Efficient Single-Column Extractive Distillation Process Achieved through Vapor–Liquid Separation of Feed
Bibliographic record
Abstract
The composition of azeotropic mixtures has the probability to deviate significantly from their azeotropic point. This study proposes a single-column side-stream extractive distillation (ED) combined with a front-side reboiler process to address the separation challenges posed by such azeotropic mixtures. The proposed process integrates the functions of preconcentration, ED, and entrainer recovery within a single distillation column. This integrated process improves economic performance and reduces energy consumption in ED. The universality of the proposed method was validated through three case studies: acetone/ n -heptane, dichloromethane/ethanol, and methanol/toluene. Notably, our process exhibits a significantly higher potential for heat integration compared with the conventional single-column side-stream ED scheme. It reduces the compression ratio and power of the compressor during the vapor recompression process. Moreover, the process is simple, avoiding unnecessary complexity.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".