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Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

Author

Listed:
  • Abdulrahman Basahel

    (Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia)

  • Mohammad Amir Sattari

    (Friedrich Schiller University Jena, Fürstengraben 1, 07743 Jena, Germany)

  • Osman Taylan

    (Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia)

  • Ehsan Nazemi

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

Abstract

The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works.

Suggested Citation

  • Abdulrahman Basahel & Mohammad Amir Sattari & Osman Taylan & Ehsan Nazemi, 2021. "Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter," Mathematics, MDPI, vol. 9(11), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1227-:d:563774
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    Citations

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    Cited by:

    1. Jie He & Farong Gao & Jian Wang & Qiuxuan Wu & Qizhong Zhang & Weijie Lin, 2022. "A Method Combining Multi-Feature Fusion and Optimized Deep Belief Network for EMG-Based Human Gait Classification," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
    2. 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.
    3. 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.
    4. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Zana Azeez Kakarash & Ali Awadh Al-Qahtani & Abdullah K. Alanazi & Hala H. Alhashimi & Ehsan Eftekhari-Zadeh & Ehsan Nazemi, 2022. "Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime," Mathematics, MDPI, vol. 10(10), pages 1-13, May.

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