Assessment of the innovative freezing-melting technology for desalination of the Mediterranean seawater in the Gaza Strip, Palestine
Bibliographic record
Abstract
ABSTRACT Although the freezing-melting process is not widely used commercially, perhaps the most significant potential advantage of desalination by freezing is the very low energy required compared with other desalination processes. Five seawater samples of 3,000 mL each were collected from different locations at the Gaza Strip beach. The physicochemical characteristics of the raw seawater samples were tested. The seawater samples were poured into an identical flask connected directly to an external stainless steel single-phase freezer (thermally protected-Sichuan Dandy Co. Ltd. 220 Volt, 50 Hz) with an energy consumption of 0.1 kW/h to be crystallized by direct freezing (at −20°C). Then the physicochemical analysis was undertaken on the water produced from three repeated freezing- melting (FM) cycles for each seawater sample. The average water mineral reduction percentages ranged from 39.0% to 45.5%, (49.7%–52.8%), and (56.0%–59.0%) for the 1st, 2nd, and 3rd FM cycles, respectively. The overall average removal percentage of dissolved minerals and constituents after the 3rd FM cycle for North Gaza, Gaza, Middle area, Khan Younis, and Rafah seawater samples was 84.7%, 85.6%, 87.3%, 86.4%, and 87.6%, respectively. The time of crystallization in the 1st, 2nd, and 3rd freezing cycles was 80, 50, and 30 min, respectively. The consumed energy for produced water after the three cycles of freezing was 0.018, 0.022, 0.018, 0.023, and 0.021 kW/L for the North Gaza, Gaza, Middle Area, Khan Younis, and Rafah seawater samples, respectively. The FM technique could be used as a pretreatment method for other methods of desalination.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".