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Application of Artificial Neural Networks to Combinatorial Catalysis: Modeling and Predicting ODHE Catalysts
Author(s) -
Corma Avelino,
Serra José M.,
Argente Estefania,
Botti Vicente,
Valero Soledad
Publication year - 2002
Publication title -
chemphyschem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.016
H-Index - 140
eISSN - 1439-7641
pISSN - 1439-4235
DOI - 10.1002/1439-7641(20021115)3:11<939::aid-cphc939>3.0.co;2-e
Subject(s) - catalysis , artificial neural network , chemistry , computer science , nanotechnology , combinatorial chemistry , materials science , artificial intelligence , organic chemistry
This paper shows how artificial neural networks are useful for modeling catalytic data from combinatorial catalysis and for predicting new potential catalyst compositions for the oxidative dehydrogenation of ethane (ODHE). The training and testing sets of data used for the neural network studies were obtained by means of a combinatorial approach search, which employs an evolutionary optimization strategy. Input and output variables of the neural network include the molar composition of thirteen different elements presented in the catalyst and five catalytic performances (C 2 H 6 and O 2 conversion, C 2 H 4 yield, and C 2 H 4 , CO 2 , and CO selectivity). The fitting results indicate that neural networks can be useful in high‐dimensional data management within combinatorial catalysis search procedures, since neural networks allow the ab inito evaluation of the reactivity of multicomponent catalysts.