Constrained model predictive control with economic optimization for integrating process
Bibliographic record
Abstract
The close relationship between steady‐state prediction outputs and actual inputs results in the existence of model uncertainty in the steady‐state prediction equation for integrating processes. This paper establishes a steady‐state prediction model that can reflect the dynamic execution process of the manipulated variables. Based on integration of the steady‐state optimization layer and dynamic optimization layer, the input increment sequences of multi‐step prediction are regarded as the decision variables. A quadratic programming model with inputs, outputs, and input increment constraints was developed, which simultaneously solved the problems of steady‐state optimization and dynamic control of integration process, as well as the sub‐optimal solution of the steady‐state targets in each cycle. Simulation examples illustrate that the optimal setpoints and the actual values of the inputs and outputs are all within the constraint ranges and the actual values settle to the optimal setpoints, and demonstrate that the method proposed in this paper can effectively solve the steady‐state optimization problem for integrating processes when economical optimization of the inputs and outputs is considered.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".