PDF 209.pdf | Request a copy Full text restricted Available under University of Tasmania Standard License.

Abstract

The separation process in capillary micellar electrochromatography (MEKC) can be modelled using artificial neural networks (ANNs) and optimisation of MEKC methods can be facilitated by combining ANNs with experimental design. ANNs have shown attractive possibilities for non-linear modelling of response surfaces in MEKC and it was demonstrated that by combining ANN modelling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. A new general approach for computer-aided optimisation in MEKC has been proposed which, because of its general validity, can also be applied in other separation techniques.

Item Type:	Article
Authors/Creators:	Havel, J and Breadmore, MC and Macka, M and Haddad, PR
Keywords:	Metal complexes; Neural networks, artificial; Optimisation; Computer modelling; Experimental design
Journal or Publication Title:	Journal of Chromatography A
ISSN:	0021-9673
DOI / ID Number:	https://doi.org/10.1016/S0021-9673(99)00634-2
Additional Information:	The definitive version is available at http://www.sciencedirect.com
Item Statistics:	View statistics for this item

Actions (login required)

Item Control Page