Thermo-economic analysis of solar humidification-dehumidificationdesalination system with subsurface condenser
Bibliographic record
Abstract
The solar humidification-dehumidification (HDH) desalination system with a subsurface condenser is a promising renewable energy-based desalination system to provide a sustainable water supply. In this study, the computational model of the solar HDH desalination system with the subsurface condenser is formulated based on thermodynamic analysis and the conservation of mass and energy. The developed model is used to evaluate the thermo-economic performance of the solar HDH desalination system with the subsurface condenser. For this purpose, the long-term performance of open-loop and closed-loop configurations of the system is evaluated. Moreover, to use the desalination system in remote areas facing a lack of electricity, photovoltaic cells are designed to supply the necessary electricity and are also included in the economic analysis. It is demonstrated that the average daily water yield and Gained Output Ratio (GOR) of the closed-loop system are 70% higher than the open-loop system. In addition, the cost of fresh water production in the closed-loop system is about 0.037 US $/lit which is 41% less than the cost of water production in the openloop system. With the addition of photovoltaic cells, the cost of fresh water production in the closed-loop system increases by 44% and reaches 0.054 US $/lit which is 41% less than the cost of water production in the open-loop system with photovoltaic cells. Therefore, according to the thermo-economic analysis, it is recommended that the solar HDH desalination system with the subsurface condenser be designed as a closed cycle.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".