PEM fuel cell heat recovery for preheating inlet air in standalone solar-hydrogen systems for telecommunication applications: An exergy analysis
Bibliographic record
Abstract
In this paper, a theoretical model of a PEMFC with heat recovery system (PEMFC-HR) for preheating its inlet air to mitigate the performance degradation, when the fuel cell operates in an extreme cold environment, is proposed and evaluated by using exergy analysis. In this modeling study, HOMER and TRNSYS software tools are used to simulate the yearly load profile of a PEMFC in the context of a standalone hybrid solar-hydrogen system (hybridized with batteries) for telecommunication application in cold climate conditions. A dynamic theoretical model of the PEMFC-HR is then built in MATLAB environment to simulate and investigate the impacts of input parameters on the performance of such system. Furthermore, by using a high effectiveness heat exchanger (HE), the inlet air temperature of the PEMFC can be increased from sub-zero to well above the freezing point temperature. The impacts of the various ambient temperatures and fuel cell power on the exergetic efficiency of PEMFC-HR are then theoretically investigated and compared with the PEMFC coupled with an external electric heater (PEMFC-EH). Based on a case study for Eureka, Canada, the modeling results showed that approximately equivalent to 30% of the electrical energy, generated annually by the PEMFC, can be saved by replacing the PEMFC-EH arrangement with a PEMFC-HR system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".