Feeding strategies of methanol and lipase on eversa® transform‐mediated hydroesterification for FAME production
Bibliographic record
Abstract
This work reports the influence of different feeding strategies of methanol and catalysts on fatty acid methyl esters (FAME) yield in a beef tallow hydroesterification reaction for biodiesel production catalyzed by liquid lipase. Soluble lipase from the Thermomyces lanuginosus microorganism, named Eversa® Transform, was selected for the reactions in this work. The feeding of methanol and lipase into the system was evaluated varying the methanol to fat molar ratio of 4.0:1 and 4.5:1 and the lipase load of 1.0 wt% and 1.45 wt% in relation to the mass of beef tallow utilized in each assay. The highest yield (85.08 %) of FAME was reached at 35 °C, methanol to fat molar ratio of 4.5:1, 1.0 wt% of lipase, 6.0 wt% of water in only 8 h of reaction, with a one‐step addition of lipase and methanol fed to the process at a constant flow of 3.0 g · h −1 . Moreover, the reutilization process of the liquid lipase after four cycles was evaluated, which is a point rarely addressed in similar works available in the open literature. The findings of this paper demonstrate that the adoption of an appropriate strategy to feed inputs to the system is fundamental in order to take advantage of the maximum catalytic capacity of the lipase and consequently to obtain high yields in the process.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".