Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

Daou, Alan S.; Fang, Hanjun; Boulfelfel, Salah Eddine; Ravikovitch, Peter I.; Sholl, David S.

doi:10.1021/acs.jpcc.4c00066

Title: Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

Journal Article · 28 March 2024 · Journal of Physical Chemistry. C

DOI: https://doi.org/10.1021/acs.jpcc.4c00066 · OSTI ID:2351063

Daou, Alan S. ^[1];

^[1]; Boulfelfel, Salah Eddine ^[1]; Ravikovitch, Peter I. ^[2];

^[3]

Georgia Institute of Technology, Atlanta, GA (United States)
ExxonMobil Research and Engineering Co., Annandale, NJ (United States)
Georgia Institute of Technology, Atlanta, GA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

We present an approach for quantitatively predicting the temperature-dependent single-component adsorption behavior of linear alkanes in silica and Na-exchanged cationic zeolites using machine learning (ML) models trained from extensive molecular simulations based on force fields with coupled cluster accuracy. A high-performing classification model was developed to distinguish between instances with negligible and non-negligible adsorption. Subsequently, two ML models were trained to predict the single-component adsorption loading and the heat of adsorption at any pressure at 300 K for any zeolite topology and silicon-to-aluminum ratio. The ML models were trained on International Zeolite Association (IZA) zeolites, and their transferability to hypothetical zeolites was successfully validated. We then expand the power of these predictions to adsorbed mixtures at arbitrary temperatures by integrating them with the Clausius–Clapeyron equation and ideal adsorbed solution theory (IAST). This approach was validated and then applied to a temperature swing adsorption separation process to demonstrate its practical utility. We demonstrate how predictions from this ML-enabled approach can allow the selection of high-performing materials that are then validated using detailed molecular simulations based on quantitatively accurate force fields.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2351063

Journal Information:: Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 14 Vol. 128; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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