KONA Powder and Particle Journal No. 41 (2024) 78–107/https://doi.org/10.14356/kona.2024017

Review Paper

DEM Modelling of Segregation in Granular Materials:
A Review †Ahmed Hadi ‡, Raïsa Roeplal ‡, Yusong Pang and Dingena L. Schott *Department of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, The Netherlands

Segregation control is a challenging yet crucial aspect of bulk material handlingprocesses. The discrete element method (DEM) can offer useful insights into
segregation phenomena, provided that reliable models are developed. The main
challenge in this regard is finding a good balance between including particle-level
details and managing the computational load. This is especially true for industrial
applications, where multi-component flows consisting of particles with various
irregular shapes and wide size distributions are encountered in huge amounts. In thiswork, we review the state of the art in DEM modelling of segregation in industrial
applications involving the gravity-driven flow of dry, cohesionless granularmaterials. We start by introducing a novel scientific notation to distinguish between
different types of mixtures. Next, we review how parameters for mixture models are
determined in the current literature, and how segregation is affected by material,
geometric and operational parameters based on these models. Finally, we review
existing segregation indices and their applicability to multi-component segregation.
We conclude that systematic calibration procedures for segregation models are
currently missing in the literature, and realistic models representing multicomponent mixtures have not yet been developed. Filling these gaps will pave theway for optimising industrial processes dealing with segregation.
Keywords: segregation, granular materials, discrete element method, granular
flow, DEM calibration

1. Introduction

Particulate materials are ubiquitous on Earth and are thesecond-most common materials handled in the industry(Richard et al., 2005). Nowadays, almost all commodities
are composed of, and/or derived from granular materials
through agriculture, mining, chemical, and/or mechanicalprocessing (Coulson, 2012). Particulate materials being
handled in the real world exhibit complex behaviour arising from the fact that almost all of them are either mixturesof different sizes, shapes, densities, or combinations
thereof. Taking into account the market-driven demand forincreasing production, lowering costs and the developmentof new sustainable products and systems, it is important tostudy and unravel the complex behaviour of granular materials, as well as to advance the technologies related to producing, processing, and transporting them (Rosato andWindows-Yule, 2020).
Segregation, also referred to as reverse mixing orde-mixing, is a phenomenon happening in moving granular
†
‡

*

78

Received 5 April 2023; Accepted 6 June 2023
J-STAGE Advance published online 15 September 2023
Authors with equal contribution
Corresponding author: Dingena L. Schott;
Add: Delft 2628 CD, The Netherlands
E-mail: d.l.schott@tudelft.nl
TEL: +31152783130

materials in which particles with similar properties, e.g.,size, density and shape, tend to collect in certain parts of a
mixture. Except for a few specific applications in mining
and agricultural engineering (Zhang et al., 2004), segregation is generally considered an undesirable occurrence affecting product homogeneity in a negative way. A
well-known example is the blast furnace, where segregation adversely influences the distribution of materials at theburden surface, which has a detrimental effect on the bed
permeability (Yu and Saxén, 2010). This negative effect on
permeability introduces an inconsistency in the pressure
drop which leads to the inefficient use of reductant gas andresulting in both economic and environmental consequences (Bhattacharya and McCarthy, 2014). This example, along with several cases of other industries (e.g., foodprocessing and pharmaceuticals industries) highlights theimportance of increasing knowledge about the underlying
roots of segregation, as well as investigating how segregation is influenced by various factors.
According to de Silva et al. (2000), there are thirteen
segregation mechanisms in general. However, some ofthese mechanisms are either rare or special cases of others.
Therefore, past researchers attempted to condense these
segregation roots in different ways (Carson et al., 1986;
Tang and Puri, 2004; Williams, 1991). Based on the size,
Williams mentioned the four main mechanisms as

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open
access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

trajectory, percolation, the rise of coarse particles due tovibration (or Brazil-nut effect) and elutriation (Rhodes,
2008; Williams, 1991). In addition to these size-based
mechanisms, the buoyancy mechanism in which the difference in density drives segregation should be considered for
multi-component mixtures (Ottino and Khakhar, 2000).
Having said that, multiple mechanisms might take place
simultaneously, making the prediction of the segregation
pattern extremely challenging. For instance, in the case of
the mixture of small light and large heavy particles, percolation and buoyancy mechanisms oppose each other andthe overall segregation behaviour of such a mixture is notknown a priori (Jain et al., 2005). Furthermore, since segregation only occurs for particles that are in motion—due toeither shearing, by means of moving walls (such as in ashear cell or bladed mixer) or as a result of gravitational
force (as is the case in hoppers) or vibration—it can be deduced that the formation of segregation patterns depends
not only on the properties of the components relative toeach other, but also on the system configuration and thedegree and type of agitation imposed on the material. Although having knowledge of the mentioned mechanisms
can shed light on the segregation behaviour of particulate
mixtures, it is necessary to investigate each case independently to determine the dominant segregation mechanism(s).
Researchers have attempted to experimentally observe
segregation phenomena in common applications since theearly ’70s in order to first understand why and how granular mixture components tend to separate spatially during
different types of agitation (Gray, 2018) and subsequently
developed models capable of capturing the observed phenomena. Given the complexity of the segregation problem,
the general approach was to scrutinize the effects of different material properties separately by considering material
mixtures differing only in either size (Duffy and Puri, 2002)
or density (Shlnohara and Mlyata, 1984) and, ultimately,
the combined effect of size and density (Jain et al., 2005).
This allowed researchers to systematically observe and incorporate the effect of each material property in mathematical models. However, there are several limitations
associated with the experimental investigation of segregation. First, despite the recent advances in measurement
techniques (Asachi et al., 2018; Bowler et al., 2020), extracting data on the composition of a granular mixture is
still not a trivial task. Second, it is nearly impossible toobtain all the information on a particle scale which canshed light on the macro-scale granular behaviour. Finally, it
is expensive and time-consuming to systematically study
the effect of various factors on segregation in experiments.
As computational models gained popularity, the Discrete
Element Method (DEM) initially introduced by Cundall
and Strack (1979) became a widely used tool for simulating
granular phenomena. The main advantage of DEM is that

Review Paper

any mixture of materials—whether they have different
sizes, densities, shapes, or combinations thereof—can bemodelled to provide detailed, particle-level insight on segregation patterns, which is both difficult and expensive toachieve experimentally and is not yet possible with mathematical models for segregation. This is highly relevant
since most materials encountered in industry are of thiscomplex nature. Hence, DEM has expedited researchers’
ability to predict segregation in multi-component mixtures,
irrespective of the application, and can therefore be considered a practical tool for modelling and optimising industrial
processes. However, until now it is unclear to what extent
the existing DEM models of segregation are representativeof the complex multi-component mixtures often encountered in real-world applications. Moreover, a DEM model
can accurately represent granular material behaviour only
if its parameters are reliably chosen, i.e., the model is calibrated and validated.
The objective of this paper is to provide an overview ofthe state of the art in modelling the segregation behaviourof complex multi-component mixtures using DEM. Specifically, this review assesses to what extent the existing DEM
studies of segregation in cohesionless materials represent
actual mixtures encountered in industry. Furthermore, thereliability of these existing studies in terms of their approach to calibration and validation is evaluated. The paper
is structured as follows. For the sake of consistency, a novel
way of defining and using consistent terminology for thedescription of mixtures is proposed in Section 2. Then in
Section 3, we first evaluate different ways of analysing
segregation employed by past studies including their applicability to multi-component mixtures. Next, the approachof past DEM studies on segregation for determining DEM
parameters is critically reviewed. Thereafter, an overview
of the methods used for the validation of DEM models ofsegregation is presented and evaluated. Finally, the resultsof past DEM studies on the effect of several factors on
segregation in different systems are reviewed in Section 4.
Section 5 concludes the review and provides recommendations for future work.
The scope of this paper is limited to DEM-based studies
of gravity-driven segregation of dry and cohesionless granular materials. Cohesionless (or free-flowing) granularmaterials are of interest because they are more susceptible
to segregation compared to cohesive particles (Schulze etal., 2008). Studies on fluidised segregation are excluded
since investigating such a phenomenon is conducted using
the coupled CFD-DEM approach, which does not fit in thisreview. Also, applications such as rotating drums and various types of mixers in which gravity is not the only sourceof energy causing segregation are beyond the scope of thisreview.

