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Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

Author

Listed:
  • Abdullah K. Alanazi

    (Department of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Seyed Mehdi Alizadeh

    (Petroleum Engineering Department, Australian College of Kuwait, West Mishref 13015, Kuwait)

  • Karina Shamilyevna Nurgalieva

    (Department of Development and Operation of Oil and Gas Fields, Saint-Petersburg Mining University, 199106 Saint-Petersburg, Russia)

  • John William Grimaldo Guerrero

    (Department of Energy, Universidad de la Costa, Barranquilla 080001, Colombia)

  • Hala M. Abo-Dief

    (Department of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ehsan Eftekhari-Zadeh

    (Institute of Optics and Quantum Electronics, Friedrich-Schiller-University Jena, Max-Wien-Platz 1, 07743 Jena, Germany)

  • Ehsan Nazemi

    (Imec-Vision Laboratory, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium)

  • Igor M. Narozhnyy

    (Department of Commercialization of Intellectual Activity Resultse Center for Technology Transfer, Mining Oil and Gas Department, RUDN University, 117198 Moscow, Russia)

Abstract

To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages ( GVP s) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP . The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error ( MAE ) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.

Suggested Citation

  • Abdullah K. Alanazi & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & John William Grimaldo Guerrero & Hala M. Abo-Dief & Ehsan Eftekhari-Zadeh & Ehsan Nazemi & Igor M. Narozhnyy, 2021. "Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13622-:d:698743
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    References listed on IDEAS

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    1. Sławomir Francik & Adrian Knapczyk & Artur Knapczyk & Renata Francik, 2020. "Decision Support System for the Production of Miscanthus and Willow Briquettes," Energies, MDPI, vol. 13(6), pages 1-24, March.
    2. Betzabe Ruiz-Morales & Irma Cristina Espitia-Moreno & Victor G. Alfaro-Garcia & Ernesto Leon-Castro, 2021. "Sustainable Development Goals Analysis with Ordered Weighted Average Operators," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
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    Cited by:

    1. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Karina Shamilyevna Nurgalieva & Robert Hanus & Ehsan Nazemi & Igor M. Narozhnyy, 2022. "Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems," Energies, MDPI, vol. 15(6), pages 1-19, March.
    2. Abdulilah Mohammad Mayet & Karina Shamilyevna Nurgalieva & Ali Awadh Al-Qahtani & Igor M. Narozhnyy & Hala H. Alhashim & Ehsan Nazemi & Ilya M. Indrupskiy, 2022. "Proposing a High-Precision Petroleum Pipeline Monitoring System for Identifying the Type and Amount of Oil Products Using Extraction of Frequency Characteristics and a MLP Neural Network," Mathematics, MDPI, vol. 10(16), pages 1-20, August.
    3. Abdullah M. Iliyasu & Dakhkilgova Kamila Bagaudinovna & Ahmed S. Salama & Gholam Hossein Roshani & Kaoru Hirota, 2023. "A Methodology for Analysis and Prediction of Volume Fraction of Two-Phase Flow Using Particle Swarm Optimization and Group Method of Data Handling Neural Network," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    4. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    5. Mohammed Balubaid & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Eftekhari-Zadeh & Ehsan Nazemi & Mohammed Alamoudi, 2022. "Central Nervous System: Overall Considerations Based on Hardware Realization of Digital Spiking Silicon Neurons (DSSNs) and Synaptic Coupling," Mathematics, MDPI, vol. 10(6), pages 1-20, March.

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