Modeling of experimental adsorption isotherm data

Abstract

Adsorption is considered to be one of the most effective technologies widely usedin global environmental protection areas. Modeling of experimental adsorption isotherm datais an essential way for predicting the mechanisms of adsorption, which will lead to animprovement in the area of adsorption science. In this paper, we employed three isothermmodels, namely: Langmuir, Freundlich, and Dubinin-Radushkevich to correlate four sets ofexperimental adsorption isotherm data, which were obtained by batch tests in lab. Thelinearized and non-linearized isotherm models were compared and discussed. In order todetermine the best fit isotherm model, the correlation coefficient (r2) and standard errors(S.E.) for each parameter were used to evaluate the data. The modeling results showed thatnon-linear Langmuir model could fit the data better than others, with relatively higher r2values and smaller S.E. The linear Langmuir model had the highest value of r2, however, themaximum adsorption capacities estimated from linear Langmuir model were deviated fromthe experimental data.