Performance characterization and misplacement studies of liquid-solid fluidized bed density separator for coal beneficiation using Taguchi-ANOVA method

Kumari, A and Tripathy, A and Rayasam, V (2021) Performance characterization and misplacement studies of liquid-solid fluidized bed density separator for coal beneficiation using Taguchi-ANOVA method. Particulate Science and Technology, 39(4). pp. 436-448.

Full text not available from this repository. (Request a copy)

Abstract

Coal preparation plants commonly use density-based separators to remove high-density inorganic matter (rock) from low-density carbonaceous matter (coal). Liquid-solid fluidized bed separators (FBS), segregates particles with different size, shape, and density. Particles with different terminal velocities report to various heights of the fluidization column. This property has been used to beneficiate the low-grade coal particles. As the material systems in mineral processing vary broadly in physical characteristics, misplacement of particles to an undesired destination is a prevalent occurrence. The present work aims at understanding the influence of input variables on the separation performance and particle misplacement in FBS. Quantitative and qualitative analyses of the performance characteristics and misplacement have been studied using Taguchi statistical design and ANOVA. Misplacement and normalized misplacement indexes were calculated to quantify the extent of misplacement during segregation. The optimized mean separation efficiency was obtained at optimized combination of mean particle size of 400 mu m, bed height of 20 cm, superficial velocity of 2.83 cm/s, and overflow tap height of 12 cm, whereas the optimized mean normalized misplacement index was obtained at mean particle size of 400 mu m, bed height of 20 cm, superficial velocity of 2.12 cm/s, and overflow tap height of 12 cm.

Item Type: Article
Uncontrolled Keywords: Liquid-solid fluidization; Taguchi design; misplacement study; normalized misplacement index; ANOVA; particle hydrodynamics; layer inversion; neural-network; prediction; sedimentation; optimization; segregation; behavior; velocity; minimum
Subjects: Minerals and Mining
Divisions: Material Science and Technology
Depositing User: Users 43 not found.
Date Deposited: 24 Nov 2021 06:45
Last Modified: 24 Nov 2021 06:45
URI: http://eprints.nmlindia.org/id/eprint/8470

Actions (login required)

View Item
View Item