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Developing Design Guidelines for an SCR Assembly Equipped for RF Sensing of NH 3 Loading
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The Cu-zeolite (CuZ) SCR catalyst enables higher NOx conversion efficiency in part because it can store a significant amount of NH3. “NH3 storage control”, where diesel exhaust fluid (DEF) is dosed in accord with a target NH3 loading, is widely used with CuZ catalysts to achieve very high efficiency. The NH3 loading actually achieved on the catalyst is currently estimated through a stoichiometric calculation. With future high-capacity CuZ catalyst designs, it is likely that the accuracy of this NH3 loading estimate will become limiting for NOx conversion efficiency. Therefore, a direct measurement of NH3 loading is needed; RF sensing enables this.
Relative to RF sensing of soot in a DPF (which is in commercial production), RF sensing of NH3 adsorbed on CuZ is more challenging. Therefore, more attention must be paid to the “microwave resonance cavity” created within the SCR assembly. The objective of this study was to develop design guidelines to enable and enhance RF sensing. Catalyst dimensions, cavity-defining design features, and antenna location and length were considered. RF measurements were made on a commercial SCR unit outfitted with antennas in various locations. In addition, a simulation tool was used to characterize the RF signals obtained with different design features within the context of an L12 screening DOE. Results of the simulations identified means for optimizing the cavity design and geometry, as well as antenna position, to enhance RF measurements of stored ammonia and enable direct feedback control based on the actual SCR ammonia storage state.
CitationBromberg, L., Harris, T., Sappok, A., Guarino, A. et al., "Developing Design Guidelines for an SCR Assembly Equipped for RF Sensing of NH3 Loading," SAE Technical Paper 2018-01-1266, 2018, https://doi.org/10.4271/2018-01-1266.
Data Sets - Support Documents
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