engleski

Development of artificial neural network retention model in ion chromatography by using different training methodologies

When facing the separation problem where gradient elution is needed the retention modelling in ion chromatography becomes even more complex problem then in isocratic elution mode. One possibility to solve gradient elution optimization problem is by the prediction of retention in gradient elution mode by using isocratic experimental data. The predictive ability of such gradient retention model is than compromised by the model used for isocratic retention modelling. Therefore it is extremely important to develop isocratic elution retention model with as highest predictive ability as possible. The aim of this work is development of the suitable artificial neural network retention model, which can be used in a variety of applications for method development in ion chromatography, particularly if isocratic data are used for gradient predictions and extremely accurate isocratic models are needed for gradient modelling. Different training algorithms are tested: (1) gradient descent algorithm with adaptive learning rate ; (2) Fletcher– Reeves conjugate gradient algorithm ; (3) Polak– Ribiére conjugate gradient algorithm ; (4) Powell– Beale conjugate gradient algorithm ; (5) Quasi-Newton algorithm with Broyden, Fletcher, Goldfarb, and Shanno (BFGS) update ; and (6) Levenberg– Marquardt algorithm with Bayesian regularization ; in order to improve predictive ability of the final retention model. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized until minimum on error surface has been found. The results of the extensive testing shows that the developed artificial neural network retention model predicts data well and it can be successfully used for the retention modelling in ion chromatography.

artificial neural network; retention model; ion chromatography

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