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 two-phase 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 two-phase 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 are highlighted or suppressed respectively. Oil-water two-phase flow experiment was conducted in the NEL facility to collect EIT voltage data. Compared with six state-of-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.