Improving ethanol productivity through self-cycling fermentation of yeast: a proof of concept
Bibliographic record
Abstract
BACKGROUND: The cellulosic ethanol industry has developed efficient strategies for converting sugars obtained from various cellulosic feedstocks to bioethanol. However, any further major improvements in ethanol productivity will require development of novel and innovative fermentation strategies that enhance incumbent technologies in a cost-effective manner. The present study investigates the feasibility of applying self-cycling fermentation (SCF) to cellulosic ethanol production to elevate productivity. SCF is a semi-continuous cycling process that employs the following strategy: once the onset of stationary phase is detected, half of the broth volume is automatically harvested and replaced with fresh medium to initiate the next cycle. SCF has been shown to increase product yield and/or productivity in many types of microbial cultivation. To test whether this cycling process could increase productivity during ethanol fermentations, we mimicked the process by manually cycling the fermentation for five cycles in shake flasks, and then compared the results to batch operation. RESULTS: Mimicking SCF for five cycles resulted in regular patterns with regards to glucose consumption, ethanol titer, pH, and biomass production. Compared to batch fermentation, our cycling strategy displayed improved ethanol volumetric productivity (the titer of ethanol produced in a given cycle per corresponding cycle time) and specific productivity (the amount of ethanol produced per cellular biomass) by 43.1 ± 11.6 and 42.7 ± 9.8%, respectively. Five successive cycles contributed to an improvement of overall productivity (the aggregate amount of ethanol produced at the end of a given cycle per total processing time) and the estimated annual ethanol productivity (the amount of ethanol produced per year) by 64.4 ± 3.3 and 33.1 ± 7.2%, respectively. CONCLUSIONS: This study provides proof of concept that applying SCF to ethanol production could significantly increase productivities, which will help strengthen the cellulosic ethanol industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".