Simulation of a 25 kW Steam-Methanol Fuel Processor/PEM Fuel Cell System
Bibliographic record
Abstract
The transition to a hydrogen economy will require an intermediate energy carrier until a sufficient hydrogen infrastructure can be implemented. A likely near-term candidate is the on-board or on-site production of hydrogen from steam-methanol reforming. The low tolerance of PEM fuel cell anode electrocatalyst, to the carbon monoxide produced during reforming, necessitates a hydrogen purification or carbon monoxide clean-up sub-system. Considerable advantages can be gained from the use of a steam-methanol reformer with a palladium-silver alloy membrane, hydrogen purification unit. In the present work we have examined such a system. A simulation comprised of a Polymer Electrolyte Membrane Fuel Cell electrochemical model, a membrane permeation model and a commercially available thermodynamics calculation package was constructed. The case investigated in this work is of a 25 kW nominal DC power generating system. A maximum efficiency of 40% was achieved at reformer and membrane unit conditions of 200°C and 300 psia with 97% conversion of the inlet methanol. The effects of variation in temperature and pressure where also investigated. It was found that the reformer and membrane unit pressure had the most significant effect on overall system efficiency. The system efficiency increases with pressure reaching a maximum at the upper limit of the operating region, 300 psia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".