A Comprehensive Review on Emulsions and Emulsion Stability in Chemical and Energy Industries
Bibliographic record
Abstract
Abstract Emulsion refers to a mixture that includes two or more liquid phases. The uses of emulsions are found in several chemical, energy, and environmental industries such as the food, health care, chemical synthesis, and firefighting sectors. Water‐in‐oil emulsions are formed spontaneously during oil production when oil and water are mixed together and in the presence of asphaltene as a naturally occurring surfactant. For operational and economic reasons, oil emulsions need to be treated to recover both oil and water phases. To develop more efficient emulsion treatments, it is essential to have a better understanding of the factors that affect emulsion formation and stability. The droplet size variation is an important parameter that influences the stability and rheological characteristics of the emulsions. In addition, the available interfacial area for any possible chemical reactions might affect the behaviours and properties of the emulsions in various transport phenomena systems. The adequate knowledge of the factors and mechanisms affecting the droplet size and emulsion stability still needs further engineering and research activities. This study is aimed to provide a comprehensive literature review on the formation of water/oil emulsions and their stability in various physical systems (e.g., pipeline networks and porous media). In this review, fundamental aspects of emulsions, emulsion formation mechanisms, analytical models, and numerical solutions for the description and characterization of the behaviours of emulsions in porous media and/or separators are discussed. The effects of different fluid properties, physical model characteristics, and operational conditions on emulsion behaviours are studied. This paper also summarizes the previous experimental and modelling studies and methodologies with a focus on reliable laboratory equipment/tools and simulation and modelling packages/strategies for the investigation of emulsion stability and droplet size distribution where a systematic parametric sensitivity analysis to study various effects of important thermodynamic, process, and medium properties on the targeted variables is conducted. This review manuscript provides useful guidelines to characterize and model emulsions and their behaviours in different industrial sectors, which will considerably help to conduct better design and optimal operation of corresponding equipment.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".