Once CSS is reached, process-level performance is evaluated using CO2 purity, recovery, energy required, and cycle productivity, defined as follows: The objective of cycle optimization is to maximize process productivity and minimize energy requirements under the constraints of purity and recovery. To obtain the optimal cycle configuration, we consider several design variables: high pressures (PH), adsorption step operation time, flow rate of the flue gas feed, and the light reflux ratios, Void fraction, porosity of the pellet, radius of pellets. The operating parameter values used in the process modeling are listed in Table -S5. Adsorbents and their cycle configurations are screened for minimum energy required and maximum productivity, with purity > 0.9 and recovery > 0.9 constraints, forming Pareto fronts. We employ the NSGA method, implemented within Pymoo , for the PSA process optimization using 200 generations of process variables with a population of 70.