[Column efficiency prediction of two dimensional chromatography by artificial neural network]

This paper used artificial neural network (ANN) modeling method to study the relationship between the column efficiency and the operating conditions. This method solved the problem that it is not easy to establish a quantitative model between the column efficiency and its main effecting factors using those traditional modeling methods, as the relationship is usually quite complex and non-linear in fact. The varied-pace BP (back-propagation) learning algorithm was adopted, and a three-layer weight-connected ANN model for a typical dual column system was established. The effective plate number representing the column efficiency acted as the output vectors, while the temperature of the pre-column, the temperature of the main column, the pressure difference between the columns and the vent rate acted as the input vectors. Then the model acquired was used to predict column efficiency (characterized by "effective plate number") under different operating conditions. The results showed that the model predicting value was in consistent with the value found. This work proved that ANN modeling method was suitable for the study on the relationship between the column efficiency of two-dimensional column chromatography system and the operating conditions.