Development of a thermodynamic identification tool for CO<sub>2</sub> capture by chemical absorption
Bibliographic record
Abstract
Abstract The extended UNIQUAC model has been used for the representation of the thermodynamic behaviour of CO2 absorption in aqueous amine solutions. Based on available experimental data, an identification methodology has been developed to fit the extended‐UNIQUAC model parameters. In the scope of providing a robust methodology, a combination of two successive optimization methods has been chosen: a genetic algorithm and a quasi‐Newton method. The first quasi‐global method allows to screen the entire search space without a precise initialization, and provides an approximate solution which is then refined by the second local method. The developed multi‐step regression strategy has been successfully applied to the H2O‐ monoethanolamine(MEA)‐CO2 system. The model gives a good agreement with the experimental vapour‐liquid equilibria for CO2 partial pressures and total pressures for all MEA concentrations and for a wide range of temperature with an average absolute relative deviation of around 20 %. Furthermore, the model predicts accurately literature data on excess enthalpy and bubble point of the system. This identification procedure has been successfully extended on several ternary H2O‐amine‐CO2 solvent systems such as methyldiethanolamine (MDEA) and 2‐amino‐2‐methyl‐1‐propanol (AMP). The wide variety of operating configurations and solvent types used and presented in this work proves the robustness and the efficiency of the developed identification method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".