On the Energy Performance of the Geothermal Dehumidifier of the Humidification-Dehumidification Desalination System
Bibliographic record
Abstract
The solar-driven humidification-dehumidification desalination system with a geothermal dehumidifier (SD-HDH-GD) has been given special attention, especially for subsurface irrigation. This contribution scrutinizes the performance of the geothermal dehumidifier of the SD-HDH-GD system. To this aim, a comprehensive three-dimensional transient computational fluid dynamics model of the geothermal dehumidifier and the surrounding soil is developed. An experimental setup is established to verify the developed numerical model. The effect of several operating parameters on the performance of the system is examined. The sensitivity of the water yield, recovery ratio, and gain output ratio to the studied parameters are analyzed. It is demonstrated that the system has different behaviors regarding these measures. Therefore, to gain a comprehensive understanding of the performance of the SD-HDH-GD system, all measures should be considered. According to the obtained results, the maximum amount of condensed water, gain output ratio, and recovery ratio obtained using the performed optimization are 180.05 kg/12 h, 41.20%, and 0.31, respectively, for the open-air HDH cycle. Moreover, to reach the optimum system performance, the temperature of the inlet air stream to the geothermal dehumidifier should be in the range of 60–70 °C, and its relative humidity must be as much as possible.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".