Effect of nanoparticles on oxygen absorption enhancement during sulfite forced oxidation

• Published in: International journal of heat and mass transfer Vol. 90; pp. 1098 - 1104
• Main Authors: Jiang, Jia-Zong, Zhao, Bo, Cao, Meng, Zhuo, Yu-Qun, Wang, Shu-Juan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2015
• Subjects: Mass transfer enhancement; Mass transfer model; Nanofluid; Sulfite forced oxidation; Sulfite forced oxidation; Mass transfer enhancement; Mass transfer model; Nanofluid
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2015.07.054

•The tests investigated the influences of nanoparticle solids loading, stirring speed, temperature and particle size on the average oxygen absorption rate.•A three-dimensional unsteady heterogeneous mass transfer model was developed based on the shuttle effect and mixing of gas–liquid boundary layer to better understand the experimental findings.•A three-dimensional mass transfer model can be used to predict the experimental results reasonably. TiO2–Na2SO3 and SiO2–Na2SO3 nanofluids were prepared using the ultrasonic dispersion method without any surfactant to study the influence of nanoparticles on the mass transfer during forced sulfite oxidation in a thermostatic stirred tank. The tests investigated the influences of nanoparticle solids loading, stirring speed, temperature and particle size on the average oxygen absorption rate. The TiO2 and SiO2 nanoparticles both remarkably improve the gas–liquid mass transfer. The oxygen absorption enhancement factor increases with increasing nanoparticle solids loading to a critical value and then decreases with further increase in the solids loading. Increasing the stirring speed also increases the oxygen absorption rate as well as the oxygen absorption enhancement factor. However, the oxygen absorption enhancement factor decreases with increasing temperature and particle size. A three-dimensional unsteady heterogeneous mass transfer model was developed based on the shuttle effect and mixing of the gas–liquid boundary layer to better understand the experimental findings. The numerical predictions agree well with the experimental findings; thus, the model can be used to predict the experimental results.