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2. Terminology

3. Discrete element method (DEM) forsegregation

For consistency and to make a clear distinction between
various terminologies used in this paper regarding mixtures, Fig. 1 is presented. In this figure, a component isdefined as a material with constant particle density. In casea mixture is composed of more than one component, it isgenerically referred to as a multi-component or more specifically, as two-component, three-component, etc. bymentioning the exact number of components. Moreover,the particles constituting each component can be mono- topoly-sized and/or mono- to multi-shaped. In this regard,referring to complex mixtures composed of several components is not yet straightforward. To overcome this issue, we
present in Eqn. (1) a novel way to define multi-component
mixtures:nC  nk 1 (ik S / jk Sh)

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3.1 Overview of DEM

A DEM model calculates the forces and moments of inertia acting on all particles and subsequently uses Newton’s
second law to compute their positions at each time step
through numerical integration. The interaction forces between particles and their surroundings are determined using contact models such as the Linear spring-dashpot(Luding, 2008) and Hertz-Mindlin model (Zhu et al.,2007), which are the most widely used for cohesionlessgranular materials. The model inputs can generally be divided into three categories: (i) morphological parameterssuch as particle size and shape distributions; (ii) material
parameters such as particle density (ρ), shear modulus (G)and Poisson ratio (ϑ); and (iii) interaction parameters suchas sliding and rolling friction coefficients (μs and μr, respectively) and the restitution coefficient (CoR).
The first step in developing a DEM model is generally
measuring morphological parameters, and determining
how they will be included in the model. In industrial applications, millions of particles with many irregular shapes
and wide size distributions are typically handled. From acomputational standpoint, modelling the true size andshape distributions for such applications can be very challenging for several reasons (Marigo and Stitt, 2015;
Roessler and Katterfeld, 2018; Sakai, 2016). First of all,tracking a large number of particles and their mutual interactions demands huge amounts of computational power,
even for simulating a few seconds of a simple process.
Secondly, modelling the actual size distribution can prolong simulation times significantly since the numericaltime step is determined by the smallest particle in the flow.
Finally, realistic (non-spherical) particle shapes can be(1)

where n is the number of components and the compositionof each component is specified in the corresponding paren(1) That is, i is the number of sizes in kth component
thesis.
kwhich could be 1, 2, 3, M or P representing a mono-, binary-, ternary-, multi- or poly-sized component. Similarly
representing the shape, jk might take the value of 1, 2, 3 orM denoting a mono-, binary-, ternary- or multi-shaped
component. For instance, 2C[(2S/1Sh + (MS/1Sh)] represents a two-component (2C) mixture composed of a binary-sized (2S) mono-shaped (1Sh) component mixed with amulti-sized (MS) mono-shaped (1Sh) one. This formula
will be consistently used in this review to refer to different
types of mixtures in a concise way.

Complex mixture composition

C1

...

Ci

...

Cn

Par�cle density = constantPar�cle sizePar�cle shape

Mono-sized

Mono-shaped

Binary-sized

Binary-shaped

Ternary-sized

Ternary-shaped
1

Poly-sized

...

1

N

2

Multi-shaped

Frequency

Multi-sized2

Particle diameter

Fig. 180

Terminology to describe various types of mixtures.

N...

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

modelled using multi-spheres or superquadrics which require computationally expensive algorithms (Soltanbeigi etal., 2018). Several solutions have been proposed to reduce
computational effort such as downscaling the full threedimensional system to a 2D representation or simulating a
portion using periodic boundaries (Yang et al., 2014; 2015)
using larger, so-called “coarse-grained” or “upscaled” particles (Coetzee, 2019; Lommen et al., 2019; Roessler andKatterfeld, 2018), ignoring small sizes of the full particle
size distribution (PSD—also referred to as “scalping” or“cut-off” (Cleary and Sawley, 2002)—and reducing theshear modulus (G), although the latter should be carried out
with caution as it might cause serious errors (Lommen etal., 2014). Besides all these techniques, DEM code development in conjunction with parallel computing techniques
using high-performance clusters (HPC) look promising toovercome the high computational time in due course(Marigo and Stitt, 2015).
The next step is determining the remaining model parameters such that the simulated flow behaviour matches
the real behaviour to an acceptable degree (Marigo andStitt, 2015). When it comes to modelling segregation, theparameters should be determined in a way that not only the

Review Paper

global behaviour (e.g., angle of repose, mass flow rate, etc.)
but also the local behaviour, i.e., spatial concentration ofcomponents, are captured. The reliability of DEM predictions depends on the proper selection of a contact model fora specific application and the values assigned to the model
parameters, given the simplifications which have been
made for computational reasons. Model validation is therefore an important final step for both verifying and demonstrating the model’s credibility.
We encountered 63 papers on the segregation of thegravity-driven flow of cohesionless granular materials in
the literature. These studies are mostly focused on segregation during hopper filling and discharge, chute flow andheap formation and are summarised in Table 1. Thesestudies have been categorised into four groups based on
their approach to obtaining DEM parameters. In the remainder of this section, we will review the studies from
Table 1 with respect to the methods used to quantify segregation (Section 3.2) and model development practices(Sections 3.3 and 3.4).

3.2 Methods for assessing segregation

Since DEM allows tracking the position of each

Table 1 Overview of DEM studies for segregation (LSD = Linear spring dashpot, HM = Hertz-Mindlin, n.s. = not specified, N/A = notapplicable, *for the definition and notation of the mixture type see Eqn. (1) in Section 2). (continued on next page)

II. Parameter assumption

I. Parametric sensitivity studies

Group

SourceSoftwareContact
model

Mixture type*

Material(in experiments)

Particle shape(Tripathi and Khakhar, 2011; 2013)

n.s.

LSD

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

N/A

Sphere(Ketterhagen et al., 2008)

n.s.

LSD

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Sphere(Pereira and Cleary, 2013)

n.s.

LSD

2C[(1S/1Sh) + (1S/1Sh)]

Sphere(Panda and Tan, 2020a; 2020b)

LIGGGHTS

HM

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Sphere(Huang et al., 2022)

n.s.

n.s.

1C[(2S/1Sh)]

Sphere(Yu and Saxén, 2014)

EDEM

HM

1C[(3S/1Sh)]

Sphere(Xu et al., 2019)

n.s.

HM

3C[(MS/1Sh) + (MS/1Sh) + (MS/1Sh)]

Sphere(Li et al., 2022)

n.s.

HM

2C[(2S/1Sh) + (2S/1Sh)]

Sphere(Vuilloz et al., 2021)

LMGC90n.s.

1C[(2S/1Sh)]

Sphere(Zhao et al., 2022)

PFC3D

LSD

1C[(3S/1Sh)](Tao et al., 2013)

n.s.

n.s.

1C[(2S/2Sh)]

SoybeanEllipsoidal,
corn-shaped,
cylinder, spherical(Shirsath et al., 2015)

n.s.

LSD

2C[(1S/1Sh) + (1S/1Sh)]

Glass beads

Sphere(Xu et al., 2017)

n.s.

n.s.

1C[(2S/1Sh)]

Alumina spheres

Sphere(Zhang et al., 2018)

n.s.

HM

1C[(2S/1Sh)]

Plastic pellets, rape seeds

Sphere(Mantravadi and Tan, 2020)

LIGGGHTS

HM

1C[(2S/1Sh)]

Glass beads

Sphere(Mio et al., 2020)

n.s.

Voigt

1C[(MS/1Sh)]

Sintered ore, coke

Sphere(Zhang et al., 2014)

n.s.

