Qussai M. Marashdeh & Fernando L. Teixeira, 2022. "Review of Selected Advances in Electrical Capacitance Volume Tomography for Multiphase Flow Monitoring," Energies, MDPI, vol. 15(14), pages 1-22, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5285-:d:868093
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5285/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/14/5285/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bin Yang & Xin Zhu & Boan Wei & Minzhang Liu & Yifan Li & Zhihan Lv & Faming Wang, 2023. "Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review," Energies, MDPI, vol. 16(3), pages 1-24, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5285-:d:868093. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.