Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design

Abstract

This work applied a Bayesian computational technique for parameter estimation of adsorption breakthrough curve models with experimental data of caffeine (CAF) adsorption onto granular activated carbon (GAC). Different operational conditions were evaluated (volumetric flow: Q, adsorbent mass: W, and initial CAF concentration: C0) by a two-level factorial experimental design (23) to determine the best operational conditions. The models (Thomas, Yoon–Nelson, Yan, Clark, Gompertz, and Log-Gompertz) were fitted to the experimental data, estimating and not estimating the maximum adsorption capacity (qS). For model selection, five statistical metrics were calculated. The results showed that the proposed Bayesian technique, not estimating qS, was effective and all analyzed operational conditions obtained 95% of CAF removal. In the best condition, when qS reached 7.317 mgCAF/gGAC, the model that best adjusted the experimental data was Log-Gompertz, being suitable for practical approaches, and for its mechanisms, the Clark model best predicted the evaluated fixed-bed column.