Cellulosic Ethanol: Improving Cost Efficiency by Coupling Semi-Continuous Fermentation and Simultaneous Saccharification Strategies
Bibliographic record
Abstract
A novel approach to improve ethanol production from sugarcane bagasse is proposed. Biomass was pretreated with sodium hydroxide, sulfuric, oxalic, and maleic acids (1% w/v) at different temperatures (130–170 °C) and times (10–30 min). The pretreatment with NaOH at 160 °C for 20 min was found to be the most efficient for further enzymatic saccharification. A semi-continuous fermentation system coupled with a simultaneous saccharification and fermentation strategy was used, attaining fermented liquor every 24 h. The amount of enzymes needed for saccharification was optimized, as well as the production time and ethanol concentration. The process occurred with near to complete depletion of glucose, obtaining ethanol concentrations ranging from 8.36 to 10.79% (v/v). The whole system, at bench scale, showed stability over 30 days, and ease of management and control. This strategy may improve cost efficiency in the production of cellulosic ethanol at industrial scale.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".