Application of Economic Model Predictive Control on a Lab Scale Rotomolding Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of economically achieving a user specified set of product qualities in an industrial batch process is presented in the current manuscript, demonstrated using a lab-scale uni-axial rotational molding process. To achieve a product with specified qualities, a data driven Economic model predictive control (EMPC) formulation is proposed through constraints on quality variables. A state-space model of the rotational molding process is first identified from previously generated data in the lab. The evolution of the internal mold temperature for a given set of input moves (combination of two heaters and compressed air) is captured by the state space model. Further, this model is augmented with a partial-least-squares based quality model, which relates the terminal (states) prediction with key quality variables (sinkhole area and impact energy). This augmented model is then integrated within the EMPC scheme that penalizes excessive energy consumption while aiming to achieve on-spec products via constraints on the quality variables. Results obtained from experimental studies illustrates the capability of the proposed EMPC scheme in lowering the process cost (energy requirements) while achieving user specified product.
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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 | 0.000 |
| 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