非表示:
キーワード:
Preparative chromatography; Gradient elution; Equilibrium stage model; Optimization of gradient shapes; Artificial neural networks
要旨:
Gradient elution is widely applied in analytical chromatography to reduce the separation time and/or to improve the selectivity. Increasingly the potential of modulating the solvent strength during gradient operation is exploited in preparative liquid chromatography. The purpose of this paper is to investigate theoretically the effect of optimizing free parameters available in gradient chromatography (extents and shapes of gradients) on the productivity of isolating a target component in a multicomponent mixture. An equilibrium stage model was used to quantify and compare different modes of operation (isocratic and various variants of gradient elution). By combining experimental design and artificial neural network concepts, optimal conditions were identified for the production of the second eluting component in a ternary mixture. The strong impact of the shape of gradients on process performance is elucidated.
© 2005 Elsevier B.V. All rights reserved. [accessed 2013 November 27th]
