Effect of extrusion conditions and hydrolysis with fiber-degrading enzymes on the production of C5 and C6 sugars from brewers’ spent grain for bioethanol production
Bibliographic record
Abstract
The bioconversion of brewers’ spent grain into bioethanol was investigated in the present study using thermoplastic extrusion and the use of fiber degrading enzymes. The extrusion conditions i.e. tempering moisture, screws speed, and temperature of last zone of the barrel were taken into account in order to optimize the yield of C5 and C6 sugars during the subsequent enzymatic hydrolysis step of the fibers. The most important variable that affected the sugar yield was the extrusion temperature, followed by the screws speed. The best extrusion conditions were 20% tempering moisture, 200 rpm and 50 °C. No enzymatic and yeast inhibitors were detected in any of the enzymatically-treated fiber hydrolyzates. The fermentation resulted in 5.43 mL bioethanol per 100g of extruded brewers’ spent grain (dry weight basis). The only sugar consumed was glucose. The free amino nitrogen amount quantified in the hydrolyzates was as low as >20 mg L-1, negatively affecting sugars consumption during the fermentation and consequently the ethanol yield.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".