High Temperature Membrane Reactor System for Hydrogen Permeation Measurements and Validation with Pd Based Membranes
Bibliographic record
Abstract
Hydrogen separation membranes are under development for integration with a coal gasifier or natural gas reformer for pre-combustion separation of hydrogen and carbon dioxide. Because of the high operating temperatures and pressures, a robust reactor and associated control systems are required for fast screening of membrane materials with a strong emphasis on operator and plant safety. In this paper, the design, construction and commissioning of a reliable membrane reactor and a versatile test station for evaluation of hydrogen permeation membrane materials (metals, ceramics or cermets) at high temperatures and high differential pressures has been described. The membrane reactor system has been designed to operate at temperatures up to 800oC and pressure differentials across the membrane to 1.0MPa. The system has multiple levels of safety redundancy built-in which include a range of controls and monitors for both operator and system safety. A number of Pd and Pd-Ag alloys of nominal thicknesses in the 20 and 140µm range were sourced and alumina based porous ceramic support structure were fabricated for evaluation of metal membranes. The test station has been validated with Pd and Pd-Ag alloys of different thicknesses. The data obtained from the reactor for various membrane types and thicknesses are in agreement with those reported in the literature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".