Improved bioethanol productivity through gas flow rate-driven self-cycling fermentation
Bibliographic record
Abstract
BACKGROUND: The growth of the cellulosic ethanol industry is currently impeded by high production costs. One possible solution is to improve the performance of fermentation itself, which has great potential to improve the economics of the entire production process. Here, we demonstrated significantly improved productivity through application of an advanced fermentation approach, named self-cycling fermentation (SCF), for cellulosic ethanol production. RESULTS: The flow rate of outlet gas from the fermenter was used as a real-time monitoring parameter to drive the cycling of the ethanol fermentation process. Then, long-term operation of SCF under anaerobic conditions was improved by the addition of ergosterol and fatty acids, which stabilized operation and reduced fermentation time. Finally, an automated SCF system was successfully operated for 21 cycles, with robust behavior and stable ethanol production. SCF maintained similar ethanol titers to batch operation while significantly reducing fermentation and down times. This led to significant improvements in ethanol volumetric productivity (the amount of ethanol produced by a cycle per working volume per cycle time)-ranging from 37.5 to 75.3%, depending on the cycle number, and in annual ethanol productivity (the amount of ethanol that can be produced each year at large scale)-reaching 75.8 ± 2.9%. Improved flocculation, with potential advantages for biomass removal and reduction in downstream costs, was also observed. CONCLUSION: Our successful demonstration of SCF could help reduce production costs for the cellulosic ethanol industry through improved productivity and automated operation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".