Biodiesel from Crude Tall Oil and Its NOx and Aldehydes Emissions in a Diesel Engine Fueled by Biodiesel-Diesel Blends with Water Emulsions
Bibliographic record
Abstract
Using biodiesel in diesel engines is beneficial for reducing emissions of carbon monoxide (CO), hydrocarbons (HC) and particulate matters (PM). Biodiesel is usually produced from vegetable oils or animal fats. When produced from plant oil or woody plant sources, biodiesel can reduce a significant amount of carbon dioxide on a life cycle basis. The objective of this study is to produce biodiesel from a non-conventional woody plant source that is, crude tall oil, which is a dark brown viscous liquid extracted and processed in wood pulping plants. It contains a high percentage of fatty acids. From raw crude tall oil, tall oil fatty acids were separated and were successfully used for the production of biodiesel in this study. Although biodiesel produces lower CO, HC and PM than petroleum diesel fuel, it produces higher oxides of nitrogen (NOx) emissions in diesel engines. Water emulsifications of diesel-biodiesel blends are investigated in a direct injection (DI) diesel engine in this work to understand their potential for NOx reduction. When using 10% water in the emulsions, NOx was reduced by nearly 15%. In aldehyde emissions, B100 showed 35% lower aldehydes and B100 with 10% water emulsion produced nearly 90% lower aldehydes than diesel fuel—a substantial reduction. Therefore, this study accomplished the desired goal of producing biodiesel from a non-conventional source, which satisfies ASTM biodiesel standard and results in lower NOx and aldehydes emissions with water emulsifications of diesel-biodiesel blends in a diesel engine compared to that of diesel fuel.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".