Prediction of chromatographic behaviors with Langmuir-artificial neural
network adsorption isotherm models
Abstract
In order to accurately predict the complex chromatographic behaviors of
the components to be separated, by the combination of the Langmuir
adsorption formula and the back propagation-artificial neural network
(BP-ANN), Langmuir-BP-ANN adsorption model was established. Herein,
based on a series of different traditional adsorption isotherms such as
with or without competition, one or two kinds of adsorbed sites,
monomolecular or multilmolecular adsorption, and so on, the Langmuir
adsorption formula was deduced, where the major adsorption parameter Ci
was the function of the component concentration, expressed as matrix
forms constructed by BP-ANN and obtained by solving the equilibrium
dispersive (ED) chromatography model with the inverse method and genetic
algorithm (GA). The Langmuir-BP-ANN model was applied to study
chromatographic behaviors of m-cresol and p-cresol on MIL-53 (Al)
stationary phase.