Abstract
Direct Air Capture (DAC) is a negative emissions technology whose performance is inherently linked to ambient conditions, which directly affect its primary feed stream (air). A common simplification in DAC model simulations is the use of fixed weather conditions, which can bias the predicted performance under weather variability. In response, this study quantifies the impact of local meteorological variability and temporal weather aggregation on the performance of DAC units. Building on a previously developed and validated 1D mechanistic model of a fixed-bed Steam-assisted Temperature Vacuum Swing Adsorption (S-TVSA) DAC process, we simulate its operation using weather data from the Met Office station at Buchan (UK), near the Saint Fergus terminal - a strategic hub for Carbon Capture and Storage (CCS) activities in Scotland. A two-branch methodological framework is developed combining optimization and forward simulations. Operating conditions are optimized using a multi-objective genetic algorithm (NSGA-II) to maximize productivity (Pr) and minimize specific equivalent work (Weq) at two temporal resolutions. Furthermore, daily weather inputs are aggregated on a monthly and yearly scale to assess the impact of data resolution on model predictions and real operational gains. Results show that temporal weather aggregation to yearly averages biases DAC key performance indicators, overestimating Pr by up to 5% while underestimating Weq by up to 31%, relative to performance based on daily weather variations. Moreover, optimization strategies that explicitly account for monthly weather variability present monthly gains, by increasing Pr by up to 10%. Yet, these monthly gains do not necessarily translate into significant operational performance benefits at the annual scale when daily weather data is propagated in the process model.