Production of biodiesel catalyzed by lipase from <i>Thermomyces lanuginosus</i> in its soluble form
Bibliographic record
Abstract
Abstract Biodiesel production through the transesterification of oleaginous sources utilizing alkaline catalysts shows many process drawbacks like the need of a raw material with high purity degree and a high amount of alkaline wastewater generated from the biodiesel washing step. Such drawbacks can be overcome using enzymes as catalysts. In this context, the commercial Callera™ Trans L lipase arises as an interesting alternative catalyst for hydrolysis‐esterification reaction. In order to evaluate the use of Callera™ Trans L on biodiesel synthesis and optimize the main variables of the hydrolysis‐esterification process (temperature, methanol to oil molar ratio, and catalyst concentration), this work adopted the Central Composite Statistical Design to find out the combination of factors that lead to maximum biodiesel yield. It was observed that the Callera™ Trans L lipase presents a high catalytic activity, mainly in the initial reaction steps. The optimized variables for biodiesel synthesis were 35 °C methanol to oil molar ratio of 4.5:1, and 1.45 wt% of catalyst load, which allowed reaching a biodiesel yield of 96.9 % in 24 h of reaction.
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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".