Electropermanent Magnet Eddy Current Separator to Recycle Non-Ferrous Metals
Bibliographic record
Abstract
This paper proposes and demonstrates the idea of using electropermanent magnet (EPM)-based actuator in the structure of eddy current separator to recycle non-ferrous metals. It uses a coil excited by a full-bridge inverter to statically switch the direction of magnetic flux in the EPM, inducing eddy current within metal particles. This provides a pulsating magnetic actuator to repel non-ferrous metals mixed with other materials on a recycling conveyor. Conventional eddy current separators typically use a costly and bulky rotating permanent magnet drum or a static electromagnetic coil to generate the required repulsive forces. The static EPM-based separator eliminates the need for the expensive rotating drum structure. Furthermore, it reduces power losses compared to static electromagnetic coils by using narrow-band current pulses instead of continuous sinusoidal current. A quadratic approximation of the magnetic flux density is proposed to improve the accuracy of the repulsive force calculation. The paper also elaborates on a core optimization and electromagnetic heating analysis. A proof-of-concept EPM separator is developed and experimentally tested to calculate the repulsive force based on measured deviations in particle angle and distance. Comparing the test results shows an average error of less than 5%, confirming the improved accuracy of the quadratic approximation.
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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 | 0.001 | 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".