Soft computing optimization of a renewable energy-integrated multigeneration system with liquid air energy storage
Bibliographic record
Abstract
The presented study provides the results of a comprehensive assessment of a multigeneration system integrating renewable energy with liquid air energy storage systems , through exergoeconomic and exergoenvironmental evaluations. Applying soft-computing techniques for optimization powered by artificial neural networks , the research aims to improve the introduced configuration's efficiency, economic feasibility , and environmental sustainability . The system aims to generate power, desalinated water, heating/cooling loads, and other products to leverage the synergies between renewable energy contributions and the advanced Liquid air energy storage (LAES) for energy storage. From the exergoenvironmental evaluation, the sustainability index for energy storage facilities, desalination systems , and multigeneration systems is 1.92, 1.43, and 1.88, respectively. The obtained outcomes from the technical analysis indicate that the exergetic term of the round-trip efficiency and exergy destruction are 61.11 % and 15.59 MW, respectively. The optimized values for the levelized costs of hydrogen and water from exergoeconomic analysis are 1.52 and 5.22. The obtained findings from the optimization process revealed that the produced hydrogen and freshwater can exceed 6.49 × 10 7 m 3 and 7.59 × 10 4 m 3 per year. Besides, the optimum working condition pushes the system toward 74.75 % exergetic round-trip efficiency and a 0.48 US$/kWh levelized cost of production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".