Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design
Júlia Toffoli de Oliveira, Ana Beatriz da Luz Arsufi, Diego Cardoso Estumano, Liliana Amaral Féris
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: C 0 ) by a two-level factorial experimental design (2 3 ) 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 ( q S ). For model selection, five statistical metrics were calculated. The results showed that the proposed Bayesian technique, not estimating q S, was effective and all analyzed operational conditions obtained 95% of CAF removal. In the best condition, when q S reached 7.317 mg CAF /g GAC, 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.