Exploring the use of high solid loadings in enzymatic hydrolysis to improve biobutanol production from brewers' spent grains
Bibliographic record
Abstract
Abstract Brewers' spent grain (BSG) is a promising agroindustrial waste for the production of biobutanol. One critical point in the butanol production process is the optimization of the enzymatic hydrolysis step. In order to obtain the maximum efficiency, it is necessary to use high solids loadings in this process to obtain high concentrations of monosaccharides that allow high titres of butanol to be produced in the ABE fermentation process. The optimum enzyme and solids load maximizing the monosaccharide concentrations and minimizing the phenolic compounds concentrations in the enzymatic hydrolysates from pretreated BSG have been investigated. A dilute sulphuric acid pretreatment was carried out previously to the optimization of the enzymatic hydrolysis. Under optimal conditions (28.1% w/w solids load and 15.4 FPU/g DM), 47.0 g/L of glucose, 16.8 g/L of xylose, and 1.2 g/L of phenolic compounds were attained in the enzymatic hydrolysates. The enzymatic hydrolysates were subjected to an ABE fermentation process (with and without previous detoxification with activated carbon) to evaluate the production of butanol by C. beijerinckii . Maximum global yields of 31.0 g butanol/kg pretreated BSG and 46.4 g ABE/kg pretreated BSG were obtained. The detoxification process had little to no effect on the ABE fermentation process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".