Performance evaluation of a novel fuel cell and wind-powered multigeneration system
Bibliographic record
Abstract
The introduction of promising energy sources to satisfy human desires is extremely crucial. The present work studies the performance of an original solid oxide fuel cell-wind-based polygeneration system for producing hydrogen, sodium hypochlorite, potable water, heating, cooling, and electrical power. In the methodological concept, energy, exergy, economic, and exergoenvironmental assessments are carried out to evaluate the system performance concerning different parameters. The technical outcomes depict that the system can produce 363 kW of electrical power, 162 kW of cooling, and 46.5 kW of heating at the design operating settings while the energy and exergy efficiencies are 55.5% and 38.1%, respectively. Moreover, over a year of operation, the system can provide 19.9 × 10 4 m3 gaseous hydrogen, 50.9 × 103 m3 potable water, and 5.31 tonnes of sodium hypochlorite to increase the gained benefits from this efficient and cost-effective system. To evaluate the prevalence of the designed system, the economic outcomes demonstrate that the payback period is 1.5 years while the internal rate of return is 0.70. Moreover, the system presents relatively proper environmental benefits based on the obtained outcomes from the exergoenvironmental study; the exergoenvironmental factor, exergy stability factor, and sustainability index are 0.50, 0.56, and 1.6, 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".