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

Abstract

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.

Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

“…1. The actual measurement and reference voltages are measured in the National Engineering Laboratory (NEL) oil-water two-phase flow facility and the detailed information is described in [22]. The simulation is carried out in COMSOL Multiphysics.…”