Issue 9, 2023

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

Abstract

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, 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(xCO2)) 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.

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

Supplementary files

Article information

Article type
Paper
Submitted
22 noy 2022
Accepted
22 fev 2023
First published
28 fev 2023

Green Chem., 2023,25, 3475-3492

Author version available

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

M. Mohan, O. Demerdash, B. A. Simmons, J. C. Smith, M. K. Kidder and S. Singh, Green Chem., 2023, 25, 3475 DOI: 10.1039/D2GC04425K

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements