Research on intelligent recommendation model for blast furnace blowing parameters
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
With the continuous exploitation of iron ore resources, the supply of high-quality ores is increasingly constrained, while low-grade ores reduce blast furnace iron output. Improving iron production remains a key research focus, with scholars emphasising optimisation of blast air parameters. This study proposes an intelligent recommendation model for blast air parameters to enhance iron output via parameter optimisation. Using real production data from a steel plant's No. 1 blast furnace, a whale-optimised random forest model for iron output prediction is developed, achieving 95% accuracy within ±4 tons. Additionally, an enhanced particle swarm optimisation algorithm is proposed to build an intelligent blast decision-recommendation model, which optimises blast parameters for production. Simulation and field tests show the improved algorithm outperforms the traditional PSO in accuracy and stability, boosting iron output by 5.66% per furnace on average. This research provides theoretical and technical support for intelligent blast furnace operation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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