pyIAST: Ideal adsorbed solution theory (IAST) Python package

详细信息    查看全文

• 作者：Cory M. Simon^a ; ^b ; Berend Smit^c ; ^a ; Maciej Haranczyk^b ; ^{mharanczyk@lbl.gov" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Ideal adsorbed solution theory ; IAST ; Mixed-gas adsorption
• 刊名：Computer Physics Communications
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：200
• 期：Complete
• 页码：364-380
• 全文大小：2078 K

文摘

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.