Aplikace genetického algoritmu a neuronové sítě pro objevování a optimalizaci nových pevných katalytických materiálů

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F04%3A00103297" target="_blank" >RIV/67985807:_____/04:00103297 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Application of a Genetic Algorithm and a Neural Network for the Discovery and Optimization of New Solid Catalytic Materials

Popis výsledku v původním jazyce

In the process of discovering new catalytic compositions by combinatorial methods in heterogeneous catalysis usually various potential catalytic compounds have to be prepared and tested. To decrease the number of necessary experiments an optimization algorithm based on a genetic algorithm for deriving subsequent generations from the performance of the members of the preceding generation is described. This procedure is supplemented by using an artificial neural network for establishing relationships between catalyst compositions - or more general speaking - materials properties and their catalytic performance. By combining a trained neural network with the genetic algorithm software virtually computer experiments were done aiming at adjusting the control parameters of the optimization algorithm to the special requirement of catalyst development. The approach is illustrated by the search for new catalytic compositions for the oxidative dehydrogenation of propane.

Název v anglickém jazyce

Application of a Genetic Algorithm and a Neural Network for the Discovery and Optimization of New Solid Catalytic Materials

Popis výsledku anglicky

In the process of discovering new catalytic compositions by combinatorial methods in heterogeneous catalysis usually various potential catalytic compounds have to be prepared and tested. To decrease the number of necessary experiments an optimization algorithm based on a genetic algorithm for deriving subsequent generations from the performance of the members of the preceding generation is described. This procedure is supplemented by using an artificial neural network for establishing relationships between catalyst compositions - or more general speaking - materials properties and their catalytic performance. By combining a trained neural network with the genetic algorithm software virtually computer experiments were done aiming at adjusting the control parameters of the optimization algorithm to the special requirement of catalyst development. The approach is illustrated by the search for new catalytic compositions for the oxidative dehydrogenation of propane.

Druh

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

CEP obor

BA - Obecná matematika

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2004

Kód důvěrnosti údajů

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

Název periodika

Applied Surface Science

ISSN

0169-4332

e-ISSN

—

Svazek periodika

223

Číslo periodika v rámci svazku

-

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

7

Strana od-do

168-174

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—