Dynamic Optimization of Multiproduct Cryogenic Air Separation Unit Startup
Bibliographic record
Abstract
The startup of multiproduct air separation units (ASUs) is of relatively long duration with limited revenue generation, during which high costs are incurred due to the energy-intensive nature of ASU operations. With current energy market trends, there is a strong incentive to improve the startup operation of multiproduct ASUs. In this paper, we focus on the development of a dynamic optimization framework for improving the startup of multiproduct ASUs. The underlying model of the ASU utilized in the framework captures discontinuities present at startup, and both time and profit metrics are used for the objective function in the formulation. In the case studies presented here, improvements and trade-offs of the respective objective functions are assessed. The time-based formulation is also used for a liquid-assisted startup study using process liquid collected from a preceding shutdown. An increase in profits of 7% over a simulated base case startup is shown, and the time taken to reach steady state is reduced by 16%.
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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 | 0.001 | 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) | 0.004 | 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".