Abstract
The cross-sectional shape has great influence on the magnetic field characteristics of magnetic matrices in high gradient magnetic separation (HGMS). Suitable cross-sectional matrices can improve the recovery of fine weakly magnetic minerals and reduce mineral processing energy consumption. The performance of four types of cross-sectional shape matrices in the axial configuration of HGMS was studied through numerical simulation combined with experimental tests. The magnetic field generated by the matrices was simulated with ANSYS software, and the magnetic field strength, gradient, and magnetic force were analyzed and compared. The simulation results showed that diamond shaped, elliptical, square, and circular steel matrices reach magnetization saturation successively with increasing the magnetic induction, and the required magnetic induction is approximately 0.7, 0.8, 1, and 1.1 T, respectively. The magnetic field gradient increases with increasing the magnetic induction and then keeps constant when the matrices reach magnetization saturation. Within a wide range of the magnetic induction, the elliptical and square matrices present good magnetic field characteristics. The magnetic force near the surface of the square matrices is the largest but decreases rapidly and consequently has a small effect depth. The magnetic force near the surface of the elliptical matrices is relatively lower but decreases slowly and has a larger effect depth. The diamond-shaped matrices are easy to reach magnetization saturation, and the magnetic force decreases most rapidly. Magnetic matrices were manufactured, and magnetic separation experiments were conducted to verify the simulation results. The experimental results show that the elliptical and square matrices can obtain higher recovery in a wide range of magnetic induction, and the recovery of the elliptical matrices is the highest. The experimental results correspond well with the numerical simulation results, and the results indicate that the magnetic force effect depth is a very important factor influencing the performance of the matrices.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
