Title of article :
Effective additives of Ni/image-Al2O3 catalyst at low methane conversion of oxidative reforming for syngas formation Original Research Article
Author/Authors :
Kohji Omata، نويسنده , , Yosuke Endo، نويسنده , , Hidetomo Ishii، نويسنده , , Akihiro Masuda، نويسنده , ,
Muneyoshi Yamada، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Abstract :
Oxidative reforming of methane was conducted at 650 imageC, 1 MPa, and GHSV= 2,000,000 N ml/(g h) using Ni/image-Al2O3 catalyst. Dilution of catalyst bed with image-Al2O3 prevented the formation of hot-spots in the catalyst bed. Element X (image, P, Ca, Mn, Fe, Cd, Ce, Gd and Re) was added to Ni/image-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/image-Al2O3 catalyst were used for ANN training. After the training, the ANN was able to predict the catalytic performance of Ni–Z/image-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/image-Al2O3. The experimentally observed activity of Ni–Sc/image-Al2O3 was five times higher than that of unpromoted Ni/image-Al2O3 catalyst.
Keywords :
Oxidative reforming of methane , Ni/View the MathML source-Al2O3 , Artificial neural network , Physicochemical properties
Journal title :
Applied Catalysis A:General
Journal title :
Applied Catalysis A:General