"Fine-tuned values of $k_{ij}$ can be provided when instantiating the model, for Peng-Robinson and SRK. A complete matrix of all the $k_{ij}$ values must be provided. This allows for asymmetric mixing models in which $k_{ij}\\neq k_{ji}$."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a32c41b5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<teqp.teqp.PCSAFTEOS at 0x19478910bf0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_01 = 0.01; k_10 = k_01\n",
"kmat = [[0,k_01,0],[k_01,0,0],[0,0,0]]\n",
"teqp.PCSAFTEOS(coeffs, kmat)"
]
},
{
"cell_type": "markdown",
"id": "ca52e844",
...
...
%% Cell type:markdown id:f406bbb5 tags:
# PC-SAFT
The PC-SAFT implementation in teqp is based on the implementation of Gross and Sadowski (https://doi.org/10.1021/ie0003887), with the typo from their paper fixed. It does NOT include the association contribution, only the dispersive contributions.
The model in teqp requires the user to specify the values of ``sigma``, ``epsilon/kB``, and ``m`` for each substance. A very few substances are hardcoded in teqp, for testing purposes.
Fine-tuned values of $k_{ij}$ can be provided when instantiating the model, for Peng-Robinson and SRK. A complete matrix of all the $k_{ij}$ values must be provided. This allows for asymmetric mixing models in which $k_{ij}\neq k_{ji}$.
%% Cell type:code id:a32c41b5 tags:
``` python
k_01=0.01;k_10=k_01
kmat=[[0,k_01,0],[k_01,0,0],[0,0,0]]
teqp.PCSAFTEOS(coeffs,kmat)
```
%% Output
<teqp.teqp.PCSAFTEOS at 0x19478910bf0>
%% Cell type:markdown id:ca52e844 tags:
## Superancillary
The superancillary equation for PC-SAFT has been developed, and is much more involved than that of the cubic EOS. As a consequence, the superancillary equation has been provided as a separate package rather than integrating it into to teqp to minimize the computational overhead. It can be installed from PYPI with: ``pip install PCSAFTsuperanc``