HM

1C[(MS/1Sh)]

Coke, iron ore

Sphere(Zhao et al., 2018)

LIGGGHTS

HM

1C[(PS/1Sh)]

Glass beads

Sphere(Zhao and Chew, 2020b)

LIGGGHTS

HM

1C[(PS/1Sh)]

Glass particles

Sphere, ellipsoid,
cylinder, cuboid(Kumar et al., 2020)

LIGGGHTS

HM

1C[(PS/1Sh)]

Glass beads

SphereSphere81

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

Review Paper

Table 1 Overview of DEM studies for segregation (LSD = Linear spring dashpot, HM = Hertz-Mindlin, n.s. = not specified, N/A = notapplicable, *for the definition and notation of the mixture type see Eqn. (1) in Section 2). (continued from previous page)

IV. Parameter calibration/ measurement

III. Parameters from literature

Group

82

SourceSoftwareContact
model

Mixture type*

Material(in experiments)

Particle shape(Ketterhagen et al., 2007; 2009)

n.s.

LSD

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Glass beads, cast steel
shot

Sphere(Ketterhagen and Hancock, 2010)

n.s.

LSD

1C[(2S/1Sh)]

Glass beads

Sphere(Kou et al., 2015; 2018)

n.s.

HM

4C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)+ (1S/1Sh)]

Pellet, ore, coke, flux

Sphere(Cliff et al., 2021)

MFiX

LSD

1C[(2S/1Sh)]

Mustard seed

Sphere(Yu and Saxén, 2013)

EDEM

HM

1C[(2S/1Sh)]

PelletSphere(Liao et al., 2023)

n.s.

n.s.

2C[(1S/1Sh) + (1S/1Sh)]

Lump ore, pellet

Sphere, clumped
spheres(Asachi et al., 2021)

EDEM

HM

3C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)]

TAED, BP, EPG(detergent powder)

Sphere, clumped
spheres(Tian et al., 2022)

n.s.

HM

1C[(3S/1Sh)]

Coke

Sphere, clumped
spheres(Yu et al., 2018)

EDEM

HM

1C[(2S/1Sh)]

PelletSphere(Xu et al., 2018b)

n.s.

HM

1C[(2S/1Sh)]

Mung bean

Sphere(Mandal and Khakhar, 2019)

n.s.

LSD

1C[(2S/1Sh)]

N/A

Spheres, dumbbells(Zhao and Chew, 2020a)

LIGGGHTS

HM

1C[(1S/2Sh)]

N/A

Ellipsoids,
cylinders, cuboids(Zhang et al., 2021)

n.s.

HM

1C[(2S/1Sh)]

Coke

Sphere(Yu and Saxén, 2010)

EDEM

HM

1C[(3S/1Sh)]

Pellets

Sphere(Yu and Saxén, 2012)

EDEM

HM

1C[(3S/1Sh)]

Pellets, coke, steel ball

Sphere, clumped
spheres(Kou et al., 2013)

n.s.

Voigt

1C[(3S/1Sh)]

Coke

Sphere(Wu et al., 2013)

n.s.

HM

1C[(3S/1Sh)]

SinterSphere(You et al., 2016)

n.s.

HM

1C[(3S/1Sh)]

Coal

Sphere(Xu et al., 2021)

n.s.

n.s.

1C[(MS/1Sh)]

Coke

Sphere(Kou et al., 2019)

n.s.

HM

3C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)]

Sinter, pellet, lump ore

Sphere(Chibwe et al., 2020)

LIGGGHTS

HM

2C[(MS/1Sh) + (1S/1Sh)]

Sinter, pellet

Sphere(Hong et al., 2021)

n.s.

HM

3C[(1S/1Sh) + (1S/1Sh) + (1S/1Sh)]

Sinter, pellet, ore

Sphere(Z. Zhang et al., 2020)

EDEM

HM

1C[(2S/1Sh)]

Acrylic spheres

Sphere(Izard et al., 2021)

EDEM

HM

1C[(2S/1Sh)]

SinterClumped spheres(Alizadeh et al., 2017)

EDEM

HM

2C[(1S/1Sh) + (1S/1Sh)]

TAED, BP (detergent
powder)Clumped spheres(Kim et al., 2020)

EDEM

HM

2C[(3S/1Sh) + (3S/1Sh)]

Sinter, briquette

Sphere, clumped
sphere(Qiu and Pabst, 2022)

PFC3D

LSD

1C[(MS/1Sh)]

Waste rockSphere(Wang et al., 2023)

LIGGGHTS

HM

2C[(1S/1Sh) + (1S/1Sh)]

Steel and aluminium
spheres

Sphere(Barik et al., 2023)

LIGGGHTS

HM

1C[(3S/1Sh)]

Microcrystalline cellulose(MCC)

Sphere(Mio et al., 2008a; 2009; 2010; 2012)

n.s.

Voigt

1C[(3S/1Sh)]

Sintered ore

Sphere(Nakano et al., 2012)

n.s.

Voigt

1C[(3S/1Sh)]

SinterSphere(Bhattacharya and McCarthy, 2014)

n.s.

HM

1C[(3S/1Sh)]

Polystyrene spheres

Sphere(Li et al., 2017)

n.s.

HM

1C[(MS/1Sh)]

Iron ore

Sphere(Li et al., 2019)

LIGGGHTS

HM

1C[(MS/1Sh)]

Iron ore

Sphere(Shimosaka et al., 2013)

n.s.

LSD

1C[(MS/1Sh)]

Glass bead, sand, alumina

Sphere(Combarros et al., 2014)

LIGGGHTS

HM

1C[(2S/1Sh)]
2C[(1S/1Sh) + (1S/1Sh)]

Aluminium oxide

Sphere, cylinders(Combarros et al., 2016)

LIGGGHTS

HM

1C[(2S/1Sh)]

Sand

Sphere(Terui et al., 2017)

n.s.

Voigt

2C[(1S/1Sh) + (1S/1Sh)]

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individual particle, it becomes relatively simple to assessthe degree of segregation within a system. This can be donein different ways. One approach is to divide the system into
subdomains and subsequently determine how different
mixture components are distributed in the subdomains bymeans of plots. Gradient plots are useful for visualising the
distribution of a single mixture component in a system. For
example, a gradient plot as in Fig. 2(a) has been used tovisualise how small particles within a binary mixture were
distributed after filling a hopper (Xu et al., 2017). Since
they were dealing with a binary mixture, showing the distribution of one component was sufficient to demonstrate
how well the material was mixed in the hopper. Whendealing with multi-component mixtures, monitoring only
one component’s distribution is no longer sufficient, andother types of plots can be used. Fig. 2(b) shows an example of line plots for monitoring the radial distribution offour different particle sizes in a blast furnace. The methodof plots is beneficial as it provides two important insights:(1) whether or not different mixture components are evenly
distributed and (2) where the different components areconcentrated in the case of uneven distribution.
Another method is to use an index which reflects thedegree of segregation based on its value. There are generally two types of segregation indices: grid-dependent andgrid-independent (Bhalode and Ierapetritou, 2020). In thefirst type, the system is divided into subdomains, similar to
the method of plots. The distribution of one mixture component (referred to as the tracer) is evaluated, and the segregation index is then determined through statistical(a)

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analysis of the tracer distribution. If the tracer is distributed
evenly, then the material is considered to be well-mixed,
and this is reflected by the value of the segregation index.
In the second type, the value of the segregation index isdetermined by considering how the tracer particle is distributed within the system as a whole, rather than in subdomains. The tracer’s distribution is generally evaluated on a
distance or contact basis, as described by Bhalode and
Ierapetritou (2020). The index value reflects how spreadout the tracer particles are in the whole system.
The benefit of using an index is that it allows for a quantitative comparison of segregation for different scenarios
by means of a single value. This might be more difficult to
do with the method of plots, which provides a more qualitative comparison. However, it is generally known that
grid-dependent indices depend on the chosen size of the
subdomains (Cho et al., 2017; Jin et al., 2022; Rosato andWindows-Yule, 2020), which makes it difficult to judge thedegree of segregation based on the index value. For thisreason, researchers often investigate how sensitive theirsegregation predictions are to the grid size. While thegrid-independent indices do not have this shortcoming,
they are often computationally intensive, especially for industrial applications where millions of particles are involved.
The majority of literature from Table 1 used the plot
method to quantify segregation, with only a few used segregation indices. These indices are presented in Table 2
and are grouped by the calculation method (grid dependent/
independent). The equations for calculating the indices are(b)

Fig. 2 (a) Gradient plot for demonstrating how a single component is spatially distributed inside a system, where white colour represents a wellmixed status and red and blue colours denote segregated areas. Reproduced with permission from Ref. (Xu et al., 2017). Copyright 2017, Elsevier. and(b) line plots for different coke sizes, showing how they are distributed inside a blast furnace after being charged using various chute angles Reproduced with permission from Ref. (Zhang et al., 2014). Copyright 2014, Elsevier.

