Experimental and Mathematical Modeling of Wax Deposition and Propagation in Pipes Transporting Crude Oil
Bibliographic record
Abstract
One of the problems faced by the petroleum industry is the wax deposition in pipelines during transportation of waxy crude oil. Oil companies dealing with waxy crude often spend millions of dollars in remedial procedures. An ideal design should use an accurate mathematical model that would include all salient features of wax deposition and waxy crude transport to predict wax deposition during crude oil transportation. In this article, a comprehensive mathematical model, both in laminar and turbulent flow regimes, is developed. The model couples energy equation with deposition and removal kinetics model and thermodynamic model. The k − ϵ turbulent flow model and energy equation were used to predict velocity and temperature distributions in the turbulent flow regime. Molecular diffusion of wax, as a mechanism of deposition and sloughing effect due to the hydrodynamic forces of fluid on deposited wax, have been considered. Parametric studies on the variation of the amount of wax deposition were performed for a mixture of toluene and oil wax cut in an experimental setup. Overall predictive ability of the proposed model is excellent for the laminar flow. For the turbulent flow regime, no necessary complete experimental data for model were available. Consequently, qualitative results were presented and discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".