Investigating the gelling behaviour of ‘waxy' paraffinic mixtures during flow shutdown
Bibliographic record
Abstract
Abstract The flow of waxy or paraffinic crude oils in a pipeline could be shutdown for a variety of reasons, resulting in their cooling and subsequent gelling. Gel formation from a multicomponent wax‐solvent mixture during flow shutdown was investigated experimentally and analyzed with a transient heat‐transfer model based on the moving boundary problem formulation. The gelling experiments were performed with a 0.10 g/g wax‐solvent mixture in a flow‐loop apparatus, following the formation of a steady‐state deposit layer in turbulent flow regime, at two initial wax‐solvent mixture temperatures, with a constant coolant temperature, and for different shutdown times. The gel formation was found to be a fast process, which continued until the gel fully occupied the deposition tube. Gas chromatographic analyses of the deposit samples (under sheared cooling) and the gel samples (under static cooling during flow shutdown) indicated significant differences in the composition and the total wax content. The deposit samples showed an enrichment of heavier paraffins, whereas the composition of gel samples was comparable to that of the original wax‐solvent mixture. The predictions from the transient model showed that a lower initial oil temperature, a lower coolant temperature, and a smaller pipe diameter would result in a faster blockage of the pipe. The predictions from the moving boundary problem formulation agreed well with the flow shutdown data, which further confirmed that the solid and gel formation from wax‐solvent mixtures is modelled satisfactorily as a heat transfer process.
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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) | 0.000 | 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) | 0.001 | 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".