Separation of ethylene glycol, 1,2‐butanediol and 1,2‐propanediol with azeotropic distillation
Bibliographic record
Abstract
Abstract 1,2‐butanediol (1,2‐BDO) and 1,2‐propanediol (1,2‐PDO) are inevitably side produced in the ethylene glycol (EG) production processes from non‐petroleum routes, but are very difficult to separate by the ordinary distillation method because of the closeness of their boiling temperatures to EG, thus compromise the economy of these processes. The azeotropic distillation process using 1‐octanol (CPO) as an entrainer to separate EG and 1,2‐BDO mixture with or without 1,2‐PDO was studied in this paper. Four binary vapour–liquid equilibrium data of EG‐1,2‐BDO, EG‐CPO, 1,2‐BDO‐CPO, and 1,2‐PDO‐CPO were measured using an Ellis equilibrium kettle and regressed with the thermodynamic model of non‐random two liquid to obtain the corresponding binary interaction parameters. On this basis, azeotropic distillations with CPO as an entrainer were designed to separate EG and 1,2‐BDO with or without 1,2‐PDO. The complete separation processes, including the azeotropic distillation and CPO recovery process consisting of extraction with H 2 O and subsequent distillation, were simulated and optimized with Aspen Plus for both the EG‐1,2‐BDO binary mixture and the EG‐1,2‐BDO‐1,2‐PDO ternary mixture. The simulation results show that the azeotropic distillation method with CPO as an entrainer can effectively separate the mixture of EG‐1,2‐BDO and EG‐1,2‐BDO‐1,2‐PDO, achieving EG of 99.90% purity with 99.98% recovery and 1,2‐BDO of 99.30% purity with 99.45% recovery for the binary mixture, and achieving EG of 99.90% purity with 99.80% recovery, 1,2‐BDO of 99.35% purity with 99.35% recovery, and 1,2‐PDO of 90.59% purity with 94.38% recovery for the ternary mixture. These processes are promising for industrial application and can significantly improve the economy of non‐petroleum EG production.
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 | 0.000 | 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 | 0.000 |
| 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".