pyIAST: Ideal adsorbed solution theory (IAST) Python package
Ideal adsorbed solution theory (IAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of pure-component adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of...
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| Vydané v: | Computer physics communications Ročník 200; číslo C; s. 364 - 380 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
01.03.2016
Elsevier |
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| ISSN: | 0010-4655, 1879-2944 |
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| Abstract | Ideal adsorbed solution theory (IAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of pure-component adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of components. pyIAST supports several common analytical models to characterize the pure-component isotherms from experimental or simulated data. Alternatively, pyIAST can use numerical quadrature to compute the spreading pressure for IAST calculations by interpolating the pure-component isotherm data. pyIAST can also perform reverse IAST calculations, where one seeks the required gas phase composition to yield a desired adsorbed phase composition.
Source code: https://github.com/CorySimon/pyIAST
Documentation: http://pyiast.readthedocs.org/en/latest/
Program title: pyIAST
Catalogue identifier: AEZA_v1_0
Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEZA_v1_0.html
Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland
Licensing provisions: MIT
No. of lines in distributed program, including test data, etc.: 38478
No. of bytes in distributed program, including test data, etc.: 1918879
Distribution format: tar.gz
Programming language: Python.
Operating system: Linux, Mac, Windows.
Classification: 23.
External routines: Pandas, Numpy, Scipy
Nature of problem: Using ideal adsorbed solution theory (IAST) to predict mixed gas adsorption isotherms from pure-component adsorption isotherm data.
Solution method: Characterize the pure-component adsorption isotherm from experimental or simulated data by fitting a model or using linear interpolation; solve the nonlinear system of equations of IAST.
Running time: Less than a second. |
|---|---|
| AbstractList | Ideal adsorbed solution theory (IAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of pure-component adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of components. pyIAST supports several common analytical models to characterize the pure-component isotherms from experimental or simulated data. Alternatively, pyIAST can use numerical quadrature to compute the spreading pressure for IAST calculations by interpolating the pure-component isotherm data. pyIAST can also perform reverse IAST calculations, where one seeks the required gas phase composition to yield a desired adsorbed phase composition.
Source code: https://github.com/CorySimon/pyIAST
Documentation: http://pyiast.readthedocs.org/en/latest/
Program title: pyIAST
Catalogue identifier: AEZA_v1_0
Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEZA_v1_0.html
Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland
Licensing provisions: MIT
No. of lines in distributed program, including test data, etc.: 38478
No. of bytes in distributed program, including test data, etc.: 1918879
Distribution format: tar.gz
Programming language: Python.
Operating system: Linux, Mac, Windows.
Classification: 23.
External routines: Pandas, Numpy, Scipy
Nature of problem: Using ideal adsorbed solution theory (IAST) to predict mixed gas adsorption isotherms from pure-component adsorption isotherm data.
Solution method: Characterize the pure-component adsorption isotherm from experimental or simulated data by fitting a model or using linear interpolation; solve the nonlinear system of equations of IAST.
Running time: Less than a second. Ideal adsorbed solution theory (IAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of pure-component adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of components. pyIAST supports several common analytical models to characterize the pure-component isotherms from experimental or simulated data. Alternatively, pyIAST can use numerical quadrature to compute the spreading pressure for IAST calculations by interpolating the pure-component isotherm data. pyIAST can also perform reverse IAST calculations, where one seeks the required gas phase composition to yield a desired adsorbed phase composition. Source code: https://github.com/CorySimon/pyIAST Documentation: http://pyiast.readthedocs.org/en/latest/ Program summary Program title: pyIAST Catalogue identifier: AEZA_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEZA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: MIT No. of lines in distributed program, including test data, etc.: 38478 No. of bytes in distributed program, including test data, etc.: 1918879 Distribution format: tar.gz Programming language: Python. Operating system: Linux, Mac, Windows. Classification: 23. External routines: Pandas, Numpy, Scipy Nature of problem: Using ideal adsorbed solution theory (IAST) to predict mixed gas adsorption isotherms from pure-component adsorption isotherm data. Solution method: Characterize the pure-component adsorption isotherm from experimental or simulated data by fitting a model or using linear interpolation; solve the nonlinear system of equations of IAST. Running time: Less than a second. |
| Author | Simon, Cory M. Smit, Berend Haranczyk, Maciej |
| Author_xml | – sequence: 1 givenname: Cory M. surname: Simon fullname: Simon, Cory M. organization: UC Berkeley, Department of Chemical and Biomolecular Engineering, Berkeley, CA 94720-1462, USA – sequence: 2 givenname: Berend surname: Smit fullname: Smit, Berend organization: UC Berkeley, Department of Chemical and Biomolecular Engineering, Berkeley, CA 94720-1462, USA – sequence: 3 givenname: Maciej surname: Haranczyk fullname: Haranczyk, Maciej email: mharanczyk@lbl.gov organization: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8139, USA |
| BackLink | https://www.osti.gov/biblio/1246683$$D View this record in Osti.gov |
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