Research output per year
Research output per year
Research output: Contribution to journal › Article › Academic › peer-review
Industrial exhaust gases have a strong environmental impact, including on global warming. Carbon dioxide (CO2) is a prominent example of such an exhaust gas. Therefore, CO2 capture and storage in industrial processes is becoming increasingly important. Preferably, these emitted gases are separated before their release into the environment. Such applications require selective gas separation to isolate the harmful gases or to allow recycling of industrially relevant gases. Porous materials are promising candidates to achieve gas separation, since their large surface area enables them to adsorb large quantities while their selectivity can be tuned by controlling their chemical composition. Modelling adsorption behavior and calculating corresponding selectivities in multicomponent gas mixtures of such porous materials, which is essential to quantify their gas separation performance, can be achieved through the Ideal Adsorption Solution Theory (IAST), which can be challenging to perform. The current available softwares for IAST calculations demand programming knowledge that not every materials scientist has or has access to, limiting the development of new porous materials for gas separation purposes. In this paper, we present a simple, user-friendly program for IAST loading and selectivity predictions for binary gas mixtures based on the Python module pyIAST. We have developed a graphical user interface resembling commonly known software and made three-dimensional selectivity predictions easily accessible within just a few clicks. The input and output data structure relies on the widely used *.csv format and isotherm data can be fitted with various established models. Therefore, our software provides a platform for IAST calculations for non-programming researchers, which is expected to enable more materials scientists to screen their porous materials for desired gas separation properties. Program summary: Program Title: GraphIAST CPC Library link to program files: https://doi.org/10.17632/ytc64xwcr5.1 Developer's repository link: https://github.com/ORC-WUR/GraphIAST Licensing provisions: MIT Programming language: Python External Routines: Math, Matplotlib, Numpy, Pandas, PIL, pyIAST, Tkinter Supplementary material: User Manual, Case studies Nature of problem: Using IAST to predict the selectivity in binary gas mixtures based on their pure component isotherms. Solution method: Employing the pyIAST package  and incorporating this into a GUI surrounding for a facilitated use of IAST analysis. Additionally, the selectivity predictions have been extended to multiple selectivities at different mole fractions and pressures. References:  C.M. Simon, B. Smit, M. Haranczyk, Comput. Phys. Commun. 200 (2016) 364–380.
Research output: Non-textual form › Software