The in-process removal of sterol glycosides by ultrafiltration in biodiesel production
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it