High-speed imaging and electrical diagnostics of interacting arcs in dual-cathode electric arc furnace
Bibliographic record
Abstract
Summary form only given. Electric arc furnaces (EAFs) are ubiquitous in the steelmaking industry, and find an increasing number of applications in municipal solid waste gasification and resource (energy and material) recovery from industrial wastes (e.g. recovery from mining residues). The dual-cathode configuration is used to form two individual arcs with a common anode, which contains the material under treatment. A multi-cathode arrangement enables different heat load patterns on the anode and more flexibility in the operating conditions. Such system is also the host of complex arc interactions through the Lorentz force. At short cathode tip-to-anode distances, two relatively independent arcs are formed in the dual-cathode configuration. As the arcs are elongated, partial merging of the arcs at the anode occurs, and a complete merging on the anode takes place at large gaps. Complex and intermittent arc patterns form with their associated voltage and current signatures. In this contribution we describe the distinct arc structures and corresponding voltage/current signatures that occur in a dual-cathode DC EAF using graphite electrodes and argon as the working gas. A 5 kHz synchronized data acquisition system for voltage, current and high-speed imaging was used to observe the arcs' dynamics and the resulting voltage and current signature. An image processing code was developed to measure the length of the arcs and to identify arc structure on each frame captured. The algorithm first locates the arcs' attachment points on the electrodes and then Dijkstra's shortest path algorithm is used to reconstruct the path of highest brightness between the anode and cathode attachment points. Preliminary experimental observations and image analyses revealed the existence of four different arc structures. We report on the possibility of distinguishing these four structures by analyzing the voltage and current signals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".