Study of Solid−Liquid Mixing in Agitated Tanks through Computational Fluid Dynamics Modeling
Bibliographic record
Abstract
Solid−liquid mixing is one of the most important mixing operations due to its vast applications in many unit operations such as crystallization, adsorption, solid-catalyzed reaction, suspension polymerization, and activated sludge processes. In this study, a computational fluid dynamics (CFD) model was developed for solid−liquid mixing in a cylindrical tank equipped with a top-entering impeller to investigate the effect of impeller type (Lightnin A100, A200, and A310), impeller off-bottom clearance ( T /6− T /2, where T is tank diameter), impeller speed (150−800 rpm), particle size (100−900 μm), and particle specific gravity (1.4−6) on the mixing quality. An Eulerian−Eulerian (EE) approach, standard k− ε model, and multiple reference frames (MRF) techniques were employed to simulate the two-phase flow, turbulent flow, and impeller rotation, respectively. The impeller torque, cloud height, and just suspended impeller speed ( N js ) computed by the CFD model agreed well with the experimental data. The validated CFD model was then employed to calculate the solid concentration profiles by which the degree of homogeneity was quantified as a function of operating conditions and design parameters.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".