Comparisons of existing pretreatment, saccharification, and fermentation processes for butanol production from agricultural residues
Bibliographic record
Abstract
Abstract Some of the most recent, relevant, industrial and academic contributions made in the field of butanol production are reviewed here. The focus on butanol is due to the growing demand for non‐fossil biofuels. In addition, butanol can be mixed with fossil fuels or can be used alone, allowing an alternative to gasoline. Butanol can be synthesised biologically using sugars extracted from biomass such as agricultural waste. This agricultural waste must be pretreated before it is suitable for sugar extraction. Following this stage, enzymatic hydrolysis is employed, before performing fermentation using microorganisms. This article summarises some of the economical methods such as simultaneous saccharification and fermentation (SSF). Different pretreatment and saccharification processes were compared. Acid pretreatment and saccharification achieved the highest sugar concentrations from wheat straw. Monoethanolamine pretreatment achieved highest sugars from hardwood. Comparisons and analysis of different types of fermentation processes illustrated that immobilised reactor provided the best butanol rate of production. Integration of fermentation with product removal process improved butanol production in immobilised reactor. Gas stripping method was illustrated to be the product removal process. © 2011 Canadian Society for Chemical Engineering
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".