An Investigation on the Regeneration of Lean NOx Trap Using Ethanol and n-Butanol
Bibliographic record
Abstract
Reduction of nitrogen oxides (NOx) in lean burn and diesel fueled Compression Ignition (CI) engines is one of the major challenges faced by automotive manufacturers. Lean NOx Trap (LNT) and urea-based Selective Catalytic Reduction (SCR) exhaust after-treatment systems are well established technologies to reduce NOx emissions. However, each of these technologies has associated advantages and disadvantages for use over a wide range of engine operating conditions. In order to meet future ultra-low NOx emission norms, the use of both alternative fuels and advanced after-treatment technology may be required. The use of an alcohol fuel such as n-butanol or ethanol in a CI engine can reduce the engine-out NOx and soot emissions. In CI engines using LNTs for NOx reduction, the fuel such as diesel is utilized as a reductant for LNT regeneration. In the present work, a detailed evaluation of the performance of long breathing LNT (requiring fewer regenerations than conventional LNT) is carried out using ethanol and n-butanol as the reductants and are compared with diesel as the reductant. For this purpose, a long breathing LNT catalyst is examined on a flow bench under simulated exhaust conditions. The NOx adsorption period is decoupled from regeneration, and reductant quantities are varied at 3% and 8.5% oxygen concentration. Ethanol and n-butanol are found to be more effective as reductants compared to diesel in terms of NOx conversion and hydrogen yield during the LNT regeneration at the tested conditions. In order to further understand the impact of using ethanol and n-butanol, the formation of different hydrocarbon species due to reforming on the Diesel Oxidation Catalyst (DOC) and LNT catalyst has been studied as well.
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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".