Bibliographic record
Abstract
The accurate modeling of systems containing water and hydrocarbons is important to support key decisions related to the design, simulation, and optimization of a variety of industrial processes, ranging from refineries, to gas plants and liquified natural gas processing facilities. Processes of interest for the production of hydrocarbons now include widely different temperatures and pressure ranges, making the use of empirical models to simulate the behavior of water and hydrocarbon mixtures awkward and prone to inconsistencies. In this work we show that the use of the Peng–Robinson equation of state using the Huron–Vidal mixing rule combined with the nonrandom two-liquid model and temperature-dependent interaction parameters provide an accurate platform to correlate mutual solubility data for a variety of hydrocarbons. Moreover, the interaction parameters were correlated on the basis of simple molecular descriptors such as molecular weight and the paraffin, iso-paraffin, olefin, naphthene, and aromatic (PIONA) chemical family classification and Watson-K factor. The model shows an absolute average error in water mole fraction in the hydrocarbon phase equal to 34% and an absolute average error in the hydrocarbon mole fraction in the aqueous phase equal to 98% using the PIONA-based parameters and an absolute average error in water mole fraction in the hydrocarbon phase equal to 34% and an absolute average error in the hydrocarbon mole fraction in the aqueous phase equal to 148% using the Watson-K factor based parameters. The method can be used for systems defined using pure or pseudocomponents and is easily integrated within the structure of existing process simulators.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".