Estimation of bubble size distributions and shapes in two-phase bubble column using image analysis and optical probes

Abstract

A precise estimation of bubble size distributions and shapes is required to characterize the bubble column fluid dynamics at the “bubble-scale”, and to evaluate the heat and mass transfer rate in bubble column reactors. Image analysis methods can be used to measure the bubble size distributions and shapes; unfortunately, these experimental techniques are limited to resolve bubble clusters and large void fractions, and can not be applied under relevant operating conditions (e.g., high temperature and pressure). On the other hand, needle probes (i.e, optical and conductive probes) can be used to measure bubble sizes in dense bubbly flows and under relevant operating conditions; however, needle probes measure chord length distributions, which should be converted into bubble size distributions by using statistical algorithms. These algorithms rely on correlations—generally obtained for single droplets/bubbles—that predicts the bubble shapes, by relating the bubble equivalent diameter to the bubble aspect ratio. In this paper, we contribute to the existing discussion through an experimental study regarding the bubble sizes and aspect ratio in a large air-water bubble column. The experimental investigation has consisted in gas holdup, image analysis and optical probe measurements. First, the gas holdup measurements have been used to identify the flow regime transition between the homogeneous flow regime and the transition flow regime. Secondly, the homogeneous flow regime has been described at the “bubble-scale”: chord length distributions and bubble size distributions have been obtained by using an optical probe and image analysis, respectively. Based on the experimental data from the image analysis, a correlation between the bubble equivalent diameter and the bubble aspect ratio has been proposed and has been compared with existing correlations. Finally, the chord length distributions have been converted into bubble size distributions using a statistical method, supported by the aspect ratio obtained through image analysis. The proposed approach has been able to estimate correctly the bubble size distributions at the center of the column then near the wall. We have also demonstrated that the correlations used to predicts the bubble shapes are the main point of improvement in the method.