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Study on Artificial Neural Network for Predicting Gas-Liquid Two-Phase Pressure Drop in Pipeline-Riser System

Author

Listed:
  • Xinping Li

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Nailiang Li

    (School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Xiang Lei

    (School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Ruotong Liu

    (School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Qiwei Fang

    (School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Bin Chen

    (State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

The pressure drop for air-water two-phase flow in pipeline systems with S-shaped and vertical risers at various inclinations (−1°, −2°, −4°, −5° and −7° from horizontal) was predicted using an artificial neural network (ANN). In the designing of the ANN model, the superficial velocity of gas and liquid as well as the inclination of the downcomer were used as input variables, while pressure drop values of two-phase flows were determined as the output. An ANN network with a hidden layer containing 14 neurons was developed based on a trial-and-error method. A sigmoid function was chosen as the transfer function for the hidden layer, while a linear function was used in the output layer. The Levenberg-Marquardt algorithm was used for the training of the model. A total of 415 experimental data points reported in the literature were collected and used for the creation of the networks. The statistical results showed that the proposed network is capable of calculating the experimental pressure drop dataset with low average absolute percent error (AAPE) of 3.35% and high determination coefficient ( R 2 ) of 0.995.

Suggested Citation

  • Xinping Li & Nailiang Li & Xiang Lei & Ruotong Liu & Qiwei Fang & Bin Chen, 2023. "Study on Artificial Neural Network for Predicting Gas-Liquid Two-Phase Pressure Drop in Pipeline-Riser System," Energies, MDPI, vol. 16(4), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1686-:d:1061511
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    References listed on IDEAS

    as
    1. Woo-Shik Kim & Jae-Bong Lee & Ki-Hwan Kim, 2021. "Development of Empirical Correlation of Two-Phase Pressure Drop in Moisture Separator Based on Separated Flow Model," Energies, MDPI, vol. 14(15), pages 1-13, July.
    2. Hieu Ngoc Hoang & Nurlaily Agustiarini & Jong Taek Oh, 2022. "Experimental Investigation of Two-Phase Flow Boiling Heat Transfer Coefficient and Pressure Drop of R448A inside Multiport Mini-Channel Tube," Energies, MDPI, vol. 15(12), pages 1-18, June.
    3. Bin Wang & Jianguo Hu & Weixiong Chen & Zhongzhao Cheng & Fei Gao, 2021. "Flow Pattern and Resistance Characteristics of Gas–Liquid Two-Phase Flow with Foam under Low Gas–Liquid Flow Rate," Energies, MDPI, vol. 14(13), pages 1-15, June.
    4. Alireza Sarraf Shirazi & Ian Frigaard, 2021. "SlurryNet: Predicting Critical Velocities and Frictional Pressure Drops in Oilfield Suspension Flows," Energies, MDPI, vol. 14(5), pages 1-20, February.
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