Ferronickel recovery from 2-stage thermally treated ultramafic nickel sulfide concentrate
Bibliographic record
Abstract
• Recovery of a high-grade FeNi concentrate from ultramafic Ni sulfides is verified. • >26 % Ni and > 80 % recovery in ferronickel from ultramafic Ni sulfides was obtained. • 98.5% Ni in FeNi phase recovered, concentrate assaying 4% S, 1.6% Si, and 1.6% Mg. • Rejection rates of ∼ 95.6 % for S, ∼93.6 % for Si, and ∼ 93.9 % for Mg were achievable. • The FeNi phases in the product are iron-nickel (Fe 44 Ni 56 ) and kamacite (Fe 90 Ni 10 ). Nickel (Ni) is a critical metal facing a sharp increase in demand as it is a key ingredient in clean energy technologies such as lithium-ion batteries (LIBs) for electric vehicles (EVs). The gradual depletion in the active high-grade Ni sulfide deposits has garnered more attention toward Ni extraction from low-grade ultramafic Ni sulfides. Although the low-grade ultramafic sulfide deposits have the benefits of being amenable to surface mining and low sulfur content, which translates to fewer sulfur emissions, their high MgO content raises the slag liquidus temperature and viscosity pushing the need for higher smelting temperatures. Our previous work developed a novel 2-stage thermal treatment process for extracting nickel from low-grade ultramafic nickel concentrates. Although promising results were obtained, further work was required to understand and fully optimize the separation process of the magnetic FeNi alloy from the non-magnetic gangue. Thus, this study comprehensively assesses the feasibility and conditions needed for producing high-grade ferronickel products. An efficient magnetic separation process flowchart detailing the optimum conditions for each process stage was developed. The optimal conditions were grinding the thermal treatment product to below 38 μm followed by magnetic separation using a magnetic field intensity of 0.025T. Under these conditions, the nickel recovery reached 80 %, the nickel grade was 26 %, and the Ni separation efficiency was above 65 %, within the acceptable ranges. Lastly, the study systematically investigated the phase transformations, micromorphology, and Ni distribution in the alloy, magnetic concentrate, and tails, aiming to fully understand the effect of variable factors such as particle size of the ground product and magnetic field intensity.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".