PyIAST: Ideal adsorbed solution theory (IAST) Python package

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/pyIASTDocumentation: http://pyiast.readthedocs.org/en/latest/ Program summaryProgram title: pyIASTCatalogue identifier: AEZA_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEZA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: MITNo. of lines in distributed program, including test data, etc.: 38478No. of bytes in distributed program, including test data, etc.: 1918879Distribution format: tar.gzProgramming language: Python.Operating system: Linux, Mac, Windows.Classification: 23.External routines: Pandas, Numpy, ScipyNature 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.