Fractionation of fatty acid methyl esters via urea inclusion and its application to improve the low-temperature performance of biodiesel
Bibliographic record
Abstract
Biodiesel is viewed as the alternative to petroleum diesel, but its poor low-temperature performance constrains its utilization. Cloud point (CP), the onset temperature of thermal crystallization, appropriately shows the low-temperature performance. The effective way to reduce CP is to remove saturated fatty acid methyl esters (FAMEs). Compared to current methods, this work describes an extraordinary approach to fractionating FAMEs by forming solid urea inclusion compounds (UICs). Urea inclusion fractionation reduces the CPs by removing high melting-point linear saturated FAME components. Urea inclusion fractionation in this study was performed under various processing conditions: mass ratios of urea to FAMEs and solvents to FAMEs, various solvents, FAMEs from various feedstocks, and processing temperatures. Supersaturation of urea in the solution is the driving force, and it significantly affects yield, composition, CP, separation efficiency, and selectivity. Through a single urea inclusion fractionation process, FAMEs, except palm oil FAMEs, resulted in CP reduction ranging from 20 to 42 oC with a yield of 77–80% depending on the compositions. CP of palm oil FAMEs could reach as low as -17 oC with a yield of 46% after twice urea inclusion fractionation. According to the model prediction, the cetane number after urea inclusion fractionation decreased about 0.7–2 but was still higher than the minimum biodiesel requirement. Oxidation stability after urea inclusion decreased according to the proposed model, but this can be mitigated by adding antioxidants. Emission evaluation after urea inclusion fractionation indicated decreased hydrocarbons, carbon monoxide, and particulate matter. However, it resulted in the increasing emission of nitrogen oxides.
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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 | 0.001 |
| 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".