Statistical Associating Fluid Theory, or SAFT, is an advanced molecular thermodynamic method that can predict a wide variety of thermodynamic properties of mixtures accurately based on physically-realistic models of molecules and their interactions with other molecules.
PSE has implemented the SAFT-VR and the SAFT-γ Mie group contribution methods in the gSAFT product. gSAFT properties can be used within any applicable gPROMS family product.
The advantages of SAFT
Because SAFT uses a more physically-realistic underlying model than standard cubic equations of state, it has many advantages when it comes to accurately predicting properties of pure components and mixtures over a wide range of operating conditions from limited experimental data.
In particular, it can predict properties of complex mixtures with an accuracy that has not been possible in the past, including:
- polar fluids (e.g. CO2, refrigerants)
- strongly associating / hydrogen bonding (e.g. carboxylic acids, HF, water)
- mixed electrolytes (e.g. inorganic salts, charged surfactants)
- gas hydrates & asphaltenes.
The power of group contribution
A key advantage of the PSE gSAFT implementation is the use of group contribution methods that allow highly-accurate prediction of a wide range of properties and mixture equilibria from a very limited set of measured data.
This is particularly important in industries dealing with non-standard chemicals such as surfactants, oleochemicals or polymers.