Comparative assessment and optimization of fuel cells
Bibliographic record
Abstract
In this study, a comprehensive exergoeconomic analysis and a multi-objective optimization study are performed for four different types of fuel cell systems , in order to determine their maximum power production capacities, exergy efficiencies , and minimum production costs, by use of a genetic algorithm method . The investigated fuel cell types are Polymer Electrolyte Membrane (PEMFC) and Direct Methanol (DMFC) for low temperature fuel cells , and Solid Oxide (SOFC) and Molten Carbonate (MCFC) for high temperature fuel cells. The selected fuel cell systems are modeled exergetically and exergoeconomically. After modeling, the cases are studied parametrically with various available operating conditions, such as temperature, pressure, surrounding temperature and pressure, current density, and relative humidity , using the developed computer program MULOP (Multi-Objective Optimizer). For the low temperature fuel cells it is observed that the efficiencies are in the range of 10–30% and the costs are around $3–4/kW. On the other hand, for the high temperature fuel cell systems, efficiencies are in the range of 15–45% and the costs seems to be $0.003–0.01/kW. The results show that high temperature fuel cells operate more effectively for large scale applications.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".