Oil Fraction Measurement of Nonuniform Dispersed Oil–Water Two-Phase Flow Based on Ultrasonic Attenuation

Han, Yongdian; Tan, Chao; Wu, Hao

doi:10.1109/tim.2021.3117069

Cited by 5 publications

(1 citation statement)

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“…Yu et al [8] established a fractal-based modified attenuation model to estimate the oil VF of oil-water two-phase flow using the ultrasonic sensor. The effectiveness of the model was validated by numerical simulation and experiment results.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Voltage Reconstruction With Attention-Based Network for Volume Fraction Prediction of Industrial Oil–Water Two-Phase Flow by EIT

Zhang

Gao

et al. 2022

IEEE Trans. Instrum. Meas.

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Oil-water two-phase flow as a typical twophase flow type widely exists in various industrial processes and the accurate measurement of oil volume fraction plays a significant role in transporting and separating oil-water mixture in the processes. Electrical Impedance Tomography (EIT) as a merging technology with the advantages of non-invasive, low cost and real-time measurement is widely applied in the industrial field to measure the volume fraction for different types of twophase flows. However, the measurement process of taking homogeneous reference voltages is time-consuming and costly. To cope with the problem, in the paper, by establishing an end-to-end mapping between measurement voltages and volume fraction, we propose an Attention UNet-Fully Connected (AU-FC) architecture. Relying on the attention mechanism, the reconstructed voltages having a strong correlation or a weak correlation with volume fraction is highlighted or suppressed respectively. Oil-water two-phase flow experiment was conducted in the NEL facility to collect EIT voltage data. Compared with six stateof-the-art and existing machine learning methods, the proposed method performs better in predicting volume fraction. The results indicate that the proposed AU-FC architecture can accurately and real-time predict the volume fraction of oil-water two-phase flow, which improves the application potential of EIT combined with deep learning method in the industrial field.

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Section: Introductionmentioning

confidence: 99%