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Fuel Processing for Fuel Cells: A Model for Fuel Conversion and Carbon Formation in the Adiabatic Steam Reformer

机译:燃料电池的燃料加工:绝热蒸汽重整器中燃料转化和碳形成的模型

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In present fuel cell power plants the fuel processor is a catalytic steam reformer which is limited to the use of fuels such as naphtha and natural gas. The sulfur content of these feeds must be reduced to low levels by hydrotreatment before contacting the nickel catalyst in the reformer. However, future fuel cell power plants may be required to ue coal-derived liquid fuel or heavy petroleum distillates which are more difficult to hydrotreat and reform. To meet this requirement, an adiabatic steam reformer is being developed by United Technologies Corporation with the support of the Electric Power Research Institute. In the adiabatic reformer, air is added to the process stream to provide, by combustion, the heat for endothermic reforming in a catalyst bed. In the inlet section of the reformer, air and fuel combust, and reforming is initiated on special catalysts whose primary functon is to prevent formation and accumulation of carbon. Following the inlet section, catalysts with high activity for steam reforming complete the conversion of the remaining fuel, principally methane. The objective of the present program is to establish a reactor model for the adiabatic reformer which would predict process stream compositions and temperatures and include carbon formation processes. Progress is reported on the four tasks: (1) determine rate expressions for catalytic reactions occurring in the adiabatic reformer; (2) establish a reactor model to predict process stream compositions in the adiabatic reformer using data from Task 1 for cataytic reactions and data from the literature for homogeneous gas-phase reactions; (3) determine critical conditions for carbon formation on selected catalysts using microbalance experiments; and (4) establish a model to predict carbon formation by combination of the model for process stream composition from Task 2 and data for carbon formation from Task 3. (ERA citation 07:043912)

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