Model Predictive Control Embedding a Parallel Hybrid Modeling Strategy
Bibliographic record
Abstract
This work addresses the problem of implementing a model predictive control (MPC) scheme that embeds a parallel hybrid subspace model as the predictive component of the control strategy. The hybrid model considered here is inspired by the framework proposed by Ghosh et al. (Hybrid Modeling Approach Integrating First-Principles Models with Subspace Identification. Ind. Eng. Chem. Res. 2019, 58, 13533−13543), but it is adapted to make it amenable to online control. In particular, the framework uses a first-principles model and a subspace-based residual model (built with error between the process measurement data and the first-principles output of historical batches) in a parallel fashion. The present manuscript adapts this framework in a way that retains the linearity of the model utilized within the MPC. This is achieved by first building a subspace model (built with output data of the first-principles model) and then appending it with the residual model to have the same parallel hybrid model structure. This linear hybrid MPC is applied on a seeded batch crystallization process to reduce the volume of fines or crystals generated due to nucleation during the crystallization process, while maintaining a desired product quality at batch termination. The closed-loop results using the proposed control methodology are compared with a purely data-driven subspace-based model predictive controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".