Solar PV System Design for Enhancing Sustainability in SWRO Desalination: The Deir El‑Balah Case Study
Bibliographic record
Abstract
The Gaza Strip faces a dual challenge of severe freshwater scarcity and chronic electricity shortages, constraining the operation of critical infrastructure such as seawater desalination plants. This study investigates the design and feasibility of integrating a solar photovoltaic (PV) system into the Deir El-Balah seawater reverse osmosis (SWRO) desalination plant to enhance sustainability, reduce dependency on external electricity supplies, and minimize environmental impacts. Using the Helioscope simulation tool, both on-grid and off-grid scenarios were evaluated to assess system performance under local solar conditions. The optimized design requires 2,663 Canadian Solar HiKu CS3W-415P modules with Enphase M250 inverters, yielding a total installed capacity of 1.11 MWp and an AC output of 639 kW. Modules were allocated across rooftop structures and ground-mounted plots to maximize land-use efficiency. The system can meet the plant’s daily demand of approximately 1,100 kWh, thereby reducing reliance on fossil fuels and mitigating greenhouse gas emissions. Beyond technical performance, the integration of solar PV offers strategic benefits, including cost savings, improved energy security, and alignment with global sustainability agendas. The findings highlight the potential of renewable-powered desalination to contribute to Sustainable Development Goals (SDGs 6, 7, and 13) while advancing resilience and energy–water security in resource-constrained regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".