Comparison of in‐situ recovery methods of gas stripping, pervaporation, and vacuum separation by multi‐objective optimization for producing biobutanol via fermentation process
Bibliographic record
Abstract
Acetone, butanol, and ethanol (ABE) continuous fermentation for biobutanol production was simulated and optimized for three case studies where the ABE fermentation process was integrated in turn with gas stripping, pervaporation, and vacuum separation methods in an attempt to partly mitigate the product inhibition effect. Following the multi‐objective optimization of the three integrated processes, the Pareto‐optimal solutions were ranked using the net flow method to determine the best operating conditions. Results were compared to a standard continuous fermentation process without an integrated recovery method. The integrated butanol recovery methods improved butanol productivity by a factor of 6–10, and sugar conversion by a factor of 3, while butanol yield remained essentially unchanged. In addition, the butanol average concentration based on all exit streams for the fermentation process integrating one of the separation methods is approximately 2.25 times higher when compared to the non‐integrated fermentation process, with values in the vicinity of 30 g/L. Results of the gas stripping and vacuum fermentation processes were very similar to each other but superior to pervaporation in terms of butanol productivity and average concentration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".