Factors affecting cellulose hydrolysis and the potential of enzyme recycle to enhance the efficiency of an integrated wood to ethanol process
Bibliographic record
Abstract
Past technoeconomic modeling work has identified the relatively large contribution that enzymatic hydrolysis adds to the total cost of producing ethanol from lignocellulosic substrates. This cost was primarily due to the high concentration of enzyme and long incubation time that was required to obtain complete hydrolysis. Although enzyme and substrate concentration and end-product inhibition influenced the rate of hydrolysis, the effect was less pronounced during the initial stages of hydrolysis. During this time most of the cellulases were adsorbed onto the unhydrolyzed residue. By recycling the cellulases adsorbed to the residual substrate remaining after an initial 24 h, a high rate of hydrolysis, with low overall residence time and minimal cellulase input, could be achieved for several rounds of enzyme recycle. A comparison of the front end (pretreatment, fractionation, and hydrolysis) of a softwood/hardwood to ethanol process indicated that the lignin associated with the softwood-derived cellulose stream limited the number of times the cellulose containing residue could be recycled. (c) 1996 John Wiley & Sons, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".