Experimental Investigation of Using Porous Bodies to Enhance the Solar Still Performance
Bibliographic record
Abstract
The demand for good quality drinking water is increasing.Indeed, the population is increasing rapidly, and the water needs of industry and agriculture are increasingly high.The drinking water crisis strongly revives interest to rapidly develop desalination techniques that are cheaper, simpler, and more robust, more reliable, if possible less energy consuming and environmentally friendly.The desalination of sea and brackish water using solar energy is the most profitable and where the most urgent demand is that drinking water be distributed by droppers.To answer to the water deficit in the world, various technical processes have been developed, in recent decades to demineralize sea and brackish water.Solar stills are a promising solution for addressing the lack of water in areas with limited water resources.To effectively utilize solar stills to address water scarcity, many strategies were used to improve water production, energy efficiency, and overall system reliability.Using porous bodies within a solar still can be an effective way to enhance the performance and efficiency of the solar still.In this paper, the effect of using rocks in solar still was investigated.Different experiments were performed under the same real climate conditions, and the solar still's performance was enhanced and compared in the two cases: conventional solar still and solar still with porous bodies.The results have shown that the incorporation of porous bodies can lead to significant improvements in the water production rate, and overall performance of the solar still system.The experimental findings showed that the incorporation of porous bodies led to the most significant improvements in the solar still's water production and thermal efficiency, with increases of up to 30% compared to the control still.Findings from these studies can inform the design and optimization of integrated solar-driven water treatment systems, contributing to the development of sustainable and cost-effective solutions for water desalination and purification.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".