Use of Isomerization and Hydroisomerization Reactions to Improve the Cold Flow Properties of Vegetable Oil Based Biodiesel
Bibliographic record
Abstract
Biodiesel is a promising alternative to petroleum diesel with the potential to reduce overall net CO2 emissions. However, the high cloud point of biodiesel must be reduced when used in cold climates. We report on the use of isomerization and hydroisomerization reactions to reduce the cloud point of eight different fats and oils. Isomerization was carried out at 260 °C and 1.5 MPa H2 pressure utilizing beta zeolite catalyst, while hydroisomerization was carried out at 300 °C and 4.0 MPa H2 pressure utilizing 0.5 wt % Pt-doped beta zeolite catalyst. Reaction products were tested for cloud point and flow properties, in addition to catalyst reusability and energy requirements. Results showed that high unsaturated fatty acid biodiesels increased in cloud point, due to the hydrogenation side reaction. In contrast, low unsaturated fatty acid biodiesels yielded cloud point reductions and overall improvement in the flow properties. A maximum cloud point reduction of 12.9 °C was observed with coconut oil as the starting material. Results of the study have shown that branching can reduce the cloud point of low unsaturated fatty acid content biodiesel.
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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) | 0.001 | 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".