Towards accurate modeling of surface tension of dry gas constituents using linear gradient theory and improved volume-translated SRK EOS
Bibliographic record
Abstract
• Integrate linear gradient theory with SRK EOS and four volume-translated SRK EOSs. • This model yields more accurate surface tension and saturation property predictions. • This model performs best in predicting density profiles among these models. • This model exactly reproduces the critical volumes of dry gas constituents. Dry gas, mainly comprised of methane, ethane, and other light alkanes, is a crucial energy source with widespread applications. Accurate surface tension predictions of dry gas constituents are important for the design and optimization of various industrial dry-gas-based systems. In this study, we integrate the linear gradient theory with five Soave-Redlich-Kwong equations of state (SRK EOSs) (including the original SRK EOS and several volume-translated SRK EOSs), leading to the development of various linear-gradient-theory-based surface tension models. Among these five models, the linear gradient theory coupled with the volume-translated SRK EOS proposed in our previous work demonstrates the highest accuracy in predicting the surface tension for the 10 dry gas constituents considered in this study, with an %AAD of 0.90. The linear gradient theory combined with our previously proposed volume-translated SRK EOS also performs best in calculating density profiles among the five surface tension models. Moreover, the previously proposed volume-translated SRK EOS is capable of accurately predicting the saturation properties and exactly replicating the true critical volumes of the 10 dry gas constituents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".