Exergy and exergoeconomic analysis of a hybrid airborne wind and solar energy system for power, liquid nitrogen and carbon dioxide production
Bibliographic record
Abstract
Airborne wind energy (AWE) systems have emerged as cost-effective and sustainable solutions that have not yet been coupled with solar technologies and integrated power plants to produce energy and on-demand substances. This study proposes an integrated system driven by an innovative AWE and photovoltaic (PV) hybrid system. This combination can harness stronger and more stable wind energy while decreasing system costs and power intermittency. The proposed system combines seven subsystems, including AWE, PV, air separation unit, oxyfuel power plant, absorption refrigeration , a nitrogen liquefaction process, and Organic Rankine Cycle (ORC) to simultaneously generate power, liquid nitrogen , and liquid carbon dioxide . The hybrid AWE-PV system can generate 10.8 MW power to initiate the system to produce 55 MW power, 127.2 m 3 / h liquid nitrogen , and 98.4 m 3 / h liquid carbon dioxide . The exergy analysis has been conducted, showing maximum exergy destruction in heat exchangers , and the total exergy efficiency of the integrated structure reaches 90.21 %. The exergoeconomic analysis illustrates that the maximum capital cost occurs in compressors and turbines with a percentage of 51 % (∼4600 $/h) and 26 % (∼2400 $/h), respectively. This first demonstration of implementing hybrid AWE-PV renewable energy sources in an integrated structure can open new perspectives and avenues toward using AWE and its combination with other renewable energy sources in the future.
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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 | 0.001 | 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".