Bibliographic record
Abstract
The modelling of viscosity of asphaltene solutions is a longstanding unresolved problem. A number of empirical and semi‐empirical viscosity models have been proposed. However, no single equation is found to adequately describe all the asphaltene systems. In this article, a new viscosity model for asphaltene solutions is developed taking into consideration the clustering of asphaltene nano‐aggregates. At low concentrations of asphaltenes, the asphaltenes are assumed to exist in the form of isolated “disk‐shaped” nano‐aggregates. At high concentrations, clustering of nano‐aggregates is allowed, resulting in an increase in the effective volume fraction of asphaltenes due to continuous‐phase immobilization within the clusters. A model is proposed to relate the effective volume fraction of asphaltenes to the actual volume fraction. The ratio of effective volume fraction to actual volume fraction of asphaltenes is dependent on the type of packing of nano‐aggregates within the clusters. The viscosity model is developed using the effective medium approach, taking into account the clustering of nano‐aggregates and the relationship between effective volume fraction and actual volume fraction of asphaltenes. Twenty‐one sets of literature data covering different sources of asphaltenes, different types of asphaltenes, different types of solvents, and broad ranges of temperature and volume fraction of asphaltenes, are used to validate the proposed model. All the viscosity data could be described very well with the proposed model assuming random close packing of nano‐aggregates within the clusters. Only intrinsic viscosity is required to predict relative viscosity of concentrated asphaltene solutions from the model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".