A novel quasi plug‐flow reactor design for enzymatic hydrolysis of cellulose using rheology experiment and CFD simulation
Bibliographic record
Abstract
One of the obstacles constraining bio‐ethanol production using cellulose is enzymatic hydrolysis. Uniform distribution of slurry residence time is important to improve the efficiency. Efforts were made to develop a novel quasi plug‐flow reactor on industrial scale for enzymatic hydrolysis of cellulose by CFD simulation. Ahead of numerical simulation, the rheological properties of the cellulose enzymolysis slurry of furfural residues were studied based on experiments. A turbulence model called k‐kl‐ω was employed in CFD simulation, which was good at describing the flow field with great variation on the extent of turbulence in the stirred tank. Vertical blades, pitched blades, helical ribbons, and their combination were investigated firstly, and then the internal structures were optimized. The calculated residence time distribution curve showed that the quasi plug‐flow reactor had the following features: (1) combination of double helical ribbons and vertical blade, (2) two partitions and four baffles, (3) flow area was 0.1 m2 on partitions. The CFD simulation methods and results could provide a theoretical guidance for the development of a novel quasi plug‐flow reactor to improve the efficiency of the cellulose enzymatic hydrolysis on industrial scale.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".