Utilizing Neural Networks for Image-based Model Predictive Controller of a batch Rotational Molding process
Bibliographic record
Abstract
We present a data-driven modelling and control approach for batch processes utilizing information from thermal images for feedback control. This work is driven by the requirement of utilizing the thermal image data that is the sole output of the system for feedback control. The overall goal here, like in many batch processes, is to obtain products with quality variables which match the user’s specifications. The quality variables of the product cannot be measured online and is only measurable after the batch has terminated. The control problem is therefore not a setpoint tracking problem. We propose a multi-layered modelling approach. We first have a dimensionality reduction technique to reduce the high dimensional image to a set of few representative outputs. Then, we apply subspace Identification (SSID) to identify a Linear Time Invariant (LTI) State space (SS) model between the inputs and the reduced outputs, and finally we construct a Partial Least Squares (PLS) model between the terminal states of a batch (identified using SSID) and the product qualities obtained for that particular batch. This model is utilized in a Model Predictive Control (MPC) formulation. We demonstrate the working of the MPC by showing its ability to achieve products with good quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".