Monitoring silicone oil droplets during emulsification in stirred vessel: Effect of dispersed phase concentration and viscosity
Bibliographic record
Abstract
Abstract Reliable measurement of drop size distributions (DSD) in liquid–liquid dispersions are necessary for industrial process monitoring and control, as well as the in‐depth study of emulsification mechanisms in order to develop accurate and phenomenological models to be used in population balance modelling. Two experimental devices were assessed: an in situ video probe coupled with an automated image analysis algorithm based on a circular Hough‐transform and a focused beam reflectance measurement (FBRM). Their applicability was evaluated for o/w emulsions of silicone oil with mean droplet sizes between 50 and 200 µm. The in situ techniques have been compared to off‐line laser diffraction, which was considered as the standard technique. The automated video treatment algorithm was improved to provide accurate detection rates for dispersed phase concentrations (by weight) ranging between 5% and 10–20% depending on the droplet sizes. The in situ nature of the video probe allows for a much finer temporal resolution during the early times of the emulsification, as well as giving more reliable measurements of not yet stabilised emulsions, when compared to off‐line laser diffraction. The reconstructed DSDs from FBRM data consistently under‐predicted the DSDs given by the other two methods, as it missed the largest droplets in the DSD. The influences of dispersed phase viscosity and concentration on the DSD, and the maximum and mean diameters have been evaluated. As the viscosity and concentration increased, the distributions move away from a classical uni‐modal shape to more complex, multi‐modal distributions due to more complex break‐up phenomena.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".