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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

Author

Listed:
  • Abdullah M. Iliyasu

    (Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan)

  • Dakhkilgova Kamila Bagaudinovna

    (Department of Programming and Infocommunication Technologies, Institute of Mathematics, Physics and Information Technology, Kadyrov Chechen State University, 32 Sheripova Str., Grozny 364907, Russia)

  • Ahmed S. Salama

    (Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

  • Gholam Hossein Roshani

    (Electrical Engineering Department, Kermanshah University of Technology, Kermanshah 6715685420, Iran)

  • Kaoru Hirota

    (School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan
    School of Automation, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Determining the volume percentages of flows passing through the oil transmission lines is one of the most essential problems in the oil, gas, and petrochemical industries. This article proposes a detecting system made of a Pyrex-glass pipe between an X-ray tube and a NaI detector to record the photons. This geometry was modeled using the MCNP version X algorithm. Three liquid-gas two-phase flow regimes named annular, homogeneous, and stratified were simulated in percentages ranging from 5 to 95%. Five time characteristics, three frequency characteristics, and five wavelet characteristics were extracted from the signals obtained from the simulation. X-ray radiation-based two-phase flowmeters’ accuracy has been improved by PSO to choose the best case among thirteen characteristics. The proposed feature selection method introduced seven features as the best combination. The void fraction inside the pipe could be predicted using the GMDH neural network, with the given characteristics as inputs to the network. The novel aspect of the current study is the application of a PSO-based feature selection method to calculate volume percentages, which yields outcomes such as the following: (1) presenting seven suitable time, frequency, and wavelet characteristics for calculating volume percentages; (2) the presented method accurately predicted the volume fraction of the two-phase flow components with RMSE and MSE of less than 0.30 and 0.09, respectively; (3) dramatically reducing the amount of calculations applied to the detection system. This research shows that the simultaneous use of time, frequency, and wavelet characteristics, as well as the use of the PSO method as a feature selection system, can significantly help to improve the accuracy of the detection system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:916-:d:1065231
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    References listed on IDEAS

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    1. 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.
    2. Anatoliy Andreevich Isaev & Mekhrali Mirzali Ogly Aliev & Alexander Nikolaevich Drozdov & Yana Alekseevna Gorbyleva & Karina Shamilyevna Nurgalieva, 2022. "Improving the Efficiency of Curved Wells’ Operation by Means of Progressive Cavity Pumps," Energies, MDPI, vol. 15(12), pages 1-14, June.
    3. 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.
    4. 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.
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