Emissions from Compression Ignition Engines with Animal-Fat-Derived Biodiesel Fuels
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Biodiesel and other renewable fuels are of interest due to their impact on energy supplies as well as their potential for carbon emissions reductions. Waste animal fats from meat processing facilities, which would otherwise be sent to landfill, have been proposed as a feedstock for biodiesel production. Emissions from biodiesel fuels derived from vegetable oils have undergone intense study, but there remains a lack of data describing the emissions implications of using animal fats as a biodiesel feedstock. In this study, emissions of NO<sub>x</sub>, unburned hydrocarbons and particulate matter from a compression ignition engine were examined. The particulate matter emissions were characterized using gravimetric analysis, elemental carbon analysis and transmission electron microscopy. The emissions from an animal fat derived B20 blend were compared to those from petroleum diesel and a soy derived B20 blend. No statistically significant differences were observed between the fuels in the gaseous emissions. Notable differences in engine-out particulate matter emissions characteristics were observed between the B20 blends and the petroleum diesel, but few differences were observed between the B20 blends using animal fat based biodiesel and those using soy based biodiesel. Given very similar engine-out particulate matter and gaseous emissions compositions for animal fat and soy B20 blends, it is suggested that diesel exhaust aftertreatment systems designed to operate with soy biodiesel blends are likely also compatible with animal fat derived biodiesel fuel blends.</div></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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".