Experimental and model‐based investigation of the droplet size distribution during the mixing process in a batch‐settling cell
Bibliographic record
Abstract
Abstract The design of a liquid–liquid gravity settler relies on the experimental investigation and model‐based description of the phase separation process in a batch‐settling cell. However, according to the current state of the art, the modelling assumes a monodisperse droplet size distribution (DSD), which can lead to an inaccurate settler design. This study considers the polydispersity of the initial DSD resulting from the mixing process in the settling cell to enhance the model accuracy. DSDs of o/w and w/o dispersions during the mixing process in a settling cell are investigated in this work for 2‐methyltetrahydrofuran/water and decane/water material systems at hold‐ups of 25–50 vol.% and stirrer speeds of 400–850 min −1 . Sauter mean diameters (SMD) and DSD shapes are analyzed to identify the influence of the investigated parameters on the SMD and DSD and to model the SMD and DSD. The experimental investigation shows that stirrer speed, hold‐up, and interfacial tension significantly affect the DSD, while the viscosity of the continuous phase plays a minor role. The SMD is correlated to the Weber number, viscosity group, and hold‐up by a model with a mean absolute percentage error (MAPE) of 3.6%. The DSD is described by a log‐normal distribution function with a MAPE of 5%. The SMD and DSD models presented in this work can be used to describe the initial DSD of the phase separation process in a batch‐settling cell, considering polydispersity and thus increasing the modelling accuracy of the phase separation process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".