Direct estimation of gas holdup in gas–liquid bubble column reactors using ultrasonic transmission tomography and artificial neural processing

Abstract

Ultrasonic transmission tomography is an effective non-intrusive method for detecting gas–liquid two-phase flow patterns. A specific interest is the many processes whose reaction utilizes a bubble column, where the fast estimation of cross-sectional gas-holdup ratio is important for monitoring and control. In this study reference indirect image-based estimates were obtained from reconstructed tomographic data. Direct (non-image) estimation of the gas holdup ratio was also obtained using trained neural processing networks. Two forms were trialled: a generalized regression neural network (GRNN); and a long short-term memory (LSTM) network. Comparison trials were carried out for single-bubble, dual-bubble, circulation and laminar flows. Relative cross-sectional gas holdup error was selected for evaluation. For the image-based indirect trials the Tikhonov regularization algorithm had the lowest error range: 2.15%–15.64%. For direct methods the LSTM network had the lowest error range: 0.41%–9.63%, giving better performance than the image-based methods. The experimental data were used to verify the effectiveness of the network. The root-mean-square error of the test metrics for GRNN and LSTM network were 6.4260 and 5.4282, respectively, indicating that LSTM network has higher performance in processing the data in this paper.