A New Model for Predicting the Retention of Stationary Phase in Countercurrent Chromatography = A New Model for Predicting the Retention of Stationary Phase in Countercurrent Chromatography

다국어 초록 (Multilingual Abstract)

The retention of stationary phase (Sf) is a crucial factor in countercurrent chromatography (CCC), which is used for the derivation of the column efficiency, peak resolution, and solute retention. Both operation conditions (flow-rate of mobile phase and rotation speed) and physical properties of two-phase systems (density difference and viscosity) exert important effects on Sf and have been studied to improve the efficiency in various CCC separations. In this study, an artificial neural network (ANN) was used to simultaneously predict the effects of operation conditions and physical properties of two-phase systems on the retention of stationary phase in CCC. The agreement between the predicted and the literature data, which contained the retention of stationary phase for 16 two-phase systems under different operation condition with 194 data points, was excellent with the total average absolute deviation (AAD) of 1.86% and the maximum deviation of 8.79%. By comparing the results predicted by ANN with the results calculated by the mathematical model proposed in literature, it was found that more accurate predictions were achieved by means of the ANN.