Successive Substitution Augmented for Global Minimization of the Gibbs Free Energy
Bibliographic record
Abstract
Abstract The conventional approach to multiphase flash is the sequential usage of stability and flash calculations. It is a series of local minimizations of the Gibbs free energy, in which a false solution is obtained from fugacity equations for a fixed number of phases and corrected in the subsequent stability analysis. The robustness and efficiency of multiphase flash have been important issues to be resolved for compositional reservoir simulation with complex phase behavior. This paper presents a new algorithm to solve the correct set of equations for global minimization of the Gibbs free energy for isothermal, isobaric, multiphase flash. The Peng-Robinson equation of state with the van der Waals mixing rules is used for thermodynamic properties. The number of equilibrium phases is part of the solution in the new algorithm, in contrast to the sequential stability/flash approach. Therefore, false solutions are not necessary for multiphase flash with the new algorithm. The advantage of the new algorithm in terms of robustness and efficiency is more pronounced for more complex phase behavior, in which multiple local minima of the Gibbs free energy are present. It is straightforward to implement the algorithm because of the simple formulation, which also allows for an arbitrary number of initial compositions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".