Modeling of the air gap membrane distillation (AGMD) process for whey treatment: A CFD approach with RSM
Bibliographic record
Abstract
• Integrating CFD with Response Surface Methodology (RSM) to model and optimize AGMD, followed by experimental validation. • Implemented a detailed mass transfer mechanism in the membrane, incorporating Knudsen, viscous, and molecular diffusions. • Conducted a detailed parametric analysis on permeate flux, thermal efficiency, temperature polarization coefficient (TPC), and overall heat transfer coefficient. • Achieved optimal conditions delivering 6.94 LMH permeate flux, 92 % thermal efficiency, and 6.8 % whey concentration at the outlet. • Provided practical design and operational recommendations for energy-efficient AGMD application in dairy wastewater valorization. The dairy industry is a key source of industrial wastewater. Given the global water shortage and a majority of seawater is unsuitable for drinking or most daily human needs, many industries rely on wastewater treatment to reclaim and reuse their water. This study presents a computational fluid dynamics (CFD) simulation of an Air Gap Membrane Distillation (AGMD) system. The governing equations for momentum, heat, and mass conservation in channels were solved using the finite element method in COMSOL Multiphysics. This work presents the first CFD-based RSM optimization of AGMD for whey treatment, providing a high-fidelity model that integrates Knudsen-molecular-viscous diffusion mechanisms to identify globally optimal operating conditions. The model matches experiments with 14 % average error, confirming its reliability. A comprehensive parametric analysis was performed to evaluate permeate flux, thermal efficiency, temperature polarization, and overall heat transfer coefficient. Optimization using Response Surface Methodology (RSM) identified the best operating conditions. The workflow yields an operating envelope that elevates permeate flux and thermal efficiency, while quantifying Temperature Polarization Constant (TPC) and overall heat transfer coefficient trade-offs, informing module design. These findings provide valuable insights into the design and operation of AGMD systems for energy-efficient wastewater valorization in the dairy sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".