Optimization of a hybrid system for solar-wind-based water desalination by reverse osmosis: Comparison of approaches
Why this work is in the frame
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Bibliographic record
Abstract
Nineteen evolutionary algorithms, including single and hybrid optimization algorithms, are used for determining the optimum size of a hybrid renewable energy system (HRES) that is comprised of a wind turbine, a photovoltaic panel, a battery bank, and a reverse osmosis desalination unit. The main source electrical power for the reverse-osmosis desalination unit for producing fresh water is solar and wind energy, and batteries are used as back up units. Integer and continuous variables in the HRES optimization model for a remote area of Iran are considered. The optimization aims to meet the load continuously while minimizing the HRES life cycle cost subject to relevant constraints. Also, to ensure reliability, the reliability index is assessed for the loss of power supply probability. In order to achieve optimal performance, various versions well-known optimization approaches are used: particle swarm optimization, bee swarm optimization, harmony search, simulated annealing, chaotic search, and tabu search algorithm. The results show that hybrid optimization techniques provide the best performance among the considered evolutionary algorithms and that using the HRES reduces system costs and increases system reliability in general and for increasing fresh water availability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it