(329f) Elucidating the Optimal Reaction Pathway of a Functionalized Metal Organic Framework for Post-Combustion CO2 Capture

Recent research has been directed into finding viable solid sorbents for post-combustion CO₂ capture that can be alternatives to aqueous solvents which are the current leading commercial technology. To realize the full potential of these systems, accurate predictive models that can be used for system design is absolutely necessary. Specifically, accurate modeling of the adsorption equilibrium for CO₂ as well as the other components present in a flue gas source is a very important and crucial step in developing these full system models. Current modeling for most sorbents uses classical isotherm models, such as a Langmuir isotherm model, to predict the adsorption equilibrium, but these models fail to accurately predict the complicated equilibrium. A fundamental understating of the reaction pathways can not only more accurately represent the pressure and temperature dependence of loading, but can also be instrumental in optimizing the composition of the functionalized MOFs for better system performance.

To determine which set of reactions can best represent the system chemistry, first a set of plausible reactions including chain formation reactions and numerous ways that these chains can interact are proposed based on the information from the sparse NMR data. To account for nonideality which arises due to interactions of the charged intermediate species and between molecular and charged species present in this system and to account for physical equilibrium of the molecular species, a plausible thermodynamic model is also developed. A mixed integer non-linear programming (MINLP) problem is solved to identify the optimal kinetic pathway as well as the model parameters for chemisorption and physisorption. To avoid overparameterization of the model, an information-theoretic criterion is considered as the optimization objective. The study shows that the model is not only thermodynamically consistent but also leads to superior prediction compared to weighted isotherms.