Effect of fine coke particles on rheological properties of the binder matrix of carbon anodes in aluminium production process
Bibliographic record
Abstract
Abstract Aluminium production through the Hall‐Héroult process relies on an extensive use of carbon anodes, which are originally made of coal tar pitch and fine and coarse carbon particles. During the forming process of green anodes, the mixture of the fine particles and the coal‐tar pitch (i.e., the binder matrix) moves between the coarse particles and produces a uniform agglomerated paste. The efficiency of the forming process directly affects the final quality of the baked anode and consequently the efficiency of the Hall‐Héroult process. The rheological properties of the binder matrix play a crucial role in the forming process. In this study, rotation and oscillation tests are used to study the effects of the fine particle concentration and temperature on the viscoelastic properties of the binder matrix. The rotation tests demonstrate that the binder matrix is a shear‐thinning material and that the viscosity is reduced by increasing the shear rate and temperature while it is increased by increasing the concentration of the fine particles. Moreover, the proposed model can predict the viscosity of the binder matrix precisely. The oscillation tests confirm that the elastic and viscous properties are reinforced by increasing the concentration of the fine particles and they are reduced by increasing the angular frequency and temperature. The three‐element Maxwell model is shown to predict the elastic and viscous moduli of the binder matrix. Finally, in a case study and based on the rheological results, the permeation velocity of the binder matrix between the coarse particles is calculated.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".