Asphaltene deposition in a Taylor‐Couette device and a pipe: Theoretically achievable critical deposition regimes
Bibliographic record
Abstract
In this paper, the effects of major asphaltene deposition model parameters, particle‐particle collision, and particle‐wall sticking efficiencies as well as asphaltene content in a hydrocarbon fluid on asphaltene deposition in both a Taylor‐Couette device and production tubing are numerically studied. The possibility of an increase in asphaltene deposition rate as a result of adding asphaltene inhibitors is investigated. The computations show that this critical effect is achievable in a Taylor‐Couette device operating in a batch regime only if the particle collision and the particle‐wall sticking efficiencies are more than an order of magnitude higher than those identified for asphaltenes present in hydrocarbon fluids. Calculations of deposition in the Taylor‐Couette device show also that an increase in the asphaltene content causes an increase in the rate of fluid depletion of particles able to deposit. The deposition calculations for a vertical pipe demonstrate that, due to continuous precipitation of primary particles along the tubing, a hydrocarbon fluid cannot be depleted of particles able to deposit; therefore the maximum deposit thickness always increases with an increase either in the major model parameters or the asphaltene content. Thus, the model parameter values indicating critical deposition regimes are different from those inherent to real hydrocarbon fluids; therefore, these regimes cannot be achieved in practical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".