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Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography

Citation

Havel, J and Breadmore, MC and Macka, M and Haddad, PR, Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography, Journal of Chromatography A, 850, (1-2) pp. 345-353. ISSN 0021-9673 (1999) [Refereed Article]

DOI: doi:10.1016/S0021-9673(99)00634-2

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. Copyright (C) 1999 Elsevier Science B.V.

Item Details

Item Type:Refereed Article
Research Division:Chemical Sciences
Research Group:Analytical Chemistry
Research Field:Separation Science
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Chemical Sciences
Author:Havel, J (Professor Josef Havel)
Author:Breadmore, MC (Professor Michael Breadmore)
Author:Macka, M (Professor Mirek Macka)
Author:Haddad, PR (Professor Paul Haddad)
ID Code:16216
Year Published:1999
Web of Science® Times Cited:36
Deposited By:Chemistry
Deposited On:1999-08-01
Last Modified:2011-08-04
Downloads:0

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