Characteristics of anthraquinone hydrogenation catalysts in a liquid‐solid fluidized bed
Bibliographic record
Abstract
Abstract The fluidization characteristics of anthraquinone hydrogenation catalysts were investigated in a liquid–solid fluidized bed. The effects of the initial bed conditions such as particle size, bed depth‐to‐column diameter ratio and liquid density and viscosity on the fluidization behaviour, bed expansion and applicability of the Richardson–Zaki equation were studied. The results reveal a strong particle size effect on the Richardson–Zaki (R‐Z) expansion index which in general decreased as the particle diameter increased. One type of particles exhibited two distinct bed expansion behaviours, depending mainly on the bed depth‐to‐column diameter ratio, with an experimentally established boundary at $h_{\rm b}0/D \approx 1.75$ . This behaviour could be attributed to increasing wall friction and a tendency to exhibit slugging. The dependence of the Richardson–Zaki exponent on the liquid dynamic viscosity confirms the classic result $n_{\rm a} = f(1/Re_{t})$ .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".