Model predictive controller design with process constraints and implicit economic criteria [gasoline blending]
Bibliographic record
Abstract
A method for designing a QDMC (Quadratic/Dynamic Matrix Control) controller is developed such that, at steady-state, it will maintain the process near the economic optimum, without explicitly calculating profit as part of its objective function. The method for designing the QDMC controller involves selecting the adjustable parameters of the QDMC controller such that the total profit is maximized for a given set of disturbances; this set of disturbances acts as a "training set" for the controller. The steady-state performance of an optimally-tuned QDMC controller was compared against both the optimum profit case and multi-loop PID control of a gasoline blending process with five manipulated and three controlled variables. The QDMC controller outperformed the multi-loop PID controller both in economic performance and in the size of its operating window. Furthermore, the QDMC controller achieved no less than 99.3% of the optimum total profit for both disturbance sets examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".