Integration of Design and Control of Dynamic Systems under Uncertainty: A New Back-Off Approach
Bibliographic record
Abstract
A new methodology for simultaneous design and control under process disturbances and parameter uncertainty is presented using power series expansions (PSE) approximations. The key idea in this methodology is to back-off from the optimal steady-state design, which might be infeasible because of process dynamics and parameter uncertainty, to obtain the optimal design parameters that result in a dynamically feasible and economically attractive process. The work focuses on calculating various optimal design and control parameters by solving a set of optimization problems in an iterative manner using mathematical expressions obtained from PSE. These approximations are used to determine the direction in the search of optimal design parameters and operating conditions that is required for an economically attractive and dynamically feasible process. The proposed method was tested on a nonisothermal CSTR, and the results were compared with the formal integration process. The effect of the methodology’s key tuning parameters is also presented. The results show that this method has the potential to address the integration of design and control of dynamic systems under uncertainty at low computational costs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".