Artificial neural network-aided development of supported Co catalyst for preferential oxidation of CO in excess hydrogen

作者：Omata K^*; Kobayashi Y; Yamada M

来源：Catalysis Communications, 2005, 6(8): 563-567.

DOI：10.1016/j.catcom.2005.05.004

• preferential oxidation of carbon monoxide
• Co/SrCO3 catalyst
• hydrogen
• artificial neural network
• design of experiment
• full factorial design

Preferential oxidation (PROX) of 1 vol% CO was investigated using the stoichiometric amount of O-2 in excess hydrogen. Cobalt supported on alkali earth metal carbonate, especially Co/SrCO3, showed high selectivity to PROX of CO while the assistance of methanation of CO was necessary to achieve over 99% conversion of CO. Cobalt loading and preparation conditions of Co/SrCO3 was optimized by using a full factorial design of experiment, an artificial neural network and a grid search. Over 99% O-2 conversion and 78% selectivity for PROX at 200 degrees C was achieved by 3.2 mol% Co/SrCO3 pretreated at 345 degrees C.

• 出版日期2005-8