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Table 2 Common segregation indices used in DEM studies of Table 1. Legend: refer to the Appendix for symbol definitions. Subscripts: “t” refers to“tracer” when a single tracer is used; ti refers to the i-th tracer when multiple tracers are used(*).

No.

SourceSegregation index

Values(mixed–segregated)

Classification0–(**)

Variance-based / single tracer

0–1

Variance-based / single tracer

0–(**)

Variance-based / single tracer

1–0(***)

Variance-based / single tracer

0–(**)

Variance-based / single tracerj

0–1

Distance-based / two tracers

logσ 02  logσ 2logσ 02  logσ r2

1–0

Variance-based / single tracer

1–0

Contact-based

COM final COM initial
δ

0–0.5

Distance-based / single tracerl  lminlud  lmin

1–0Distance-based / single tracer

Grid-dependent
1(Panda and Tan, 2020a; 2020b)
SI 

2(Li et al., 2022)

3(Mantravadi and Tan, 2020)

2

m

j
2
m N
 j 1  y tj  y t  
N

SI 
[31]
y t 1  y t 

[32]
SI 
4

(Mantravadi and Tan, 2020)

5(Zhang et al., 2014), (Ketterhagenand Hancock, 2010)

SI 

[33]
[34]

SI 

6

(Zhao et al., 2018)7(Combarros et al., 2014)

 x tj  xtj  m  
 j 1  xt  xt  avg.
m 1

SI 

[36]

(Zhang et al., 2018)2

σ 02  σ 2
σ 02  σ r2
 x tjm j 1 
 xt

 xj   t   xt  avg.
m mj 1 xt1  h j 
1jm
 j 1 xt 2  h j 
SI 
[35]
2 x t 2  x t1
1
x t1

Grid-independent8jm
 j 1 c t  c t 

σ σ
[37]
SI  0
2

2

(*)

2

σ 02  σ r2

9(Mandal and Khakhar, 2019)10(Chibwe et al., 2020)

SI 

[38]
SI 

[39]

(*) The readers are referred to Table A1 in the Appendix for detailed equations on calculating component concentrations of indices 1–8.

(**) For these indices, the maximum value of SI depends on the mixture composition, as the equations contain the variables c , y and xt ttwhich represent the concentration of the tracer[40]in the entire mixture.

(***) In these equations, σ2, σ 2 and σ 2 are calculated by0

σ 2  in1ry 1  y t  y ti  y t  2 , 
σ 2 y 1  y  , σ 2  t

n 1

0

t

t

r

Ni

[41] [42]given along with [44]the range of values that can be assigned to[43]each index. Additionally, the[45]type of segregation index
based on the classification according to Bhalode and Ierapetritou (2020) is indicated. It is important to note that,similar to the method of plots, the single-tracer approach isonly useful when considering a binary mixture. We havetherefore also indicated whether the segregation indices are
based on the distribution of a single tracer. The common
symbols used in index no. 1–8 from Table 2 are defined in84

the Appendix, and for index no. 9–10, the reader is referred to the respective references.
We conclude that all grid-dependent indices in Table 2
are based on a statistical analysis of a single tracer, which is
not surprising since they were applied to studies of binary
mixtures. In studies of three or more particle types(Bhalode and Ierapetritou, 2020), the plot method was used
to visualize the degree of segregation. For indices which
can be applied to multi-component segregation, the reader

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

is referred to the work of Cho et al. (2017), who studiedsegregation in a mixer.

3.3 Determination of DEM parameters forsegregation

The literature mentioned in Table 1 has been categorizedbased on the way in which the authors dealt with model
parameters. We identified the following four categories:
I. Parametric sensitivity studies. This group of studies
carried out parametric sensitivity studies to identify thecritical factors (i.e., model, material, geometric and operational parameters) affecting the segregation behaviour of
mixtures in different systems. Hence, parameter values inthese studies are systematically varied. Since they are notaimed at modelling a specific material, calibration andvalidation are not performed in these studies.
II. Parameter assumption. The second group contains
studies in which the authors assumed parameters without
any justification or referring to related resources (for instance chosen by experience). Although these studies attempted to justify the parameter values by comparing theDEM results with physical tests, this is insufficient because
more than one parameter set can yield the same bulk behaviour (Roessler et al., 2019). Furthermore, major errors
were reported in some cases (Tao et al., 2013), which could
be the consequence of not calibrating the parameters.
III. Parameters from literature. Many DEM studies on
segregation have taken either all or merely a selection of
the parameters from the literature (Asachi et al., 2021;
Chibwe et al., 2020; Cliff et al., 2021; Hong et al., 2021;
Ketterhagen et al., 2007, 2009; Ketterhagen and Hancock,2010; Kou et al., 2013, 2015, 2018, 2019; Liao et al., 2023;
Mandal and Khakhar, 2019; Tian et al., 2022; Wu et al.,2013; Xu et al., 2018b, 2021; You et al., 2016; Yu et al.,2018; Yu and Saxén, 2010, 2012, 2013; Zhang et al., 2021;
Zhao and Chew, 2020a). This approach has the same shortcoming as the previous group, since the parameters are
obtained from resources that have not conducted calibration. Furthermore, it is unclear if the material under study issimilar or exactly the same as in the literature (Zhang et al.,2021).
IV. Parameter calibration/ measurement. Several past

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studies have established DEM parameters through either
direct measurements or bulk calibration (Alizadeh et al.,2017; Barik et al., 2023; Bhattacharya and McCarthy,
2014; Combarros et al., 2014; Combarros Garcia et al.,2016; Izard et al., 2021; Kim et al., 2020; Li et al., 2019,2017; Mio et al., 2010, 2009, 2008a, 2012; Nakano et al.,2012; Qiu and Pabst, 2022; Shimosaka et al., 2013; Terui etal., 2017; Wang et al., 2023; Z. Zhang et al., 2020). Each ofthese approaches comes with a set of advantages and disadvantages as summarized in Table 3. The calibration approaches of the studies in this group are summarized inTable 4. All calibration tests presented in this table capture
only the global behaviour of granular materials (e.g., angleof repose) and none of them has calibrated the DEM model
for local behaviour using a segregation test.
Calibration of DEM models against experimental data
ensures that the model captures the material behaviour andtherefore provides credibility (Coetzee, 2017). However,calibration can be very time-consuming. This is especiallytrue for multi-component mixtures since the number ofmodel parameters increases with the number of components in the mixture. Sensitivity studies enable us to explore the importance of the model parameters for inclusionin the calibration step. Table 5 presents a summary of theresults of the existing DEM sensitivity studies on segregation. As can be seen, the significance of DEM parameters
depends on the system and the granular flow regime being
studied.
Fig. 3 visually presents deeper insight into studies listed
in Table 1 to highlight the percentage of studies with respect to the approach for determining DEM parameters,
reproducibility and the types of the mixtures being studied.
According to Table 1, not all past DEM studies havedetermined the parameters in a proper way. To highlight
this with respect to segregation, Fig. 3(a) shows the percentage of studies in groups II to IV, where only studies ingroup IV include calibration. It should be noted that group
I is excluded since calibration is not necessary for parametric sensitivity analysis. As can be seen, 66 % of the existingstudies have omitted the calibration of the DEM model.
This is relevant since calibrated DEM models are required
if they are intended to be used as predictive tools that yield

Table 3 Pros and cons of two main approaches to determining DEM parameters (Coetzee, 2017; Marigo and Stitt, 2015; Wang et al., 2022).

