Identification of reheat furnace temperature models from closed‐loop data—an industrial case study
Bibliographic record
Abstract
Abstract This work deals with the application of prediction error (PE) method to identify the furnace temperature models from a set of closed‐loop data. In this study, we gather a set of measurement data of the set points of the furnace zone temperatures as inputs and the furnace sidewall temperatures as outputs under a dynamic change of the slab pace rate. Owing to the complexity of its process dynamics, we assume no knowledge about the nature of the feedback mechanism. By treating the slab pace rate as an additive external signal, we show that the closed‐loop data is informative for applying a direct approach to the closed‐loop identification using the PE method, but only for a particular class of model structures. Model validation results support this analysis, in which the identified ARX, Box–Jenkins, and state‐space models are reasonably better than the identified FIR models, according to the Akaike's index and the one‐step‐ahead prediction criteria. The residual analysis reveals that the identified ARX, Box–Jenkins, and state‐space models do satisfy a 99% confidence region of its auto‐ and cross‐correlation functions. Moreover, we find out that, for the collected data, there is no significant difference in the model predictive quality when applying the MISO and MIMO PE methods using the state‐space model structure. © 2006 Curtin University of Technology and John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".