## 5 Conclusions  
This study showed that the use of Bayesian inference for model fitting in continuous adsorption systems is promising, enabling better understanding and optimization of gas separation and purification processes.  
Overall, models that included the estimation of maximum adsorption capacity provided more accurate descriptions of adsorption system behavior. On the other hand, it was noted that when maximum adsorption capacity is treated as a deterministic parameter, the difference between estimated and experimental values tends to be larger.  
The analysis of determination coefficients (R\({}^{2}\) and R\({}^{2}\)\({}_{\text{Adjusted}}\)) and Bayesian Information Criterion (BIC) reinforced the efficiency of the fitted models. For H\({}_{2}\)S, the Yan model best described the gas adsorption, obtaining R\({}^{2}\) values above 0.998 and a lower BIC value of 11.2732, whereas for CO\({}_{2}\), the standout models were Adams-Bohart and Yoon-Nelson, with R\({}^{2}\) values close to 0.995 and a BIC of 153.3. These results confirm the effectiveness of the analytical models in representing fixed-bed adsorption processes, offering a viable alternative with lower computational effort compared to models based on complex differential equations.  
Thus, the study highlights the importance of modeling in fixed-bed column adsorption, contributing to the development of more efficient systems for capturing atmospheric pollutants, such as H\({}_{2}\)S e CO\({}_{2}\), with the potential for large-scale implementation. Additionally, the application of Bayesian inference provides a promising alternative to overcome the limitations of traditional parameter estimation methods, offering improved fits/descriptions and enhanced reliability in analyses.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14166956/s1](https://www.mdpi.com/article/10.3390/app14166956/s1)]([https://www.mdpi.com/article/10.3390/app14166956/s1](https://www.mdpi.com/article/10.3390/app14166956/s1)), Table S1: Elements used for parameter estimation of models for hydrogen sulfide (H\({}_{2}\)S) gas.; Table S2: Elements used for parameter estimation of models for hydrogen sulfide (CO\({}_{2}\)) gas.  
Conceptualization, methodology, validation, writing--original draft preparation, software H.B.S.L. and A.P.S.d.S.; writing--review and editing, visualization W.B.d.S.; supervision, visualization, writing--review and editing D.S.d.C.; project administration, formal analysis,E.C.R.; software, validation, project administration, supervision, writing--original draft preparation D.C.E. All authors have read and agreed to the published version of the manuscript. This research was funded by Pro-Reitoria de Pesquisa e Pos-Graduacao (PROPESP) of Federal University of Para (UFPA). Not applicable. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Upon request, the data will be made available from the corresponding author. The data are not publicly available due to privacy. We thank FAPESPA and PROPESP (UFPA) for financially supporting this research through financing the project entitled \"Adsorcao De Gases Em Leito Fixo: Uso De adsorventes Produzidos A Partir De Residuos De Mineracao Em Sistema Com Escala Semi Piloto\" agreement N\"13/2022. The authors declare no conflict of interest.