Advantage

Disadvantage

Direct measurement+++

Independent of the contact model
Independent of the DEM code
Maintaining physical meaning

–
–
–
–

Mostly limited to mm-sized particles
Not practical for irregular particles
Not practical for all parameters
Not considering the stochastic nature of the parameters

Bulk Calibration+

 ompensating for the inaccuracy of –C
the parameters–
Obtaining values for all parameters ––Probability of losing physical meaning on particle level
DEM code dependency
Probability of not resulting in a unique parameter set
Challenging in the case of a high number of parameters+

85Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

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Table 4 Detailed calibration approach of studies in group IV of Table 1.

SourceDirect measurement

Bulk calibration test

(Z. Zhang et al., 2020)

μs,pp, μs,pw, CoR—(Izard et al., 2021)

μs,pw, ρs

μs,pp (angle of repose)(Alizadeh et al., 2017)

CoR, μs,pw, particle shape

The number of spheres in clumped approach, μr (angle ofrepose), μs,pp (sliding process),(Kim et al., 2020)

PSD, ρs, CoR, μs, E

μr (angle of repose)(Qiu and Pabst, 2022)

PSD

μs, μr (angle of repose, both at lab and field scales)(Wang et al., 2023)

ρs, CoR—(Barik et al., 2023)—

μs (silo discharging)(Mio et al., 2008a; 2009; 2010; 2012) PSD, ρs

μs (shear test), μr (particle rolling test)(Nakano et al., 2012)—μr (angle of movement)(Bhattacharya and McCarthy, 2014)

PSD, ρs

Tuning the contact model(Li et al., 2017)

PSD, ρs, G

μs, μr (angle of repose)(Li et al., 2019)

PSD, ρs, G, μs, μr—(Shimosaka et al., 2013)—

μr (inclined plate test)(Combarros et al., 2014)

PSD, ρs

μs, μr (sensitivity analysis + dynamic and static angle of repose)(Combarros Garcia et al., 2016)

ρs, D50

μs, CoR (sensitivity analysis + shear test, angle of repose anddrop fall test)(Terui et al., 2017)—

μs (stationary bed angle), μr (angle of repose)

Table 5 A summary of the results of DEM sensitivity studies on the significance of model parameters for segregation.

DEM parameter

Range (source)

SystemSignificanceParticle-particle sliding friction (μs,p-p)(0.0–0.5) (Ketterhagen et al., 2008)

Hopper discharge

High

Particle-wall sliding friction (μs,p-w)

Particle-particle rolling friction (μr,p-p)(0.01–0.9) (Yu and Saxén, 2010)Low(0.15–0.95) (Z. Zhang et al., 2020)

High(0.1–1.0) (Zhang et al., 2018)

Hopper fillingHigh(0.3–0.9) (Li et al., 2022)

Blast furnace throat

Low(0.0–0.5) (Ketterhagen et al., 2008)

Hopper dischargeHigh(0.01–0.9) (Yu and Saxén, 2010)High(0.05–0.85) (Z. Zhang et al., 2020)High(0.0–0.045dc) (Ketterhagen et al., 2008)

Hopper discharge(0.05–0.9) (Yu and Saxén, 2010)
Particle-wall rolling friction (μr,p-w)

Low
Low

(0.001dp–0.2dp) (Zhang et al., 2018)

Hopper fillingHigh(0.0–0.045dc) (Ketterhagen et al., 2008)

Hopper discharge

Low(0.01–0.9) (Yu and Saxén, 2010)

High

Particle-particle coefficient of restitution (ep-p)(0.2–0.94) (Ketterhagen et al., 2008)

Hopper filling

High

Particle-wall coefficient of restitution (ep-w)(0.2–0.9) (Ketterhagen et al., 2008)

Hopper filling

High

Local damp

(0.2–0.8) (Qiu and Pabst, 2022)

Pile formation

Low

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Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107(a)Review Paper

(b)

(c)
7%

20%
34%

23%

40%
60%

70%

46%

II. Parameter assumption

Not specified

III. Parameters from literature
IV. Parameter calibration/measurement
Fig. 3SpecifiedSingle-component
Two-component
Three-component and more

Distribution of past DEM-based studies of segregation by (a) approach of calibration, (b) DEM software and (c) types of mixtures.

reliable results (Coetzee, 2017). Even though the studies ingroup IV have applied calibration to some extent, not all ofthem have carried it out in a systematic way (cf. Table 4).
In several studies, only a portion of the parameters has beencalibrated (Nakano et al., 2012; Shimosaka et al., 2013).
Also, in several cases, a single experiment is used to determine the values of more than one parameter (Izard et al.,2021; Li et al., 2017). As shown by Wensrich and Katterfeld(2012), this approach leads to ambiguity since an infinite
combination of parameter values might lead to the samebulk behaviour.
There are good examples of systematic calibration of
DEM models with respect to bulk material behaviour forvarious operational conditions in the literature (Coetzee,
2016; Do et al., 2018; El Kassem et al., 2021; Marigo andStitt, 2015; Mohajeri et al., 2020; Roessler et al., 2019).
When it comes to the calibration of models for segregation,
several authors have shown that bulk calibration also ensures that the local material behaviour is accurately captured. For example, Li et al. (2017) calibrated the friction
coefficients of iron ore pellets using a repose angle test andverified that the calibrated model could reproduce the experimentally observed size segregation in simulations.
Similarly, Izard et al. (2021) modelled the size segregationof sinter particles after determining the model parameters
through bulk calibration. However, it has not yet been addressed to what extent the DEM parameters obtained
through bulk calibration (e.g., dynamic and static angle ofrepose) can be employed to accurately replicate the segregation of multi-component mixtures, which is essentially a
local occurrence. Furthermore, the systematic calibrationof mixture models has received little attention. One of the
few studies where mixture calibration was performed is thework of Alizadeh et al. (2017). The authors modelled thesegregation of a detergent mixture during heap formationand found that the segregation behaviour was not accurately captured by their model, which was calibrated using
a combination of direct measurement and bulk calibration.
They attributed the model’s inaccuracy to the fact thatspheres were used to model the particle shape. When using
clumped spheres to approximate the actual particle shape,
the predicted segregation behaviour was in good agreementwith the experiments. Other researchers have also modelled the segregation of mixtures, but the interaction parameters between mixture components were not mentioned(Combarros et al., 2014; Terui et al., 2017).
Fig. 3(b) illustrates the various DEM software that has
been used by past researchers to investigate segregation. It
is notable that 60 % of the studies have not specified theDEM software they employed. This is highly importantbecause not only the same DEM parameters might be defined in different ways in various software, but also models
may be differently implemented, making the parameterscode-dependent (Coetzee, 2017; González-Montellano etal., 2012). It means that the calibrated parameters of thesestudies cannot be reliably and directly used by others to
reproduce the work or to model the same material. Therefore, it is essential to specify in detail the DEM code orsoftware used in future studies.
The different types of mixtures that have been modelled
and studied are shown in Fig. 3(c). A vast majority of paststudies (i.e. 70 %) have been dedicated to singlecomponent mixtures i.e., with the general form of 1C[(iS/jSh)] (see Section 2), most of which have modelled simple
binary- or ternary-sized mixtures with spherical particles(i.e., 1C[(2S/1Sh)] or 1C[(3S/1Sh)]). In addition, a few

87

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studies considered two or three components (Chibwe et al.,2020; Combarros et al., 2014; Hong et al., 2021; Ketterhagen
et al., 2007; Kou et al., 2019; Li et al., 2022; Panda and Tan,
2020a; Pereira and Cleary, 2013; Shirsath et al., 2015;
Terui et al., 2017; Xu et al., 2019), but they also simplified
the mixtures by modelling mono-sized and mono-shaped
components. In conclusion, although past DEM studies
have attempted to shed light on segregation in granularmaterials, they have failed to represent the real-world complex mixtures which are mostly multi-component witheach component having a size distribution and different
particle shapes (Gao et al., 2021).
As can be seen in Table 1 (columns Mixture type andParticle shape), the particle shape and size distribution
have often been simplified, mainly to prevent high computational time. The effect of particle shape on segregation is
not crystal clear yet as can be illustrated by seemingly
contradictory results in the literature. For instance, Yu andSaxén (2014) asserted that particle shape has an insignificant effect on the size segregation during charging anddischarging from a hopper since spherical particles yielded
the best agreement with the experimental results ofStandish (1985). It is notable that they used the same DEM
parameters for both spherical and non-spherical particles,
which can lead to misinterpretation. In another study, Alizadeh et al. (2017) concluded that modelling non-sphericalparticles as spherical with rolling friction underestimates
the segregation extent during heap formation. Despite thisconflict, Lu et al. (2015) and Combarros Garcia et al.
(2016) concluded that particle shape has to be modelled as
accurately as possible to ensure that the DEM model is accurate and predictive. Similarly, particle size distribution
has usually been approximated either by using a limited
number of sizes or scalping (cf. Section 3.1). However,since particle size is the most crucial parameter affecting
segregation (Williams, 1976), size distribution should bemodelled as precisely as possible. As stated by Coetzee(2017), particle shape along with the particle size distribution should be explicitly included in the calibration process
and they have to be determined prior to other parameters.

