Modeling the phase equilibria of asymmetric hydrocarbon mixtures using molecular simulation and equations of state

Journal article (2019)

Authors

I. Nikolaidis National Technical University of Athens, National Centre for Scientific Research Demokritos
A. Poursaeidesfahani Engineering Thermodynamics - Mechanical, Maritime and Materials Engineering
M. Ramdin Engineering Thermodynamics - Mechanical, Maritime and Materials Engineering
T.J.H. Vlugt Engineering Thermodynamics - Mechanical, Maritime and Materials Engineering
Ioannis G. Economou Texas A&M University at Qatar, National Centre for Scientific Research Demokritos
O. Moultos Engineering Thermodynamics - Mechanical, Maritime and Materials Engineering
Research Group
Engineering Thermodynamics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2019 I. Nikolaidis, A. Poursaeidesfahani, Zsolt Csaszar, M. Ramdin, T.J.H. Vlugt, Ioannis G. Economou, O. Moultos
DOI: https://doi.org/10.1002/aic.16453
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Published Date

2019

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Process and Energy

Research Group

Engineering Thermodynamics

Abstract

Monte Carlo simulation (MC) is combined with equations of state (EoS) to develop a methodology for the calculation of the vapor–liquid equilibrium (VLE) of multicomponent hydrocarbon mixtures with high asymmetry. MC simulations are used for the calculation of the VLE of binary methane mixtures with long n-alkanes, for a wide range of temperatures and pressures, to obtain sufficient VLE data for the consistent fitting of binary interaction parameters (BIPs) for the EoS. The Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Perturbed Chain - Statistical Associating Fluid Theory (PC-SAFT) EoS are considered. The ability of each EoS to correlate the VLE data is assessed and the selected ones are used to predict the VLE of multicomponent gas condensate mixtures. MC simulations proved to be very accurate in predicting the VLE in all conditions and mixtures considered. The BIPs regressed from the simulation dataset lead to equally accurate modeling results for multicomponent mixtures, compared to those regressed from experimental data.

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