Numerical simulation of a continuous sonoreactor for cotton cellulose residues recovery
Bibliographic record
Abstract
Numerical simulations are a tool for sonoreactors design and reaction parameters choice. We modeled a two parts sonoreactor with six lateral flat transducers along the walls and a concentric high intensity focused ultrasound (HIFU) transducer at the bottom. We examined the effects of the gap between the reflector and transducers ( h ), cone radii in the lower part and ultrasound frequency ( f ) on the cavitation activity of cellulose esters solutions. Then, we investigated the effects of the properties of cellulose solutions on the cavitation activity The simulation accounts for the attenuation due to the cavitation bubbles and considers the propagation of sound waves from the HIFU as linear. We measured the speed of sound in the cellulose esters solutions and included it in our simulations: 1545 m s −1 for 6.25 g L -1 . h , f , the density ( ρ ) and the viscosity ( μ ) of the cellulose solutions have the most significant effects − accounting for 34 % to 61 % of the variance − on the total acoustic pressure ( p T ) and active cavitation surface area ( V ). p T and V increase as f and ρ increase, and as h and μ decrease. At 78 kHz and h = 0.075 m, with μ = 5.3 × 10 -3 Pa.s and ρ = 941.8 kg m −3 , the simulation resulted in the highest p T and largest V : 1.96 × 10 6 Pa and 3.99 × 10 −2 m 2 . These data provide a basis to optimize sonoreactor design and operating conditions for enhanced cavitation performance in cellulose processing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".