Parameter Estimation of Breakthrough Curve Models in the Adsorption Process of H₂S and CO₂ Using the Markov Chain Monte Carlo Method

Abstract

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 R2Adjusted) and the Bayesian Information Criterion (BIC). The models showed better agreement using the estimation of maximum adsorption capacity (qs, N0) and the constants of each model (kth, kyn, and kba). In the adsorption of H2S, the Yan model stood out for its precision in estimating qs. 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.

Keywords

• analytical models
• fixed bed
• gas purification
• Bayesian statistics