Phase Behavior Modeling for Carbon Dioxide/Brine Mixtures Using PR EOS and Huron-Vidal Mixing Rule
Bibliographic record
Abstract
Abstract Accurately predicting CO2 solubility in saline aquifers is very important for CO2 capture and storage. A reliable and accurate thermodynamic model is needed to predict the phase equilibrium of the CO2+brine systems over a wide range of temperature, pressure, and molality. In this study, a cubic-EOS-based thermodynamic model is presented to predict the phase equilibrium of CO2+brine mixtures. Peng-Robinson equation of state and Huron-Vidal mixing rule are applied to predict the phase behavior of CO2+brine systems containing salt species including NaCl, KCl, CaCl2, and MgCl2. Binary interaction parameters for specific CO2+single-salt+H2O systems are established as functions of temperature and salt molality. To investigate the impact of multiple salts mixtures on CO2 solubility in brine solutions, the model is extended to CO2+mixed-salt+H2O systems under the practical geological conditions (273–550K, 0–800 bar, 0–6 mol/kg). PR EOS with a modified BIP model in the HV mixing rule is implemented to capture the phase compositions in vapor-liquid equilibria (VLE). The collected experimental data are used to determine the optimal BIP model. Comparison of the experimental data and the computed data indicates that the average absolute deviation (AAD) in reproducing the CO2 concentration in the mixed-salt brine is 0.0015. Compared to other state-of-the-art models in the literature, the new model can more accurately predict the VLE of CO2+brine systems over a large temperature, pressure, and molality range.
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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 | 0.001 | 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".