Development of dissolved carbon dioxide‐driven‐and‐controlled repeated batch fermentation process for ethanol production
Bibliographic record
Abstract
Abstract We previously observed that as glucose is completely exhausted during ethanol fermentation, the dissolved carbon dioxide (DCO2) level in the fermenter will suddenly decline. This observation was implemented to design and develop a DCO2‐driven‐and‐controlled repeated batch fermentation process for ethanol production. The process was tested at four different glucose concentrations (~150 g/L, ~200 g/L, ~250 g/L, and ~300 g/L), and each glucose concentration was controlled under three respective DCO2 control levels (without DCO2 control, and DCO2 controlled at either 1000 mg/L or 750 mg/L). The results show that reported process features complete glucose utilization and is self‐driven. For glucose concentration less than 200 g/L, ~41%‐50% of fermentation time per batch was saved during the repeated batch operation. It took 12.1 ± 1.1 hours‐14.9 ± 1.9 hours to complete a batch with glucose feed at ~250 g/L and 21.7 ± 6 hours‐31.5 ± 7 hours to complete a batch with glucose feed at ~300 g/L. The reported process is time saving and stable, but the ethanol yield is ~20% lower than the operation without DCO2 control. Dissolved CO2 control became essential for glucose concentrations greater than 250 g/L if zero glucose discharge in each batch during the operation is desired.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".