Pyisotherm: Python Library for Single-Component Isotherms

Objective: The objective of this study is to present the development and validation of the pyIsotherm library, created in Python, designed for estimating parameters of single-component adsorption isotherm models, aiming to enhance the precision and efficiency of modeling in materials engineering and water treatment fields. Theoretical Framework: This study is grounded in classical and modern adsorption isotherm models, such as Langmuir, Sips, Toth, BET, BET-Aranovich, GAB, and Multi-site Langmuir. The literature highlights the importance of robust computational methods for the proper characterization of adsorbent materials. Method: The library was implemented in Python and applied to 31 datasets extracted from ten recent scientific articles. Parameter estimation was performed using the Particle Swarm Optimization algorithm. The quality of the fitting was assessed based on the coefficient of determination (r²) and the Mean Absolute Error (MAE), complemented by statistical analysis using the Kruskal-Wallis test. Results and Discussion: The library achieved r² values greater than 0.99 in 80% of the evaluated isotherms. Only one isotherm showed a significant difference between experimental and simulated data (p<0.05). The Sips and Toth models performed best in 60% of the cases, highlighting the tool’s effectiveness. Research Implications: pyIsotherm offers practical and theoretical advances, enabling more accurate modeling and accelerating the development of new adsorbents for environmental and industrial applications. Originality/Value: This study contributes by offering an innovative, open-access tool that overcomes the limitations of traditional isotherm fitting methods, adding scientific and technological value to the adsorption field.