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Sorting rare earth magnets motors for recycling without opening the motors

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Abstract A novel approach for sorting electric motors without dismantling them is reported in this article. This approach increases the likelihood that a motor selected from a mixture for recycling would contain rare earth elements (REEs), which are pivotal for numerous advanced green technologies. The challenge of inadvertently dismantling motors without REEs during recycling is addressed by this proposed innovative method. It relies on cogging interactions and power density for sorting motors with REEs. The process uses an algorithm introduced in this work to sort motors in two steps by (a) distinguishing induction motors from permanent magnet motors, and (b) distinguishing motors with critical REEs (e.g., Nd, Pr, Dy, Tb) from ferrite-based motors, all without opening the motors. The former was accomplished with 100% accuracy while the latter was accomplished with ~ 78% accuracy. Such high accuracy improves the recycling efficiency of critical REEs from motors, alleviating concern about supply disruption and supporting resource conservation and sustainability.
Title: Sorting rare earth magnets motors for recycling without opening the motors
Description:
Abstract A novel approach for sorting electric motors without dismantling them is reported in this article.
This approach increases the likelihood that a motor selected from a mixture for recycling would contain rare earth elements (REEs), which are pivotal for numerous advanced green technologies.
The challenge of inadvertently dismantling motors without REEs during recycling is addressed by this proposed innovative method.
It relies on cogging interactions and power density for sorting motors with REEs.
The process uses an algorithm introduced in this work to sort motors in two steps by (a) distinguishing induction motors from permanent magnet motors, and (b) distinguishing motors with critical REEs (e.
g.
, Nd, Pr, Dy, Tb) from ferrite-based motors, all without opening the motors.
The former was accomplished with 100% accuracy while the latter was accomplished with ~ 78% accuracy.
Such high accuracy improves the recycling efficiency of critical REEs from motors, alleviating concern about supply disruption and supporting resource conservation and sustainability.

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