Beyond dividing wall columns: Improved process intensification through liquid-only transfer and heat integration
Bibliographic record
Abstract
Many chemical companies aim to achieve climate neutrality by 2050, requiring raw material changes and significant reductions in process energy. Since distillation accounts for a large share of energy use, it is a key target for process improvements. One promising approach is thermal coupling between columns, which is already industrially implemented, especially in dividing wall columns. However, such configurations often suffer from limited operational flexibility due to the fixed vapor split between parallel sections, which is largely fixed during design and difficult to adjust during operation. This limitation can be overcome by replacing each bidirectional vapor-and-liquid connection with a liquid-only transfer side stream. This concept allows each column to operate at an individual pressure and enables new options for heat integration. The present study introduces a structured approach for assessing and optimizing such systems with one or two liquid-only transfer side streams, particularly when combined with direct heat integration. Promising configurations are first identified through an efficient shortcut screening and can further be optimized using superstructure optimization. A case study on separating benzene, toluene, and para-xylene demonstrates that liquid-only transfer configurations with direct heat integration can significantly reduce energy and costs, and in many cases outperform conventional thermally coupled 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".