Experimental investigation, modelling, and order of magnitude analysis of oxygen mass transfer in pulsed plate column with α‐Fe2O3 nanofluid

• Published in: Canadian journal of chemical engineering Vol. 102; no. 7; pp. 2608 - 2627
• Main Authors: Shet, Amruta S., Shetty K., Vidya
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-07-2024; Wiley Subscription Services, Inc
• Subjects: Bioreactors; Critical loading; Design parameters; Dimensionless analysis; enhancement factor; Fluid dynamics; Fluid flow; Gas flow; Mass transfer; Multiple regression analysis; Nanofluids; Nanoparticles; Oxygen; Plate columns; pulsed plate column; pulsing velocity; Regression models; Reynolds number; Schmidt number; Titanium dioxide; α‐Fe2O3 nanofluid
• ISSN: 0008-4034; 1939-019X
• DOI: 10.1002/cjce.25207

Volumetric oxygen mass transfer coefficient (kLa) is an important parameter in the design of various reactors and bioreactors. In the present work, the influence of α‐Fe2O3 nanofluid on the enhancement of kLa is studied in a pulsed plate column (PPC). An enhancement factor of greater than one showed that the nanofluid is favourable in enhancing the mass transfer rate. The effect of pulsing velocity on kLa is observed to fall under two regimes: the dispersion regime and emulsion regime. The kLa enhancement factor is found to be higher in TiO2 nanofluid than in α‐Fe2O3 nanofluid, indicating that the type of nanofluid influences the enhancement factor. The order of magnitude analysis showed that localized convection triggered by the Brownian motion of nanoparticles is the phenomenon responsible for kLa enhancement. A dimensionless multiple regression analysis (MRA) model was developed to predict kLa in the nanoparticle loading range of 0.003–0.019 (v/v%), relating the Sherwood number with oscillating Reynolds number (1200 ≤ Reo ≤ 20,000), gas flow Reynolds number (0.135 ≤ Reg ≤0.370), Schmidt number (1300 ≤ Sc ≤2700), and Brownian Reynolds number (2.81 × 10−4 ≤ ReB ≤5 × 10−4). The pseudo‐homogeneous model could accurately predict the enhancement until critical loading conditions.