Module optimization and array design of moisture swing direct air capture based on 2D-3D coupled analysis

Abstract

Direct Air Capture (DAC) is crucial for offsetting carbon emissions. Moisture Swing Adsorption (MSA), a DAC method switches between adsorption and desorption through humidity adjustment without relying on heat, offering significant energy-saving potential but lacking engineering solutions. Therefore, using a validated 2D-3D coupled model, a moisture swing DAC array was proposed, and multi-dimensional evaluations were used to optimize the system. Unlike the preferred capture rate around 90 % for post-combustion gas, the ultra-low CO2 concentration in the air resulted in a different rate range for DAC. Simulation indicated that although the capture rate of DAC could reach 90 %, the trade-off was energy consumption exceeding 3000 kWh/t CO2 with low adsorbent utilization. Consequently, a detailed evaluation method based on adsorbent utilization, energy consumption and cost analysis optimized the preferred capture rate range to 50–60 %, and the total energy consumption was reduced to 873.55 kWh/t CO2, including 276.33 kWh/t CO2 for capture energy and 597.22 kWh/t CO2 for vacuum-concentration energy. The lowest cost was $209.17/t CO2 at the optimal capture rate of 53.91 %. Afterwards, the Ordos Plateau, as a typical preferred sub-environment with abundant wind resources and dry climate, was choose for DAC system deployment study. Wind field simulation determined the layout of a 10000-ton array occupying only 0.298 km2. Finally, based on savings in capture energy due to prevailing winds, regeneration energy savings from the utilization of low-grade waste heat, and the improved performance of the adsorbent itself, it was found that this DAC unit showed great potential to reduce the energy and capture costs to 564.91 kWh/t CO2 and $140.25/t CO2, and at wind speed exceeding 16.8 m/s, the unit switched to passive adsorption, further reducing energy consumption to 408.89 kWh/t CO2, with the minimum fix investment estimated at 21.71 million USD.