Optimizing Direct Air Capture: Evaluating the Impact of Process Parameters on Productivity, Energy Requirement, and Cost

Direct air capture (DAC) of CO2 via adsorption is a promising technology for mitigating climate change, but its high cost remains a major barrier to large-scale deployment. This study presents a comprehensive global sensitivity analysis to evaluate how various process parameters affect the performance and cost of an adsorbent-based DAC system, considering 34 parameters across operating conditions, adsorbent properties, bed configurations, and economic factors. Using a detailed dynamic process model, both temperature-vacuum swing adsorption (TVSA) and steam-assisted TVSA (S-TVSA) are simulated in a fixed adsorbent bed. Key techno-economic metrics, including productivity, energy consumption, system size, and levelized cost of CO2 capture, are assessed. The results indicate that parameters influencing productivity, such as adsorption-desorption temperature swings, feed velocities, and bed thickness, play a critical role in optimizing system performance. Energy consumption is minimized through careful control of desorption pressure and reduced steam purge. Additionally, productivity gains and reduced energy requirements directly impact the levelized cost of CO2 capture, which can be lowered to 350-400 €/tCO2 with optimal parameter combinations identified in this study. However, suboptimal configurations may cause costs to exceed 1000 €/tCO2, underscoring the need for precise process optimization. The cost distribution of the lowest-cost scenarios reveals that energy and adsorbent related operational expenditures dominate the total cost. This study provides a versatile modeling framework to identify cost-reduction strategies for DAC, paving the way for more economically viable carbon removal solutions.