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https://github.com/cadet/rdm-example-characterize-chromatographic-system

https://github.com/cadet/rdm-example-characterize-chromatographic-system

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 3 committers (66.7%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: cadet
  • Language: Python
  • Default Branch: main
  • Size: 172 KB
Statistics
  • Stars: 0
  • Watchers: 3
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Created over 1 year ago · Last pushed 12 months ago
Metadata Files
Readme

README.md

Characterization of Chromatographic Systems in CADET

This repository contains example simulations demonstrating the workflow for characterizing chromatographic systems using CADET-Process and CADET-RDM. The steps required to model a chromatographic system are outlined, including how to fit the model to experimental data. The characterization of the system periphery, such as tubing and valves, is not yet included in this example. The following contents are covered:

  • Fit Column Transport Parameters - column_transport_parameters.py
    A system with a non-binding, non-pore-penetrating tracer is simulated using the Lumped Rate Model With Pores unit operation model to optimize the parameters: Bed porosity and Axial Dispersion

  • Fit Pore Transport Parameters - pore_transport.py
    A system with a non-binding, pore-penetrating tracer is simulated using the Lumped Rate Model with Pores unit operation model to optimize the parameters: Particle porosity and Film diffusion

  • Fit Binding Model Parameters - binding_model_parameters.py
    A system with a pore-penetrating tracer is simulated using the Lumped Rate Model with Pores unit operation model to estimate Steric Mass Action (SMA) binding parameters based on multiple gradient elution chromatograms.

Authors

  • Johannes Schmölder
  • Katharina Paul
  • Ronald Jäpel
  • Hannah Lanzrath

Running the Example Simulation

  1. Clone this repository.
  2. Set up the environment using the environment.yml file.
  3. Run the simulation:

bash python main.py

The results will be stored in the src folder inside the output directory.

Note: Running cadet-rdm requires Git LFS, which needs to be installed separately.

  • Ubuntu/Debian:

bash sudo apt-get install git-lfs git lfs install

  • macOS (with Homebrew):

bash brew install git-lfs git lfs install

Output Repository

The output data for this case study can be found here:
Link to Output Repository

Owner

  • Name: CADET
  • Login: cadet
  • Kind: organization
  • Email: cadet@fz-juelich.de

GitHub Events

Total
  • Issues event: 2
  • Delete event: 1
  • Member event: 1
  • Push event: 13
  • Create event: 6
Last Year
  • Issues event: 2
  • Delete event: 1
  • Member event: 1
  • Push event: 13
  • Create event: 6

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 24
  • Total Committers: 3
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.458
Past Year
  • Commits: 24
  • Committers: 3
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.458
Top Committers
Name Email Commits
Katharina Paul k****l@r****e 13
r.jaepel r****l@g****m 10
Ronald r****l@f****e 1
Committer Domains (Top 20 + Academic)
fz-juelich.de: 1 rwth-aachen.de: 1

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 2
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 minute
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 minute
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ronald-jaepel (2)
Pull Request Authors
  • ronald-jaepel (1)
Top Labels
Issue Labels
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