A Study of the Effects of Fuel Type and Emission Control Systems on Regulated Gaseous Emissions from Heavy-Duty Diesel Engines
Bibliographic record
Abstract
The New York State Department of Environmental Conservation (DEC) and Environment Canada have jointly participated along with partners the New York City Metropolitan Transit Agency (MTA); Johnson Matthey, Environmental Catalysts & Technologies; Equilon Enterprises, LLC and Corning, Inc. in a project to evaluate the effect of various combinations of fuels and aftertreatment configurations on diesel emissions. Emissions measurements were performed during engine dynamometer testing of an International DT 466E heavy-duty diesel engine. Fuels tested in the study were Diesel Fuel 1 and 2, low sulfur diesel (150 ppm), two ultralow sulfur fuels (<30 ppm), Fischer-Tropsch, Biodiesel, PuriNOx™ and two Ethanol-Diesel blends. Configurations tested were: engine out, and diesel oxidation catalyst, continuously regenerating diesel filter, and exhaust gas recirculation aftertreatment. In general, the use of more aggressive aftertreatment (ie. DOC vs engine out, CRDPF vs DOC, etc) had a much more significant effect on emissions of PM, NOx, NO, HC and CO than the use of non-standard fuels, including the blended fuels. EGR-DPF was the only after treatment technology that significantly affected NOx emissions, reducing them an average of 42% from the DOC case for all fuels. NOx was reduced 41% from the Engine Out case for EULSD, the only fuel that was tested with both configurations. The only exception to this general trend was that PNOx fuel produced similar NOx emissions in the DOC configuration to the use of EGR-DPF after treatment with the ultralow sulfur fuels.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".