Renewable energies powering seawater desalination in Morocco
Bibliographic record
Abstract
Several reasons including fast-paced development, global warming, unsustainable water use, and many others have prompted many areas around the world to experience water shortages. But with salt water accounting for approximately 97.5% of total water on Earth and its availability in almost every country, desalination has emerged as the go-to solution. This comes with some associated issues, namely, significant energy requirements. These are largely satisfied by fossil fuels, which are becoming increasingly scarce and contribute to greenhouse gas (GHG) emissions and, therefore, global warming. Thanks to renewables, desalination plants could be able to sustainably overcome such shortages, particularly in Morocco. This is attributable to the country’s favorable meteorological conditions for solar and wind plants, and to the large amount of land available for hydroelectric plants. This paper discusses the main benefits associated with the use of renewables in seawater desalination plants. Notably, the contribution to increase the share of renewables in the energy mix and to reduce GHG emissions. Moreover, it reviews certain obstacles encountered, including costs and grid stability issues, which could be complex and costly to overcome. The paper also highlights the necessity of adopting AI, IoT, and waste management technologies to further optimize processes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".