Modeling of Oxygen Delignified Wheat Straw Enzymatic Hydrolysis as a Function of Hydrolysis Time, Enzyme Concentration, and Lignin Content
Bibliographic record
Abstract
Enzymatic hydrolysis is one of the most expensive operations of producing lignocellulosic ethanol, primarily due to high enzyme costs. Enzyme loadings must be reduced, and a well-developed kinetic model that can be easily implemented in process simulation software would greatly assist in determining optimum processing conditions. Oxygen-delignified wheat straw with different lignin contents was subjected to enzymatic hydrolysis at two different enzyme loadings for 72 h. Glucan conversion increased with increasing enzyme loading, decreasing lignin content, and decreasing solids concentration. By measuring total protein concentration and predicting the Novozyme 188 protein concentration, it was possible to calculate the cellulase protein concentration as a function of time. This work is the first report of a mass-based kinetic model capable of predicting glucose production during enzymatic hydrolysis of oxygen-delignified wheat straw, at different cellulases loadings (20 and 40 filter paper units/g glucan), lignin contents (5 and 9 wt%), and solids concentrations (5 to 10 wt% dry basis). The presented hydrolysis model includes a novel lignin factor to describe the amount of cellulases irreversibly adsorbed on lignin. The lignin factor also links glucose production during enzymatic hydrolysis to pretreatment severity. Mass transfer limitations present at 10 wt% solids were accounted for using a diffusion factor. Due to the model's simple solution and use of only five parameters, it can be easily implemented in process simulations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".