Properties Analysis of Spent Catalyst for Fixed-Bed Residue Hydrotreating Unit: Radial Distribution of Deposited Elements in Spent Catalyst Particles
Bibliographic record
Abstract
14 spent catalysts, which were sampled from a commercial residue hydrotreating unit at the end of an operation cycle, were analyzed by SEM to describe elements distributions along the radial direction of particles. Different from laboratory equipment, on the whole, V, Ni and S showed U-shaped pattern along the radial direction of spent catalysts. The catalyst bed has been penetrated by deposited metals and deposited massive metals on catalyst, so the pore size of catalyst decreased, diffusion resistance increased and reactants entered internal of the catalysts more difficultly. Most of the organometallic compounds hydrotreated and deposited on outside of the catalyst particles. It is showed that metals deposited on catalyst in forms of metal sulfides because the points of high metal content also have high sulfur contents unexceptionally. The structure of high metal deposition catalyst was destroyed obviously or massive irregular material has deposited on the external surface. It is verified by SEM that there is no any distribution law for deposited elements along the bed height. The change laws of deposited elements along the bed height and radial direction of particles were influenced by various factors in commercial residue hydrotrating. Key words : Residue hydrotreating unit; Spent catalyst; SEM
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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 | 0.000 |
| 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".