Application of different artificial neural networks retention models for multi-criteria decision-making optimization in gradient ion chromatography (CROSBI ID 155419)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bolanča, Tomislav ; Cerjan Stefanović, Štefica ; Luša, Melita ; Ukić, Šime ; Rogošić, Marko
engleski
Application of different artificial neural networks retention models for multi-criteria decision-making optimization in gradient ion chromatography
In this work, the principles of multi-criteria decision-making were used to develop an efficient optimization strategy in gradient elution ion chromatographic analysis. Two different artificial neural network retention models (multi-layer perceptron and radial basis function), three different separation criterion functions (chromatography response function, separation factor product and normalized retention difference product) and four different robustness criterion functions (CR1-CR4) were examined. The shape of the calculated separation vs robustness response surface was used as principal criterion. Analysis time and minimum separation of adjacent peaks were additional criteria. The results showed that the radial basis artificial neural network retention model in combination with normalized retention difference product separation criterion function and CR3 robustness criterion function provided the optimal gradient ion chromatographic analysis.
ion chromatography ; artificial neural networks ; multi-criteria decision-making
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Podaci o izdanju
45 (2)
2010.
236-243
objavljeno
0149-6395
1520-5754
10.1080/01496390903417958
Povezanost rada
Kemija, Kemijsko inženjerstvo