IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v303y2024ics0360544224017171.html
   My bibliography  Save this article

Intelligent predictions for flow pattern and phase fraction of a horizontal gas-liquid flow

Author

Listed:
  • Ma, Huimin
  • Xu, Ying
  • Huang, Hongbo
  • Yuan, Chao
  • Wang, Jinghan
  • Yang, Yiguang
  • Wang, Da

Abstract

In-situ measurement of phase fraction of a gas-liquid flow is closely related to the production efficiency in natural gas extraction. However, the measurement accuracy can be affected by the co-existed multiple flow patterns. This study proposes an intelligent strategy that identifies the flow pattern followed by a phase fraction prediction. For flow pattern recognition, we establish a bidirectional long short-term memory (BI-LSTM) network whose inputs are time-series phases of a Radio Frequency Sensor (RFS). The accuracy is 92.4 % over four classical flow patterns. The time-series phases of RFS are agreed well with the axial imaging from a Wire-Mesh Sensor (WMS). Two predictive models are developed for gas fraction: dimensionless analysis model (DAM) based on RFS and gas Froude number, and neural network model (NNM) with the phases of RFS and the recognized flow pattern. The mean absolute errors (MAE) are 3.2 % and 1.5 % for DAM and NNM, respectively. It is concluded that a NNM, incorporated with RFS and flow pattern by BI-LSTM, can intelligently predict gas fraction with high-accuracy. As the present strategy decouples the pattern recognition and gas fraction prediction into two networks, the complexity of a NNM is reduced which benefits the in-situ measurement practice.

