Cost optimization of scrap when making steel with an electric arc furnace
Why this work is in the frame
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Bibliographic record
Abstract
In steel production, an electric arc furnace (EAF) is most commonly used to melt raw material in order to produce liquid steel. Scrap is the main raw material which differs in regard to the content of iron and of some chemical elements. The price of scrap depends on these attributes. In order to obtain the desired quality and quantity, each melting bath unit of steel has either its own material constraints or the constraints for electric arc furnace such as the capacity of EAF. In addition, the availability and transportation of scrap are also restricted because they need space. The research in this thesis is to create an optimization model which minimizes the cost of raw material and charges the EAF efficiently while meeting the constraints of the scrap recipe and scrap transportation system. This problem is a combinational optimization problem and the model is developed based on linear programming theory. The running speed of the model is reasonably guaranteed by properly designing the combinatorial structure with branch and bound rules and heuristics. Finally, a software is created by representing the model in the spreadsheet, which can be used in real, everyday production. Simulation results show significant improvement compared to the strategy applied today at ArcelorMittal(Contrecoeur, Quebec): the cost of scrap steel is reduced by 2 to 6% and the time of charging buckets is 2 to 10 minutes faster.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it