Feasibility Analysis of Methanol Fuelled SOFC Systems for Remote Distributed Power Applications
Bibliographic record
Abstract
This paper analyzes the feasibility of converting a 5 kW natural gas SOFC power generator system designed to operate on natural gas to be fuelled by methanol. The analysis included laboratory methanol fuel processing experimentation, methanol reformate SOFC cell testing, and SOFC system mass and energy balance modelling. Methanol fuel processing experimentation indicated that theoretical reformate compositions can be achieved for high temperature (600–900°C) methanol steam reforming. A 6% open circuit voltage decrease and a 10% peak power current density decrease between cell operation on humidified hydrogen and methanol reformate was observed. SOFC system mass and energy balance modelling indicated that a natural gas SOFC system can be converted to operate on methanol fuel with minimal (1%) efficiency impact; however, the systems' thermal integration can be impacted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".