Bioconversion of hybrid poplar to ethanol and co‐products using an organosolv fractionation process: Optimization of process yields
Bibliographic record
Abstract
An organosolv process involving extraction with hot aqueous ethanol has been evaluated for bioconversion of hybrid poplar to ethanol. The process resulted in fractionation of poplar chips into a cellulose-rich solids fraction, an ethanol organosolv lignin (EOL) fraction, and a water-soluble fraction containing hemicellulosic sugars, sugar breakdown products, degraded lignin, and other components. The influence of four independent process variables (temperature, time, catalyst dose, and ethanol concentration) on product yields was analyzed over a broad range using a small composite design and response surface methodology. Center point conditions for the composite design (180 degrees C, 60 min, 1.25% H(2)SO(4), and 60% ethanol), yielded a solids fraction containing approximately 88% of the cellulose present in the untreated poplar. Approximately 82% of the total cellulose in the untreated poplar was recovered as monomeric glucose after hydrolysis of the solids fraction for 24 h using a low enzyme loading (20 filter paper units of cellulase/g cellulose); approximately 85% was recovered after 48 h hydrolysis. Total recovery of xylose (soluble and insoluble) was equivalent to approximately 72% of the xylose present in untreated wood. Approximately 74% of the lignin in untreated wood was recovered as EOL. Other cooking conditions resulted in either similar or inferior product yields although the distribution of components between the various fractions differed markedly. Data analysis generated regression models that describe process responses for any combination of the four variables.
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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 | 0.000 | 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".