Adapted feeding strategies in fed-batch fermentation improve sugar delivery and ethanol productivity
Bibliographic record
Abstract
Bioethanol is a renewable fuel widely used in road transportation and is generally regarded as a clean energy source. Although fermentation is one of the major processes in bioethanol production, studies on improving its efficiency through operational design are limited, especially compared to other steps (pretreatment and hydrolysis/saccharification). In this study, two adapted feeding strategies, in which feed medium addition (sugar delivery) was adjusted to increase the supply of fermentable sugar, were developed to improve ethanol productivity in 5-L fed-batch fermentation by Saccharomyces cerevisiae. Specifically, a linear adapted feeding strategy was established based on changes in cell biomass, and an exponential adapted feeding strategy was developed based on cell biomass accumulation. By implementing these two feeding strategies, the overall ethanol productivity reached 0.88±0.04 and 0.87±0.06 g/L/h, respectively. This corresponded to ~20% increases in ethanol productivity compared to fixed pulsed feeding operations. Additionally, there was no residual glucose at the end of fermentation, and final ethanol content reached 95±3 g/L under the linear adapted operation and 104±3 g/L under the exponential adapted feeding strategy. No statistical difference was observed in the overall ethanol yield (ethanol-to-sugar ratio) between fixed and adapted feeding strategies (~91%). These results demonstrate that sugar delivery controlled by adapted feeding strategies was more efficient than fixed feeding operations, leading to higher ethanol productivity. Overall, this study provides novel adapted feeding strategies to improve sugar delivery and ethanol productivity. Integration into the current practices of the ethanol industry could improve productivity and reduce production costs of fermentation processes.
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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".