Mechanical separation and acid leaching: potential to decrease the environmental impact of the graphite tailings by recovering sulfide sulfur and heavy metals
Bibliographic record
Abstract
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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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".