Numerical approach for optimization of magnetic roller and evaluating the performance of permanent magnet roller separator through design of experiment

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Date

2022

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Elsevier B.V.

Abstract

The present study is focused on numerical analysis of magnetic roller (M<inf>r</inf>) using finite element method magnetics (FEMM) software for different magnet disc-to-steel disc (MD-to-SD) width ratios. The numerical (FEMM) results reveal that, the optimized M<inf>r</inf> with the MD-to-SD width ratio of 5 mm: 2.5 mm was proved advantageous because of the effective magnetic field (M<inf>f</inf>) value of 0.89–2.59 T. The artificial neural network (ANN) modelling technique was used for the prediction analysis of obtained numerical results. Furthermore, by using optimized M<inf>r</inf>, the lab-scale permanent magnet roller separator (PMRS) was developed and parametric optimization has been carried out using Taguchi-based L<inf>27</inf> orthogonal array design. The significance of parameters on the overall quality of the product has also been evaluated quantitatively by the analysis of variance (ANOVA) method. It was found that the belt thickness was the most influential factor in the product of desired Fe grade and recovery %. The obtained regression coefficient (i.e., R2 = 87.13 and 91.69% for Fe grade and Fe recovery %, respectively) and normal probability plot show the highest correlation between the experimented and predicted data. The results suggested that the numerical approach was suitable for designing optimized M<inf>r</inf> for the processing of paramagnetic minerals. © 2022 Faculty of Engineering, Alexandria University

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Keywords

Analysis of variance (ANOVA), Design of experiments, Finite element method, Magnetism, Numerical methods, Permanent magnets, Rollers (machine components), Separators, Taguchi methods, Analyse of variance, Finite element method magnetic, Magnetic-field, Numerical approaches, Optimisations, Performance, Permanent magnet roller separator, Steel disks, Taguchi, Width ratio, Neural networks

Citation

Alexandria Engineering Journal, 2022, 61, 12, pp. 13011-13033

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