Artificial neural network aided screening for membrane disc catalysts for oxidative reforming of methane

Autor:	Kohji Omata, Hikotaro Suzuki, Akihiro Masuda, Muneyoshi Yamada, Hidetomo Ishii
Rok vydání:	2009
Předmět:	Methane reformer Chemistry Process Chemistry and Technology Catalyst support Binary compound Heterogeneous catalysis Catalysis Methane chemistry.chemical_compound Membrane Chemical engineering Organic chemistry Syngas
Zdroj:	Applied Catalysis A: General. 362:14-19
ISSN:	0926-860X
DOI:	10.1016/j.apcata.2009.04.009
Popis:	Porous alumina membrane filters (Anodisc ™ ) were applied to catalyst support for oxidative reforming of methane. Two discs were used: the 1st catalyst disc was mainly for methane combustion and the 2nd was for methane reforming. Additives for Ni/Anodisc or Co–Mg/Anodisc catalyst were screened by an artificial neural network (ANN). After an ANN was trained using the physicochemical properties of 9 elements and their catalytic performances as Ni-additive/Anodisc or Co–Mg-additive/Anodisc, the ANN can predict the catalytic performance from physicochemical properties of other elements than the 9 elements. The two kinds of optimum catalysts were determined to be Ni–Pr/Anodisc and Co–Mg–Li/Anodiscs, respectively. Hot-spot formation was avoided according to the restricted catalyst zone by overlapping these quite thin discs. The total performance of the optimized base metal catalysts was almost the same as that of Rh/Anodisc catalyst disc.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::297bef1dea085b6d300c5c88c3ee7b2b https://doi.org/10.1016/j.apcata.2009.04.009 Zobrazit plný text záznamu Full Text from ScienceDirect