Grand Equilibrium: vapour-liquid equilibria by a new molecular simulation method

TL;DR

This paper introduces a novel molecular simulation method for calculating vapour-liquid equilibria of mixtures, using independent variables of temperature and liquid composition, and demonstrates its effectiveness on Lennard-Jones systems.

Contribution

A new simulation approach that combines isobaric liquid phase simulation with pseudo grand canonical vapour simulation for accurate vapour-liquid equilibrium calculations.

Findings

Accurately predicts vapour pressure and composition for Lennard-Jones systems.

Shows very good agreement with literature data.

Applicable to pure and mixture systems.

Abstract

A new molecular simulation method for the calculation of vapour-liquid equilibria of mixtures is presented. In this method, the independent thermodynamic variables are temperature and liquid composition. In the first step, one isobaric isothermal simulation for the liquid phase is performed, in which the chemical potentials of all components and their derivatives with respect to the pressure, i.e., the partial molar volumes, are calculated. From these results, first order Taylor series expansions for the chemical potentials as functions of the pressure $\mu_i(p)$ at constant liquid composition are determined. That information is needed, as the specified pressure in the liquid will generally not be equal to the equilibrium pressure, which has to be found in the course of a vapour simulation. In the second step, one pseudo grand canonical simulation for the vapour phase is performed, where the chemical potentials are set according to the instantaneous pressure $p^v$ using the previously determined function $\mu_i(p^v)$. In this way, results for the vapour pressure and vapour composition are achieved which are consistent to the given temperature and liquid composition.The new method is applied to the pure Lennard-Jones fluid, a binary, and a ternary mixture of Lennard-Jones spheres and shows very good agreement with corresponding data from the literature.