Abstract

Abstract Oxidative reforming of methane was conducted at 650 ° C, 1 MPa, and GHSV= 2,000,000 N ml/(g h) using Ni/ α -Al2O3 catalyst. Dilution of catalyst bed with α -Al2O3 prevented the formation of hot-spots in the catalyst bed. Element X ( X = B , P, Ca, Mn, Fe, Cd, Ce, Gd and Re) was added to Ni/ α -Al2O3, and the catalyst activities were experimentally observed to obtain training data of an artificial neural network (ANN). Then the physicochemical properties of element X and the observed values (CH4 conversion, H2 selectivity, or CO selectivity) of Ni–X/ α -Al2O3 catalyst were used for ANN training. After the training, the ANN was able to predict the catalytic performance of Ni–Z/ α -Al2O3 based on the physicochemical properties of element Z where Z is a possible additive other than X. In addition to La and Ce, Sc and Nd were predicted to promote the activity of Ni/ α -Al2O3. The experimentally observed activity of Ni–Sc/ α -Al2O3 was five times higher than that of unpromoted Ni/ α -Al2O3 catalyst.

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