Comparison of general rate model with a new model--artificial neural network model in describing chromatographic kinetics of solanesol adsorption in packed column by macroporous resins.

Herein, two models, the general rate model taking into account convection, axial dispersion, external and intra-particle mass transfer resistances and particle size distribution (PSD) and the artificial neural network model (ANN) were developed to describe solanesol adsorption process in packed column using macroporous resins. First, Static equilibrium experiments and kinetic experiments in packed column were carried out respectively to obtain experimental data. By fitting static experimental data, Langmuir isotherm and Freundlich isotherm were estimated, and the former one was used in simulation coupled with general rate model considering better correlative coefficients. The simulated results showed that theoretical predictions of general rate model with PSD were well consistent with experimental data. Then, a new model, the ANN model, was developed to describe present adsorption process in packed column. The encouraging simulated results showed that ANN model could describe present system even better than general rate model. At last, by using the predictive ability of ANN model, the influence of each experimental parameter was investigated. Predicted results showed that with the increases of particle porosity and the ratio of bed height to inner column diameter (ROHD), the breakthrough time was delayed. On the contrary, an increase in feed concentration, flow rate, mean particle diameter and bed porosity decreased the breakthrough time.