Lean NO <sub> <i>x</i> </sub> trap supplemental energy savings with a long breathing strategy
Bibliographic record
Abstract
Current and upcoming diesel engine emission regulations in the USA and in Europe stipulate significant reductions of nitrogen oxide emissions. To satisfy these emission regulations and to maintain high fuel efficiency, energy efficient diesel after-treatment to remove nitrogen oxides is required. In this study, a long breathing (long adsorption) strategy was investigated for the reduction of supplemental energy consumption of a diesel lean nitrogen oxide trap. The long breathing strategy would be enabled by moderate exhaust gas recirculation to reduce the engine-out nitrogen oxide levels. With reduced feed gas nitrogen oxide levels, the adsorption time of the lean nitrogen oxide trap could be extended, leading to less frequent fuel-rich regeneration of the lean nitrogen oxide trap. Proof of concept studies were undertaken on a diesel engine to demonstrate the enabling of the long breathing lean nitrogen oxide trap strategy, while further tests were undertaken on a flow bench set-up to demonstrate the potential energy savings with the long breathing lean nitrogen oxide trap strategy. The test results indicated that, at the selected operating conditions, the long breathing strategy could be enabled by reducing the engine-out nitrogen oxide from 110 ppm to 50 ppm via moderate exhaust gas recirculation. The flow bench test results indicated that the adsorption time of the lean nitrogen oxide trap increased exponentially when the feed gas nitrogen oxide level was reduced. The longer adsorption led to supplemental energy savings in excess of 60% when the feed gas nitrogen oxide level was reduced from 110 ppm to 50 ppm. Furthermore, it was calculated that the long breathing lean nitrogen oxide trap strategy enabled a higher overall indicated efficiency of 36.4% compared to 35.9% with a conventional lean nitrogen oxide trap strategy.
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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".