Mechanical separation and acid leaching: potential to decrease the environmental impact of the graphite tailings by recovering sulfide sulfur and heavy metals
Notice bibliographique
Résumé
The electrification of the world and the green transition are expected to increase the demand for energy storage technologies such as lithium-ion batteries. This will increase the demand for raw materials used in batteries, such as graphite. Further, due to geopolitical risk and the need for sustainable sources, graphite mining is also of interest in Europe and nations like Finland. During the concentration and/or refining of ore, an enormous amount of waste is produced due to the low concentration of the desired element. Some of the material of interest ends up in these side streams, as for example, in the tailings. However, the waste can potentially be used as a secondary source of raw materials. \n \nIn this work the potential processing scenarios of graphite mine tailings were investigated. The studied tailings samples originated from pilot metallurgical test work done in Canada with Aitolampi graphite ore. According to the analysis results, the tailings contain high concentrations of sulfidic sulfur and heavy metals, posing a potential risk of acid mine drainage. In this work, the potential strategies for harmful elements removal were studied and advantages and disadvantages were evaluated. This thesis work included magnetic and gravity separation test work done for tailings samples. Additionally, leaching experiments with varying acid molarity, temperature, and solid-to-liquid ratio were conducted. \n \nBased on the results, with a low-intensity magnetic separator, 58–64% of sulfidic sulfur could be recovered. Additionally, the magnetic product is theoretically suitable to be used in sulfuric acid production. Further, with gravity separation, the grade of Ni, Co, Cu, and Zn could be increased by a ratio of two to three. By leaching experiments, the leaching orders of the sulfide minerals pyrrhotite, sphalerite, chalcopyrite, and pyrite were confirmed. However, the concentrations of Co, Ni, Cu, and Zn in the tailings were too low, and Fe was too high to be economically attractive, so that recovery utilizing only leaching would be economical. This suggests that low grade tailings will still require technological innovations to achieve concentrates suitable for state-of-the-art refining.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».