Comparative investigation for sustainable freshwater production in hybrid multigrid systems based on solar energy
Bibliographic record
Abstract
Energy storage systems are decisive players in the sustainable performance of the energy market for more beneficial outcomes from technical and economic points of view. Renewable energy-powered desalination technologies are attractive options for sustainable freshwater production for household applications. Two configurations based on the chemical and electrochemical energy storage systems are introduced to discover an effective system for this goal of sustained potable water production throughout the day. These systems' technical and economic performances present their superiorities over each other. The estimated exergy destruction for the battery and fuel cell-based systems are around 6.4 and 7.1 MW, respectively. The calculated parameters declared that the fuel cell-powered system offers higher round-trip efficiency by 16.48%. Moreover, the fuel cell-contained system has an interesting superiority in economic parameters by a lower payback period . Another captivating fact lies in the provision of the designed products. In this case, the fuel cell offers more sustainable electrical power in the discharge times for freshwater and hydrogen production . The obtained outcomes confirm that while the required investment cost for the fuel cells is relatively higher, the obtained chemical energy through the processes involved in the fuel cells can compensate for the financial stresses.
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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 | 0.000 |
| 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) | 0.003 | 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".