A new approach toward modeling of mixed‐gas sorption in glassy polymers based on metaheuristic algorithms
Bibliographic record
Abstract
Abstract Modeling mixed‐gas sorption has always been associated with computational challenges due to the existence of two or more conflicting objective functions. This study aims to use an artificial intelligence approach toward modeling mixed‐gas sorption in PIM‐1 and TZ‐PIM polymeric membranes. Non‐dominated sorting genetic algorithm (NSGA‐II) has been applied to identify the extended Henry‐Langmuir (EHL) isotherm based on CO2‐CH4 mixed‐gas sorption data. Also, the group method of data handling (GMDH) neural network is implemented to obtain a formula for the calculation of equilibrium partial pressure corresponding to three effective parameters, which are easily measurable. The formula provides an accurate estimation from the equilibrium relationship between the partial pressure of each gas in the binary gas mixtures over the PIM‐1 and TZ‐PIM membranes. Eventually, the calculated coefficients of EHL isotherm and obtained formula for computing the partial pressure of each component are simultaneously applied into the isotherm model to predict the mixed‐gas sorption behavior. The results showed that the computed lines well reproduce the experimental data points, proving that the applied artificial intelligence approach offers a suitable approximation for mixed‐gas sorption.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".