Modeling of Air-Gap Membrane Distillation and Comparative Study with Direct Contact Membrane Distillation
Bibliographic record
Abstract
Most of the developed models for the air-gap membrane distillation (AGMD) process are one-dimensional and rely on experimentally determined parameters. Herein, inspired by the effectiveness-number of transfer units method for the design of heat exchangers, a new approach of theoretical model is developed based on mass and heat transfer mechanisms in the AGMD process by considering the temperature variation in two dimensions. The results of our self-sustained model match well with the AGMD experimental results, with less than 4% deviation. Using the developed model, the AGMD performance is systematically investigated in terms of permeate flux, energy efficiency, and temperature and concentration polarization effects, and the results are compared with direct contact membrane distillation (DCMD). The results showed that the feed temperature had the most significant impact on the permeate flux and energy efficiency. The thickness of the air-gap and the flow rate were found to be the second most effective parameters. In contrast, the membrane thermal conductivity and porosity did not play a determining role. A 60% increase in the feed temperature increased the permeate flux and energy efficiency by 200 and 2%, respectively. By increasing the flow rate from 0.2 to 8 liters per minute, the permeate flux was enhanced by 67.19%. The air-gap thickness increment from 0.6 to 5.6 mm caused a 36.8% reduction in the permeate flux. In our comparative study, the permeate flux and the gained output ratio for DCMD were 56.6 and 27.3% higher as compared to AGMD at the same conditions. However, the thermal efficiency of the AGMD process was 24.7% larger than that of the DCMD process. The developed model provides solutions to minimize the undesirable effects of temperature and concentration polarization and proposes an optimum design map to achieve higher energy efficiency and permeate flux.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".