Naslov
Optimization of artificial neural networks used for retention modelling in ion chromatography

Autori
Srečnik, Goran ; Debeljak, Željko ; Cerjan-Štefanović, Štefica ; Nović, Milko ; Bolanča, Tomislav

Izvornik
Journal of Chromatography A (0021-9673) 973 (2002), 1-2; 47-59

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
neural networks; artificial; retention modelling; optimization; fluoride; chloride; nitrite; sulfate; bromide; nitrate; phosphate

Sažetak
The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written ; the division of experimental data set on training and test set ; selection of data for training and test set ; Dixon's outlier test ; retraining procedure routine ; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network. (C) 2002 Elsevier Science B.V. All rights reserved.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Kemijsko inženjerstvo, Farmacija

POVEZANOST RADA