UNIFAC method limitations

Care must be exercised in applying the UNIFAC method. The specific limitations of the method are ... 

The compositions of the vapor and liquid phases in equilibrium for partially miscible systems are calculated in the same way as for miscible systems. In the regions where a single liquid is in equilibrium with its vapor, the general nature of Fig. 13.17 is not different in any essential way from that of Fig. I2.9< Since limited miscibility implies highly nonideal behavior, any general assumption of liquid-phase ideality is excluded. Even a combination of Henry s law, valid for a species at infinite dilution, and Raoult s law, valid for a species as it approaches purity, is not very useful, because each approximates real behavior only for a very small composition range. Thus GE is large, and its composition dependence is often not adequately represented by simple equations. However, the UNIFAC method (App. D) is suitable for estimation of activity coefficients. 

Modeling of a semibatch reactor (Figure 16.1) enables to determine the reaction rate pseudoconstants. For lack of physical data, a number of assumptions have to be made. The volume of the liquid phase is the function of composition, temperature, pressure, and mass of EO reacted with raw material. At a constant temperature (185 5°C), the volume of the liquid phase increases due to an increased solubility of EO. However, the rate of change is relatively low compared to the reaction rate. The universal functional activity coefficient (UNIFAC) method [43] was used to calculate the activity coefficients. The method was adopted for the heterogeneous liquid-liquid-vapor system as the limited solubility of liquid components was observed. The... 

UNIFAC was developed for situations where experimental data are scarce, and its appHcation should generally be that of last resort (3,7,162,178). That is, UNIFAC is not a method for comparative testing of methods based on experimental data even though the method is sometimes Hsted in commercial computer simulator menus without indication of its limitations. 

Another estimation method of mixture flashpointe was sugg ed by Gmehling (note p.63). The method uses the forecast technique of activity coefficients of iiquid mixtures called UNIFAC that would therefore enable calculation of the vapour pressure of the mixtures and, thanks to Le Chdtelier equation, calculate the temperature to which the mixture has to be heated so that its equilibrium concentration reaches the lower explosive limit. 

The group contribution method UNIFAC [18] is based on the UNIQUAC thermodynamic model as well. It thus suffers from the same thermodynamic approximations as UNIQUAC, especially for strong interactions in the infinite dilution limit. 

When gas solubility data are lacking or are unavailable at the desired temperature, they can be estimated using available models. The method of Prausnitz and Shair (1961), which is based on regular solution theory and thus has the limitations of that theory. The applicability of regular solution theory is covered in detail by Hildebrand et al. (1970). A more recent model, now widely used, is UNIFAC, which is based on structural contributions of the solute and solvent molecular species. This model is described by Fredenslund et al. (1977) and extensive tabulations of equilibrium data, based on UNIFAC, have been published by Hwang et al. (1992) for aqueous systems where the solute concentrations are low and the solutions depart markedly from thermodynamic equilibrium. 

The importance of a suitable G.C. method for evaluating the pure solid properties was evidenced. The limitations of the different G.C. methods are due to the limited experimental data available in the literature for heavy multifunctional compounds. The PR EOS with classical mixing rules gives the same results than the most complex UNIFAC approach. The importance of the sublimation pressure for correlating solubility data was underlined. 

In the exploratory phases of product and process research and development, generally little or limited data are available for model parameterization. However, chemists and engineers need to evaluate a multitude of possible molecular or process variations. Rather than high accuracy, successful evaluation depends on the ability to discard the least viable options and select better options for further detailed studies. Estimation methods that give reliable results for new or unknown species are required. The well-known group-contribution methods, like UNIFAC, have demonstrated their value in cases where the molecules can be decomposed to functional groups for which parameters are already available. 

Estimation of liquid mixture viscosity without any mixture data is difficult because the viscosity is strongly affected by large molecular size differences and strong cross interactions between the different types of molecules. Viscosity-composition plots for aqueous mixtures can have maxima or minima, and viscosities for these mixtures are particularly difficult to estimate. The UNIFAC-VISCO method described below can be used to predict liquid viscosity of organic mixtures without any mixture data. It is relatively successful even for large differences in molecular size, but it is currently limited in scope by the small number of group contributions available. 

When it is necessary to estimate activity coefficients where no data or very limited data are available, estimates may be made by using a group contribution method. In this case, a molecule is divided into fimctional groups, or subgroups of the molecule. These subgroups are assumed to act independently of the molecule in which they appear. Molecular interactions are accounted for by properly weighted sums of group interactions. Fredenslund, Jones, and Prausnitz developed the method for UNIQUAC and named it as universal functional activity coefficient (UNIFAC). Smith, van Ness, and Abbott report the equations for the activity coefficients of multicomponent solutions and their parameters. These equations are very... 

This method deserves special mention because, unlike all of the previous methods, it allows the prediction of activity coefficients based entirely on tabulated parameters i.e., no fitting of parameters is necessary. It builds on UNIQUAC and is based on the premise that a solution maybe regarded as a mixture of structural units rather than of chemical species. For example, a mixture of n-pentane and n-heptane is considered as a mixture of CHa and CH3 subgroups and so is a mixture of cyclohexane and ethane. In this approach, interaction parameters are determined between a finite number of subgroups and are tabulated. It is then possible to calculate activity coefficients for any solution, binary or multicomponent, from a relatively small number of tabulated values. This is the main advantage of the method. Its applicability is limited to components that are liquid at 25 C. Parameters for the UNIFAC equation have been... 

This kind of computation can be done using the UNIFAC contribution group method [11], but its empirical character and lack of available parameters at high temperatures limit drastically its usefulness. 

A step forward in modelling is provided by the use of activity coefficient models and group contribution methods. One of the most valuable features of these methods is their applicabihty to multi-component systems imder the assumption that local compositions can be described in this case by a relationship similar to that obtained for binary systems. However, one of the main disadvantages of these methods is that they depend on an extremely large amount of experimental data. Furthermore, the absence of the volume and surface p>arameters p>oses a hindrance in the calculation of the binary interaction parameters for UNIQUAC and UNIFAC models. These limitations can be overcome by the use of quantum-based models, such as COSMO-RS (see, for instead, the works of Shah et al., (2002) and of Guo et al. (2007)). In this method no experimental data is needed as an input to model the ionic hquids, being the main constraint the extensive computational time and also that, in some cases, the comparison with experimental data is only qualitative. 

---