Effect of particle size distribution on the dewatering circuit design; case study: iron ore tailing of the Gol-E-Gohar mining and industrial company
Bibliographic record
Abstract
Iron ore processing plants of the Gol-E-Gohar mining and industrial company have three thickeners. The thickeners’ underflow's is pumped to a tailings dam, with the solid content of about 50%. To increase water recovery, this research investigated the effect of the particle size distribution on the dewatering circuit design of tailings. Physical and chemical characteristics of the thickeners’ underflows (solid content, XRF, XRD, density, etc.) are determined and three different dewatering circuit designs considered. In the first design, only a pressure filter was considered for each stream separately, and in the second design, all streams were combined, and then particles coarser than 1 mm were removed using the screen and dewatered by a pressure filter. In addition, in the third design, all streams are combined and particles coarser than 250 microns are removed using hydrocyclone-screen. Particles smaller than 250 microns are dewatered using a pressure filter. The results showed that the required filtration area (m2) related to the dewatering circuits to reach 20% moisture of plants’ tailings are 1074, 926, and 912 m2, respectively. Finally, it is determined that because of the positive impact of coarse particles on the filtration performance and the operational problems related to the particles larger than 1 mm, all streams must be combined and dewatered using a pressure filter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".