Analysis of the Hydrodynamic Effects of Gas Permeation in a Pilot-Scale Fluidized Bed Membrane Reactor
Bibliographic record
Abstract
This study experimentally investigates the effects of gas extraction/addition, via multiple vertical membrane panels, on the hydrodynamics in different regions of a pilot-scale gas fluidized bed membrane reactor (FBMR), based on differential pressure signals measured at different vertical bed sections at high temperature. In a bed section where membrane panels were installed and activated, the extraction of gas caused the average bubble size to increase, but decreased the number of small- and medium-sized bubbles. This effect of gas extraction penetrated into bed sections above the active membrane panel, but attenuated with increasing distance away from the extraction location. The attenuation rate was much faster in FBMR with lower bed voidage, mainly due to the large decrease of the drag force exerted by gas extraction on fluidizing gas in a denser bed. With the same inlet gas velocity, gas addition favored the growth of bubbles, especially in the upper bed sections compared with operation without gas permeation. The increase of the effective fluidizing velocity was the major reason for the increase of the bubble size during gas addition. These findings preliminarily suggest that membrane units should not be installed in or below fast-reacting zones in a scale-up FBMR, and operation with a lower bed voidage is preferable to avoid the formation of large bubbles enhanced by gas extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".