Experimental determination of intensification limits and artificial intelligence-enabled energy-optimal operating windows in rotating packed bed CO2 capture

Rotating packed bed (RPB) enhance mass transfer; however, energy demand limits their industrial applicability. In addition, the lack of data-driven tools to generalize performance across operating conditions hinders the identification of energy- optimal operating windows. The operational boundaries governing this trade-off, particularly at high gas-to-liquid (G/L) ratios, are yet defined. This study investigates intensification limits and energy-efficient operating regimes in a countercurrent RPB using Monoethanolamine, evaluated over a range of G/L and rotational speeds. Thus, identifying a threshold at 1600 RPM, beyond which increase in rotational speed result in negligible improvement in capture performance while significantly increasing energy demand. At high G/L, solvent limitations reduce absorption efficiency from 95% to 75%, while the mass transfer coefficient reaches a plateau, diminishing returns from mechanical intensification. An energy-optimal operating window is established, where high CO2 capture efficiency is achieved with marginal energy input. The machine learning model identifies mass transfer coefficient as the dominant parameter and enables rapid prediction of system performance across untested conditions, supporting real-time optimization and process design. This combined experimental and data-driven framework reduces scale-up uncertainty, and provides guidance for designing energy-efficient RPB systems, supporting the deployment of efficient RPB systems for industrial CO2 capture.