Application of the Generic Fluidized-Bed Reactor Model to the Fluidized-Bed Membrane Reactor Process for Steam Methane Reforming with Oxygen Input
Bibliographic record
Abstract
A generic fluidized-bed reactor model (Abba et al. Chem. Eng. Sci . 2002, 57, 4797-4807; AIChE J . 2003, in press) is adapted to evaluate the performance of a fluidized-bed membrane reactor for steam reforming with oxygen input in a large-scale unit (16 m high and 2 m wide) described by Adris and Grace ( Ind. Eng. Chem. Res . 1997, 36, 4549-4556). This allows flow regimes beyond bubbling to be modeled and facilitates the treatment of the impacts of changes in volumetric flow due to variations in molar flow, temperature, and hydrostatic pressure. Improvement in the reactor performance is shown when one considers these changes. Permselective membrane tubes are shown to substantially improve the performance of the reactor. The simulation results show that an ultrathin membrane coating could result in reversal in hydrogen diffusion with height, especially at elevated temperatures. The influences on the reactor performance of several other parameters such as the superficial gas velocity and steam-to-carbon ratio are also examined. Given the right combinations of key operating parameters such as the methane-to-oxygen ratio, feed temperature, and reactor temperature, the reactor can be operated autothermally.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".