Assessment of mass transfer performance using the two-film theory and surrogate models for intensified CO2 capture process by amine solutions in rotating packed beds

Abstract

CO₂ capture is a crucial aspect of attempts to mitigate climate change. The purpose of this study is to investigate the impacts of essential operating parameters on the mass transfer performance of absorbing CO₂ in rotating packed beds (RPBs). A multilayer perceptron neural network (MLPNN) model with the Levenberg-Marquardt learning algorithm, a mass transfer model using the two-film theory, and an empirical correlation model were developed to predict the overall gas-phase volumetric mass-transfer coefficient ($K_G{a}_V$) for RPB-based CO₂ absorption. The developed MLPNN model showed excellent agreement with the actual data, with an MSE of 0.0357, an AARD of 7.4%, and an R² of 0.9839. A sensitivity analysis was conducted using Taguchi orthogonal array design on distinct mass transfer correlations. The results of the two-film theory and surrogate models for the diethylenetriamine (DETA) solvent were compared. The MLPNN model provided better predictions than other developed models with an AARD of 13% for CO₂H₂O-DETA system. Therefore, the effects of operating parameters such as concentration, temperature, solvent flow rate, and rotational speed on $K_G{a}_V$and CO₂ removal efficiency were evaluated using the MLPNN model. Finally, an empirical correlation was proposed to predict $K_G{a}_V$as a function of operational parameters.