Modeling and Optimization of Spinning Parameters on Selectivity of Polysulfone Hollow Fiber Membrane for CO2/CH4 Separation
Bibliographic record
Abstract
Hollow fiber membrane (HFM) and related technologies have recently become highly in demand and widely used in most industries and other areas recently. HFM could be used in different functions, from water and gas treatment to blood and medical applications. The paper describes the modeling of the effects of the spinning condition of a nonporous HFM on the membrane’s characteristics with the use of an automated hollow fiber fabrication system. An integrated, systematic experimental strategy based on the design of experiments was used to elaborate the effects of each parameter on selectivity. To obtain and sustain a satisfactory, significant selectivity of HFM, an automated system controlled the parameters during the fabrication process. The parameters involved in the fabrication of HFM and used in modeling are dope flow rate, bore flow rate, draw force, and air gap, which are considered inputs to fabricate polysulfone membrane. The fabrication process is improved by automating and instrumenting fabrication systems. An empirical model of the effects of fabrication parameters on the selectivity of the membrane is extracted experimentally. The mathematical model effectively explains the selectivity of CO2/CH4 with 95% fit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".