Cycle-resolved dynamic modeling of atmospheric CO2 separation systems under time-varying ambient and electricity conditions

Atmospheric CO 2 separation using solid-sorbent adsorption systems is strongly influenced by ambient temperature and humidity, which vary over hourly and seasonal timescales. However, detailed cyclic adsorption models that resolve adsorption-desorption dynamics are computationally intensive, limiting their application to long-term, ambient condition-resolved analysis and system-scale optimization. In this work, we develop a physics-informed dynamic reduced-order model (ROM) for a solid-sorbent temperature-vacuum swing adsorption system. The model retains the dominant physical mechanisms governing cyclic operation, including CO 2 -H 2 O co-adsorption, adsorption–desorption kinetics, incomplete regeneration, and cycle-to-cycle sorbent-state memory. It is formulated as a computationally efficient, control-oriented state-transition model rather than an equipment-level representation of a specific Direct air capture plant. Using this framework, approximately 120,000 cycle-resolved simulations are generated across environmental conditions, initial sorbent states, system designs, and operating strategies. The resulting dataset reveals a structured performance landscape and supports the development of a neural-network surrogate for rapid cyclic prediction and optimization. Results demonstrate that system performance is highly sensitive to environmental variability and that cycle-resolved optimization under time-varying conditions can simultaneously reduce energy demand and improve CO 2 productivity compared with fixed operating strategies. Overall, this work presents a scalable physics-to-surrogate workflow that links adsorption physics, reduced-order dynamic modeling, and neural-network-assisted optimization for atmospheric CO 2 separation systems operating under realistic environmental variability.