Bibliographic record
Abstract
Research has shown that asphaltenes are the prime stabilizers of water-in-oil emulsions and that resins are necessary to solvate the asphaltenes. Research has also shown that many compositional factors play a role including the amount of saturates and the properties of viscosity and density. These factors can then be used to develop models of emulsion formation. A review of the formation processes of these emulsions and water and oil types is given. This applies to all four water-in-oil types: stable, meso-stable, unstable emulsions and entrained water. The differences among these four types are high-lighted. A number of other techniques have also been used to model emulsions including neural networks. These are noted and compared to the regression models. A data set of more than 400 oils and their water-in-oil mixtures are used for the comparison. Numerical modeling schemes for the formation of water-in-oil emulsions are reviewed. New models are based on empirical data and the corresponding physical knowledge of emulsion formation. The density, viscosity, asphaltene and resin contents were correlated with a stability index. The establishment of an index for emulsion stability enables the use of this value as a target for the optimization of regressions to form a new model. The predictions of the new model are much simpler and better than old models and some that have been in the literature for some time. The new model is more accurate than the old models, although some improvement could still be made. The benefit of the new model is that it is more accurate and simpler than former regression models. The different approaches to these models and older regression models are highlighted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".