Enhancing Accuracy in Gas-Water Two-Phase Flow Sensor Systems Through Deep-Learning-based Computational Framework

Multiphase flow is a critical component in contemporary industrial operations, yet the accurate quantification of multiphase parameters presents a substantial obstacle.This study enhances gas-water two-phase flow measurement accuracy via a deep learning framework, leveraging a multi-sensor array in a lab-simulated dual-layer pipeline.Employing electrical resistance tomography, electromagnetic flow meters, and temperature and pressure sensors, it captures real-time data for a deep learning model integrating a classical drift flux model for a non-intrusive, comprehensive measurement system.Two models, One Dimensional Convolutional Bidirectional Long Short-Term Memory Neural Network (1D CNN-BiLSTM) and Multiphase Flow Estimation Neural Network (MFENet)-featuring positional encoding, multi-attention mechanisms, and a sliding window-were developed.Testing across 185 different flow conditions demonstrated superior precision of MFENet in flow predictions with 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.