PDMS coated asymmetric PES membrane for natural gas sweetening: Effect of preparation and operating parameters on performance
Bibliographic record
Abstract
The present study is an attempt to investigate the effects of preparation and operation parameters on ideal and real selectivity of polydimethylsiloxane (PDMS) coated asymmetric polyethersulfone (PES) membranes for natural gas sweetening. Scanning electron microscopy (SEM) was used to study the effect of PES concentration and solvent type on membrane morphology. The effects of operating parameters on the performance of membranes were investigated by using binary CO2/CH4, H2S/CH4 and ternary H2S/CO2/CH4 gas mixtures. Higher concentrations of PES and a higher sequential coating number increase CO2/CH4 selectivity and decrease CO2 permeance. As a notable and interesting result, the membrane exhibits rubbery PDMS behaviour for H2S containing feeds and displays a glassy PES membrane for CO2/CH4 mixed gas. An increase in the feed pressure decreases CO2 and CH4 permeance and increases CO2/CH4 selectivity for CO2/CH4 feed. For H2S/CH4 and H2S/CO2/CH4 mixed gas, enhancing the feed pressure results in higher CH4 and lower CO2 and H2S permeance and a declined CO2/CH4 and H2S/CH4 selectivity. Increasing temperature in binary CO2/CH4 enhances CO2 and CH4 permeance and decreases CO2/CH4 selectivity. Increasing the temperature increases CH4 permeance and decreases H2S permeance for binary H2S/CH4 mixture. For ternary mixture, increasing the temperature leads to a higher permeance for CO2 and H2S and a lower CH4 permeance. For binary CO2/CH4, a higher CO2 concentration increases the membrane gas permeance and decreases CO2/CH4 selectivity. Increasing the H2S concentration in the feed results in a reduction in gas pressure normalised flux of gases in ternary gas feed because of an increase in Flory–Huggins interaction parameter.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".