Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks

Abstract

Computer-aided optimization of micellar electrokinetic capillary chromatography (MEKC) separations was demonstrated by artificial neural networks (ANNs) using a Levenberg-Marquardt algorithm and an orthogonal experimental design. A novel criterion, named Q, for evaluating the separation quality of MEKC was firstly presented, which considered both separation selectivity and analysis time. MEKC separation conditions of seven plant hormones were then simulated and optimized using ANNs based on this novel criterion. The result was further compared to that obtained using ANNs based on a traditionally used criterion of overall normalization resolution (named r). Finally, the separation under optimum conditions predicted by ANNs using the criterion Q was compared to, and proved to be better than that obtained by empirical step-by-step optimization procedures. This method may also be adapted to other separation methods due to its generality.

Effects of CO2 and H2S on Corrosion of Martensitic Steels in Brines at Low Temperature

“…The effects of CO 2 and H 2 S on corrosion processes are of considerable interest to the oil and gas industry for a number of reasons. [1][2][3][4][5][6][7][8][9][10] Though the individual impacts of each gas are fairly well agreed on, more understanding is still needed for mixed cases and extreme conditions. In the case of sweet corrosion (CO 2 corrosion), it is well known that dissolved CO 2 lowers the solution pH and increases the rate of corrosion.…”