Modeling CO2 loading capacity of triethanolamine aqueous solutions using advanced white-box approaches: GMDH, GEP, and GP
Bibliographic record
Abstract
Abstract The equilibrium solubility of carbon dioxide (CO2) in the solvents is a key essential characteristic that has to be evaluated for successful absorption-based CO2 capture procedures. In this study, the CO2 loading capacity of triethanolamine (TEA) aqueous solutions was estimated using three famous white-box algorithms namely gene expression programming (GEP), genetic programming (GP), and group method of data handling (GMDH). For achieving the aim of this study, 258 data in a wide range of pressure, temperature, and amine concentration were collected from literature. Temperature, partial pressure of CO2, and amine concentration were used as input parameters. The results demonstrated that GMDH correlation is more accurate than GEP and GP with a determination coefficient (R2) of 0.9813 and root mean square error of 0.0222. The R2 values of 0.9713 and 0.9664 for the GEP and GP, respectively, demonstrated that the GEP and GP also showed accurate predictions. In addition, GMDH approach accurately predicted the anticipated trends of the CO2 loading in response to changes in the partial pressure of CO2 and temperature. The Pearson and Spearman correlation analyses were also incorporated in this research which showed that temperature and CO2 partial pressure have almost the same relative effect on CO2 loading, while amine concentration has the lowest effect on it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".