How does spatio-temporal optimisation affect the siting and operation of direct air capture systems?
Year: 2026
Type: preprint
Abstract: The climate crisis is driving the urgent development of negative emission technologies, such as adsorption-based direct air capture (DAC). Although DAC holds promise, it remains costly and requires further technological innovation, design optimisation, and supply chain development to scale effectively and achieve meaningful climate impact. DAC performance is influenced by both local ambient (e.g., temperature, humidity) and energy conditions (e.g., upstream emissions, costs). Previous studies have typically evaluated DAC systems under constant ambient and energy conditions, used static models that do not provide temporally resolved results, or restricted their analysis to limited case studies. These restrictions may lead to an incomplete assessment of DAC performance and suboptimal decision-making. This study presents the dynamic DAC + energy supply (DACES) optimisation framework, which explicitly accounts for spatial and temporal variations in ambient and energy conditions, and demonstrates its application to the province of Alberta, Canada using highresolution data from 2024. The DACES model can address two essential questions without reverting to static modelling or time-averaged input assumptions: (i) which locations are better suited for DAC deployment than others and (ii) how DAC should be operated. Coupled with a genetic algorithm that minimises net carbon capture costs, the DACES model answers these questions while maintaining computational tractability. The results demonstrate that spatio-temporal optimisation—through the selection of more favourable DAC locations combined with adaptive cycle designs—reduces estimated costs by up to 20 %. Specifically, the use of dynamic conditions in the decision-making process favours the positioning of DAC systems in northern Alberta, whereas constant conditions would have placed them several hundred kilometres further south. An analysis of 32 external conditions across different stages of the cycle identified ambient temperature at the start of adsorption, relative humidity at the end of adsorption, and upstream emissions and costs at the start of desorption as the parameters most influential to cycle performance. Results further show that, for optimal cycle designs, the system should (i) align cycle design with the minima of these parameters at their respective stages, since they determine the extent of pre-desorption conditions and the associated costs of net removal during desorption; and (ii) introduce strategically timed waiting periods between DAC phases to align operation with external conditions, trading DAC productivity for reduced regeneration energy. Overall, the findings highlight that accounting for spatio-temporal variability and optimising cycle design at the individual-cycle level can considerably enhance DAC performance and reduce operational costs.
Source: ChemRxiv
Authors: Malte Glaser, Sean T. McCoy
Institution: University of Calgary
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221116777