Evaluation of chemical equilibria with the use of artificial neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F02%3A00007642" target="_blank" >RIV/00216224:14310/02:00007642 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Evaluation of chemical equilibria with the use of artificial neural networks

Original language 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 onnon-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 re

Czech name

Evaluation of chemical equilibria with the use of artificial neural networks

Czech 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 onnon-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 re

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

CB - Analytical chemistry, separation

OECD FORD branch

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Project

<a href="/en/project/GA203%2F02%2F1103" target="_blank" >GA203/02/1103: Artificial neural networks and experimental design in analytical chemistry, especially in separation methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2002

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

POLYHEDRON

ISSN

0277-5387

e-ISSN

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Volume of the periodical

21

Issue of the periodical within the volume

14-15

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

1375-1384

UT code for WoS article

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EID of the result in the Scopus database

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