On-road and In-Laboratory Testing to Demonstrate Effects of ULSD, B20 and B99 on a Retrofit Urea-SCR Aftertreatment System
Bibliographic record
Abstract
<div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">Results from this testing showed that oxides of nitrogen (NO<sub>x</sub>) 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 NO<sub>x</sub> emissions by 64%–87% and its performance was not affected by the use of biodiesel</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".