Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Mohan, Mood; Demerdash, Omar; Simmons, Blake A.; Smith, Jeremy C.; K. Kidder, Michelle; Singh, Seema

doi:10.1039/d2gc04425k

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Journal Article · Mon Feb 27 23:00:00 EST 2023 · Green Chemistry

DOI:https://doi.org/10.1039/d2gc04425k· OSTI ID:1960697

^[1]; Demerdash, Omar ^[2]; ^[3]; ^[4]; ^[2]; ^[5]

Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)

Carbon dioxide (CO₂) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, in this study, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO₂ in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO₂ solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO₂ solubility (ln(x_CO₂)) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO₂ solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO₂ capture and utilization.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725; AC02-05CH11231; SC0022273

OSTI ID:: 1960697

Alternate ID(s):: OSTI ID: 1960474

Journal Information:: Green Chemistry, Journal Name: Green Chemistry Journal Issue: 9 Vol. 25; ISSN 1463-9262

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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