Energy and Entropy Analyses of a Pilot-Scale Dual Heating HDH Desalination System
Bibliographic record
Abstract
This study focuses on energy and entropy analysis to theoretically investigate the performance of a pilot scale dual heated humidification-dehumidification (HDH) desalination system. Two cases of HDH systems are considered in the analysis. The first case is a dual heated (DH) cycle consisting of 1.59 kW air heater and 1.42 kW water heater with a heat rate ratio of 0.89 (CAOW-DH-I). Whereas the second case is a dual heated HDH cycle comprising of 1.59 kW air heater and 2.82 kW water heater with a heat rate ratio of 1.77 (CAOW-DH-II). As a first step, mathematical code was developed based on heat and mass transfer and entropy generation within the major components of the system. The code was validated against the experimental data obtained from a pilot scale HDH system and was found to be in a good agreement with the experimental results. Theoretical results revealed that there is an optimal mass flowrate ratio at which GOR is maximized, and entropy generation is minimized. Furthermore, the degree of irreversibility within the humidifier component is low and approaches zero, while the specific entropy generation within other components are relatively high and are of the same order of magnitude. Entropy analysis also showed that the dual heated system with heat rate ratio greater than unity is better than the one with heat rate ratio less than unity.
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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".