A Phenomenological Model for Particle Kinetics in Drum-type Wet Low-Intensity Magnetic Separation*
Bibliographic record
Abstract
Drum-type wet low-intensity magnetic separation (WLIMS) is a versatile technique widely employed in the mining industry for the treatment of iron ores. Its design and operation are rather simple and straightforward. Yet, understanding the process performance from a fundamental point of view still remains a puzzling task due to a number of complex subprocesses involved in the separation. Most of the models for drum-type WLIMS thus are based on empirical approaches. This work presents a modeling strategy that integrates ore properties and equipment characteristics to describe the behavior of iron ore particles. It relies on interpreting a laboratory-scale drum-type wet magnetic separator as a continuously stirred tank reactor. It merges the benefits of phenomenological and empirical modeling to express the particle kinetic rate constants as a function of the separation principles and ore characteristics. Results suggest that the kinetic model satisfactorily reproduces the experimental observations in terms of particle-classified magnetite recovery. The approach is promising for obtaining early information on the behavior of the particles at different stages of the iron ore beneficiation chain, especially for production planning, circuit layout and optimization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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".