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
(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 (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 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.