Bibliographic record
Abstract
Demulsifiers are a class of surfactants used to destabilize emulsions. This destabilization is achieved by reducing the interfacial tension at the emulsion interface, often by neutralizing the effect of other, naturally occurring surfactants which are stabilizing the emulsion. Demulsifier performance is routinely characterized using simple test procedures developed for use in the field. Because of the complexity of factors determining emulsion stability and, therefore, the effectiveness of any given demulsifier chemical, the wide variety of fundamental, mechanistic approaches to demulsifier selection often give way to empirical methods. A discussion of some of the common demulsifier performance characterization techniques is given along with some empirical methods for demulsifier selection. Introduction Several excellent reviews of demulsifier chemistry and properties can be found in the literature. For this chapter, the important factors in demulsifier selection and characterization will be discussed, accompanied with specific examples. Chemical demulsification is commonly used to separate water from heavy oils in order to produce a fluid suitable for pipelining (typically less than 0.5% solids and water). A wide range of chemical demulsifiers are available in order to effect this separation. In order to develop the fundamental understanding necessary to optimize demulsifier selection for a particular emulsion, it should be sufficient, in principle, to obtain a complete chemical and physical characterization of both the emulsion to be separated and the demulsifier to be used.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".