Rapid multi-criteria screening of energy-integrated distillation processes for nonideal mixtures
Bibliographic record
Abstract
Several thousand distillation columns are industrially employed for various separations, accounting for a substantial share of the industrial energy demand. In order to reduce their energy requirements various means for energy integration, such as direct heat integration, multi-effect distillation, thermal coupling, or vapor recompression can be applied. Considering these options and combinations of these, several hundred possible process configurations can be designed even for separations into three product streams, while the choice for a best option depends strongly on the specific separation task and system properties. In order to enable a reliable case-specific evaluation, which avoids simplified heuristics or simplified thermodynamics, this article presents a computationally efficient, algorithmic framework for a multi-criteria evaluation of more than 750 energy-integrated distillation sequences for multicomponent separations in three product streams. The framework employs thermodynamically sound pinch-based shortcut models that do not rely on constant relative volatility and constant molar overflow assumptions, making it applicable to nonideal and azeotropic mixtures. Based on the minimum energy duties and the respective flowsheet information, classical estimation methods for equipment sizes, operating costs, and capital investment, are employed. Several case studies demonstrate the framework’s applicability to azeotropic systems, its computational efficiency benefits that enable performing sensitivity analyses for varied process, thermodynamic, and economic scenarios.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".