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.
['Cory M. Simon', 'Berend Smit', 'Maciej Haranczyk']
pyIAST: Ideal adsorbed solution theory (IAST) Python package