Design of In-Line Emulsification Processes for Water-in-Oil Emulsions
Bibliographic record
Abstract
The fabrication of water-in-oil emulsions is a process with widespread applications in formulation engineering. The most common process approach is to use a stirred vessel provided with a high speed dispersing impeller or a rotor-stator head and operated in batch or semi-batch mode. The mean drop size and the drop size distribution are usually correlated by the properties of the surfactants and the specific mechanical energy dissipated by the mixer among others. The present paper addresses an application in the oil industry: the large-scale manufacturing of a fine water-in-oil emulsion. Instead of using a tank-based operation, the idea is to create the emulsion in line and operate the process in a continuous mode. Several commercial in-line dispersing technologies are available and the purpose is here to determine the process and dispersing technology parameters that make possible the fabrication of a stable emulsion. Likewise in stirred tank, it is shown that apart from the energy dissipation rate, the kinetics properties of the surfactants and the process configuration also play a major role in obtaining a stable emulsion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".