Multi-Objective Optimization of CO2 Capture from Ambient Air via TVSA Process Modeling

Abstract

Direct Air Capture (DAC)- a negative emissions strategy that involves removing carbon dioxide directly from the atmosphere- is one of the key technological approaches to meeting global climate targets. Among the various sorbent materials explored for CO2 capture, amine-functionalised metal organic frameworks (MOFS) have attracted considerable attention due to their high selectivity and tunable adsorption properties, particularly for post-combustion CO2 capture from flue gas streams. In this study, the performance of mmen-Mg2(dobpdc) for DAC under atmospheric conditions (∼400 ppm CO2) is evaluated through detailed process simulation using a temperature vacuum swing adsorption (TVSA) cycle. The simulation model is integrated with the NSGA-II algorithm to perform multi-objective optimisation, targeting the trade-off between CO2 recovery and specific energy consumption. Results indicate that this sorbent can achieve CO2 recovery above 90% under feed and process conditions with purity exceeding 94%. The corresponding specific energy consumption varies with the selected operating point, ranging from 3 to 7.5 MJ/Kg CO2, highlighting the importance of balanced process design.