Exergy and Exergoeconomic Analyses of a Combined Power Producing System including a Proton Exchange Membrane Fuel Cell and an Organic Rankine Cycle
Bibliographic record
Abstract
Comprehensive exergy and exergoeconomic assessments are reported for a proposed power producing system, in which an organic Rankine cycle is employed to utilize the waste heat from the fuel cell stack. A complete mathematical model is presented for simulating the system performance while considering water management in the fuel cell. The simulation is performed for individual components of the fuel cell system, e.g., the compressor and humidifiers. A parametric study is conducted to evaluate the effects on the system’s thermodynamic and economic performance of parameters, such as the fuel cell operating pressure, current density, and turbine back pressure. The results show that an increase in the fuel cell operating pressure leads to a higher exergy efficiency and exergoeconomic factor for the overall system. In addition, it is observed that the overall exergy efficiency is 4.16% higher than the corresponding value that is obtained for the standalone fuel cell for the same value of fuel cell operating pressure. Furthermore, the results indicate that the compressor and condenser exhibit the worst exergoeconomic performance and that the exergoeconomic factor, the capital cost rate and the exergy destruction cost rate for the overall system are 40.8%, 27.21 $/h, and 39.49 $/h, respectively.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".