The structure of deposited coke on hydrocracking and reforming catalysts: Coke deactivation and kinetics
Bibliographic record
Abstract
Abstract This comprehensive study investigates the structural deposition of coke, particularly its association with sulphides, in spent hydrocracking and reforming catalysts, leveraging advanced kinetic modelling and characterization techniques. Its objective is to elucidate coke removal kinetics, offering insights into optimizing catalyst regeneration. Utilizing thermogravimetric analysis, spectroscopic methods, and modelling, the research outlines the characteristics and kinetic behaviour of coke during regeneration. The novelty aspect of this study is the use of a reaction‐based model for accurate kinetic analysis and identifying optimal conditions for coke removal. The study recognized both hydrogen‐deficient and hydrogen‐rich amorphous coke, alongside graphitic coke spent catalysts. Thermogravimetric analysis revealed that coke constitutes ~23.8% and 4.6% of the total weight in hydrocracking and reforming catalysts, respectively. The traditional model‐free method for deactivation was found inadequate in predicting coke in the spent reforming catalyst, likely due to the presence of low‐temperature hydrocarbons of soft coke within the catalyst pores. In contrast, a reaction model‐based deactivation approach yielded more consistent evaluations for soft and hard coke of spent catalyst. For the spent hydrocracking catalyst, the estimated activation energies for coke decomposition were 41.56 and 128.97 kJ/mol for soft and hard coke, respectively, during regeneration. Although the coke in the spent reforming catalyst displayed similar temperature trends for decomposition, the average activation energies were 52.61 and 128.62 kJ/mol for soft and hard coke, respectively. The theoretical results from the multi‐reaction model for coke decomposition are consistent with the experimental results for both catalysts.
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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) | 0.000 | 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 | 0.000 | 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".