Activity coefficient-models recommended

Plyasunova et al. critically evaluated the standard thermodynamic quantities of Ni, its hydrolysis reactions and hydroxo-complex formation on the basis of published experimental studies and the specific interaction theory (SIT) for activity coefficient modelling [97GRE/PLY2]. Recommended thermodynamic functions and interaction coefficients relevant for the present review and its compounds were presented, see Table A-... 

Activity-coefficient models, however, can only be used to calculate liquid-state fugacities and enthalpies of mixing. These models provide algebraic equations for the activity coefficient (y,) as a function of composition and temperature. Because the activity coefficient is merely a correction factor for the ideal-solution model (essentially Raoult s Law), it cannot be used for supercritical or noncondensable components. (Modifications of these models for these types of systems have been developed, but they are not recommended for the process simulator user without consultation with a thermodynamics expert.)... 

Equations of state are recommended for simple systems (nonpolar, small molecules) and in regions (especially supercritical conditions for any component in a mixture) where activity-coefficient models are... 

The recommended strategy for choosing a liquid-state activity-coefficient model is as follows. 

Use of the models outside of the classes should be done with caution. In some cases none of the models may be recommended for a particular class. This means that although a particular class was included in the evaluations, the best model still gives unreasonably high errors. Therefore, alternative means should be used if possible to get an activity coefficient for these classes. Finally, for some classes the finite and infinite recommendations disagree. This disagreement only occurred in systems where the data were limited and may change if more data are evaluated. 

Overall, UNIFAC-FV is the best model for predicting the weight fraction activity coefficient. It is recommended for 7 of the 16 finite concentration data classes and for 15... 

Liquid Mixtures Compositions at the liquid-vapor interface are not the same as in the bulk liquid, and so simple (bulk) composition-weighted averages of the pure-fluid values do not provide quantitative estimates of the surface tension at the vapor-liquid interface of a mixture. The behavior of aqueous mixtures is more difficult to correlate and estimate than that of nonpolar mixtures because small amounts of organic material can have a pronounced effect upon the surface concentrations and the resultant surface tension. These effects are usually modeled with thermodynamic methods that account for the activity coefficients. For example, a UNIFAC method [Suarez, J. T. C. Torres-Marchal, and P. Rasmussen, Chem. Eng. Set, 44 (1989) 782] is recommended and illustrated in PGL5. For nonaqueous systems the extension of the parachor method, used above for pure fluids, is a simple and reasonably effective method for estimating a for mixtures. 

Click and then the box on the left (in the Data Browser option). The part of the process represented by that box is then described. You can also click on the box and then the FI key to obtain the same information. The Help menu provides detailed help in all areas. For example, the Help/Physical Property Methods/Choosing a Property Method gives advice about which thermodynamic model is recommended for different applications. The Guidelines for Choosing a Property Method and Guidelines for Choosing an Activity Coefficient Method are sub-menus that outline decision trees to guide your choice. 

The Davies correction has no theoretical substantiation. Equation (1.77) ignores ion sizes and produces equal values y. for equal charges. It is not recommended for moderate and high ionic strength for calculation of activities coefficient of micro-concentrations. The Davies equation gives sufficiently accurate results at ionic strength of up to 0.5 mole-kg . It is handy for the application for geochemical models imder standard conditions. 

Our recommended approach is to examine the model and the speciflc inflows and to try to estimate pH. Given this estimate, the actual K values and an initial estimation of all activity coefficients of 1.0, one can usually estimate the other species concentrations. A general application of this approach c n be seen in Butler (6) where the concept of "predominance plots" is discussed. In "predominance plots" various species concentrations are plotted versus pH. Normally, these plots are developed based upon actual K values along with a very simple estimate of activity coefficients. Figure 9.2 illustrates a typical predominance plot for phosphoric acid. 

Often semiempirical models for determination of the activity coefficients that account for nonideal solution behavior are used. An application-oriented introduction is given in Ref [12]. Here, several easy-to-use approximate calculations for solubility estimation are discussed. In principle, such methods are recommended to be applied as complementary tools to experimental solubility determination that can help to reduce the experimental efforts required. 

Most process simulators include these types of correlations but they are largely of historical interest or used to maintain compatibility with old models. We do not recommend using simple methods, since they cannot adequately quantify the transition from vapor to liquid phases beyond the original correlation. In addition, these correlations tend to be thermodynamically poor (do not consider any interactions between components and thermodynamically inconsistent at higher pressures) and we cannot integrate models using these correlations into new models that use an equation of state or activity coefficient approach without significant efibrts. 

The use of the following statistical characteristics of the test set was also recommended [33] (1) correlation coefficient between the predicted and observed activities, (2) coefficients of determination (predicted versus observed activities R, and observed versus predicted activities Rq), (3) slopes fc and k of the regression lines through the origin. Thus, we consider a QSAR model predictive if the following conditions are satisfied [33] ... 

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