Fine coal desulphurization and microwave energy absorption behaviour by microwave magnetic separation
Bibliographic record
Abstract
Abstract Microwave energy has been used to improve the coal‐pyrite magnetism, while magnetic medium has been added to enhance the microwave energy. Hence, there will be a secondary gradient magnetic chain, which can improve the desulphurization process through magnetic separation. Microwave energy and medium synergistic effects are able to readily improve the magnetic desulphurization process for fine coal. The present paper has studied the impact of microwave energy on the magnetic desulphurization process for fine coal. Intrinsic electromagnetic parameters of different materials were used to analyze and predict the equivalent dielectric parameters of the fine coal layer. The electromagnetic parameter according to the effective medium theory equation for fine coal was established. Coal specific susceptibility is related to pyrite content and pyrrhotite content in pyrite. The specific susceptibility values for the three types of coals were found to be in the following order: Lu'an (LA) coal > Weinan (WN) coal > Yiluo (YL) coal. The most suitable value for LA high‐sulphur coal's sulphur content after being separated by dry type rare earth roll strong magnetic separator appeared to be 60 s delayed in nitrogen atmosphere compared to the corresponding value in an air atmosphere. LA fine coal sulphur content reduced to 2.05 % from 3.66 %, and the microwave magnetic separation desulphurization rate was 44 %.
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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) | 0.001 | 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".