3.4 Validation of DEM models for segregation

The parameters obtained through calibration must be
independent of the application (Coetzee, 2016). Therefore,
once the model is calibrated, it should be capable of accurately reproducing other experiments. In order to provethis, the results of the DEM simulation have to be compared with either experimental tests, analytical or “wellestablished” numerical results related to segregation which
are available in the literature. If a “good agreement” is observed, the model is validated. Regardless of theabove-mentioned approaches being adopted for determining the DEM parameters, the studies in groups II, III and IV
of Table 1 (except for parametric studies in group I) at-

88

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tempted to validate their DEM model for segregation. The
validation techniques found in the literature overview ofTable 1 can be classified into two main categories as illustrated in Table 6: (1) using other research results as thebenchmark and (2) using first-hand experimental results.

3.4.1 Validation using other work’s results

In this approach, existing experimental, mathematical or
numerical (i.e., DEM) results on segregation are used tovalidate the DEM model. Although adopting this approachfacilitates the process of developing a DEM model, it suffers from several drawbacks. Firstly, some of these references have not particularly investigated the local behaviour(i.e., segregation) of granular materials and as a result, evenin the case of an acceptable agreement, one cannot makesure the DEM model is valid for segregation. Secondly, insome cases, the size distribution of the DEM model differsfrom the source study against which the model has beenvalidated. For example, Zhao et al. (2018) compared theresults of the model for mono-sized particles and used thesame model for modelling the segregation behaviour of
granular materials with log-normal size distribution. However, according to Coetzee (2017), if particle size in themodel changes, the model parameters must be recalibrated, i.e., the previous parameters’ values are notvalid anymore. Thirdly, some studies employed a different
setup in DEM than the experiment in the benchmark, whichmight significantly affect the simulation results. For instance, Wu et al. (2013) simulated a virtual factory insteadof a conveyor belt in the original study (Kajiwara et al.,1988). While simplifying real-life scenarios is a common
practice among researchers, it’s crucial to ensure that thevelocity field is accurately mimicked, as it has a significant
impact on segregation.
All in all, the approach of using other work’s results tovalidate the DEM model can have serious shortcomings. In
other words, the model can be validated in this way only if
all the details are accurately documented. This is often
challenging since some data may have been omitted due toconfidentiality. In addition, this approach is limited to specific materials (e.g., glass beads, iron ores), because there isa shortage of experimental data on the segregation of various kinds of granular materials in the literature.

3.4.2 Validation by conducting experiments

Unlike the above-mentioned category which relied on
existing data in the literature, other studies have conducted
experiments themselves and used first-hand experimental
data to validate the DEM model. Two types of experiments
have been carried out (cf. Table 6): the flow-related measurements of the granular materials, i.e., global/bulk behaviour, or segregation-related measurements, i.e., local
behaviour.
Examples of the first type of experiment in whichAhmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

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Table 6 Validation approaches of past DEM studies on segregation (cf. Table 1 for the type of mixture in each study).
Approach for validation
Other work’s results

First-handexperimental results

SourceExperimental(Liao et al., 2023; Wu et al., 2013; T.F.
Zhang et al., 2020; Zhang et al., 2018;
Zhao et al., 2018; Zhao and Chew,
2020b)

DEM(Kumar et al., 2020; Mandal andKhakhar, 2019; Mantravadi and Tan,
2020)

Mathematical(Ketterhagen et al., 2008; Ketterhagenand Hancock, 2010; Yu and Saxén,
2013; Zhao and Chew, 2020a)

Flow-related(bulk behaviour)

Segregation(local behaviour)

The mass fraction of the whole particles discharged
from the hopper(Tao et al., 2013)

The velocity profile of particles using particle tracking
velocimetry (PTV)(Shirsath et al., 2015)

Angle of repose measured in practice(Kou et al., 2015; Tian et al., 2022)

The discharge time of the particles from the hopper(Xu et al., 2017; 2018b)

Burden profile(Kou et al., 2013; Yu and Saxén, 2013)

Mass distribution of “all” particles in the radial direction in furnace throat(Kou et al., 2013)

Laser grid (Gao et al., 2010) measurement of burden
falling trajectories(Zhang et al., 2014)

Bed height(Li et al., 2019)

Invasive

Non-invasive

Sampling spaces composed of 24
cuboid-shaped cells inside thehopper(Combarros Garcia et al., 2016)

The discontinuous start-stopsampling method (Standish andKilic, 1985) has been mostly
adopted(Ketterhagen et al., 2007; Yu and Saxén,
2010; Z. Zhang et al., 2020)

Including a number of samplingboxes at the bottom to capture thetrajectory segregation of materials(Bhattacharya and McCarthy, 2014; Mioet al., 2008b, 2009, 2010, 2012; Yu andSaxén, 2012)

Heaps are divided into a number of
sub-regions in the horizontal and/ orvertical direction(Li et al., 2017; Mio et al., 2020; Qiu
and Pabst, 2022; Terui et al., 2017; Youet al., 2016)

Using rotating sampling table withpie slicing configuration(Cliff et al., 2021)

Measuring mass flow rate of fineparticles using Copley flowability
tester BEP2 model(Barik et al., 2023)

Sampling in the horizontal andvertical direction of the sinter cooler(Izard et al., 2021)

Sampling box in QPM segregation
tester(Combarros et al., 2014)

Taking pictures and counting thenumber of different components(Yu et al., 2018)

Colouring equal-sized components
and using image analysis to analyse
segregation(Combarros et al., 2014)

Taking pictures and using image
processing to measure the fractionof components(Alizadeh et al., 2017; Wang et al.,2018)

physical measurements related to global behaviour havebeen carried out are listed in Table 6. With respect to segregation, this approach fails to provide detailed spatial andtemporal or “local” information about segregation such as
the fraction of different components. Although this approach can be adequately adopted to validate DEM models

89Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

for “flow behaviour” on the bulk level, it cannot necessarily
prove the validity of the model for segregation. In otherwords, to validate the models for segregation, component
fractions are required to obtain local information and assess
segregation.
The second type of the studies relied on segregationrelated measurements. As can be seen in Table 6, there are
two main ways of measuring segregation, namely invasive
and non-invasive methods (Huang and Kuo, 2014). Invasive methods for evaluating segregation simply involve
taking a number of samples, separating the components ineach sample and weighting each component to obtain its
mass fraction. Several sampling methods have been
adopted by researchers for different systems as listed inTable 6. Although all the mentioned invasive methods are
relatively simple and can provide valuable information
about the local composition of granular mixtures, they havea number of disadvantages. First and foremost, invasive
methods disturb the original structure of the mixture which
affects the accuracy of the measurement. Moreover, particles from locations other than the target points might becollected during sampling (Muzzio et al., 1997). And, as
mentioned above, components in each sample must be
separated to further calculate their fractions. This can bestraightforward for components differing in size, however
for multi-component mixtures with overlapping particle
sizes, this can be challenging and time-consuming. Terui etal. (2017) successfully employed the gravity separation
method to separate sinter and coke particles based on thedensity difference. However, besides being time-consuming,
this method is not applicable to all types of materials.
The mentioned drawbacks of invasive methods pushed
the search for non-invasive techniques as an alternative.
Because they do not disturb the granular structure, thesetechniques have been increasingly employed in recent
years, especially in the powder blending industry (Nadeem
and Heindel, 2018). There are several comprehensive review papers in the literature explaining the pros and cons aswell as the applications and limitations of these methods indetail (Asachi et al., 2018; Bowler et al., 2020; Huang andKuo, 2014; Nadeem and Heindel, 2018). Because of theinherent limitation of those techniques, not all of them areapplicable to the segregation analysis of any granular mixture. For instance, the electrical conductivity method,(a)

