FeOs-torch combines the FeOs thermodynamics engine with the machine learning/automatic differentiation framework PyTorch.
import torch
from feos_torch import PcSaftPure
# define PC-SAFT parameters
# m, sigma, epsilon_k, mu, kappa_ab, epsilon_k_ab, na, nb
params = torch.tensor([1.5, 3.5, 250.0, 0, 0.03, 1500.0, 1, 1], dtype=torch.float64, requires_grad=True)
pcsaft = PcSaftPure(params.repeat(5, 1))
# evaluate vapor pressures (in Pa)
temperature = torch.tensor([250., 300., 350., 400., 450.], dtype=torch.float64)
_, vp = pcsaft.vapor_pressure(temperature)
print(vp)
# determine the derivatives of the first vapor pressure w.r.t. PC-SAFT parameters
vp[0].backward()
print(params.grad)tensor([ 20693.5960, 216164.6184, 1049770.6187, 3281855.9640, 7875531.7021],
dtype=torch.float64, grad_fn=<MulBackward0>)
tensor([-6.7923e+04, -1.7737e+04, -7.0413e+02, 0.0000e+00, -5.7458e+05,
-6.9122e+01, -3.6892e+04, -3.6892e+04], dtype=torch.float64)
The following models and properties are currently implemented in FeOs-torch
| model | vapor pressure | liquid density | equilibrium liquid density | bubble point pressure | dew point pressure |
|---|---|---|---|---|---|
| PC-SAFT | ✓ | ✓ | ✓ | ✓ | ✓ |
| gc-PC-SAFT | ✓ | ✓ |
If you find FeOs-torch useful for your own research, consider citing our publication from which this library resulted.
@article{rehner2023mixtures,
author = {Rehner, Philipp and Bardow, André and Gross, Joachim},
title = {Modeling Mixtures with PCP-SAFT: Insights from Large-Scale Parametrization and Group-Contribution Method for Binary Interaction Parameters}
journal = {International Journal of Thermophysics},
volume = {44},
number = {12},
pages = {179},
year = {2023}
}