Advances in synthesis of azeotropic distillation column sequences: internal secondary recycles
Bibliographic record
Abstract
Abstract An algorithm for automatic generation of sequences of distillation columns and decanters for separation of azeotropic mixtures has been developed (S.K. Wasylkiewicz, 54th Canadian Chemical Engineering Conference, paper No. 243, Calgary, October 2004; S.K. Wasylkiewicz, AIChE Spring National Meeting, paper No. 83e, Atlanta, GA, April 2005) where distillation boundaries can be crossed by moving them with pressure change, by exploring curvatures of distillation boundaries, or by liquid‐liquid splits in decanters. In the first step of the algorithm, open‐loop sequences are generated and primary recycles are automatically detected. Then, mass balances are calculated to finish the sequences. In this paper, we are focused on internal secondary recycles where species present in the sequence feed are introduced as separating agents. This can be a pure component produced somewhere downstream in the sequence or any other intermediate stream. On the basis of a broad knowledge about distillation regions and boundaries for the separated mixture, a preferred distillation region can be identified and a suitable recycle stream can be selected. These types of recycles can simplify tremendously, the whole sequence and reduce significantly the total cost of separation. We present in detail an example based on an industrial case where the internal secondary recycle was efficiently found and calculated during synthesis of column sequences by using the Split Generator in Distil and the mass balance calculation in MS Excel. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".