sgtpy is an open-source python package of SAFT-VR-Mie and SAFT-Gamma-Mie Equations of State (EOS). sgtpy allows to work with pure fluids and fluid mixtures, additionally the fluids can be modeled considering association, cross-association and polar contributions. sgtpy was built on top of phasepy's phase equilibrium and Square Gradient Theory (SGT) functions. These functions were updated to speed-up calculations of associative mixtures.
sgtpy relies on numpy, scipy and phasepy.
New in v0.0.12: Group contribution SAFT-Gamma-Mie
Note in v0.0.13: Due to PEP-8 standard's the module name has change from SGTPy to sgtpy.
- numpy
- scipy
- cython
- pandas
- numba
Get the latest version of sgtpy from https://pypi.python.org/pypi/sgtpy/
If you have an installation of Python with pip, simple install it with:
$ pip install sgtpy
To get the git version, run:
$ git clone https://github.com/gustavochm/sgtpy
Note for Apple Silicon users: It is recommended to install python and sgtpy dependencies (numpy, scipy, cython, pandas and numba) through conda miniforge, then you can install sgtpy running pip install sgtpy.
SGTPy's documentation is available on the web (under development):
sgtpy easily allows you to perform phase equilibria and interfacial properties calculations using SAFT-VR-Mie or SAFT-gamma-Mie EoS. First, components are defined with their molecular parameters, then a mixture can be created with them.
>>> import numpy as np
>>> from sgtpy import component, mixture, saftvrmie
>>> ethanol = component('ethanol2C', ms=1.7728, sigma=3.5592 , eps=224.50,
lambda_r=11.319, lambda_a=6., eAB=3018.05, rcAB=0.3547,
rdAB=0.4, sites=[1,0,1], cii=5.3141080872882285e-20)
>>> hexane = component('hexane', ms=1.96720036, sigma=4.54762477,
eps=377.60127994, lambda_r=18.41193194,
cii=3.581510586936205e-19)
>>> mix = mixture(hexane, ethanol)
>>> # fitted to experimental data
>>> kij = 0.011818492037463553
>>> Kij = np.array([[0, kij], [kij, 0]])
>>> mix.kij_saft(Kij)
>>> eos = saftvrmie(mix)The eos object can be used to compute phase equilibria.
>>> from sgtpy.equilibrium import bubblePy
>>> # computing bubble point
>>> T = 298.15 # K
>>> x = np.array([0.3, 0.7])
>>> # initial guesses for vapor compotision and pressure
>>> y0 = 1.*x
>>> P0 = 8000. # Pa
>>> sol = bubblePy(y0, P0, x, T, eos, full_output=True)Finally, the equilibria results can be used to model the interfacial behavior of the mixture using SGT.
>>> from sgtpy.sgt import sgt_mix
>>> # reading solution object
>>> y, P = sol.Y, sol.P
>>> vl, vv = sol.v1, sol.v2
>>> #density vector of each phase
>>> rhox = x/vl
>>> rhoy = y/vv
>>> bij = 0.05719272059410664
>>> beta = np.array([[0, bij], [bij, 0]])
>>> eos.beta_sgt(beta)
>>> #solving BVP of SGT with 25 colocation points
>>> solsgt = sgt_mix(rhoy, rhox, T, P, eos, n = 25, full_output = True)For more examples, please have a look at the Jupyter Notebook files located in the examples folder of the sources or view examples in github.
The latest development version of SGTPy's sources can be obtained at
git clone https://github.com/gustavochm/SGTPy
To report bugs, please use the SGTPy's Bug Tracker at:
This package is part of the article SGTPy: A Python open-source code for calculating the interfacial properties of fluids based on the Square Gradient Theory using the SAFT-VR Mie equation of state by Andrés Mejía, Erich A. Müller and Gustavo Chaparro. J. Chem. Inf. Model., 2021, https://doi.org/10.1021/acs.jcim.0c01324.
See LICENSE.txt for information on the terms & conditions for usage of this software, and a DISCLAIMER OF ALL WARRANTIES.
Although not required by the sgtpy license, if it is convenient for you, please cite sgtpy if used in your work. Please also consider contributing any changes you make back, and benefit the community.
