Regulated Emissions from Biodiesel Tested in Heavy-Duty Engines Meeting 2004 Emission Standards
Bibliographic record
Abstract
Biodiesel produced from soybean oil, canola oil, yellow grease, and beef tallow was tested in two heavy-duty engines. The biodiesels were tested neat and as 20% by volume blends with a 15 ppm sulfur petroleum-derived diesel fuel. The test engines were a 2002 Cummins ISB and 2003 DDC Series 60. Both engines met the 2004 U.S. emission standard of 2.5 g/bhp-h NOx+HC (3.35 g/kW-h) and utilized exhaust gas recirculation (EGR). All emission tests employed the heavy-duty transient procedure as specified in the U.S. Code of Federal Regulations. Reduction in PM emissions and increase in NOx emissions were observed for all biodiesels in all engines, confirming observations made in older engines. On average PM was reduced by 25% and NOx increased by 3% for the two engines tested for a variety of B20 blends. These changes are slightly larger in magnitude, but in the same range as observed in older engines. The cetane improver 2-ethyl hexyl nitrate was shown to have no measurable effect on NOx emissions from B20 in these engines, in contrast to observations reported for older engines. The effect of intake air humidity on NOx emissions from the Cummins ISB was quantified. The CFR NOx/humidity correction factor was shown to be valid for an engine equipped with EGR, operating at 1700 m above sea level, and operating on conventional or biodiesel.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".