Transesterification of Canola Oil to Fatty Acid Methyl Ester (FAME) in a Continuous Flow Liquid−Liquid Packed Bed Reactor
Bibliographic record
Abstract
Experiments were performed to study the mass-transfer limitations during the acid-catalyzed transesterification reaction of triglyceride (TG) with methanol (MeOH) to fatty acid methyl ester (FAME or biodiesel). The experiments were carried out as both agitated two- and single-phase reactions in an empty pipe and a packed bed reactor. The TG conversion rate increased with an increase in total superficial velocity and a decrease in packing particle diameter for the two-phase reactions. The TG conversion rate did not increase significantly with the reaction temperature for the two-phase reactions, whereas for the single-phase reactions, the TG conversion rate increased significantly as the reaction temperature increased. The rate constant at two-phase conditions (largest velocity, smallest packing particle size, and maximum pressure gradient) was comparable to that obtained at single-phase conditions, indicating that the mass-transfer limitations for two-phase experiments can be effectively overcome using a liquid−liquid packed bed reactor. The diminished mass transfer was explained by the formation of a new interfacial area between the two liquid phases, caused by the droplets being momentarily deformed into an elongated nonspherical shape as they passed through the openings between the solid particles of the packed bed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".