Measurement and Modeling of Wax Deposition in Crude Oil Pipelines
Bibliographic record
Abstract
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 paper, a comprehensive mathematical model for quantitative prediction of wax deposition for a multicomponent hydrocarbons mixture (oil) has been developed. The wax deposition is estimated from the diffusion of wax from the bulk towards the wall as a result of temperature gradients. 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".