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Figure 7 – uploaded by G.Ali Mansoori

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where JN is the number of data points. solution. Figure 8 shows the variation of activity coefficient versus temperature at constant

Figure 8 where JN is the number of data points. solution. Figure 8 shows the variation of activity coefficient versus temperature at constant

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equations: As it is seen from these figures, closer agreement with experimental data even at higher concentrations where other models generally fail is observed. Of course, some models which have several aajustable parameters can give a better fit with experimental data However, the dependency of these models on their adjustable parameters imposes limitations on their applications since the parameters should primarily be evaluated fron experimental data. Also Table 1 shows the accuracy of the results obtained via th proposed model. The root mean square of the errors have been calculated by the followin; aniiatinne: However, the dependency of these models on their adjustable parameters impose: concentrations where other models generally fail is observed. Of course, some models
equations: As it is seen from these figures, closer agreement with experimental data even at higher concentrations where other models generally fail is observed. Of course, some models which have several aajustable parameters can give a better fit with experimental data However, the dependency of these models on their adjustable parameters imposes limitations on their applications since the parameters should primarily be evaluated fron experimental data. Also Table 1 shows the accuracy of the results obtained via th proposed model. The root mean square of the errors have been calculated by the followin; aniiatinne: However, the dependency of these models on their adjustable parameters impose: concentrations where other models generally fail is observed. Of course, some models
Related topics:
Thermodynamics of Electrolyte SolutionsOsmotic CoefficientsActivity CoefficientRadial Distribution FunctionsKirkwood-Buff Solution Theory
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