A study on liquid‐liquid dispersions in a continuous mixer via computational fluid dynamics (CFD) simulation combined with population balance model (PBM)
Bibliographic record
Abstract
Abstract Liquid‐liquid two‐phase flow in a mixer of mixer‐settler has been studied via a computational fluid dynamics (CFD) simulation combined with the population balance model (PBM) and verified with particle image velocimetry (PIV) experiments. The simulation was performed using the multiple reference frame (MRF) approach, the Eulerian‐Eulerian two‐fluid model, and the standard k‐ϵ model. The effects of impeller speed, flow ratio, and impeller type on flow field, droplet diameter, and dispersed phase holdup were investigated. The results showed that CFD simulation combined with PBM could predict droplet size distribution (DSD). The smaller droplets were mainly in the bottom region of the mixer, larger ones were in the top part of the mixer, and the largest droplets appeared in the impeller centre region. The DSD and holdup were more sensitive to impeller speed than to the organic/aqueous flow ratio. A dual‐impeller mixer configuration was designed to enhance the mixing performance. Compared with single‐impeller, the installation of dual‐impellers could effectively avoid the dispersed phase dead zone above the lower impeller. When hc /T = 0.3, the best dispersing effect, such as uniform DSD and high mixing chamber space utilization, could be obtained.
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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 | 0.000 |
| 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 | 0.000 | 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".