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Szijjarto G.P.,Hungarian Academy of Sciences | Tompos A.,Hungarian Academy of Sciences | Heberger K.,Hungarian Academy of Sciences | Margitfalvi J.L.,Combitech Nanotech Ltd. Budapest
Combinatorial Chemistry and High Throughput Screening | Year: 2012

Effects of different catalyst components on the catalytic performance in steam reforming of ethanol have been investigated by means of Artificial Neural Networks (ANNs) and Partial Least Square regression (PLSR). The data base consisted of ca. 400 items (catalysts with varied composition), which were obtained from a former catalyst optimization procedure. Marten's uncertainty (jackknife) test showed that simultaneous addition of Ni and Co has crucial effect on the hydrogen production. The catalyst containing both Ni and Co provided remarkable hydrogen production at 450°C. The addition of Ceas modifier to the bimetallic NiCo catalyst has high importance at lower temperatures: the hydrogen concentration is doubled at 350°C. Addition of Pt had only little effect on the product distribution. The outliers in the data set have been investigated by means of Hotelling T2 control chart. Compositions containing high amount of Cu or Ce have been identified as outliers, which points to the nonlinear effect of Cu and Ce on the catalytic performance. ANNs were used for analysis of the non-linear effects: an optimum was found with increasing amount of Cu and Ce in the catalyst composition. Hydrogen production can be improved by Ce only in the absence of Zn. Additionally, negative cross-effect was evidenced between Ni and Cu. The above relationships have been visualized in Holographic Maps, too. Although predictive ability of PLSR is somewhat worse than that of ANN, PLSR provided indirect evidence that ANNs were trained adequately. © 2012 Bentham Science Publishers.

Tompos A.,Hungarian Academy of Sciences | Margitfalvi J.L.,Combitech Nanotech Ltd. Budapest | Szabo E.G.,Hungarian Academy of Sciences | Vegvari L.,Combitech Nanotech Ltd. Budapest
Topics in Catalysis | Year: 2010

Multi-component Au/Al2O3 catalysts were designed and tested for PROX reaction using holographic research strategy. On the bases of our previous study Pb has been selected as the main modifier of the Au. In addition to Au and Pb the catalysts library contained V, Ba, Ce, Sm, Ag and Cu resulting in multi component catalysts tailored for PROX reaction. After preparation and testing of 173 catalysts within five generations new catalyst compositions with excellent performance have been obtained. Upon using the best catalyst CO could be removed almost completely and the selectivity of oxygen towards the CO oxidation was around 75%. In the course of catalyst library design it has been revealed that the selection of the objective function (OF) has high impact both on the rate of optimization and the performance of catalysts designed. The complex OF was created from two single desirability functions related to CO conversion and oxygen selectivity towards CO oxidation. In order to maintain high optimization rate there was a need to change the weights of single desirability functions in the course of catalyst library design. The results show that Pb, Sm, Cu and Ag are the key modifiers for PROX reaction under experimental conditions applied. © 2009 Springer Science+Business Media, LLC.

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