High-solids enzymatic hydrolysis of biomass: Hydrodynamics and reaction kinetics integration via numerical modeling
Bibliographic record
Abstract
This study presents a novel computational fluid dynamics (CFD) model to investigate important aspects of the complex high-solids enzymatic hydrolysis (HSEH) process. The uniqueness of this CFD model lies in integrating the biochemical reaction taking place in the secondary phase and the corresponding mass transfer of the products from the secondary phase to the non-Newtonian primary phase, while dual axial impellers blend the multiphase system. The distribution of the reactants and products in the non-Newtonian primary phase affects the overall conversion of glucan to glucose, which, in turn, affects the commercial deployment of these systems for the production of renewable sugars. We investigated the effect of slurry viscosity on insoluble and soluble solids distribution, the impact of initial insoluble solids loading on total solids distribution, and varying the initial chemical composition of the insoluble solids on the total solids distribution. The comprehensive CFD model results show that variations in the chemical composition of the insoluble solids and the solids loading can each have a pronounced effect on the distribution of solids. This behavior would then affect the rate and extent of conversion of insoluble solids to soluble solids. Thus, the comprehensive CFD model can account for the interactions between independent variables, facilitating the design of small and large-scale reactors, while improving the conversion of insoluble solids to soluble solids. This novel CFD model thus represents the combined effects of key factors that influence HSEH in a realistic process environment.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".