Design and Optimization of Solid Amine CO₂ Adsorbents Assisted by Machine Learning

In the development of solid amine CO2 adsorbents, the CO2 adsorption performance of amine-functionalized adsorbents, with various novel porous supports or modification of the amine structure, has been widely studied. However, a lack of systematic research limits the industrial application of amine-functionalized CO2 adsorbents, especially the adsorbents prepared from inexpensive and readily available commercial porous supports. In this work, machine learning (ML) was employed to explore how the CO2 adsorption performance of amine-functionalized adsorbents is correlated with five factors: amine loading, amine type, pore volume, pore size, and specific surface area. We found that amine loading contributed the most to the effect of CO2 adsorption capacity, followed by pore volume. Pore size was the most important factor affecting amine efficiency, while the cycle stability of the adsorbent was basically related to the amine type, and the interaction effect between the influencing factors was explored by ML. In addition, the CO2 adsorption capacities of TEPA/KXY and PEI/KYX adsorbents were predicted by ML, and the results of ML prediction were consistent with our experimental results. Furthermore, we constructed a “five-in-one” comprehensive comparison of the CO2 adsorption performance of 45TEPA/KYX and 60PEI/KYX adsorbents through a radar diagram, and it was considered that the 45TEPA/KYX adsorbent had a better comprehensive CO2 adsorption performance. Our study provides insights into the development and optimization of solid amine CO2 adsorbents using commercial porous supports