(58f) The Development of Machine Learning, Group Contribution and Molecular Modeling Approach to Screen Physical Solvents for Gas Separation | AIChE

(58f) The Development of Machine Learning, Group Contribution and Molecular Modeling Approach to Screen Physical Solvents for Gas Separation


Shi, W. - Presenter, LRST/battelle/NETL
Macala, M., NETL/AECOM
Thompson, R. L., National Energy Technology Laboratory
Tiwari, S., National Energy Technology Laboratory
Resnik, K. P., URS - US DOE/NETL
Siefert, N., National Energy Technology Laboratory
In this work, we focus on screening and designing solvents to physically and selectively absorb one gas from the other in a gas mixture. Specifically, a hybrid computational approach was developed to systematically screen solvents to separate CO2 from a fuel gas mixture which contains CO2, H2 and H2O. We have identified a solvent which is hydrophobic, non-foaming, less volatile and less viscous than the commercial Selexol solvent. Additionally, this solvent exhibits a large CO2 loading and large CO2/H2 loading selectivity compared with Selexol. Due to its very high hydrophobicity, water presence in the gas stream will not affect CO2 loading in this solvent. In contrast, water presence in the gas stream will adversely decrease CO2 loading and CO2/H2 loading selectivity by 2-3 times in the hydrophilic Selexol. Along with other favorable properties for this solvent, such as the large overall CO2 mass transfer coefficient obtained from simulations and physical model estimation and the reasonable price, this solvent is promising to be used in CO2 pre-combustion capture applications and is being experimentally tested at NETL. Additionally, some solvents with much higher CO2 loadings than the commercial methanol solvent used at low temperatures have also been identified. From our in-house database and molecular simulations, the largest CO2 loading in any organic compound at 298 K was estimated to be 11 mol/MPa.L and the theoretical minimal CO2 loading was approximately calculated to be 0.4 mol/MPa.L. A literature survey of experimental CO2 loadings in ~100 compounds shows that most of those CO2 loadings are between 0.4 - 11 mol/MPa.L; the largest experimental CO2 loading obtained from the literature survey is about 3.3 mol/MPa.L, which is about 3.3 times smaller than the theoretical largest value of 11 mol/MPa.L. This result indicates that there is still much room for improvement to find better solvents with higher CO2 loadings. Finally, some findings obtained from machine learning algorithms including artificial neural network will also be presented. It was found that the H2 loading in a solvent correlates almost linearly with the solvent free volume fraction. For CO2, a relationship between CO2 loading and three other factors, that is, the solvent free volume fraction, effective CO2-solvent interaction by including solvent molar volume, and functional availability was built. The best machine learning model gives a CO2 solubility of about 0.4 mol/MPa.L different than the experimental data. Although more accurate models are being pursued, this preliminary model can be used for screening purposes to select solvents with large CO2 loadings.