Flow Velocity Computation in Solid–Liquid Two-Phase Flow by a Hybrid Network CNN–RKSVM

Abstract

As an advanced detection technique, electrical resistive tomography (ERT) has been applied to detect the solid–liquid two-phase flow velocity based on available ERT measurements. The flow velocity computation by ERT must depend on the relative algorithms, including both the cross-correlation (CC) principle and convolutional neural networks (CNNs). However, these two types of algorithms have poor accuracy and generalization under complex measuring conditions and various flow patterns. To address this issue, in this paper, a hybrid network is proposed that combines a CNN with a reproducing kernel-based support vector machine (RKSVM) technique. The features hidden in ERT measurements are extracted using the CNN, and then the flow velocity is computed by the RKSVM in a high-dimensional feature space. According to the ERT measurements in an actual experimental platform, the results show that the hybrid network has higher accuracy and generalization ability for flow velocity computation compared with the existing CC, RKSVM, and CNN methods.