Operating Mode Recognition Based on Fluctuation Interval Prediction for Iron Ore Sintering 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 operating mode is an essential factor affecting product quality and yield of the sinter ore, which inspires the realization of operating mode recognition. Taking burn-through point (BTP) as the decision parameter of operating mode, an operating mode recognition method based on the fluctuation interval prediction is presented. First, combining the principal component analysis and the fuzzy information granulation method, a fluctuation interval prediction model of the BTP is established through utilizing the Elman neural network. Then, the operating mode classification rules are built according to the data distribution of the BTP in the fluctuation interval. Finally, experiments are executed with the data collected from a factory. The results indicate that it can effectively predict the fluctuation interval of the BTP, and then successfully recognize the operating mode. In this article, the proposed method provides a valid reference to control the stable operation of the iron ore sintering process.
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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) | 0.000 | 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