From CO <sub>2</sub> Solubility to the Carbon Capture Process: Thermodynamic Modeling, Electrolyte Speciation, Phase Behavior, Solidification, and Regeneration Energy in the CO <sub>2</sub> –NH <sub>3</sub> –H <sub>2</sub> O System
Bibliographic record
Abstract
This study investigates and validates a thermodynamic model for aqueous ammonia (NH 3 ) in postcombustion CO 2 capture, focusing on both vapor–liquid equilibrium (VLE) and NH 4 HCO 3 precipitation. Experimental VLE data at 4–8 wt % NH 3 (40–80 °C) and existing literature data sets were used to refine an electrolyte nonrandom two-liquid (e-NRTL) model. The results highlight that lower NH 3 concentrations (under 7 wt %) eliminate solid precipitation (solidification) risk but may raise regeneration energy, while higher concentrations provide reduced circulation flow rates yet risk solid formation at high CO 2 loadings. By applying a simplified regeneration energy analysis, we illustrate how stripper temperature, stripper pressure, and NH 3 concentration influence the components of regeneration energy: reaction heat, sensible heat, and latent heat. For a 10 wt % NH 3 solution, an optimal stripper temperature of about 130.0 °C and total pressure of 800 kPa are identified to minimize the total reboiler duty (2.90 GJ/tCO 2 ). However, a further pressure increase reduces reaction and latent heat but simultaneously boosts lean loading, raising the sensible heat requirement. Overall, the thermodynamic model and parametric study provide operational strategies to reduce CO 2 capture costs, highlighting temperature control and NH 3 concentration as dominant factors.
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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 | 0.000 |
| 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 | 0.000 | 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".