Properties Analysis of Spent Commercial Residue Hydrotreating Catalyst: Surface Property Changes of Spent Catalysts in Commercial Residue Hydrotreating Unit
Bibliographic record
Abstract
In this study, the changes of the surface properties of 14 spent catalysts, which were sampled from a commercial residue hydrotreating unit at the end of an operation cycle, were analyzed and compared with the corresponding fresh catalysts. It was found that the changes in the surface properties do not have change laws along the bed height. Furthermore, the pore size, pore volume and surface area of most of the catalysts decreased after reaction and the number of micropores of the spent catalysts increased, due to the fact that the coke and metals deposited in the catalyst to alter the pore distribution. But some catalysts with high coke content, the pore size and pore volume decreased with the increase of surface area, which was a result of the forming of massive micro/mesopores (the pore size is mainly ranged from 3 to 10 nm), when partial soft coke desorbed under the action of the deposited active metals and micro/mesopores were formed in macropores or large mesopores. The surface properties of the spent catalysts were not only related to the deposition amounts, but also to the deposition configurations of the coke and metals on the 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 | 0.001 | 0.001 |
| 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".