An Integrated Modeling Framework for Screening CO2 Capture Materials

Abstract

In this work, we have developed and demonstrated an end-to-end modeling framework for screening solid sorbent materials for CO2 capture. The framework includes molecular simulations to estimate adsorption isotherm parameters, diffusivity, dynamic process modeling to compute the process-based figures of merit, and machine learning approaches to guide the selection of materials for screening. This framework is designed to enable accelerated discovery of solid sorbent materials for carbon capture. The molecular simulations (Monte Carlo and Molecular Dynamics) are used to generate single-component gas phase adsorption loading and diffusivity which are interpreted through a variety of isotherm and diffusivity models to be used in the process model. The process model uses a heuristic to optimize a 4-step cycle (adsorption, purge, desorption, cooling) for each MOF. About ~1000 MOFs from the CoRE 2019 database are screened for CO2 capture performance indicators such as productivity, purity, recovery, and energy consumption. Materials with the highest volumetric productivity are selected for further investigation in terms of synthesis feasibility and stability. We will present the status and summary of the screening effort for 1000 MOFs in comparison to three reference MOFs, namely Mg-MOF-74, HKUST-1 and ZIF-8.