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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Transactions on Instrumentation and Measurement

سال: 2022

ISSN: ['1557-9662', '0018-9456']

DOI: https://doi.org/10.1109/tim.2022.3169557