TY - JOUR
T1 - Prediction of thermodynamic properties of organic mixtures
T2 - Combining molecular simulations with classical thermodynamics
AU - Ravichandran, Ashwin
AU - Tun, Hla
AU - Khare, Rajesh
AU - Chen, Chau Chyun
N1 - Funding Information:
The authors gratefully acknowledge the financial support of the Jack Maddox Distinguished Engineering Chair Professorship in Sustainable Energy sponsored by the J.F Maddox Foundation , United States. Computational resources at High Performance Computing Center, Texas Tech University are also acknowledged. A.R. and H.T. thank Yifan Hao for sharing the regression based binary interaction parameters for some mixtures.
Funding Information:
The authors gratefully acknowledge the financial support of the Jack Maddox Distinguished Engineering Chair Professorship in Sustainable Energy sponsored by the J.F Maddox Foundation, United States. Computational resources at High Performance Computing Center, Texas Tech University are also acknowledged. A.R. and H.T. thank Yifan Hao for sharing the regression based binary interaction parameters for some mixtures.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/11/15
Y1 - 2020/11/15
N2 - The binary interaction parameters of the nonrandom two liquid (NRTL) thermodynamic model are predicted for several organic mixtures using molecular simulations. Based on the theoretical framework of the two-fluid theory, the binary interaction parameters are expressed in terms of the interaction energies, size of the molecules, and size of the local molecular domains; these quantities are calculated from molecular simulations. We show that our technique is robust in terms of its predictions involving organic mixtures with compatible chemical characteristics while we propose possible modifications in the case of mixtures involving incompatible chemical components or significant size disparity, where there is a notable difference between the interaction parameters calculated from simulations and those obtained from experimental data regression. We further demonstrate that the binary interaction parameters calculated from data regression are not unique and that molecular simulations can guide the parameter selection process by identifying physically relevant binary interaction parameters. Requiring only the local molecular structure information from molecular simulations, the method offers fast and reliable prediction of phase equilibrium properties, especially in cases where limited experimental data are available.
AB - The binary interaction parameters of the nonrandom two liquid (NRTL) thermodynamic model are predicted for several organic mixtures using molecular simulations. Based on the theoretical framework of the two-fluid theory, the binary interaction parameters are expressed in terms of the interaction energies, size of the molecules, and size of the local molecular domains; these quantities are calculated from molecular simulations. We show that our technique is robust in terms of its predictions involving organic mixtures with compatible chemical characteristics while we propose possible modifications in the case of mixtures involving incompatible chemical components or significant size disparity, where there is a notable difference between the interaction parameters calculated from simulations and those obtained from experimental data regression. We further demonstrate that the binary interaction parameters calculated from data regression are not unique and that molecular simulations can guide the parameter selection process by identifying physically relevant binary interaction parameters. Requiring only the local molecular structure information from molecular simulations, the method offers fast and reliable prediction of phase equilibrium properties, especially in cases where limited experimental data are available.
KW - Fluid phase equilibria
KW - Local composition models
KW - Molecular simulations
KW - NRTL activity coefficient model
KW - Two-fluid theory
UR - http://www.scopus.com/inward/record.url?scp=85089508561&partnerID=8YFLogxK
U2 - 10.1016/j.fluid.2020.112759
DO - 10.1016/j.fluid.2020.112759
M3 - Article
AN - SCOPUS:85089508561
SN - 0378-3812
VL - 523
JO - Fluid Phase Equilibria
JF - Fluid Phase Equilibria
M1 - 112759
ER -