Modelling wax deposition in oil transport pipelines
Bibliographic record
Abstract
A model of wax deposition in oil‐transport pipelines is developed. An analysis of heat and mass transfer in a turbulent pipe flow is performed. Wax deposition flux is modelled accounting for the temperature dependent wax precipitation kinetics. A deposit layer is considered as a porous medium with a variable porosity that changes as a result of wax molecular diffusion across the deposit layer. Wax solids are assumed to be in a thermodynamic equilibrium with a hydrocarbon fluid across the layer. Wax molecular diffusivity through the porous layer obeys the Archie law. Thermal conductivities of the solid wax and the hydrocarbon fluid, composing the deposit layer, are assumed to be different. The thermal conductivity of the porous material is determined by a correlation for a porous material. A deposit shear‐removal phenomenon is taken into account. An equation of the deposit layer growth is obtained in an analytical form. Model performance is illustrated by calculations of wax deposition in a flow loop. The developed model is computationally expensive for modelling wax deposition in long transport pipelines. A simplified deposition model, based on analysis of numerical results obtained using the full model, is also developed. The simplified model performance is illustrated by numerical calculations of wax deposition in a long oil transport pipeline.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".