Exploiting the prediction of mass transfer performance in aerated coaxial mixers containing biopolymer solutions using empirical correlations and neural networks

Abstract

AbstractThe volumetric mass transfer coefficient is commonly used to assess the mixing effectiveness of gas–liquid bioreactor systems. Analyzing mass transfer performance in non‐Newtonian fluids inside coaxial mixers can be challenging due to the complex interaction between process variables, which requires developing robust characterization and estimation approaches. This study aims to investigate the gas dispersion in shear‐thinning biopolymers with yield stress using coaxial mixers in order to evaluate the effects of aeration and agitation on the volumetric mass transfer coefficient. A mixing configuration comprising a pitched blade turbine and an anchor was employed to disperse air into xanthan gum solutions, and the mass transfer performance was obtained at different impeller speeds and biopolymer concentrations by measuring the dissolved oxygen concentration. A dimensionless empirical correlation was proposed, and the results showed that the mass transfer was positively influenced by aeration intensity and agitation mechanism, quantified by the gas flow number and Reynolds number, respectively. Additionally, a strategy using stacking‐ensemble artificial neural networks was developed to accurately estimate the volumetric mass transfer coefficient, with a correlation coefficient of 0.998. The proposed mass transfer characterization approach overcame the complexities of analyzing aerated coaxial mixer systems and provided a reliable design model for bioreactor systems containing non‐Newtonian fluids.