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

Corma, Avelino; Serrà, Joan; Argente, Estefanía; Botti, Vicente; Valero, Soledad

doi:10.1002/1439-7641(20021115)3:11<939::aid-cphc939>3.0.co;2-e

Cited by 82 publications

(43 citation statements)

References 5 publications

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“…Other mixed metal oxides based catalysts have also been proposed in the last years for the ODH of ethane by several authors [70][71][72][73][74][75][76][77], some of them based on combinatorial studies [72][73][74][75][76][77]. However, relatively low yields of olefins were reported, especially for the ODH of propane.…”

Section: V-containing Mixed Metal Oxidesmentioning

confidence: 99%

The selective oxidative activation of light alkanes. From supported vanadia to multicomponent bulk V-containing catalysts

Nieto

2006

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V-containing materials have been proposed as active and selective catalysts in the selective oxidative transformation of C 2 -C 4 alkanes to the corresponding olefins, oxygenates and nitriles. Although vanadium ions are generally assumed as the active sites in the oxidative activation of short chain alkanes, the coordination, the oxidation state and the environment of V atoms strongly influence both the catalytic activity and the nature of the reaction products. A comparative study on the catalytic behaviour of V-containing catalysts, i.e. metal vanadates, supported vanadium oxides, V-containing microporous/mesoporous materials and multicomponent bulk Mo-V-Te(Sb)-Nb mixed metal oxides, on the alkane oxidation is presented. The influence of the structure (including the V-coordination and V-environment) and the physico-chemical properties (especially redox and acid-base characteristics) of the catalysts on their behaviour in the selective oxidation of paraffins and alkenes is discussed.KEY WORDS: activation of light alkanes by vanadia catalysts; selective oxidative oxidation of light alkanes.

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Section: V-containing Mixed Metal Oxidesmentioning

confidence: 99%

The selective oxidative activation of light alkanes. From supported vanadia to multicomponent bulk V-containing catalysts

Nieto

2006

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113

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“…This is actually more challenging than a straightforward prediction. For modeling and optimizing the catalysis, one of the primary works was done by Corma et al, who first applied ANNs for the optimization of potential catalyst compositions for the oxidative dehydrogenation of ethane (ODHE) [49]. The scheme of the optimization strategy in their study is shown in Figure 4, with the inputs of various combinatorial compositions and the output of catalytic results.…”

Section: Optimization Of Catalysismentioning

confidence: 99%

Application of Artificial Neural Networks for Catalysis: A Review

2017

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Abstract:Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.

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“…Está basado en técnicas Soft Computing, combinando algoritmos genéticos y redes neuronales, siendo fruto de la evolución de las soluciones planteadas anteriormente en nuestros trabajos previos [Corma et al, 2002a;Valero et al, 2004b;Corma et al, 2005;Serra, 2004;Valero et al, 2004a].…”

Section: Métodos Híbridosunclassified

“…Por tanto, se analizan diferentes factores involucrados en el comportamiento ofrecido por la RNA, como la topología de la RNA, los algoritmos de entrenamiento y las funciones de activación. Diferentes experiencias que siguen este procedimiento metodológico, así como los resultados obtenidos pueden consultarse en los siguientes estudios previos [Serra et al, 2003a;Valero et al, 2004b;Moliner et al, 2005;Corma et al, 2002a].…”

Section: Obtención Del Modelo De Rnaunclassified

“…También se han aplicado RNA combinadas con estrategias evolutivas en el diseño de catalizadores para la amoxidación del propano [Cundari et al, 2001] o para el descubrimiento de nuevos materiales para la reacción deshidrogenación oxidativa del etano (ODHE) [Corma et al, 2002a], que permite el análisis y la predicción de resultados catalíticos obtenidos mediante técnicas combinatorias. Además, se ha empleado RNA en el modelado de sistemas cristalinos multifase en la síntesis de zeolitas .…”

Section: Introductionunclassified

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Arquitectura de búsqueda basada en técnicas soft computing para la resolución de problemas combinatorios en diferentes dominios de aplicación

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

Cited by 82 publications

References 5 publications

The selective oxidative activation of light alkanes. From supported vanadia to multicomponent bulk V-containing catalysts

The selective oxidative activation of light alkanes. From supported vanadia to multicomponent bulk V-containing catalysts

Application of Artificial Neural Networks for Catalysis: A Review

Arquitectura de búsqueda basada en técnicas soft computing para la resolución de problemas combinatorios en diferentes dominios de aplicación

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