Mathematical Modeling and Investigation on the Temperature and Pressure Dependency of Permeation and Membrane Separation Performance for Natural gas Treatment
Bibliographic record
Abstract
Abstract Due to special features, modules comprising asymmetric hollow fiber membranes are widely used in various industrial gas separation processes. Accordingly, numerous mathematical models have been proposed for predicting and analyzing the performance. However, majority of the proposed models for this purpose assume that membrane permeance remains constant upon changes in temperature and pressure. In this study, a mathematical model is proposed by taking into account non-ideal effects including changes in pressure and temperature in both sides of hollow fibers, concentration polarization and Joule-Thomson effects. Finite element method is employed to solve the governing equations and model is validated using experimental data. The effect of temperature and pressure dependency of permeance and separation performance of hollow fiber membrane modules is investigated in the case of CO 2 /CH 4 . The effect of temperature and pressure dependence of membrane permeance is studied by using type Arrhenius type and partial immobilization equations to understand which form of the equations fits experimental data best. Findings reveal that the prediction of membrane performance for CO 2 /CH 4 separation is highly related to pressure and temperature; the models considering temperature and pressure dependence of membrane permeance match experimental data with higher accuracy. Also, results suggest that partial immobilization model represents a better prediction to the experimental data than Arrhenius type equation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".