Control strategies to optimize adsorption-based direct air capture under varying weather conditions

Adsorption-based direct air capture (DAC) captures CO 2 directly from the atmosphere. Based on detailed DAC process models, recent studies demonstrated that diurnal variations in ambient temperature and relative humidity affect cost-optimal control of DAC. Hence, control strategies are needed to determine the control parameters of DAC under varying ambient conditions. Here, we develop and compare four control strategies to minimize the net carbon capture costs (NCC) of a dynamic DAC system model. We compare the four control strategies based on the average NCC after two weeks of operation during summer in Aachen, Germany. (1) As a reference strategy, we operate the DAC system using control parameters optimized for annual average ambient conditions, resulting in a relatively high NCC of 444 € (t CO 2 ) −1 . (2) Selecting optimal control parameters based on the ambient conditions at the start of each cycle reduces NCC by 12.3% to 389 € (t CO 2 ) −1 . (3) Optimizing the DAC system based on a weather forecast covering one DAC cycle reduces NCC by 14.6% to 379 € (t CO 2 ) −1 compared to the reference. ([1]) Using rolling-horizon optimization and a weather forecast covering four consecutive cycles results in an NCC reduction of 15.3% to 376 € (t CO 2 ) −1 compared to the reference. Thus, our study highlights that selecting optimal control parameters based on the ambient conditions at the beginning of a cycle already enables a major NCC reduction. To achieve the lowest NCC possible, control optimization must incorporate a weather forecast.