Application of Artificial Neural Networks to Combinatorial Catalysis: Modeling and Predicting ODHE Catalysts

• Published in: Chemphyschem 2002-11, Vol.3 (11), p.939-945
• Main Authors: Corma, Avelino, Serra, José M., Argente, Estefania, Botti, Vicente, Valero, Soledad
• Format: Article
• Language: English
• Subjects: Catalysis; Catalytic reactions; Chemistry; combinatorial catalysis; combinatorial chemistry; Exact sciences and technology; General and physical chemistry; heterogeneous catalysis; neural networks; ODHE processes; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
• ISSN: 1439-4235; 1439-7641
• DOI: 10.1002/1439-7641(20021115)3:11<939::AID-CPHC939>3.0.CO;2-E

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 (C2H6 and O2 conversion, C2H4 yield, and C2H4, CO2, 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. Industrial think(ing) tanks: Artificial neural networks are useful for modeling catalytic data from combinatorial catalysis and predicting new potential catalyst compositions on account of their facility for high‐dimensional data management. The training and testing sets of data used for neural network studies were obtained by means of a combinatorial approach search employing an evolutionary optimization strategy. Input and output variables of the neural network include molar composition of thirteen different elements present in the catalyst and five catalytic performances. within combinatorial catalysis search procedures.