Carbon capture technologies are expected to play a key role in the global energy system, as it is likely that fossil fuels will continue to be dominant in the world’s energy mix in the near future. Pressure swing adsorption (PSA) is a promising alternative among currently available technologies for carbon capture due to its low energy requirements. Still, the design of the appropriate PSA cycle for a given adsorbent material is a challenge that must be addressed to make PSA commercially competitive for carbon capture applications. In this work, we propose and test a model reduction-based approach that systematically generates low-order representations of rigorous PSA models. These reduced-order models are obtained by training artificial neural networks on data collected from full partial differential algebraic equation (PDAE) model simulations. The main contribution of this paper is the development of surrogate models for every possible step in PSA cycles: pressurization, adsorption, and depressurization ...