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Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net

Author

Listed:
  • Bartosz Przysucha

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Dariusz Wójcik

    (WSEI University, 20-209 Lublin, Poland
    Research & Development Center, Netrix S.A., 20-704 Lublin, Poland)

  • Tomasz Rymarczyk

    (WSEI University, 20-209 Lublin, Poland
    Research & Development Center, Netrix S.A., 20-704 Lublin, Poland)

  • Krzysztof Król

    (WSEI University, 20-209 Lublin, Poland
    Research & Development Center, Netrix S.A., 20-704 Lublin, Poland)

  • Edward Kozłowski

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

  • Marcin Gąsior

    (Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland)

Abstract

The main goal of this paper is to research and analyze the problem of image reconstruction performance using machine learning methods in 3D electrical capacitance tomography (ECT) and electrical impedance tomography (EIT) by comparing the areas inside the tank to determine the finite elements for which one of the method reconstructions is more effective. The research was conducted on 5000 simulated cases, which ranged from one to five inclusions generated for a cylindrical tank. The authors first used the elastic net learning method to perform the reconstruction and then proposed a method for testing the effectiveness of reconstruction. Based on this approach, the reconstructions obtained by each method were compared, and the areas within the object were identified. Finally, the results obtained from the simulation tests were verified on real measurements made with two types of tomographs. It was found that areas closer to the edge of the tank were more effectively reconstructed by EIT, while ECT reconstructed areas closer to the center of the tank. Extensive analysis of the inclusions makes it possible to use this measurement for energy optimization of industrial processes and biogas plant operation.

Suggested Citation

  • Bartosz Przysucha & Dariusz Wójcik & Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Marcin Gąsior, 2023. "Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net," Energies, MDPI, vol. 16(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1490-:d:1055881
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    References listed on IDEAS

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    1. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
    2. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jan Sikora & Tomasz Wołowiec & Paweł Tchórzewski & Stanisław Skowron, 2021. "Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls," Energies, MDPI, vol. 14(10), pages 1-22, May.
    3. Dariusz Majerek & Tomasz Rymarczyk & Dariusz Wójcik & Edward Kozłowski & Magda Rzemieniak & Janusz Gudowski & Konrad Gauda, 2021. "Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography," Energies, MDPI, vol. 14(22), pages 1-19, November.
    4. Adam Ryszard Zywica & Marcin Ziolkowski & Stanislaw Gratkowski, 2020. "Detailed Analytical Approach to Solve the Magnetoacoustic Tomography with Magnetic Induction (MAT-MI) Problem for Three-Layer Objects," Energies, MDPI, vol. 13(24), pages 1-17, December.
    5. Grzegorz Kłosowski & Tomasz Rymarczyk & Konrad Niderla & Magdalena Rzemieniak & Artur Dmowski & Michał Maj, 2021. "Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography," Energies, MDPI, vol. 14(21), pages 1-20, November.
    6. Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Tomasz Wołowiec & Marta Cholewa-Wiktor & Piotr Bednarczuk, 2021. "Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks," Energies, MDPI, vol. 14(23), pages 1-35, December.
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