Ultrasound‐assisted production of biodiesel FAME from rapeseed oil in a novel two‐compartment reactor
Bibliographic record
Abstract
Abstract BACKGROUND Ultrasonication has been proposed as a promising technique for enzymatic transesterification. In contrast, excess ultrasonication causes enzyme inactivation. This paper describes enzymatic transesterification to produce fatty acid methyl ester (FAME) from rapeseed oil using Callera Trans L™ and an original two‐compartment reactor. The reactor was composed of a mechanically stirred compartment (ST) and an ultrasound irradiation compartment (US). The reaction solution was recirculated between the ST and the US. The enzyme was exposed to ultrasonication only in the US. The reactor system has the option to control the direct irradiation period of ultrasonication to soluble enzyme, regulated by the mean residence time in the US. RESULTS The production of FAME with ultrasound irradiation gave a final yield of 91 wt% after 15 h. The reaction rate was enhanced up to 2‐fold through the use of the two‐compartment reactor compared with liquid lipase catalyzed transesterification without any ultrasound treatment. The Vmax with ultrasound irradiation was 2.3‐fold higher than that of the ultrasound‐free system, while the Km remained at almost the same level. The reaction rate and the conversion increased with shorter mean residence time in the US. CONCLUSION The advantages of the two‐compartment reactor were shown to produce biodiesel (FAME) resulting in acceleration of the enzyme reaction by ultrasound irradiation. Reaction enhancement was maximized by using a separate compartment of the reactor. A shorter mean residence time of reaction solution in the US and higher ultrasound power successfully realized a higher production rate of FAME. © 2016 Society of Chemical Industry
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".