Experimental and Estimation of Vapor-Liquid Equilibria in AqueousElectrolyte System: CO2-K2CO3-MDEA+DEA-H2O
Bibliographic record
Abstract
Absorption with chemical reaction process of CO2 gas using K2CO3solution or known as hot potassiumcarbonate promoted with amine was widely used in many chemical industries. DEA and MDEA mixture wasproposed as promoter. Vapor-liquid equilibrium (VLE) data of CO2-K2CO3-MDEA+DEA-H2O system areneeded for rational design and optimal operation of CO2 removal unit. The purpose of this research is todertermine solubility data of CO2 gas in aqueous solution of potassium carbonate with DEA and MDEA as apromotor at various temperatures of 30-50°C with 30% K2CO3, 1-3% MDEA and 1-3% DEA. The CO2solubility is very important property when establishing thermodynamics models for the VLE. In order to obtainthe CO2 solubility, the normal procedure is to use the N2O analogy since the CO2 solubility cannot be directlymeasured. Solubility was measured volumetrically in absorption flask using a shaking waterbath. The increase ofDEA concentration in solution gives higher Henry’s constant or lower gas solubility. It also makes the empiricalcorrelation between Henry’s constant andtemperature in various concentrations of MDEA and DEA. The resultsof N2O analogy experiment were used to calculate the vapor liquid equilibria of CO2-K2CO3-MDEA+DEA-H2Osystem by using the electrolyte NRTL model. The model gives a good representation of the experimental VLEdata for CO2 partial pressures with Root Mean Square Deviation (RMSD) of 5.93%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".