pyIsoFit 0.0.1

Package for The Fitting of Adsorption Isotherms and Prediction of Multi-component Isotherms

Extended thermodynamic models are extensions of pure-component isotherm models that can predict multi-component adsorption isotherms. Thus, they offer an alternative method of predicting multi-component isotherms to the widely used Ideal adsorbed solution theory (IAST). However, their accuracy is dependent on the pure component models that are used to fit the data and an accessible python package does not yet exist like for IAST (pyIAST). Therefore, a Python package, pyIsoFit, is presented that can fit 10 different isotherm models to pure component adsorption data and predict multi-component adsorption using an extended model. The package is created with a focus on fitting with isotherm models that exhibit thermodynamically correct behaviour. The package created is flexible and can fit any number of datasets with features such as an ability to toggle fitting constraints for the different fitting procedures implemented. Additionally, the package can be used to fit any number of isotherm data and readily generate plots of the fittings and tables of the fitting parameters for the user and predict co-adsorption using the extended dual-site Langmuir model. While currently the package is limited to only one co-adsorption fitting procedure, it sets foundational work for an extended model-based python package for multi-component prediction that can serve as an alternative to IAST.