Bao, M., Wu, R., Wang, M. et al. (2 more authors) (2024) Enhancing Accuracy in Gas–Water Two-Phase Flow Sensor Systems Through Deep-Learning- Based Computational Framework. IEEE Sensors Journal, 24 (23). pp. 39934-39946. ISSN 1530-437X
Abstract
Multiphase flow is a critical component in contemporary industrial operations, yet the accurate quantification of multiphase parameters presents a substantial obstacle. This research enhances gas-water two-phase flow measurement accuracy via a deep learning framework, leveraging a multisensor array in a laboratory-simulated dual-layer pipeline. Employing electrical resistance tomography (ERT), electromagnetic flow meters (EMFs), and temperature and pressure sensors, it captures real-time data for a deep learning model integrating a classical drift flux model (DFM) for a nonintrusive, comprehensive measurement system. Two models, 1-D convolutional bidirectional long short-term memory neural network (1D CNN-BiLSTM) and multiphase flow estimation neural network (MFENet) - featuring positional encoding, multiattention mechanisms, and a sliding window - were developed. Testing across 185 different flow conditions demonstrated superior precision of MFENet in flow predictions with the average relative errors of 2.45% for gas volumetric flow rate and 1.38% for water volumetric flow rate, outperforming 1D CNN-BiLSTM. This emphasizes the capability of deep learning to improve the accuracy of multiphase flow measurement techniques.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Deep learning; fluid flow measurement; neural networks; sensor systems; tomography |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2025 15:51 |
Last Modified: | 07 Jan 2025 15:51 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/jsen.2024.3475292 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221404 |