A Non-Destructive Technique for the On-Line Quality Control of Green and Baked Anodes †
Bibliographic record
Abstract
Carbon anodes play an important role in the electrolytic production of aluminum. They have a significant economic and environmental impact. Carbon anodes are made of dry aggregates, composed of petroleum coke, recycled rejects, and butts, bound by coal tar pitch. Due to several factors, defects (cracks/pores) appear in anodes during the fabrication process, affecting their quality. It is thus essential to control the quality of anodes before their use in the electrolysis cell. Current practice for the quality evaluation (visual inspection, core analysis) gives limited information. As an alternative to this practice, electrical resistivity measurements can be used. Electrical resistivity is one of the key indicators for anode quality and its homogeneity. A simple and non-destructive method has been developed for the specific electrical resistivity measurement of anodes (SERMA) for on-line control of anode quality. Various tests have been carried out at both lab scale and industrial scale. In this study, the electrical resistivity distributions in the lab-scale anodes were measured and compared with those of the tomography analysis. The method is able to detect defective anodes even before the baking process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".