Review Paper

electrical capacitance tomography (ECT), positron emission particle tracking (PEPT) and magnetic resonance imaging tomography (MRI) cannot be readily utilised toanalyse the mixing and segregation of all multi-component
mixtures (Asachi et al., 2018). This is because the electricalconductivity method and ECT work based on the noticeable difference in electrical conductivity and permittivity,
respectively, which is not the case in all multi-component
mixtures (Asachi et al., 2018; Shenoy et al., 2015). Also, alimited number of tracers is tracked in PEPT, which is notsuitable to measure segregation, and in MRI, the component should be coated with oil to be detectable, whichmight significantly affect its flow behaviour (Stannarius,
2017). Other techniques such as Raman spectroscopy,
near-infrared spectroscopy (NIR) and acoustic emissions
which are feasible for multi-components, are conducted onvery small samples (Bowler et al., 2020), which makes
them unsuitable for coarser particles. Image analysis is another existing non-invasive method which has been widelyused due to its simplicity and low cost. The main advantage
of image analysis over other techniques is that it is applicable to multi-component mixtures. However, this method islimited to surface analysis, it requires a transparent vessel
and the components must differ in colour (Asachi et al.,2018). This method has been successfully employed by anumber of studies to provide experimental results for thecalibration and validation of DEM models of segregation.
Yu et al. (2018) took photos of the heap, divided the heap
into sub-regions radially and counted the number of small
and coarse particles in each region to analyse segregation.
Adopting a more robust approach, Wang et al. (2018) andAlizadeh et al. (2017) employed image processing techniques to measure local concentrations of mixture components at the surface of the heap. To be fully consistent, theyused the same approach in DEM by taking snapshots of the
granular system and applying image processing (seeFig. 4).
In conclusion, all the methods being adopted to validate
DEM models come with a set of advantages and disadvantages. Although the use of existing results in the literaturefacilitates the validation, it is often difficult to precisely
replicate the experimental setup and sometimes, the otherstudy has not itself been validated. Additionally, utilisingexisting results is confined to a few materials which have(b)

Fig. 4 An example of using image analysis to measure segregation in (a) experiments and (b) DEM. Reproduced with permission from Ref. (Alizadeh
et al., 2017). Copyright 2017, Elsevier.

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already been investigated. To overcome these obstacles,
carrying out experiments and using first-hand results would
be beneficial. However, it should be noted that the experimental measurements should provide detailed spatial information for segregation on a local level supplemented by
global level or bulk behaviour such as mass flow rate. The
segregation-related measurements can be performed using
either invasive or non-invasive methods. Image analysis asa non-invasive method is considered superior to invasive
techniques since it not only maintains the structure of the
granular materials but also is applicable to multi-component
mixtures, provided that the components differ in colour.

4. Results of DEM-based studies on
segregation

The work we presented in Table 1 in the previous section will be elaborated in detail here. We gather and analyse
the findings of those studies on how segregation is affectedby adjusting material properties, system configurations andoperational parameters.

4.1 Material properties

Generally, any difference between the particles of a particulate system can cause segregation. For free-flowing
granular materials, it has been proven that particle size,
density and shape affect segregation more than other factors such as surface roughness and elasticity (Tang andPuri, 2004). The studies listed in Table 1 are analysed andthe effect of these factors are presented in the following
paragraphs. To systematically discuss the results in the literature, we present Fig. 5 which illustrates the possiblecombinations of different particle properties and Table 7
which shows the relevant subsection addressing the corresponding type of mixture.

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standing of the effect of particle size and especially particle
size distribution (PSD) on segregation.
In the case of binary-sized mixtures, i.e., mixtures in theform of 1C[(2S/1Sh)], the effect of size distribution can beinvestigated by varying either the size ratio or the massfraction of the components. It is generally accepted that alarger size ratio leads to more pronounced segregation(Bridgwater, 1994). Fig. 6 shows the investigated ranges ofsize ratio in the DEM-based studies of binary-sized
free-flowing mixtures in various systems. For hoppers andin one of the earliest attempts to study segregation using
DEM, Ketterhagen et al. (2007) concluded that the extent
of segregation significantly increases for size ratios greaterthan 1.9. Using the velocity difference between small andlarge particles as an indicator of percolation, T.F. Zhang etal. (2020) investigated the segregation during conical hopper discharging and found that in their case, percolation
was not dominant for size ratios smaller than 6.0 (i.e., corresponding to the velocity difference of 0.07 mm/s). Z.
Zhang et al. (2020) observed that the percolation which
occurred during the discharge of a wedge-shaped hopper
near the wall and the bottom of the hopper was eliminatedSize (A)Density (B)(A+B)(A+B+C)
(A+C)

(B+C)

Shape (C)4.1.1 Particle size

Particle size is unequivocally the most influential property contributing to segregation among the other particle
properties (Williams, 1976). This is why numerous research studies have been dedicated to gaining a full under-

Fig. 5Different possible combinations of particle properties.

Table 7 Corresponding subsection for different types of mixtures.

SectionType of mixture
SymbolNotation

4.1.1 Particle sizeA

1C[(iS/1Sh)]i = 2, 3, …, M, P

4.1.2 Particle density

B

nC[(1S/1Sh) + ⋯ + (1S/1Sh)]

n = 2, 3, etc.

4.1.3 Particle shapeC

1C[(1S/jSh)]

j = 2, 3, etc.

4.1.4 Combinations of particle size,
density and shape(A+B)

nC[(i1S/1Sh) + ⋯ + (inS/1Sh)]

n = 2, 3, etc. & i = 2, 3, …, M, P

(A+C)

1C[(iS/jSh)]i = 2, 3, …, M, P & j = 2, 3, etc.

(B+C)

nC[(1S/j1Sh) + ⋯ + (1S/jnSh)]

n = 2, 3, etc. & j = 2, 3, etc.

(A+B+C)nC[(i1S/j1Sh + ⋯ + (inS/jnSh)]

n = 2, 3, etc. & j = 2, 3, etc.

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SourcePereira and Cleary (2013)
Li et al. (2022)
Xu et al. (2018)
Zhang et al. (2017)
Mandal and Khakhar (2019)
Panda and Tan (2020)
Tripathi and Khakhar (2011)
Tao et al. (2013)
Yu and Saxen (2010)
Xu et al. (2017)
Z. Zhang et al. (2020)
T.F. Zhang et al. (2020)
Ketterhagen et al. (2007)
Ketterhagen et al. (2008)
1

Fig. 6

2

3

4

5

6

8

9

10

11

12

13

14

15

Size ratio

Size ratio ranges in the past DEM studies on segregation.

for a size ratio lower than 1.2. Varying size ratios from 1.3
to 4.0, Yu and Saxén (2010) observed significant segregation even for the minimum ratio of 1.3. Unlike these studies, Ketterhagen et al. (2008) found that in the case of thehopper bottom wall angle of 15° (as opposed to 90°), no
considerable segregation was observed for all size ratios
tested.
In addition to hoppers, several studies have examinedthe effect of size ratio on segregation in other systems suchas inclined planes, chutes, the throat of the blast furnace
and V-blender. In their work on the segregation of binary
mixtures down an inclined plane, Tripathi and Khakhar(2011) found that varying size ratio does not affect the extent of segregation. In the case of a chute, Panda and Tan(2020a) observed that although the stream-wise, crossstream and vertical velocities all increase with increasing
size ratio, the degree of segregation did not increase significantly in the stream-wise direction. Li et al. (2022) studied
the particle segregation in the throat of the blast furnace.
For this, they charged a layer of binary-sized cokes followed by a layer of binary-sized ore particles. Varying thesize ratio equally for coke and ore particles, they observedthat larger size ratios result in a more segregated state,
which was more evident for the coke mixture. In their study
on the de-mixing of binary-sized mixtures during discharge
from a V-blender, Pereira and Cleary (2013) observed thatthe segregation extent becomes significant for large size
ratios, i.e., 3 and 4.

