Artificial neural networks combined with experimental design: a ?soft? approach for chemical kinetics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F12%3A00059296" target="_blank" >RIV/00216224:14310/12:00059296 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.talanta.2012.01.044" target="_blank" >http://dx.doi.org/10.1016/j.talanta.2012.01.044</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.talanta.2012.01.044" target="_blank" >10.1016/j.talanta.2012.01.044</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial neural networks combined with experimental design: a ?soft? approach for chemical kinetics

Popis výsledku v původním jazyce

The possibilities of artificial neural networks (ANNs) ?soft? computing to evaluate chemical kinetic data have been studied. In the first stage, , a set of ?standard? kinetic curves with known parameters (rate constants and/or concentrations of the reactants), which is some kind of ?normalized maps?, is prepared. The data base should be built according to a suitable experimental design (ED). In the second stage, such data set is then used for ANNs ?learning?. Afterwards, in the second stage, experimental data are evaluated and parameters of ?other? kinetic curves are computed without solving anymore the system of differential equations. The combined ED-ANNs approach has been applied to solve several kinetic systems. It was also demonstrated that usingANNs, the optimization of complex chemical systems can be achieved even not knowing or determining the values of the rate constants. Moreover, the solution of differential equations is here not necessary, as well.

Název v anglickém jazyce

Artificial neural networks combined with experimental design: a ?soft? approach for chemical kinetics

Popis výsledku anglicky

The possibilities of artificial neural networks (ANNs) ?soft? computing to evaluate chemical kinetic data have been studied. In the first stage, , a set of ?standard? kinetic curves with known parameters (rate constants and/or concentrations of the reactants), which is some kind of ?normalized maps?, is prepared. The data base should be built according to a suitable experimental design (ED). In the second stage, such data set is then used for ANNs ?learning?. Afterwards, in the second stage, experimental data are evaluated and parameters of ?other? kinetic curves are computed without solving anymore the system of differential equations. The combined ED-ANNs approach has been applied to solve several kinetic systems. It was also demonstrated that usingANNs, the optimization of complex chemical systems can be achieved even not knowing or determining the values of the rate constants. Moreover, the solution of differential equations is here not necessary, as well.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

CB - Analytická chemie, separace

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Talanta

ISSN

0039-9140

e-ISSN

—

Svazek periodika

93

Číslo periodika v rámci svazku

MAY

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

72-78

Kód UT WoS článku

000303305700010

EID výsledku v databázi Scopus

—