Developing Cold Flow Technology for Pipeline Transportation of Paraffinic 'Waxy' Mixtures
Bibliographic record
Abstract
The terms hot flow and cold flow refer to the bulk temperature of a ‘waxy’ crude oil being above and below its wax appearance temperature (WAT), respectively. The deposit thickness has been reported to decrease substantially when the crude oil in a pipeline is in the cold flow regime and the deposit thickness approaches zero when the crude oil temperature is the same as the surrounding temperature because of the absent of temperature driving force. The cold flow regime was characterized by two-phase flow, in which solid wax crystals are suspended in the liquid phase. However, achieving cold flow of a ‘waxy’ crude oil would invariably involve solid deposition on the cooling surface especially in the hot flow regime. This thesis is focused on understanding and developing the cold flow technology for pipeline transportation of paraffinic ‘waxy’ crude oils. Solids deposition in the cold flow regime from a wax–solvent mixture was studied experimentally using a cold finger apparatus to develop stable two-phase solid-in-liquid suspension and to prove the reduction of deposit thickness in the cold flow regime. This study further investigated a novel methodology for accomplishing cold flow condition without any significant deposition in the hot flow regime using both cold-finger and flow-loop apparatuses. In this approach, the effect of cooling rate was investigated on the temperature difference between the mixture and the coolant as well as the extent of wax deposition. It was found that the temperature difference and the deposit mass increased with the cooling rate. With a constant temperature difference, no deposition was observed above the WAT. The results indicated that the deposition in the hot flow regime could be decreased substantially, or even be avoided when the waxy mixture is cooled at a low cooling rate. In addition, a steady-state heat-transfer model along with the effect of deposit aging was developed for the formation of a deposit-layer from wax–solvent ‘waxy’ mixtures in a pipeline under turbulent flow. The trends in the model predictions compared satisfactorily with those reported from bench-scale experimental studies as well as the predictions from an unsteady-state moving boundary problem formulation.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".