Statistical Optimization of Biodiesel Production from Salmon Oil via Enzymatic Transesterification: Investigation of the Effects of Various Operational Parameters
Bibliographic record
Abstract
The enzymatic transesterification of Atlantic salmon (Salmo salar) oil was carried out using Novozym 435 (immobilized lipase from Candida antartica) to produce biodiesel. A response surface modelling design was performed to investigate the relationship between biodiesel yield and several critical factors, including enzyme concentration (5, 10, or 15%), temperature (40, 45, or 50 °C), oil/alcohol molar ratio (1:3, 1:4, or 1:5) and time (8, 16, or 24 h). The results indicated that the effects of all the factors were statistically significant at p-values of 0.000 for biodiesel production. The optimum parameters for biodiesel production were determined as 10% enzyme concentration, 45 °C, 16 h, and 1:4 oil/alcohol molar ratio, leading to a biodiesel yield of 87.23%. The step-wise addition of methanol during the enzymatic transesterification further increased the biodiesel yield to 94.5%. This is the first study that focused on Atlantic salmon oil-derived biodiesel production, which creates a paradigm for valorization of Atlantic salmon by-products that would also reduce the consumption and demand of plant oils derived from crops and vegetables.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".