Simulation Testbed for Liquid Chromatography

The Simulation Testbed for Liquid Chromatography (STLC) package implements variations of the general rate model. The aim is to provide a basis for the development of an easy to use package for chromatography modeling in Python.

Ongoing development

Please be aware that this is software created as part of research and is provided as is.

Install

You can install by cloning the repository and running:

	> pip install setuptools
	> python setup.py sdist
	> pip install -e ./

from the project root.

Usage

Examples are provided in the examples directory. A model may also be instantiated and run as follows:

	from stlc import lkm
	zl = 1.0
	epsilon = 0.4
	u = 0.29
	tmax = 20
	a = 0.85
	D = 1e-6
	k = 111.0
	c_0 = 1.0
	b = 1.0

	parameters0 = lkm.ModelParameters(u=u, ep=epsilon, D=D, c0=c_0, k=k, a=a, b=b, ip = lambda t: t<1.)

	n = 10
	ne = 10
	dt = 0.01
	timesteps = int(tmax / dt)
	model = lkm.LumpedKineticModel(n, ne, zl, [parameters0])
	y = lkm.solve(model, tmax, dt)

Cite

@article{ANDERSSON2023108068,
title = {Numerical simulation of the general rate model of chromatography using orthogonal collocation},
author = {David Andersson and Rickard Sjögren and Brandon Corbett},
journal = {Computers & Chemical Engineering},
volume = {170},
pages = {108068},
year = {2023},
doi = {https://doi.org/10.1016/j.compchemeng.2022.108068},
}

References

Code implementing orthogonal collocation discretization: Larry C. Young 2019, Orthogonal collocation revisited, Computer Methods in Applied Mechanics and Engineering.

License

Released under GPL v3. License (see LICENSE.txt):