Non-random two-liquid model

The non-random two-liquid model (abbreviated NRTL model) is an activity coefficient model that correlates the activity coefficients formula_1 of a compound with its mole fractions formula_2 in the liquid phase concerned. It is frequently applied in the field of chemical engineering to calculate phase equilibria. The concept of NRTL is based on the hypothesis of Wilson that the local concentration around a molecule is different from the bulk concentration. This difference is due to a difference between the interaction energy of the central molecule with the molecules of its own kind formula_3 and that with the molecules of the other kind formula_4. The energy difference also introduces a non-randomness at the local molecular level. The NRTL model belongs to the so-called local-composition models. Other models of this type are the Wilson model, the UNIQUAC model, and the group contribution model UNIFAC. These local-composition models are not thermodynamically consistent for a one-fluid model for a real mixture due to the assumption that the local composition around molecule "i" is independent of the local composition around molecule "j". This assumption is not true, as was shown by Flemr in 1976. However, they are consistent if a hypothetical two-liquid model is used.
Derivation.
Like Wilson (1964), Renon & Prausnitz (1968) began with local composition theory, but instead of using the Flory–Huggins volumetric expression as Wilson did, they assumed local compositions followed
with a new "non-randomness" parameter α. The excess Gibbs free energy was then determined to be
Unlike Wilson's equation, this can predict partially miscible mixtures. However the cross term, like Wohl's expansion, is more suitable for formula_7 than formula_8, and experimental data is not always sufficiently plentiful to yield three meaningful values, so later attempts to extend Wilson's equation to partial miscibility (or to extend Guggenheim's quasichemical theory for nonrandom mixtures to Wilson's different-sized molecules) eventually yielded variants like UNIQUAC.
Equations for a binary mixture.
For a binary mixture the following function are used:
with
Here, formula_11 and formula_12 are the dimensionless interaction parameters, which are related to the interaction energy parameters formula_13 and formula_14 by:
Here "R" is the gas constant and "T" the absolute temperature, and "Uij" is the energy between molecular surface "i" and "j". "Uii" is the energy of evaporation. Here "Uij" has to be equal to "Uji", but formula_16 is not necessary equal to formula_17.
The parameters formula_18 and formula_19 are the so-called non-randomness parameter, for which usually formula_18 is set equal to formula_19. For a liquid, in which the local distribution is random around the center molecule, the parameter formula_22. In that case the equations reduce to the one-parameter Margules activity model:
In practice, formula_18 is set to 0.2, 0.3 or 0.48. The latter value is frequently used for aqueous systems. The high value reflects the ordered structure caused by hydrogen bonds. However, in the description of liquid-liquid equilibria the non-randomness parameter is set to 0.2 to avoid wrong liquid-liquid description. In some cases a better phase equilibria description is obtained by setting formula_25. However this mathematical solution is impossible from a physical point of view, since no system can be more random than random (formula_18 =0). In general NRTL offers more flexibility in the description of phase equilibria than other activity models due to the extra non-randomness parameters. However, in practice this flexibility is reduced in order to avoid wrong equilibrium description outside the range of regressed data.
The limiting activity coefficients, also known as the activity coefficients at infinite dilution, are calculated by:
The expressions show that at formula_22 the limiting activity coefficients are equal. This situation that occurs for molecules of equal size, but of different polarities.<br> It also shows, since three parameters are available, that multiple sets of solutions are possible.
General equations.
The general equation for formula_29 for species formula_30 in a mixture of formula_31 components is:
with
There are several different equation forms for formula_36 and formula_37, the most general of which are shown above.
Temperature dependent parameters.
To describe phase equilibria over a large temperature regime, i.e. larger than 50 K, the interaction parameter has to be made temperature dependent.
Two formats are frequently used. The extended Antoine equation format:
Here the logarithmic and linear terms are mainly used in the description of liquid-liquid equilibria (miscibility gap).
The other format is a second-order polynomial format:
Parameter determination.
The NRTL parameters are fitted to activity coefficients that have been derived from experimentally determined phase equilibrium data (vapor–liquid, liquid–liquid, solid–liquid) as well as from heats of mixing. The source of the experimental data are often factual data banks like the Dortmund Data Bank. Other options are direct experimental work and predicted activity coefficients with UNIFAC and similar models.
Noteworthy is that for the same liquid mixture several NRTL parameter sets might exist. The NRTL parameter set to use depends on the kind of phase equilibrium (i.e. solid–liquid (SL), liquid–liquid (LL), vapor–liquid (VL)). In the case of the description of a vapor–liquid equilibria it is necessary to know which saturated vapor pressure of the pure components was used and whether the gas phase was treated as an ideal or a real gas. Accurate saturated vapor pressure values are important in the determination or the description of an azeotrope. The gas fugacity coefficients are mostly set to unity (ideal gas assumption), but for vapor-liquid equilibria at high pressures (i.e. > 10 bar) an equation of state is needed to calculate the gas fugacity coefficient for a real gas description.
Determination of NRTL parameters from LLE data is more complicated than parameter regression from VLE data as it involves solving isoactivity equations which are highly non-linear. In addition, parameters obtained from LLE may not always represent the real activity of components due to lack of knowledge on the activity values of components in the data regression. For this reason it is necessary to confirm the consistency of the obtained parameters in the whole range of compositions (including binary subsystems, experimental and calculated lie-lines, Hessian matrix, etc.).