Triethanolamine (TEA), a tertiary alkanolamine possesses a high equilibrium capacity for CO₂ but is fundamentally constrained by slow intrinsic chemical kinetics, due to its base-catalyzed hydration mechanism, which proceeds more slowly than the direct carbamate formation pathway of primary amines. This study introduces a novel hybrid solvent system, synergistically combining TEA with the bio-derived amino acid l-arginine (Arg), and deploys it within a process-intensified microfluidic contactor to overcome this intrinsic reaction rate limitation. Detailed physicochemical characterization of the TEA-Arg blends was experimentally measured to evaluate its suitability for intensified gas-liquid contact. The absorption performance was rigorously quantified through key metrics, including CO2 absorption efficiency, overall mass transfer coefficient based on the gas phase (KGaV), and volumetric molar flux (NAaV)-parameters that reflect the combined macroscopic behavior resulting from the improved kinetics. The performance of the TEA-Arg solvent is benchmarked against a conventional 35 wt% methyldiethanolamine (MDEA) and diethanolamine (DEA) to contextualize its efficacy against a widely adopted industrial standard for tertiary amines. The TEA-Arg system demonstrated a profound performance leap, achieving a NAaᵥ of 306 kmol/h·m³ and a capture efficiency of 73 %, representing an approximate threefold enhancement over the MDEA benchmark (107 kmol/h·m³ and 23 % efficiency). Response surface methodology identified gas flow rate and arginine concentration as the dominant factors, accounting for 61.69 % and 15.15 % of the variance in the mass transfer efficiency, respectively. The experimental dataset was further leveraged to develop predictive machine learning models using the Random Forest, RF, algorithm. Three distinct RF models were constructed to predict three key mass transfer parameters: CO₂ capture efficiency (%), volumetric molar flux (NAaV, kmol/h·m³), and the overall gas-phase mass transfer coefficient (KGaV, kmol/m³·h·kPa). The RF model's ability to capture complex nonlinear interactions among multiple operating variables enables rapid and reliable prediction, facilitating optimized design and operation of the CO2 absorption process with the TEA-Arg solvent system.
