The increase in emissions of toxic gasses such as hydrogen sulfide (H₂S) and carbon dioxide (CO₂), resulting from growing urbanization and industrialization, has caused environmental and public health problems, making the implementation of air purification techniques through adsorption important. Thus, modeling the gas adsorption process is fundamental for good agreement with experimental data, employing mathematical models that enable the prediction of adsorption capacity. In this way, the present work aimed to compare different analytical breakthrough curve models (Thomas, Yoon–Nelson, Adams–Bohart, and Yan) for the adsorption of H₂S and CO₂ in fixed-bed columns, using experimental data from the literature, estimating the curve parameters through the Markov Chain Monte Carlo (MCMC) method with the Metropolis–Hastings algorithm, and ranking using the determination coefficients (R² and R²_Adjusted) and the Bayesian Information Criterion (BIC). The models showed better agreement using the estimation of maximum adsorption capacity (q_s, N₀) and the constants of each model (k_th, k_yn, and k_ba). In the adsorption of H₂S, the Yan model stood out for its precision in estimating q_s. For the adsorption of CO₂, the Adams–Bohart model achieved better results with the estimation of N₀, along with the Yoon–Nelson model. Furthermore, the use of this method allows for a reduction in computational effort compared to models based on complex differential equations.

Adsorption is a frequently used technique for pollutant removal due to its efficiency and versatility. Mathematical modeling of these processes enables understanding of adsorbent and adsorbate interactions and prediction of system dynamics, especially when experimental data are limited. Among adsorption isotherm models, the Langmuir model is one of the most applied; however, its formulation does not explicitly consider temperature variation, limiting predictive capability in practical applications. This article proposes an adaptation of the Langmuir model by incorporating temperature dependence, enabling inference of isotherms at temperatures without experimental data. The model was first validated using data from the literature at different temperatures, considering the adsorption of amoxicillin, phosphate, methylene blue, and caffeine. Subsequently, it was applied to experimental data of copper ion adsorption by 5A zeolite obtained in this work, allowing estimation of Q_max and K_L. Based on these estimates, hypotheses regarding their temperature dependence were formulated, and isotherms at 55 °C, 65 °C, and 75 °C were inferred and compared with experimental data. Finally, the model was applied to infer isotherms from 25 °C to 95 °C, demonstrating its ability to represent experimental dynamics and predict adsorption at temperatures without available data, contributing to the analysis and optimization of adsorption processes.