The in-process removal of sterol glycosides by ultrafiltration in biodiesel production
Bibliographic record
Abstract
Minor components found in biodiesel can affect its stability and cold flow properties. Without extensive post treatments, trace compounds such as sterol glycosides (SG) can remain at unacceptable levels in finished biodiesel fuels. This study proposes to remove SG from reacted Fatty Acid Methyl Ester (FAME) mixtures using ultrafiltration. Degummed soybean oil was transesterified using methanol and a catalyst (sodium methoxide). The mixtures were immediately ultrafiltered after the reaction and the FAMEs from the retentate and permeate were analyzed for SG. The highest separation for SG (86 %) was obtained when the reaction conditions were 0.7 wt.% catalyst and 4:1 MeOH:Oil ratio. The lowest separation (0%) was observed at 0.3 wt.% catalyst and 4:1 MeOH:Oil ratio. The higher separations were explained by the deprotonation of the hydroxyl groups on SG. This decreased the solubility of SG in the reacted FAME phase. The separation was lowest, when unreacted oil along with monoacylglycerides (MG) and diacylglycerides (DG) solubilized SG in the reacted mixture. The separation was also low when high methanol to oil ratios were used in the transesterification. The lowest concentration of SG measured in FAMEs treated by ultrafiltration was 3.4 ppm. The results indicate that ultrafiltration is an effective method to remove SG from soybean FAMEs.
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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".