Integrating Machine Learning With Sensor Technology for Multiphase Flow Measurement: A Review

This article reviews the integration of machine learning (ML) techniques with sensor-based technologies for multiphase flow measurement in industrial applications. Accurate measurement of multiphase flows is essential for process optimization and safety but presents challenges due to complex phase distributions and varying velocities. The review first discusses traditional sensors used in multiphase flow measurements, including differential pressure, microwave, electrical tomography, and radioactive source-based sensors. It highlights the challenges associated with these sensors. The article then explores various ML algorithms applied to multiphase flow data analysis, covering both traditional methods such as multilayer perceptrons and support vector machine networks, and advanced deep learning approaches such as convolutional and recurrent neural networks. The focus is on how sensor-based ML can enhance the accuracy of multiphase flow predictions and reduce computational demands. The review compares different sensor-based ML methods, illustrating their effectiveness in improving prediction accuracy. This review is relevant to industrial sectors that rely on accurate multiphase flow measurements and highlights the potential of ML in augmenting conventional measurement techniques.