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

Xinping Li, Nailiang Li, Xiang Lei, Ruotong Liu, Qiwei Fang and Bin Chen ()
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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

Energies, 2023, vol. 16, issue 4, 1-13

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.

Keywords: pipeline-riser; gas-liquid; pressure drop; artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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