Techno‐economic assessment and optimization of simple and complex distillation column sequences of the olefin recovery plant using an automatic approach
Bibliographic record
Abstract
Abstract The synthesis of multicomponent distillation column sequences is complex due to the numerous possible scenarios. Therefore, employing a systematic and automated approach can be highly advantageous. This study analyzes and evaluates both simple and complex distillation column sequences suitable for the cold end of olefin plants to enhance olefin's production performance. A matrix‐based algorithm is used to generate possible configurations, which are then rigorously simulated and optimized using genetic algorithm. These steps are executed systematically and automatically within an integrated development environment. Sequences are evaluated based on energy consumption, exergy losses, and economic aspects. The impact of the hydrogenation reactor's location on distillation sequence performance is also examined. In the two case studies, the symmetrical sequence demonstrated the best economic performance, achieving a total annual cost (TAC) 11.21% lower than that of conventional sequences for the given feed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".