3E assessment of a solar-driven reverse osmosis plant for seawater desalination in a small island of the Mediterranean Sea
Bibliographic record
Abstract
Water scarcity in many regions of the world and global demographic growth make the desalination of seawater and/or brackish an effective solution to meet the growing demand for fresh water. Nowadays, reverse osmosis has the largest share of the global installed desalination capacity. The impelling need to reduce greenhouse gas emissions has been pushing the search for sustainable technologies to produce the electricity needed to power reverse osmosis plants. Among solar technologies, little attention has been paid to the possibility of powering reverse osmosis with electricity from the dish-Stirling solar concentrator. To fill this knowledge gap, this paper assesses the energy-saving potential of a reverse osmosis plant coupled with a cogenerative dish-Stirling concentrator on a small island in the Mediterranean Sea. A model of the integrated systems was developed based on data measured on a real dish-Stirling concentrator. Moreover, the variation of the energy consumption of the reverse osmosis plant with the temperature of the feedwater solution was also accounted for. Hourly simulations showed that almost 36% of the annual water demand could be covered by driving the plant using electricity from the concentrator, and the solar fraction of the electricity consumed by the reverse osmosis plant accounted for 48%. Finally, economic and environmental analyses revealed that the levelized cost of water of €1.08 per cubic meter of fresh water, consistent with the literature, and the system could avoid emitting 34.16 tons of carbon dioxide equivalent emissions per year.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".