Toward Sustainable Separation of Complex Azeotropic Mixture Methanol–Ethanol–Tetrahydrofuran Based on the Ionic Liquids Screening, Global Optimization, and Mechanism Analysis
Bibliographic record
Abstract
Ionic liquids (ILs) have long been recognized as highly effective solvents for azeotrope separation and have attracted significant research attention over the past two decades. In this study, environmentally friendly and efficient ILs were screened using the COSMO-RS model for the separation of the ternary azeotropic mixture methanol/ethanol/tetrahydrofuran. Among the 4557 ILs, the ethyltrimethylammonium 2,2-dichloroethoxide (i.e., [EtMe 3 N][DCE]) was identified as a promising solvent based on its selectivity, capacity, and compliance with thermodynamic constraints. To further optimize the process, the nondominated sorting genetic algorithm-II (NSGA-II) was applied. The resulting processes were systematically evaluated in terms of economic performance and environmental impact. The results demonstrated that using [EtMe 3 N][DCE] and a mixed solvent ([EtMe 3 N][DCE] + dimethyl sulfoxide (DMSO)) reduced economic costs by 30.65% and 19.37%, and environmental burdens by 42.50% and 33.13%, respectively, compared with the conventional DMSO-based process. In the LCA, environmental impacts were further reduced by 98.32% and 31.69%, respectively. To gain molecular-level insight, quantum chemical calculations were performed to elucidate the separation mechanism. The analysis revealed that ethanol exhibited the strongest interaction with ILs, followed by methanol, while tetrahydrofuran displayed the weakest affinity, consistent with the COSMO-RS predictions. Overall, this study establishes a systematic framework for screening sustainable ILs for the separation of ternary azeotropic systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".