Thermodynamic Modeling of Zinc Distribution Among Matte, Slag and Liquid Copper
Bibliographic record
Abstract
AbstractAbstractRecently a thermodynamic database was developed to calculate equilibria involved in copper production. Zinc has now been included in the database for the matte, slag and blister copper phases, thereby permitting calculations in the 8-component system Zn-Pb-Cu-Ca-Fe-Si-O-S. Thermodynamic and phase equilibrium data from the literature have been critically assessed and optimized with the modified quasichemical model. When used with the Gibbs energy minimization software and other databases of the F*A*C*T thermodynamic computing system, this database can be used to calculate the distribution of zinc among the matte, slag, copper and gas phases during copper smelting and converting, or under various conditions which are difficult to study experimentally. The calculations predict that the presence of zinc increases the solubility of copper in the fayalite slag.Récemment, nous avons développé une banque de données thermodynamiques afin de calculer des équilibres de phases propres aux procédés d'élaboration du cuivre. Nous venons d'ajouter le zinc à cette banque de données, pour la matte, le laitier, et la phase métallique. Il est donc possible de faire des calculs pour le système à huit composants: Zn-Pb-Cu-Ca-Fe-Si-O-S. Des données sur les propriétés thermodynamiques et les équilibres de phases, prises de la littérature, ont été évaluées et optimisées en utilisant le modèle quasichimique modifié. On peut utiliser cette banque de données, avec les autres banques de données et les logiciels de minimisation de l'énergie de Gibbs du système informatisé F*A*C*T / F*A*I*T, afin de calculer la distribution du zinc entre la matte, le laitier et l'alliage lors du smeltage et du convertissage du cuivre, ou pour des conditions qui sont difficiles à réaliser expérimentalement. Les calculs prévoient que la présence du zinc sert à augmenter la solubilité du cuivre dans le laitier fayalitique.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.000 | 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 teacher head, 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".