Separating carbon dioxide from natural gas by a hollow fiber membrane contactor with various absorbents: Numerical investigation of membrane wettability
Bibliographic record
Abstract
This study investigated CO 2 separation from natural gas using hollow fiber membrane contactors, focusing on the combined effects of membrane wettability and absorbent type on separation efficiency. Unlike prior studies that primarily examined dry or fully wetted membranes, we explored intermediate wetting conditions (20 %–60%) to simulate realistic operational scenarios over time. Three absorbents—monoethanolamine (MEA), piperazine (PZ), and triethanolamine (TEA)—with distinct reaction kinetics were evaluated. The results indicated that under dry conditions, all absorbents achieve near-100 % CO 2 removal due to fast reaction kinetics relative to gas flow. However, under wetted conditions (20 %–60 %), absorbent type significantly influenced performance, with PZ (the fastest-reacting) removing up to 15 % of CO 2 at the maximum gas velocity (3.3 m/s), compared to just 5 % for TEA (the slowest-reacting) at 60 % wetting, while at 20 % wetting, the removal efficiencies were 51 % for PZ and 38 % for TEA. Furthermore, we modeled long-term performance decline due to membrane aging and wetting, providing novel insights for industrial applications. This work advances sustainable gas separation technologies by elucidating the interplay of wettability, absorbent kinetics, and temporal dynamics, and by addressing key gaps in previous research.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".