Selective catalytic reduction: Adding an ammonia slip catalyst mitigates dosing errors
Bibliographic record
Abstract
Abstract This paper uses computational simulation to compare the performance of two catalytic systems designed for the removal of oxides of nitrogen from automotive engine exhaust using selective catalytic reduction (SCR). The first system is comprised of an SCR catalyst alone, whilst the second system adds an ammonia slip catalyst (ASC) to the end of an SCR catalyst. Both systems had the same total length. Comparisons of the NOx removal efficiency and the degree of ammonia slip are made using driving cycles and an optimized ammonia dosing strategy. Comparisons are also made for the case where a constant error in the ammonia dosing occurs. The addition of the ASC reduces ammonia slip during over‐dosing scenarios and gives a greater NOx conversion during under‐dosing cases. This work concludes that the ASC is a positive addition to the SCR in meeting the exhaust emission regulations. Although it does not necessarily allow for a higher NOx conversion, its ability to cope with under‐ and over‐dosing situations can be beneficial in catalyst aging and unpredictable driving conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".