Computational modelling and optimization of physicochemical absorption of CO2 in rotating packed bed

Abstract

The current study developed a novel computational fluid dynamics (CFD) model that accounted for both physical and chemical absorption in the multiphase flow and captured the relative dominance of chemical absorption over physical by employing a tunable model parameter ‘enhancement factor’. The CFD model was validated against experimental data in a rotating packed bed, and then the validated model was used to investigate the effect of operational parameters such as rotational speed, monoethanolamine (MEA) concentration, inlet velocity, and MEA-packing contact angle on the physiochemical absorption. The significance of each operational parameter was then evaluated by the ANOVA analysis, which inferred that the enhancement factor is sensitive to rotational speed, MEA concentration, inlet velocity, and contact angle. The p-value of MEA concentration and inlet velocity was less than 0.05, which implies that these two variables are the most significant variables for the chemical absorption of CO₂. The response surface methodology (RSM) and the artificial neural network (ANN) were also employed to develop the predictive model for the enhancement factor. Among the employed techniques, ANN resulted in R² closer to 0.99 and could better predict the enhancement factor. The modelling approach and findings of the current study are useful in optimizing the operation of rotating packed-bed reactor (RPB) for CO₂ absorption on the industrial scale.