Modelling wax deposition of diesel in sequential transportation of product oil pipeline using optimized <scp>back propagation</scp> neural network
Bibliographic record
Abstract
Abstract Contamination of gasoline by wax deposit of diesel is a severe problem in sequential transportation of product oil pipelines in cold areas. However, most works on wax deposition are focused on crude oil. In response, this paper aims to investigate wax deposition from a unique perspective of diesel oil in sequential transportation. To this end, a cold finger apparatus was designed and constructed. It is found that the wax deposition rate of diesel oil increases with oil temperature and wax content, and decreases with cold finger temperature. A non‐monotonic variation trend is observed against shear stress. To predict diesel wax deposition rate, a back propagation (BP) neural network optimized by bald eagle search (BES) algorithm is proposed. Grey relational analysis (GRA) is employed to get the highly relevant factors as input parameters of the developed model. Prediction accuracy and generalization ability of the BES‐BP model is experimentally verified. This work will be helpful to schedule the transportation program of product oil to avoid contamination of gasoline by diesel wax deposit.
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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) | 0.001 | 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 | 0.001 | 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".