Publication details

Evaluation of chemical equilibria with the use of artificial neural networks

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Authors

HAVEL Josef LUBAL Přemysl FARKOVÁ Marta

Year of publication 2002
Type Article in Periodical
Magazine / Source POLYHEDRON
MU Faculty or unit

Faculty of Science

Citation
Field Analytic chemistry
Keywords artificial neural networks; chemical equilibria
Description Multivariate calibration with experimental design (ED) and artificial neural networks (ANN) modeling can be used to estimate equilibria constants from any kind of protonation or metal-ligand equilibrium data like potentiometry, polarography, spectrophotometry, extraction, etc. The method was tested on evenly or randomly distributed experimental error-free data and data with random noise and the results show that even rather higher experimental errors do not influence significantly the prediction power and correctness of ANN prediction. ANN with appropriate ED can provide accurate prediction of stability constants with the relative errors in the range of +/-4% or smaller while the approach is very robust. Comparison with a hard model evaluation based on non-linear regression techniques shows excellent agreement. Proposed ANN method is of a general nature and, in principal, can be adopted to any analytical technique used in equilibria studies. (C) 2002 Elsevier Science Ltd. All rights reserved.
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