Weather-dependent direct air capture process modeling for techno-economic assessments

Adsorption-based Direct Air Capture (DAC) is crucial for achieving negative emissions but faces significant challenges due to high energy demand and operational costs. While recent research has highlighted that weather conditions significantly affect DAC energy demand and cost, current DAC systems are typically optimized under steady-state conditions, overlooking the impact of ambient weather variability on the optimal operating point. This study addresses that gap by investigating whether dynamically adjusted adsorption and desorption durations based on hourly weather conditions can improve energy efficiency compared to static operation. Therefore, a process model incorporating co-adsorption effects was optimized for real-world weather conditions and the results are utilized as an input for a techno-economic assessment. Dynamic operation of the optimized process model was evaluated using hourly weather data from four possible DAC locations, revealing potential reductions in electrical and thermal energy demands of up to 8.8 % and 0.9 %, respectively. Additional analyses show that simplified day–night and seasonal operating strategies achieve nearly the same energy savings as hourly adaptation, substantially reducing control complexity. Integration of the optimized process model into a techno-economic assessment reveals weather-driven cost variations of up to 72 €/t CO2 and demonstrates strong sensitivity of DAC costs to renewable energy intermittency. By providing detailed data on the optimized process model, including energy consumption and productivity across diverse climatic conditions, the study supports more refined and location-specific future assessments.