pyvaporation

The solution for modelling pervaporation membrane performance based on experimental data

https://github.com/membrizard/pyvaporation

Science Score: 57.0%

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

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 2 DOI reference(s) in README
○
Academic publication links
○
Committers with academic emails
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (16.8%) to scientific vocabulary

Keywords

chemical-engineering diffusion environmental-engineering heat-capacity membranes modelling-tool pervaporation physics process-simulation python scientific scipy thermodynamics uniquac vapor-liquid-equilibrium vaporisation-heat vapour-pressure

Last synced: 6 months ago · JSON representation ·

Repository

The solution for modelling pervaporation membrane performance based on experimental data

Basic Info

Host: GitHub
Owner: Membrizard
License: apache-2.0
Language: Jupyter Notebook
Default Branch: main
Homepage: https:\\pervaporation-modelling.com
Size: 3.42 MB

Statistics

Stars: 75
Watchers: 1
Forks: 4
Open Issues: 2
Releases: 15

Topics

Created almost 4 years ago · Last pushed almost 3 years ago

Metadata Files

Readme License Citation

README.md

For simplification of the package usage we have built the Pervaporation Modelling App

The app allows performing basic calculations available in the package using a User-friendly web-based UI.

This solution is designed specifically to assist Researchers in the field of Pervaporation membranes development. By means of the proposed instrument one can easily model a performance of a particular membrane with known permeance (Pi - GPU, SI, kg/(m2 * h * kPa)) and apparent energy of transport activiation (Ea - J/mol) values for each component of a considered binary mixture, if the transport is considered Ideal (Permeances are not dependent on the mixture composition)

Or, given that the diffusion curve set of a non-ideal process is measured one can model the non-ideal process in isothermal or non-isothermal (adiabatic) mode. Non-isothermal modelling for both type of processes supports self-cooling mode, or temperature program mode.

The comprehensive review of the theoretical background, applicability and code-examples may be found here

Following mixtures are Currently built into the solution:

(Current version supports only binary mixtures)

H2O/MeOH
H2O/EtOH
H2O/IPOH
H2O/Acetic acid
MeOH/toluene
MeOH/Methyl-tert-butyl ether
MeOH/Dimethylcarbonate
EtOH/Ethyl-tert-butyl ether

Assumptions and applicability

The activity coefficients of the binary mixture are calculated by means of NRTL or UNIQUAC model
Saturated vapour pressure could be assessed using Antoine or Frost equations
Vaporisation/Condensation heat values are calculated using Clapeyron-Clausius equation
Specific heat capacities are calculated using polynomial approximation
The ideal modelling process is applicable only for the processes, where permeance values do not depend significantly on mixture composition
The non-ideal modelling is performed only on the basis of specified diffusion curves (Fluxes/Permeances of each component as a function of first component concentration in feed)
Non-Ideal modelling supports non-linear dependencies of permeances and activation energies on feed composition
Non-Isothermal processes support pre-defined temperature program (feed temperature as a function of process time may be specified for process modelling)

Installation

Requirements:

python 3.7 or higher

To install: pip install pyvaporation

Code examples

You can run code-examples.ipynb from github.com/Membrizard/PyVaporation/code-examples.ipynb in order to check the package functionality.

Hints for general usage

Pre-configured default membranes are located in ./tests/default_membranes
VLE data used to fit UNIQUAC Parameters of default mixtures is located in ./tests/VLE_data
VLE data for a mixture could be fitted with a UNIQUAC model using fit_vle( data: VLEPoints, method: typing.Optional[str] = None, ) -> UNIQUACParameters
To run automated tests for all the modules: python -m pytest -sv tests/

Owner

Name: Denis Sapegin
Login: Membrizard
Kind: user
Company: Quantori

Repositories: 2
Profile: https://github.com/Membrizard

An experienced Chemist and Chemical process engineer passionate about helpful solutions for scientists.

Citation (CITATION.cff)

cff-version: 1.1.4
message: "If you use this software, please cite it as below."
authors:
- family-names: "Sapegin"
  given-names: "Denis Andzheevich"
  orcid: "https://orcid.org/0000-0002-1446-6288"
- family-names: "Chekmachev"
  given-names: "Aleksei Viktorovich"
title: "PyVaporation"
version: 1.1.4
date-released: 2022-07-18
url: "https://github.com/Membrizard/PyVaporation"
preferred-citation:
  type: article
  authors:
  - family-names: "Sapegin"
    given-names: "Denis Andzheevich"
    orcid: "https://orcid.org/0000-0002-1446-6288"
  - family-names: "Chekmachev"
    given-names: "Aleksei Viktorovich"
  doi: "10.3390/membranes12080784"
  journal: "Membranes"
  month: 8
  article-number: 784
  title: "PyVaporation: A Python Package for Studying and Modelling Pervaporation Processes"
  issue: 8
  volume: 12
  year: 2022

GitHub Events

Total

Watch event: 3

Last Year

Watch event: 3

Committers

Last synced: about 1 year ago

All Time

Total Commits: 409
Total Committers: 4
Avg Commits per committer: 102.25
Development Distribution Score (DDS): 0.21

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Membrizard	d**n@q**m	323
Alexey Chekmachev	a**v@g**m	78
Alexey Chekmachev	a**v@a**m	7
Oleg Ivashov	i**9@g**m	1

Committer Domains (Top 20 + Academic)

assaia.com: 1 quantori.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 20
Total pull requests: 22
Average time to close issues: 25 days
Average time to close pull requests: 3 days
Total issue authors: 2
Total pull request authors: 3
Average comments per issue: 0.6
Average comments per pull request: 0.05
Merged pull requests: 15
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

Membrizard (18)
pajeeloy (2)

Pull Request Authors

Membrizard (15)
pajeeloy (6)
Ivashkaization (1)

Top Labels

Issue Labels

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 70 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 15
Total maintainers: 1

pypi.org: pyvaporation

Set of tools for modelling pervaporation processes

Homepage: https://github.com/Membrizard/PyVaporation
Documentation: https://pyvaporation.readthedocs.io/
License: Apache license 2.0
Latest release: 1.2.1
published almost 3 years ago

Versions: 15
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 70 Last month

Rankings

Dependent packages count: 6.6%

Stargazers count: 8.4%

Downloads: 12.0%

Average: 14.7%

Forks count: 15.7%

Dependent repos count: 30.6%

Maintainers (1)

Membrizard

Last synced: 6 months ago

Dependencies

requirements.txt pypi

attr ==0.3.1
attrs ==21.4.0
joblib ==1.1.0
matplotlib ==3.5.2
pandas ==1.3.5
scipy ==1.7.3

setup.py pypi

attr ==0.3.1
attrs ==21.4.0
joblib ==1.1.0
matplotlib ==3.5.2
pandas ==1.3.5
scipy ==1.7.3

.github/workflows/python-package.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite

.github/workflows/python-publish.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite
pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite