On the analysis of ionic mass transfer in the electrolytic bath of an aluminum reduction cell
Bibliographic record
Abstract
Abstract An electrolyte typically used in an aluminum electrolysis cell is composed of different ions moving in the electromagnetic field generated by the high intensity current needed for the industrial application. The flux of these ions has an important impact on the functional parameters of the cell, like current efficiency. In this study, the transient behaviour of these ions in the NaF‐AlF3‐Al2O3 mixture is modelled using a numerical finite element method. The electric potential field equation governed by electrochemical reaction kinetics at electrodes is solved to obtain the electric potential field, current density, and consequently heat generation in the cell. Subsequently, the concentration field is solved for ionic species in the bath. The results indicate formation of a high concentration gradient of electroactive ions like Al2OF62− and AlF4− at the corresponding reacting electrodes with time and diffusion as the main mechanism for these ions transfer. It is found that from the early stages of the 3 minute simulation of the electrochemical process, the difference between bulk concentration and surface concentration of electroactive ions remains constant. Moreover, the results indicate that although the flux of electroactive species is dominated by diffusion, especially for larger times, migration is the controlling mechanism of transport for the electroinactive ions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".