Closed-loop control framework for optimal startup of cryogenic air separation units
Why this work is in the frame
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Bibliographic record
Abstract
Current volatile electricity market conditions incentivize the adaptation of the operation, including the startup, of cryogenic air separation units (ASUs) which are large consumers of electricity. Improvement in ASU startups using earlier proposed open-loop control strategies may not be fully realized in the presence of uncertainties and disturbances. This paper assesses the potential benefit of using a proposed closed-loop control framework to address uncertainty and disturbances. A rolling-horizon economic nonlinear model predictive control (ENMPC) approach is considered, for which strategies are proposed to improve computation time. Online parameter estimation is performed using a computationally efficient method that is easy to implement. Through the case studies presented, it is shown that the proposed framework outperforms the use of offline pre-computed optimal inputs in response to the disturbance and uncertainty considered.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it