Long breathing lean NO <sub> <i>x</i> </sub> trap regeneration with supplemental <i>n</i> -butanol
Bibliographic record
Abstract
This paper evaluates a long breathing strategy of lean NO x trap for achieving ultra-low nitrogen oxide (NO x ) emissions, with an aim to reduce the associated fuel penalty. The fuel impacts on the long breathing strategy of lean NO x trap operation are examined on a heated flow bench with diesel and n-butanol as the reductants. Engine tests are performed to identify suitable working regions for the lean NO x trap strategies. For a very low engine-out NO x emission level of ~30 ppm, the long breathing adsorption of the lean NO x trap shows a significant improvement in NO x storage efficiency compared to a conventional lean NO x trap operational strategy for a moderate level of NO x emissions. The use of n-butanol fuel in diesel engines produces much lower NO x and particulate matter emissions, which is deemed advantageous for operating the long breathing lean NO x trap strategy. As a reductant for lean NO x trap regeneration, n-butanol is found to be more effective in terms of regeneration effectiveness, NO x conversion efficiency, and potential hydrogen (H 2 ) yield compared to using diesel fuel in the after-treatment. A fuel penalty analysis is conducted for the selected cases with combinations of different combustion modes and lean NO x trap strategies. Given a low level of NO x emissions from n-butanol combustion, the long breathing lean NO x trap strategy can potentially achieve ultra-low NO x emissions with a minimum fuel penalty.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".