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Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow

Author

Listed:
  • Abdulilah Mohammad Mayet

    (Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia)

  • Tzu-Chia Chen

    (College of Management and Design, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    International College, Krirk University, Bangkok, 3 Ram Inthra Rd, Khwaeng Anusawari, Khet Bang Khen, Krung Thep Maha Nakhon 10220, Thailand)

  • Ijaz Ahmad

    (Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences (UCAS), Shenzhen 518055, China)

  • Elsayed Tag Eldin

    (Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt)

  • Ali Awadh Al-Qahtani

    (Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia)

  • Igor M. Narozhnyy

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

  • John William Grimaldo Guerrero

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

  • Hala H. Alhashim

    (Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

Abstract

Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source ( 241 Am and 133 Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241 Am and 133 Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry.

Suggested Citation

  • Abdulilah Mohammad Mayet & Tzu-Chia Chen & Ijaz Ahmad & Elsayed Tag Eldin & Ali Awadh Al-Qahtani & Igor M. Narozhnyy & John William Grimaldo Guerrero & Hala H. Alhashim, 2022. "Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3544-:d:928330
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    References listed on IDEAS

    as
    1. Abdullah M. Iliyasu & Abdulilah Mohammad Mayet & Robert Hanus & Ahmed A. Abd El-Latif & Ahmed S. Salama, 2022. "Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines," Energies, MDPI, vol. 15(12), pages 1-12, June.
    2. Mohammed Balubaid & Mohammad Amir Sattari & Osman Taylan & Ahmed A. Bakhsh & Ehsan Nazemi, 2021. "Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products," Mathematics, MDPI, vol. 9(24), pages 1-14, December.
    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.
    Full references (including those not matched with items on IDEAS)

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