Emissions from Compression Ignition Engines with Animal-Fat-Derived Biodiesel Fuels
Bibliographic record
Abstract
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 NOx, 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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".