Computational fluid dynamic model for the estimation of coke formation and gas generation inside petrochemical furnace pipes with the use of a kinetic net
Bibliographic record
Abstract
Abstract Petrochemical furnaces are used in the petrochemical industry for the preheating of crude oil, residue, gasoil, naphtha, kerosene, and diesel through refining operations. When the fluid flows inside the furnace pipes, thermal cracking occurs. During the heating process, a generation of lighter fractions of petroleum and coke formation takes place. Coke adheres to the wall of the pipes, increasing pressure drop and internally insulating the pipes. Consequently, heat transfer is affected. In most of these processes, during heating and thermal cracking, gases are generated, forming a liquid‐gas two‐phase flow in the pipe. In this work, thermal cracking and gas generation are represented by a kinetic net which takes into account the constituent fractions of the petroleum load, which are represented by six pseudo‐components. The CFD model was able to predict the lighter petroleum fractions and the gas generation as well as the coke formation inside the tube. Coke concentration increases along the pipe as the average temperature of the mixture increases.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".