Suggested Citation

  • Ma, Huimin & Xu, Ying & Huang, Hongbo & Yuan, Chao & Wang, Jinghan & Yang, Yiguang & Wang, Da, 2024. "Intelligent predictions for flow pattern and phase fraction of a horizontal gas-liquid flow," Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:energy:v:303:y:2024:i:c:s0360544224017171
    DOI: 10.1016/j.energy.2024.131944
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224017171
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131944?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Chaofan & Song, Yajing & Xu, Long & Zhao, Ning & Wang, Fan & Fang, Lide & Li, Xiaoting, 2022. "Prediction of the interfacial disturbance wave velocity in vertical upward gas-liquid annular flow via ensemble learning," Energy, Elsevier, vol. 242(C).
    2. Lin, Zi & Liu, Xiaolei & Lao, Liyun & Liu, Hengxu, 2020. "Prediction of two-phase flow patterns in upward inclined pipes via deep learning," Energy, Elsevier, vol. 210(C).
    3. Ni, Dan & Zhang, Ning & Gao, Bo & Li, Zhong & Yang, Minguan, 2020. "Dynamic measurements on unsteady pressure pulsations and flow distributions in a nuclear reactor coolant pump," Energy, Elsevier, vol. 198(C).
    4. Mao, Ning & Azman, Amirah Nabilah & Ding, Guangxin & Jin, Yubo & Kang, Can & Kim, Hyoung-Bum, 2022. "Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning," Energy, Elsevier, vol. 239(PD).
    5. Kapustenko, Petro & Klemeš, Jiří Jaromír & Arsenyeva, Olga & Tovazhnyanskyy, Leonid & Zorenko, Viktor, 2021. "Pressure drop in two phase flow of condensing air-steam mixture inside PHE channels formed by plates with corrugations of different geometries," Energy, Elsevier, vol. 228(C).
    6. Zhang, Lifeng & Zhang, Sijia, 2023. "Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encoding," Energy, Elsevier, vol. 282(C).
    7. Cheen Sean Oon & Muhammad Ateeq & Andy Shaw & Stephen Wylie & Ahmed Al-Shamma’a & Salim Newaz Kazi, 2016. "Detection of the gas–liquid two-phase flow regimes using non-intrusive microwave cylindrical cavity sensor," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 30(17), pages 2241-2255, November.
    8. Pandelidis, Demis & Cichoń, Aleksandra & Pacak, Anna & Anisimov, Sergey & Drąg, Paweł, 2018. "Counter-flow indirect evaporative cooler for heat recovery in the temperate climate," Energy, Elsevier, vol. 165(PA), pages 877-894.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Lifeng & Zhang, Sijia, 2023. "Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encoding," Energy, Elsevier, vol. 282(C).
    2. Mao, Ning & Azman, Amirah Nabilah & Ding, Guangxin & Jin, Yubo & Kang, Can & Kim, Hyoung-Bum, 2022. "Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning," Energy, Elsevier, vol. 239(PD).
    3. Jia, Huijun & Wen, Jiaqi & Xu, Xinrui & Liu, Miaomiao & Fang, Lide & Zhao, Ning, 2024. "Spatial and temporal characteristic information parameter measurement of interfacial wave using ultrasonic phased array method," Energy, Elsevier, vol. 292(C).
    4. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    5. Li, Nianqi & Klemeš, Jiří Jaromír & Sunden, Bengt & Wu, Zan & Wang, Qiuwang & Zeng, Min, 2022. "Heat exchanger network synthesis considering detailed thermal-hydraulic performance: Methods and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Leilei Du & Fankun Zheng & Bo Gao & Mona Gad & Delin Li & Ning Zhang, 2024. "Numerical Investigation of Rotor and Stator Matching Mode on the Complex Flow Field and Pressure Pulsation of a Vaned Centrifugal Pump," Energies, MDPI, vol. 17(10), pages 1-19, May.
    7. Dan Ni & Feifan Wang & Bo Gao & Yang Zhang & Shiyuan Huang, 2022. "Experimental Investigation on the Effect of the Staggered Impeller on the Unsteady Pressure Pulsations Characteristic in a Pump," Energies, MDPI, vol. 15(23), pages 1-15, November.
    8. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    9. Arsenyeva, Olga & Klemeš, Jiří Jaromír & Tovazhnyanskyy, Leonid & Klochok, Eugeny & Kapustenko, Petro, 2023. "Estimating parameters of plate heat exchanger for condensation of steam from mixture with air as a component of heat exchanger network," Energy, Elsevier, vol. 283(C).
    10. Zhu, Hongtao & Gao, Xueping & Liu, Yinzhu & Liu, Shuai, 2023. "Numerical and experimental assessment of the water discharge segment in a pumped-storage power station," Energy, Elsevier, vol. 265(C).
    11. Arsenyeva, Olga & Klemeš, Jiří Jaromír & Klochock, Eugeny & Kapustenko, Petro, 2023. "The effect of plate size and corrugation pattern on plate heat exchanger performance in specific conditions of steam-air mixture condensation," Energy, Elsevier, vol. 263(PC).
    12. Tian, Zhongyun & Zheng, Wenke & Guo, Jiwei & Jiang, Yiqiang & Liang, Zhirong & Mi, Xiaoguang, 2024. "Fundamental research on the condensation heat transfer of the hydrocarbon-mixture energy in a spiral tube described by a universal model using flow pattern based and general modes," Energy, Elsevier, vol. 296(C).
    13. Chengshuo Wu & Jun Yang & Shuai Yang & Peng Wu & Bin Huang & Dazhuan Wu, 2023. "A Review of Fluid-Induced Excitations in Centrifugal Pumps," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    14. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    15. Dan Ni & Jinbo Chen & Feifan Wang & Yanjuan Zheng & Yang Zhang & Bo Gao, 2023. "Investigation into Dynamic Pressure Pulsation Characteristics in a Centrifugal Pump with Staggered Impeller," Energies, MDPI, vol. 16(9), pages 1-14, April.
    16. Kan, Kan & Binama, Maxime & Chen, Huixiang & Zheng, Yuan & Zhou, Daqing & Su, Wentao & Muhirwa, Alexis, 2022. "Pump as turbine cavitation performance for both conventional and reverse operating modes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    17. Zhenhua Zhou & Huacong Li & Jinbo Chen & Delin Li & Ning Zhang, 2023. "Numerical Simulation on Transient Pressure Pulsations and Complex Flow Structures of a Ultra-High-Speed Centrifugal Pump at Stalled Condition," Energies, MDPI, vol. 16(11), pages 1-17, June.
    18. Li, Lin & Tan, Dapeng & Wang, Tong & Yin, Zichao & Fan, Xinghua & Wang, Ronghui, 2021. "Multiphase coupling mechanism of free surface vortex and the vibration-based sensing method," Energy, Elsevier, vol. 216(C).
    19. Abrasaldo, Paul Michael B. & Zarrouk, Sadiq J. & Kempa-Liehr, Andreas W., 2024. "A systematic review of data analytics applications in above-ground geothermal energy operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    20. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:303:y:2024:i:c:s0360544224017171. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.