Transport Properties of Asymmetric Hollow Fiber Membrane Permeators for Practical Applications: Mathematical Modelling for Binary Gas Mixtures
Bibliographic record
Abstract
Hollow fiber membrane permeators have gained widespread acceptance for a variety of applications, including gas separation. This study presents our efforts to develop an appropriate methodology and mathematical modelling for the analysis of the transport properties and separation performance in hollow fiber membrane permeators with asymmetric structure. A relatively simplified model, developed based on ideal conditions, provides the opportunity for having a quick and overall prediction of the separation performance; while a comprehensive model developed by incorporation of non‐ideal conditions enables a more accurate prediction of the membrane performance. The real gas behaviour, temperature, pressure, and concentration dependence of gas viscosity, as well as pressure changes on both sides of hollow fibers, concentration polarization, temperature change due to permeation, and temperature dependence of permeance are considered non‐ideal parameters in development of the model. The integrated models with associated parameters in the form of differential equations are coded in MATLAB and solved as initial value problem using appropriate numerical methods. The validity of the developed models is examined, indicating close agreement between predictions and the experimental data provided in literature. The proposed methodology and developed models provide valuable opportunities for researchers in designing appropriate hollow fiber membrane permeators and processes for practical gas separation applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".