Recent innovations for reviving the ABE fermentation for production of butanol as a drop-in liquid biofuel
Bibliographic record
Abstract
Butanol is a key microbial product that provides a route from renewable carbohydrate resources to a "drop-in" liquid biofuel, broadening its market in the near future. The acceptable performance of butanol as a neat or a blended fuel in different engines both from the technical and environmental points of view has attracted a wide range of research for reviving the old acetone-butanol-ethanol (ABE) fermentation. In this review, recent findings on fuel characteristics of butanol, different generations of substrate for large scale butanol production, and alternative process designs for upstream, mainstream, and downstream operations have been critically reviewed and discussed. In the upstream, studies devoted to designing and optimization of pretreatments based on prerequisites of butanol production, e.g., maximizing cellulose and hemicellulose recovery and minimizing lignin degradation, are presented. In the mainstream, different microbial systems and process integrations developed for facilitating ABE production (e.g., in-situ butanol removal) are scrutinized. Finally, innovations in ABE recovery and purification as "Achilles Heel" of butanol production processes which directly controls the energy return on investment (EROI), are reviewed and discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".