On-road and In-Laboratory Testing to Demonstrate Effects of ULSD, B20 and B99 on a Retrofit Urea-SCR Aftertreatment System
Bibliographic record
Abstract
In order to demonstrate the performance of a retrofitted selective catalytic reduction (SCR) system while also addressing the issues associated with greater use of biodiesel, a 2005 International 9200i tractor owned by the City of Santa Monica was retrofitted with a titania-vanadia-tungsten catalyst and a urea dosing system supplied by Extengine Systems, Inc. This tractor was operated under normal service conditions within the City of Santa Monica refuse collection and transportation fleet. An on-board emissions measurement system supplied by Engine, Fuel, and Emissions Engineering, Inc. was installed on the vehicle; it measured the emissions and fuel use of the vehicle while it operated on ultra-low-sulfur diesel (ULSD), 20% biodiesel (B20), and 99% biodiesel (B99) on consecutive days. The vehicle, with a similar aftertreatment system, was then tested at the California Air Resources Board (CARB) Los Angeles Heavy-Duty Emissions Lab and tested on the Urban Dynamometer Driving Schedule (UDDS) to again assess the effects of ULSD, B20, and B99 on the performance of the urea-SCR aftertreatment system. Results from this testing showed that oxides of nitrogen (NOx) emissions increased by 6%–12% when the blend was increased from ULSD to B20, by 16%–35% when the blend was increased from ULSD to B99, and by 26%–27% when it was increased from B20 to B99. Particulate matter (PM) emissions decreased by 37%–50% when the blend was increased from ULSD to B20, by 71%–79% when it was increased from ULSD to B99, and by 60%–63% when it was increased from B20 to B99. The SCR system reduced NOx emissions by 64%–87% and its performance was not affected by the use of 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 | 0.001 | 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 | 0.001 | 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) | 0.004 | 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".