Hydrocarbon condensation modelling to mitigate fluid coker cyclone fouling
Bibliographic record
Abstract
Abstract FLUID COKING is a continuous process that thermally converts heavy hydrocarbons, such as oil sands bitumen, to lighter and higher‐value products by horizontal spray injection onto a fluidized bed of hot coke particles. The cyclone sections of commercial fluid coker reactors experience fouling during typical operation, which limits unit run lengths. The main objective of this work is to improve fluid coker reliability by proposing cyclone fouling mitigation strategies based on practical operation modifications. This study developed a process simulation in Aspen Plus to establish the combined impact of vapour‐liquid equilibrium, endothermic thermal cracking reactions, pressure changes, and overall fluid dynamics in the selected fluid coker control volumes. The hydrocarbon composition was defined by applying an assay characterization of distillation data for representative hydrocarbon streams. Case studies were performed to determine the sensitivity of the predicted temperatures and hydrocarbon condensate flow rates for: (a) the burner‐to‐fluid coker transfer line temperature; (b) the hot coke flow rate; (c) hot coke entrainment from the freeboard region; and (d) scouring coke flow rate in the horn chamber. The scouring coke flow rate was identified as the most promising process lever to mitigate fluid coker cyclone fouling.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".