Data-driven modeling of CO2 capture in rotating packed beds enhanced by carbonic anhydrase using explainable artificial intelligence methods

Reactive absorption in aqueous solutions is a widely applied CO₂ capture technology, but its efficiency can be significantly enhanced through process intensification. Rotating Packed Bed (RPB) technology offers a promising solution by intensifying mass transfer and enabling substantial equipment size reduction compared to packed columns. Incorporating biocatalysts, such as the enzyme carbonic anhydrase (CA), further boosts the efficiency of the CO₂ absorption process. This study employs a data-driven approach to model enzyme-enhanced CO₂ absorption in an RPB system using LightGBM, a gradient boosting framework that builds decision trees in a sequential manner, utilizing histogram-based learning and leaf-wise tree growth for enhanced accuracy and efficiency. The model is trained and validated based on experimental data collected from CO₂ absorption experiments with a 30 wt% N-methyldiethanolamine (MDEA) solution, with and without CA across various gas and liquid flow rates. The LightGBM model achieved a high mean cross-validation R² score (0.98) and low root mean squared error value (0.3) in predicting absorption efficiency, indicating high predictive accuracy. Shapely Additive Explanations (SHAP) are employed to analyze feature importance and understand the key parameters influencing absorption efficiency. The validated model is then used for operational analysis, offering insights into system performance optimization. Results reveal that enzyme-enhanced absorption can improve CO₂ absorption efficiency by up to 245.87% compared to the solvent without the enzyme, underscoring the potential of combining high-gravity technology with biocatalysts and machine learning techniques for next generation carbon capture systems.