期刊
COMPUTER PHYSICS COMMUNICATIONS
卷 200, 期 -, 页码 364-380出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cpc.2015.11.016
关键词
Ideal adsorbed solution theory; IAST; Mixed-gas adsorption
资金
- U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) program
- DOE [DE-AC05-06OR23100]
- U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences [DE-FG02-12ER16362]
Ideal adsorbed solution theory (LAST) 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. pylAST 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. (C) 2016 Elsevier B.V. All rights reserved.
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