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. Martens 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.
Keywords: Artificial neural networks, ethanol reforming, holographic research strategy, hydrogen production, multi component catalysts, partial least squares, visualization of experimental space, energy carriers, steam reforming, hydrocarbons, in silico, quantitative structure, activity, physicochemical fatures, amorphous phases, surface acidity, heterogeneous catalysis, catalytic performance
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