Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems
Bibliographic record
Abstract
Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.
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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 | 0.002 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".