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Reversible Sulfur Poisoning of 3-way Catalyst linked with Oxygen Storage Mechanisms
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 05, 2021 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Even though the 3-way catalyst chemistry has been studied extensively in the literature, some performance aspects of practical relevance have not been fully explained. It is believed that the Oxygen Storage Capacity function of 3-way catalytic components dominates the behavior during stoichiometry transitions from lean to rich mode and vice versa whereas a number of mathematical models have been proposed to describe the dynamics of pollutant conversion. Previous studies have suggested a strong impact of Sulfur on the pollutant conversion after a lean to rich transition, which has not been adequately explained and modelled. Lean to rich transitions are highly relevant to catalyst ‘purging’ needed after exposure to high O2 levels (e.g. after fuel cut-offs). This work presents engine test measurements with an engine-aged catalyst that highlight the negative impact of Sulfur on pollutant conversion after a lean to rich transition. Sulfur appears to impact not only the available Oxygen to treat the excess reductants but also the water-gas shift and the steam reforming pathways that are desired to minimize CO and HC slip converting them to H2. We speculate that the above H2 production pathways are linked to Oxygen Storage dynamics due to the reversible (equilibrium controlled) nature of Oxygen Storage. The proposed mechanism is based on our earlier work on Sulfation of OSC-containing Lean NOx traps where we identified the role of SO2 storage and release at moderate/high temperature leading to reversible sulfation of the active sites in rich mode. An initial modeling attempt of the above complex interactions seems to confirm the main elements of the proposed mechanism predicting the main behavioral trends of pollutant conversion at two different temperatures. This opens the way to develop predictive models to optimize lambda controls addressing the zero-impact emission challenges.
Data Sets - Support Documents
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