Styrene hydrogenation in inclined packed‐bed bubble reactors: A reaction‐transport model for the catalytic hydrogenation of pyrolysis gasoline on‐board floating reactors
Bibliographic record
Abstract
Abstract Two‐phase upflow/downflow and styrene hydrogenation were explored numerically in vertical and inclined packed‐bed bubble reactors using a dynamic three‐dimensional model which integrates the hydrodynamics via macroscopic volume‐averaged continuity and momentum balance equations, energy and mass conservative equations in liquid/gas phases, and simultaneous diffusion and chemical reaction inside Pd/Al 2 O 3 catalyst particles. Packed‐bed bubble reactors non‐verticality divert the liquid phase from its normally expected axial trajectory and generate an excessive axial symmetry distortion because of amplified secondary liquid flow associated with a larger liquid holdup and because of liquid reversal flow (two‐phase upflow). The significant liquid maldistribution over the packed beds (especially in deeper beds) substantially reduces the performance of the styrene hydrogenation process in inclined packed‐bed bubble reactors, more than in inclined trickle‐bed reactors. This drop in hydrogenation performance is more noticeable in downflow packed‐bed bubble reactors, particularly at higher packed bed inclinations, because of the reduction of catalyst wetting efficiency and overall effectiveness factor of the catalyst particles. Increasing the height and diameter of packed bed is recommended to compensate for reduction in hydrogenation performance in inclined packed‐bed bubble reactors. However, the ratio between the reactor height and diameter should be limited to a maximum value to avoid the excessive liquid maldistribution in deeper beds with a significant bypassing in the vicinity of the bottom of the packed bed.
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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".