Large‐scale, high‐solids enzymatic hydrolysis of steam‐exploded poplar
Bibliographic record
Abstract
Abstract Enzymatic hydrolysis at high solids loadings is key to scale‐up of lignocellulosic biochemical conversion processes, because of potentially higher sugar and ethanol titers and lower hydraulic loads. However, high solids loadings can pose rheological challenges, reduce mass and heat transfer efficiency, and increase the concentration of enzyme inhibitors in the system, resulting in low conversion of glucan and xylan into fermentable sugars. In this study, ten batch enzymatic hydrolyses were conducted in a 200‐liter reactor, while monitoring sugar and inhibitor profiles. The effects of enzyme cocktail, biomass loading, pre‐treatment severity, and hydrolysis temperature were assessed using techno‐economic indicators to evaluate the efficacy of the enzymatic hydrolysis. For similar experimental conditions, different enzyme cocktails produced distinct hydrolysis outcomes allowing cocktail optimization. In spite of a rapid initial reaction rate, fermentable sugars concentrations reached a plateau after about 48 h, indicating severe inhibition. Increased biomass loadings did not proportionally increase sugar production. Both observations indicated the presence of severe inhibition, likely endogenous. Pre‐treatment at a lower severity (200°C for 8 min) led to the most efficient hydrolysis, while higher severities destroyed hemicellulose and led to lower overall sugar production. Lower saccharification temperatures (30–32°C) caused a 20% decrease in sugar conversion when compared to 50°C operation. Strategies to mitigate inhibition will be required if high‐solids enzyme hydrolysis is to be successfully scaled up to commercially relevant levels. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".