Reactive distillation for producing ethyl propionate featured with extreme excess of propionic acid: Process design and analysis
Bibliographic record
Abstract
Abstract Aiming at the problem of high energy consumption in traditional water‐carrying agent process for producing ethyl propionate, based on the thermodynamic analysis of residual curve map, a low energy consumption strategy for producing ethyl propionate via reactive distillation was proposed featured with extreme excess of propionic acid. It could realize the complete conversion (>99%) of ethanol via thermodynamic driving from propionic acid excess and facilitate effective separation of the water‐ethyl propionate distillate which forms two immiscible liquid phases and enables simple decantation with 98% EP recovery from organic phase through natural phase stratification, leading to significant reduction in energy consumption. A comprehensive investigation of process design and analysis for high‐purity ethyl propionate production with extreme excess of propionic acid was carried out by integrating process simulation and experimental validation in this work. Parametric studies of reactive distillation including excessive degree of propionic acid, holdup in bottom kettle reboiler, number of theoretical plates, and reflux ratio on the process performance and energy consumption was investigated. Simulation results of this new process were validated through experiments. Subsequently, the energy‐saving designs of further EP and water purification were implemented, including a weak alkaline solution washing to remove trace propionic acid from the distillate and distillation for separation of ethyl propionate and water. Finally, under preferred conditions, the proposed process with molar ratio of propionic acid to ethanol of 30:1, only required about 1700 kJ/kg for producing high‐purity ethyl propionate (0.99999), saving 68% energy consumption by comparison with the traditional process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".