Cellulose nanocrystals as a rheology modifier in the processing of ultramafic nickel ores
Bibliographic record
Abstract
Rheological behaviors, such as high viscosity and yield stress of suspensions, present tremendous challenges during comminution, flotation, and dewatering in the processing of low-grade nickel ores. Owing to the presence of anisotropic serpentine, particle–particle interactions that are attractive in nature can lead to high yield stresses, which increases the energy costs for pumping slurry suspensions. In this study, we investigated the rheological behavior of low-grade ultramafic nickel ore suspensions at varying dosages of cellulose nanocrystals (CNC) at solid densities of 30, 40, and 50 wt.%. Based on the CNC dosage, the serpentine interparticle interaction resulted in aggregation at dosages of 0, 0.1, and 1.0 mg/g CNC and dispersion at dosages of 2.5 mg/g, for slurry concentrations of 30, 40, and 50 wt.%. The viscosity at 1s -1 shear rate for 30 wt.% suspension increased from 0.12 Pa.s to 0.18 Pa.s on increasing the dosage from 0 to 1 mg/g CNC and then decreased to 0.10 Pa.s at 2.5 mg/g CNC dosage. The rheology effect by CNC was more pronounced for higher solid density such as 50 wt.%., where the viscosity increased from 2.74 to 4.42 Pa.s and then decreased to 2.3 Pa.s. This dual effect of CNCs on the rheology of ore can reduce the need for numerous hazardous chemicals used in different unit operations during nickel processing. This study paves the way for CNC to be used as post-processing flocculants for tailings management, which is one of the major environmental concerns faced by the mining and mineral processing industry.
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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 | 0.000 |
| 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 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".