Prediction of equilibrium solubility of CO<sub>2</sub> in aqueous alkanolamines using differential evolution algorithm
Bibliographic record
Abstract
Abstract The design of sour‐gas treating processes with alkanolamine solvents requires knowledge of the vapour–liquid equilibrium (VLE) of the aqueous acid gas–alkanolamine systems. Representation of the experimental data with a thermodynamically rigorous model is required, so that one can systematically correlate and predict the VLEs of these systems. The modified Clegg–Pitzer equations have been used to correlate and predict the VLE of CO2 in the aqueous N‐methyldiethanolamine (MDEA) and 2‐amino‐2‐methyl‐1‐propanol (AMP) systems. Differential evolution (DE), an evolutionary computational technique, has been used for parameter estimation of the developed VLE model in an effort to predict the VLE of CO2 in aqueous MDEA and AMP solutions with a comparable accuracy to that by using the non‐traditional simulated annealing (SA) and deterministic technique like Levenberg–Marquardt (LM), if not better. In this work, the DE/rand‐best/1/bin strategy has been used for finding near global minimum solutions to the multivariable optimization problem as a part of the numerical solution of the developed model.
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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) | 0.000 | 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 | 0.001 | 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".