Liquid–liquid equilibrium and mechanism study on separation of short carbon chain hydrocarbon mixtures by Cyrene
Bibliographic record
Abstract
Abstract The separation of short‐chain hydrocarbon mixtures is of great significance for the efficient utilization of fossil energy. Liquid–liquid extraction, as one of the commonly used treatment methods, has significant advantages in terms of operation conditions and energy consumption. As a new dipolar aprotic solvent developed in recent years, dihydrolevoglucosenone (Cyrene) has a wide range of sources and a green composition. In this paper, the liquid–liquid equilibrium and extraction mechanism of Cyrene and five hydrocarbon mixtures with short carbon chains, including toluene/ n‐ heptane, toluene/cyclohexane, n‐ hexane/cyclohexane, n‐ pentane/pentene, and n‐ hexane/hexene, have been studied by combining experiments and quantum chemical calculations, and the extraction effects under different conditions have been investigated. The results showed that the forces between Cyrene and the different solutes are mainly van der Waals (VDW) forces dominated by dispersion forces, with some weak hydrogen bonds present. Due to the difference in interaction energy, the order of extraction selectivity was toluene‐ n‐ heptane > toluene‐cyclohexane > n‐ hexane‐hexene > n‐ hexane‐cyclohexane > n‐ pentane‐pentene, and the order of distribution coefficients of the extracted components (aromatics, olefins, and cycloalkanes) was toluene > pentene > hexene > cyclohexane. The dissolution processes of all systems were heat‐absorbing, and they all reached the extraction equilibrium within 60 s. The reliability of the experimental data was verified using the Othmer–Tobias equation and the Hand equation, and the binary interaction parameters of all systems were obtained by the non‐random two liquid (NRTL) model, providing basic data and references for the subsequent studies on the separation of Cyrene and short‐chain hydrocarbons.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".