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Dynamics of Separation Characteristics of Sieving and Flow Classification Processes

  • Martin WeersEmail author
  • Annett Wollmann
  • Ulrich Teipel
  • Alfred P. Weber
Chapter
  • 28 Downloads

Abstract

In spite of the broad range of applications of flow and sieve classification, the physical phenomena for higher particle loadings are not completely understood. As a starting point, common models such as the one of Molerus may be used and optimized to include particle-particle and particle-wall collisions. In this contribution, it is investigated to which extent single particle models may be employed to describe the performance of a deflector wheel classifier and a circular vibratory screening machine at higher loadings. For the sieving process, the Molerus model was modified with a selectivity parameter, while for the deflector wheel, a differentiation of particles with low and high Stokes numbers was made. For high Stokes numbers, in a first approximation, the particularities of the airflow can be neglected, but the impaction behavior on the wheel blades needs to be taken into account. With the detailed knowledge of the mean airflow, a much better prediction of the separation curve can be obtained. In particular, the dynamic aspects of flow and sieving classification have been studied.

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Nomenclature

T(x)

Separation efficiency (–)

x

Particle size (m)

x′

Dimensionless particle size (–)

k

Coefficient for the sharpness of cut (–)

COR

Coefficient of restitution (–)

v

Velocity (m s−1)

η

Viscosity (Pa s)

ρ

Density (kg m−3)

H

Particle layer height (cm)

τ

Characteristic particle relaxation time (s)

τ*

Particle cloud relaxation time (s)

\({\dot{\text{V}}}\)

Carrier gas volume flow rate (m3 s−1)

R

Wheel radius (m)

h

Height of the openings in the deflector wheel (m)

U

Circumference (m)

Ψ2D

2D Sphericity (–)

SEM

Scanning electron microscope

σ

Standard deviation (depends on related variable)

E

Kinetic energy (J)

JKR

Johnson-Kendall-Roberts

p

Pressure (Pa)

E*

Average Young’s modulus (Pa)

υ

Poisson ratio (–)

Θ

Angle related to the deflector wheel blade (°)

L

Impaction length (m)

f

Revolution rate (s−1), fine material fraction (–)

cD

Drag coefficient (–)

Re

Particle Reynolds number (–)

κ

Sharpness of cut (–)

F

Force (N)

c

Coarse material fraction (–)

Q

Distribution sum function (–)

a

“Dead flow” parameter (–)

α, β

Measure of selectivity (–)

Γ

Dimensionless acceleration number (–)

A

Amplitude (m)

ω

Angular velocity (° s−1)

g

Gravitational constant (m s−2)

q

Density distribution (m−1)

\({\dot{\text{m}}}\)

Massflow rate (kg s−1)

KV

Throwing coefficient (–)

Indices

p

Particle

air

Air

eff

Effective

f

Fines

G

Coarse

A

Feed

25

Particle size related to T(x) = 0.25

50

Mean particle size

75

Particle size related to T(x) = 0.75

3

Mass-weighted

t

Cut particle size related to T(x) = 0.50

v

Volume equivalent

area-equivalent

Projection area equivalent

perimeter-equivalent

Projection perimeter equivalent

r

Rebound

i

Approach

0

Initial

imp

Absolute normal impaction

rad

Radial

tan

Tangential

rel

Relative

w

Wall

kin.

Kinetic

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Weers
    • 1
    Email author
  • Annett Wollmann
    • 1
  • Ulrich Teipel
    • 2
  • Alfred P. Weber
    • 1
  1. 1.Institute of Particle Technology, Technical University of ClausthalClausthal-ZellerfeldGermany
  2. 2.Institute of Particle Technology and Raw Material Innovation, TH NürnbergNurembergGermany

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