Establishment of Fuzzy Langmuir Adsorption Model and Prediction of Chromatographic Behavior. Issue 7 (25th April 2022) - Li, Shoujiang; Wang, Shaoyan
Abstract: In order to accurately predict the complex chromatographic behaviors of the components to be separated, the fuzzy Langmuir adsorption equations and the back propagation‐artificial neural network (BP‐ANN) are combined to establish fuzzy Langmuir adsorption models. Herein, the fuzzy Langmuir adsorption equations are deduced based on a series of different traditional adsorption equations such as with or without competition, one or two kinds of adsorbed sites, monomolecular or multimolecular adsorption, etc. The major adsorption parameter Ci (form concentration) is the function of the actual concentration ci, expressed in matrix form constructed by BP‐ANN and is obtained by solving the equilibrium dispersive chromatography model with the inverse method and genetic algorithm. Finally, the fuzzy Langmuir models are applied to study the chromatographic behaviors of m ‐cresol and p ‐cresol on MIL‐53 (Al) stationary phase. The results show that the models have excellent curve fitting ability, and can be used to determine the adsorption relationship and predict the chromatographic elution curves under complex or unknown adsorption mechanism. Abstract : Fuzzy Langmuir adsorption models are proposed, which have the dual advantages of mechanism and data‐driven modeling. The calculated elution curves based on the models are almost consistent with the experimental elution curves under "breathing" effect. The proposed method can be used to determine the single‐component and two‐component competitive adsorption equations under complex adsorption mechanism.