Anaerobic fermentation of glycerol by «Escherichia coli K12» for the production of ethanol
Bibliographic record
Abstract
As a by-product of biodiesel production, glycerol has now become an abundant and cheap source of carbon. Conversion of this glycerol to higher value products will increase the economic viability of the biodiesel production process. A full factorial experimental design was used to test the effects of glycerol concentration and headspace conditions on the cell growth, ethanol and hydrogen production were investigated. The results demonstrate that increase of glycerol concentration accelerates fermentation and that hydrogen production negatively affects cell growth. Maximum ethanol yield was obtained with a glycerol concentration of 10 g/L and was 0.38 g/g glycerol under membrane condition headspace. Statistical optimization showed that optimal conditions are 20 g/L initial glycerol with initial sparging of the reactor headspace for hydrogen production and 10 g/L initial glycerol with a membrane for ethanol. The study also investigated hydrogen and ethanol production from glucose, glycerol and crude glycerol via fermentation using Escherichia coli. The optimal conditions for fermentation of crude glycerol differ from that of pure with respect to initial glycerol concentration, supplementation and mixing speed. The maximum ethanol yield for crude glycerol was 85% of the maximum yield of ethanol of 0.42 g/g obtained at the optimum conditions of pure glycerol fermentation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".