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Record W4416958421 · doi:10.1016/j.rineng.2025.108567

Modeling of the air gap membrane distillation (AGMD) process for whey treatment: A CFD approach with RSM

2025· article· en· W4416958421 on OpenAlexaff
Mohammad Z. Abedin, Arash Fassadi Chimeh, Amir Fouladitajar, Ali Kargari

Bibliographic record

VenueResults in Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputational fluid dynamicsMass transferMembrane distillationResponse surface methodologyConcentration polarizationDesalinationHeat transferParametric statisticsWork (physics)

Abstract

fetched live from OpenAlex

• 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.242
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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