Modelling coke formation in an industrial ethane‐cracking furnace for ethylene production
Bibliographic record
Abstract
Abstract The development of a model for predicting coke formation in an industrial ethylene cracking furnace is described. Expressions for predicting the rates of catalytic and pyrolytic coke formation are developed and a differential equation is derived to predict changes in coke thickness with time and position. An expression is developed to account for a decline in the rate of catalytic coke formation with increasing thickness of the coke layer. The proposed coke model equations are used to extend a previously developed ethane pyrolysis furnace model that ignored coke. Three model parameters related to coke formation are estimated using industrial data to obtain reliable model predictions. Two of these parameters are coefficients that appear in the catalytic and pyrolytic coke formation rate expressions. The third is a characteristic‐length parameter used to reduce the rate of catalytic coke formation as the coke layer grows. The resulting dynamic model matches the industrial data well and can be used to simulate furnace operation and predict coke thickness profiles over a variety of the operating conditions, thereby helping process engineers who plan the decoking 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".