The Influence of High Cetane Blending Components on Emissions From a Heavy-Duty Diesel Engine With EGR
Bibliographic record
Abstract
The exhaust emissions form a single-cylinder version of a heavy-duty diesel engine with exhaust gas recirculation (EGR) were measured with eight high-cetane components blended into an ultra-low sulphur diesel base fuel. the blending components evaluated were conventional nitrate and peroxide cetane improver additives, paraffins from two sources, three ethers, and soy methyl ester. The blending components were used to increase the cetane number of a base fuel by ten numbers, from 44 to 54. Exhaust emissions were measured using the AVL eight-mode steady-state test procedure. PM and NOx emissions from the engine were fairly insensitive to ignition quality improvement by nitrate and peroxide cetane improvers. Soy methyl ester and two of the ethers, 1,4 diethoxybutane and 2-ethoxyethyl ether, significantly reduced PM emissions, but increased ONx emissions. The two paraffinic blending components reduced both PM and NOx emissions.
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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) | 0.001 | 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".