Numerical and experimental analysis of oxygen transfer in bubble columns: Assessment of predicting the oxygen-transfer rate in clean water and with surfactant solutions

肺表面活性物质 气泡 传质 传质系数 化学 扩散 体积流量 流体体积法 极限氧浓度 热力学 曝气 氧气 机械 分析化学(期刊) 色谱法 流量(数学) 物理 生物化学 有机化学
作者
Kamal Rezk,Фредрик Андерссон,Maria Sandberg,Wamei Lin
标识
DOI:10.1016/j.eti.2023.103522
摘要

The purpose of this study was to develop a numerical model to estimate the oxygen-transfer rate for a laboratory-scale bottom aeration system at a 1.28 L reactor volume and to contribute to fundamental knowledge regarding the oxygenation of surfactant solutions. The primary goal of the study has been to develop a computational fluid dynamics (CFD) model using Euler–Euler (EE) mixture model coupled with the advection-diffusion equation to predict the oxygen-transfer rate in bubble columns containing clean water. The secondary goal has been to apply the model to water-based solutions containing the surfactant lauric acid (DDA) to identify options for further development of the model to make it applicable to surfactant solution systems. The Sauter mean diameter (SMD) was calculated to represent the average bubble diameter, based on available experimental data for different combinations of superficial velocities rate and DDA concentration. The oxygen-transfer rate in clean water fit well with experimental data at lower superficial velocities, and the differences in volumetric mass-transfer coefficients were 0.7% and 3.3% for superficial velocities of 0.24 cm/s and 0.48 cm/s, respectively. Because the flow regime is more heterogeneous at higher superficial velocities, the model tends to overestimate the oxygen-transfer rate. For surfactant solutions, the model overestimates the oxygen-transfer rate due to surfactant adsorption at the bubble/water interface and the consequent decrease in the mass-transfer coefficient not being modeled. A correction factor for the mass-transfer coefficient based on a larger sample size of experimental data may need to be calculated and applied to improve model predictability.

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