Modeling and multi-criteria assessment of polyamine-based CO2 capture in rotating packed beds using artificial intelligence

Rotating Packed Beds (RPB) are receiving wide attention as a CO₂ capture technology that intensifies mass transfer and enables substantial equipment size reduction compared to conventional packed columns. This study employs a data-driven approach to model CO₂ absorption in a RPB system through experimental literature data for five polyamines across various liquid flow rates, rotational speeds and concentration. Polyamines are promising solvents as the combination of multiple amine groups in the same molecule enables high absorption rate, kinetics and CO₂ solubility, among others. The Artificial Intelligence (AI) algorithms used are Partial Least Squares (PLS), Random Forest Regression (RFR), Light (LightGBM) and Extreme Gradient Boosting Machine (XGBoost), Categorical Boosting (CatBoost), Support Vector Regressor (SVR) and Multilayer Perceptron (MLP). Shapley Additive Explanations (SHAP) is used to analyze the combined influence of key parameters on absorption efficiency. The LightGBM model achieved the highest predictive accuracy in carbon capture rate. It was then used for factorial design of simulations, and calculation of RPB motor power requirement enabling a multi objective assessment. Results revealed that ethylenediamine (EDA) offers superior trade-offs between carbon capture and energy requirement. The work underscores the potential of using directly process-level experimental data to model and investigate the performance in polyamine-based capture systems where sufficient data are not available to develop first-principles models.