Effect of electrolyte solubility and column inclination on the performance of monoethylene glycol regeneration process
Bibliographic record
Abstract
Abstract In this study, the solubilities of potassium‐chloride (KCl) in aqueous monoethylene glycol (MEG) solution and the vapour pressure of aqueous MEG solution containing KCl were investigated. A thermodynamic model was modified based on the data obtained to predict the solubilities of KCl, which was then applied to optimize the MEG regeneration process that is widely used in offshore gas fields. The solubility of KCl in the aqueous MEG solution decreased with an increase in MEG concentration and a decrease in temperature. The presence of KCl in aqueous MEG solution decreased the vapour pressure, thus increasing the boiling temperature at the corresponding pressure. A thermodynamic model based on the electrolyte non‐random two‐liquid (NRTL) coupled with the Redlich–Kwong (RK) equation of state was employed by modifying the binary interaction parameters using the experimental data. The modified model predicted the solubilities of KCl and the vapour pressure of aqueous MEG solutions, which were in good agreement with the experimental data. Moreover, the distillation column in pilot‐scale MEG regeneration was inclined to simulate the movement of the offshore platform, showing decreasing MEG concentration, possibly due to the distribution issues inside the column.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".