Process-informed adsorbent design for direct air capture

Abstract

Direct air capture using solid adsorbents is a proven technology critical to reducing our net greenhouse gas emissions to zero and beyond. Currently, academic research into the technology mainly focuses on the development of new adsorbents. However, there is a discord between the adsorbent design and process performance. Many materials scientists focus on maximising metrics such as the CO2 capacity of their adsorbent. Here, we combine detailed process modelling, machine learning, and extensive global sensitivity analysis, which entails varying all of the model parameters together, on a direct air capture process to show that the CO2 adsorption capacity is not one of the critical parameters for an amine-functionalised adsorbent operating in a temperature vacuum swing adsorption (TVSA) process, while it is important in a steam-assisted TVSA (S-TVSA) process. In fact, adsorption kinetics, density, and thermal conductivity are all critical attributes to obtaining a low energy penalty and reduced costs. The analysis also highlights the importance of heat transfer, directing process engineers to (alternative) adsorber designs that maximise this. By an in-depth evaluation of how process performance indicators are affected by materials properties and process operating parameters, this work provides guidance to both material scientists and process engineers towards the design of a "unicorn adsorbent" and intensified DAC processes. This will improve the performance of solid adsorbent direct air capture and help drive down the costs of this vital technology to avert the worst impacts of climate change.

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

“…An alternative approach is the use of data science methods to develop a surrogate model. Such models have already been successfully used for full physical models, where the CPU time for optimization is also a problem [24][25][26].…”