Iron Extraction Efficiently from High-Iron Red Mud by Microwave Suspension Roasting Mixed by Biomass and Weak Magnetic Separation
Bibliographic record
Abstract
High-iron red mud, which is a solid waste with high iron content, is difficult to be processed and utilized by the traditional beneficiation process. In this study, it is proposed to extract iron efficiently by microwave suspension roasting followed by weak magnetic separation, and the thermodynamics, kinetics, phase, and microstructure evolution of mineral reactions during the roasting of high-iron red mud are systematically investigated. This method has the advantage of high efficiency and low energy consumption compared with the traditional roasting method. The results of thermodynamic and kinetic analyses showed that the hematite in the high-iron red mud was transformed into magnetite during the roasting process. Eventually, a magnetic separation concentrate with an iron grade of 63.12%, a yield of 85.49%, and an iron recovery of 94.99% was obtained. The reduction reaction of hematite was consistent with the stochastic nucleation and subsequent growth model at different roasting temperatures. The apparent activation energy and the pre-exponential factor decreased with the increase of roasting temperature, and the increase of the heating rate in a certain range was conducive to the reduction reaction of hematite.
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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 | 0.001 | 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".