Applying Imaging Technique to Investigate Effects of Solute Concentrations and Gas Injection Rates on Gas Bubble Generation

Abstract

This work investigated the bubble size variation under various aqueous conditions, including saline and surfactant solutions, with different gas injection rates using a commercial bubble analyzer. The results show that salt and surfactant can minimize bubble size with increasing solute concentration, and the surfactant outperforms salt. In addition, the critical coalescence concentrations (CCC) of salt and surfactant could be determined at 20 g/l and 20 mg/l, respectively, over which bubble mean size cannot be further reduced. On the other hand, the gas injection rate, compared to the solute concentration, has minor effects on bubble size variation. Nonetheless, there is a critical coalescence injection rate (CCIR) of 30 ml/min for surfactant solution, over which the standard deviation of the bubble size distribution (BSD) cannot be further increased. In principle, this work improves the accuracy and efficiency of the bubble analyzer. It also presents a sound understanding of two influential factors rather than a single-factor controlling bubble size. Most importantly, it is the first time to observe and propose the concept of CCIR to describe how the gas injection rate influences the standard deviation of BSD. Based on those results and findings, it is able to conclude that bubble size control, whose mechanism has been previously identified as the bubble collision and coalescence rather than the surface tension of water solutions, is actually dominated by not only the solute concentration but also the gas injection rate when a porous air sparger is used to generate bubbles. It is expected that this work could contribute to laboratory modeling bubbly flow in a porous medium in order to bring more insights into the mechanism of soil gas leaking through soil strata.

Soil Biopores and Non-Biopores Responses to Different Tillage Treatments in Sugarcane Fields in Guangxi, China

“…Subsequently, an image-adjust plugin in ImageJ 1.53c was utilized to convert the grayscale image into a binary image, containing only pore space and soil. Manual thresholding was performed to determine the segmentation threshold between the pore and background [47]. This threshold was adjusted by comparing the binarized pore image with the original pore image to maximize the accuracy and conformity of the segmentation.…”