Comparative Analysis of Water and Glycerin Emulsification: Particle Size, Stability, Engine Performance, and Emissions in Biodiesel Fuels
Bibliographic record
Abstract
Biodiesel has emerged as a promising alternative to conventional diesel fuel, offering potential reductions in greenhouse gas (CO2) emissions. However, its use in diesel engines results in higher levels of nitrogen oxides (NOx). This study investigates emulsification techniques for reducing NOx emissions from biodiesel combustion. Two techniques, glycerin and water emulsification, are examined. Approximately 10 vol. % of crude glycerin is produced during biodiesel manufacturing as a waste or by‐product. The study attempts on‐site purification of crude glycerin, which is then used as a phase for glycerin‐biodiesel emulsions. These emulsions are compared to water emulsions in terms of emulsion stability, mean particle droplet size, microscopic fuel structure, and fuel properties. In addition, engine performance and emissions are evaluated using a small direct injection (DI) diesel engine, with both water and glycerin emulsion fuels. Results show that both emulsion fuels significantly reduce smoke emissions and further mitigate NOx emissions from biodiesel combustion. With 10% glycerin and water emulsions, smoke emissions were reduced by over 50% compared to pure biodiesel, and NOx emissions decreased by more than 15%. Emulsification techniques in the biodiesel industry could offer a viable solution for reducing both smoke and NOx emissions. Employing glycerin emulsification not only decreases NOx emissions but also transforms crude glycerin into a value‐added resource. Otherwise, disposal of crude glycerin could pose significant challenges for small and remote biodiesel producers due to cost constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".