A unified volume translation model in SRK EOS for dry gas constituents
Bibliographic record
Abstract
Dry gas, mainly made of light hydrocarbons (such as methane and ethane), is an important type of natural gases. The PVT properties of dry gas constituents (e.g., compressibility factor) play an important role in the various stages of dry gas recovery. In this study, we develop an improved distance-function-based volume translation model in Soave-Redlich-Kwong equation of state (SRK EOS) for dry gas constituents (including carbon dioxide, nitrogen, methane, ethane, propane, n-butane, isobutane, n-pentane, isopentane, and neopentane). This model not only accurately replicates the critical compressibility factor for a specific dry gas component but also maintains strong performance across a broad range of pressures and temperatures (i.e., pressure range: from triple-point pressure to 300 MPa; temperature range: from triple-point temperature to 600 K). For the 10 dry gas constituents considered in this study, the new volume-translated SRK EOS yields an %AAD of 1.27 in reproducing saturation pressure, while it yields %AADs of 0.73, 0.38, 0.69, 1.72, and 1.55 in reproducing the liquid-phase, vapor-phase, saturated-liquid-phase, saturated-vapor-phase, and supercritical-phase compressibility factors, respectively. Moreover, the new VTR-SRK EOS does not lead to crossover of pressure-volume isotherms within the tested pressure/temperature ranges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".