The Impact of Vapour-Liquid Equilibria VLE Calculations on Scale Prediction Modelling
Bibliographic record
Abstract
Abstract Vapour-Liquid Equilibria (VLE) calculations, particularly involving the phase behaviour of CO2 and H2S, are used in scale prediction modelling. In this work, the impact of VLE calculations for CO2 and H2S-rich gas phases and for acid and sour gas mixtures on scale prediction calculations is evaluated. Three Equations of State (EOS), viz. Soave-Redlich-Kwong (SRK), Peng-Robinson (PR) and Valderrama-Patel-Teja (VPT), are implemented in the Heriot-Watt model and used in VLE calculations. The solubility of single-component CO2 and H2S in water and the solubility of a gas mixture in water were compared with experimental data in terms of the Absolute Relative Deviation (ARD). The solubility data were then used in PHREEQC to calculate the impact of using different EOS on carbonate and sulphide scales, particularly on CaCO3 and FeS. Average ARDs of 6.04%, 4.10% and 3.77% between experimental and calculated values for CO2 solubility in water were obtained for SRK, PR and VPT EOS, respectively. Similarly for H2S solubility in water, average ARDs of 6.49%, 6.66% and 6.48% were obtained. For the solubility of sour and acid gas mixtures in water, average ARDs of 13.92%, 13.25% and 10.78% were obtained. It has thus been concluded that VPT EOS performs better than SRK and PR in VLE calculations for the analysed data. The errors introduced in VLE calculations have been found to impact the calculation of the amount of CaCO3 precipitated with consequences for scale inhibitor selection. Higher deviations were found in the amount of CaCO3 precipitation for gas mixtures when compared to single component CO2 rich phase. Furthermore, the large errors occurring in VLE calculations for H2S solubility have not been found to impact the calculation of the amount of FeS precipitated when H2S is in excess with respect to Fe2+. Scale prediction calculations carried out using PHREEQC demonstrate that VLE calculations can have a high impact on mineral precipitation. Thus, it is recommended that the best VLE model available should always be used for scale prediction modelling, particularly when mixtures of gases are present.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".