Granular Prediction and Dynamic Scheduling Based on Adaptive Dynamic Programming for the Blast Furnace Gas System
Why this work is in the frame
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Bibliographic record
Abstract
A timely and effective scheduling of the byproduct gas system plays a pivotal role in realizing intelligent manufacturing and energy conservation in the steel industry. In order to realize real-time dynamic scheduling of the blast furnace gas (BFG) system, a granular prediction and dynamic scheduling process based on adaptive dynamic programming is proposed in this paper. To reflect the specificity of production reflected in the fluctuation of data, a series of information granules is constructed and described. In the dynamic scheduling phase, based on the granular feature description, a scheduling action network is established and further updates of information granules are realized. Considering a slow adjustment process and delay characteristics of the BFG system, the cumulative reward of the critic network is calculated on the basis of the data partition to construct a tendency attenuation-based cost function. In order to determine the future trends of the gas tank level that targets real-time determination of the scheduling moment, a reinforcement learning-based granulation and prediction process is also proposed. To demonstrate the performance of the proposed method, a number of comparative experiments are presented by using the practical industrial data. The results indicate that the proposed method exhibits high accuracy and can deliver an effective solution to justified scheduling of the BFG system.
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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 | 0.001 | 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