Innovations Into Minimizing Interfacial Contamination Between Crude Batches due to Dead-Legs in Pump Station Piping Layouts
Bibliographic record
Abstract
Invariably, oil pump station piping layouts may contain multiple dead-legs brought about by closed valves at one end of side branches, while flows continue through the main runs. During the transportation of heavy crude through the pump station, these dead-legs will be filled with this crude. When a light crude batch is introduced next into the pipeline, following the heavy crude ahead, two phenomena will occur. First, contamination between batches at the interface of the two crudes will occur due to axial turbulent diffusion along the length of the pipeline itself. Second, as the light crude flows through the pump station, and passes by each dead-leg containing residual heavy crude from the preceding batch, the heavy crude trapped in these dead-legs will start to drain out into the passing light crude in the main run. This causes further contamination and spreading of the mixing zone between the two batches. The second source of contamination, which is addressed in this paper, could cause appreciable contamination particularly for large dead-leg sizes and numbers. A computational fluid dynamics (CFD) model has been developed to quantify the drainage rate of the contaminating crude into the main stream and its impact on widening the mixed zone (contamination spread) between the two batches. Two drainage mechanisms of the heavy crude in the dead-legs into the main stream of the light crude have been identified and quantified. Finally, potential innovations to mitigate contamination due to dead legs are presented and quantified for their respective effectiveness in minimizing the contamination spreads between batches of different crudes.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".