92

7

Apart from the particle size ratio, the mass fraction offine particles has been proven to have a significant impact
on segregation (Tang and Puri, 2004). Artega and Tüzün(1990) proposed a model for binary-sized mixtures to determine the fine mass fraction (xf) for which segregation via
percolation becomes feasible during hopper discharge.
They claimed that percolation no longer occurs when thesurface area of the large spheres is completely covered bysmall spheres, implying that percolation only occurs if themass fraction of finer particles is lower than the limiting
value defined as xL,crit = [4/(4 + ØR)], where ØR is the size
ratio. This model is applicable to free-flowing materials of
approximately spherical shape and is independent of thehopper geometry. While the findings of Ketterhagen et al.
(2008) and Z. Zhang et al. (2020) are in accordance withArtega and Tüzün’s model, Xu et al. (2017) observed segregation for mass fractions higher than the abovementioned xL,crit. Nevertheless, all of these studies agree
that increasing the fine mass fraction decreases segregation(Barik et al., 2023; Ketterhagen et al., 2008; Tian et al.,2022; Xu et al., 2017; Zhang et al., 2018; Z. Zhang et al.,2020).
While in most of the DEM studies a limited number ofparticle sizes has been used, Zhao et al. (2018) and Kumar
et al. (2020) used a continuous distribution of particle size,i.e. poly-sized mixtures with the general form of
1C[(PS/1Sh)]. Zhao et al. (2018) studied the discharge of
mixtures with a log-normal size distribution from a conicalAhmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

hopper. As shown in Fig. 7, they examined the effect of thewidth of size distribution, defined as the ratio of standard
deviation to mean diameter (σ/µ) on segregation and concluded that the higher the width, the greater the size segregation will be. This agrees with observations on the impactof size ratio on the segregation of binary-sized mixtures, as
the size ratio in binary-sized mixtures is essentially thesame as the width of size distribution in poly-sized mixtures.
In another study, Kumar et al. (2020) studied the discharge from a hopper of mixtures with the Rosin-Rammler
size distribution as follows:
n

 x 
R
1  exp    
  x  (2)with size distribution’s width (n) and location parameters(x′). It was found that the width has more impact on the(2)segregation than location parameters. Moreover, they conducted a comparative analysis between the log-normal and
Rosin-Rammler particle size distribution (PSD) with thesame width and observed that the latter showed a higher
segregation extent. They also reported that the extent ofsegregation is increased with more fine particles which
contradicts the observations in past studies on the segregation of binary-sized mixtures (Ketterhagen et al., 2008; Xuet al., 2017; Zhang et al., 2018; Z. Zhang et al., 2020). This
conflict may lie in the fact that comparing a continuous size
distribution with a binary-sized mixture requires selectionof the right cut-off size between fine and coarse particles,
which is not always a trivial task.
To date, a large number of segregation-related studies
focused on the effect of particle size and mass fraction inbinary-sized mixtures. Generally, it has been found that
reducing the size ratio as well as increasing the mass fraction of fine particles lessen segregation. With respect to thesize ratio, however, there is no unanimous agreement either
on the critical size ratio which promotes segregation or on

Fig. 7 Different log-normal size distributions. Reproduced with permission from Ref. (Zhao et al., 2018). Copyright 2017, John Wiley and
Sons.

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the effect of increasing the size ratio on segregation in
general. Overall, there is no single limiting size ratio above
which segregation happens. Also, there is no unanimous
agreement on the effect of increasing the size ratio on segregation. The reason behind this is twofold. Firstly, apartfrom size ratio, other factors such as material properties(e.g., mass fraction), geometric, and operational parameters
also play a crucial role in segregation phenomena. Secondly, percolation rate measurements in shear cells haveshown that segregation can occur for all size ratios, andlarger size ratios only serve to accelerate its occurrence(Bridgwater, 1994; Bridgwater et al., 1978; Tang and Puri,2005). Similar to the size ratio, the effect of the mass ratioof fine particles on segregation is not consistent, leading to
the conclusion that the results of one study cannot be applied to all cases since other factors (e.g., geometrical andoperational parameters) play a key role as well.

4.1.2 Particle density

The difference in particle density is another known factor to cause segregation. As mentioned in Section 2, thedensity of particles in a single material is assumed to beconstant. Therefore, the difference in particle density isequivalent to having different materials. For instance, what
is referred to as a “binary-density” mixture is basically a
two-component mixture with the notation of
2C[(1S/1Sh) + (1S/1Sh)].
While a number of earliest experimental studies on the
mixing and segregation of granular materials argued thatparticle density does not affect segregation considerably(Vallance and Savage, 2000; Williams, 1976), some otherssuggested that the difference in density should be taken into
account in segregation analysis (Bridgwater et al., 1978;
Drahun and Bridgwater, 1983; Khakhar et al., 1999). There
has been, however, little systematic analysis of densitydriven segregation in free-flowing granular materials using
DEM. Seil et al. (2012) investigated the segregation of
mixtures discharging from two hoppers. They observedthat the heavier particles tend to gather in the centre areas
and that the extent of segregation is linear to the density
ratio. In another study on an inclined plane, Tripathi and
Khakhar (2013) observed that heavy particles sink to near
the base, and light particles form a layer close to the freesurface with a mixed region in between, which was confirmed in other studies (Panda and Tan, 2020a; Wang et al.,2018, 2023). Furthermore, they showed that higher density
ratios result in stronger segregation.
In conclusion, particle density has been shown to affectsegregation to some extent. However, the significance of its
effect compared to other factors such as the difference inparticle size is unclear as yet. More effort on densityinduced segregation in various systems is required to pave
the way for developing DEM models for multi-component
segregation.

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4.1.3 Particle shape

Only a few of the studies found focused on modelling theeffect of particle shape on the extent of segregation. Thesestudies can be classified into two groups. The first group
includes studies in which the mixtures are composed of two
particle shapes to investigate shape-induced segregation.
Using the multi-sphere approach, Mandal and Khakhar(2019) studied the flow of mixtures of spherical andnon-spherical particles of the same volume down a roughinclined plane (cf. Fig. 8). They found that the geometricmean diameter (i.e., dg = λ1/3d, where λ is the aspect ratio
and d is the diameter of the constituent spheres) is a good
representative of the effective size for irregularly shaped
particles, and the extent of segregation of two species depends on the ratio of the geometric mean diameter of particles. Moreover, it was observed that at the steady state, theparticles with a larger geometric mean diameter accumulate near the free surface.
Zhao and Chew (2020a) studied the flow behaviour ofbinary-shaped mixtures during discharge from a hopper.
They modelled seven different particle shapes (a sphere,
two ellipsoids, two cylinders, and two cuboids (as shown in
Fig. 9), all with an aspect ratio between 0 and 2 and thesame volume. Considering various combinations of theseparticle shapes, they observed that mixtures of cylinders
and cuboids exhibited the least segregation and the ellipsoids mixed with cylinders were the most segregated mixtures.
Second, studies in which the segregation behaviour ofdifferent mixtures, each component composed of a particular shape, is analysed. Zhao and Chew (2020b) compared
the discharging behaviour of mixtures with log-normal
PSD composed of ellipsoids, cylinders or cuboids and observed that ellipsoids show a very similar segregation behaviour to spheres. The cuboids and ellipsoids exhibit thelowest and the highest segregation extent, respectively.
Concluding, very little attention has been paid toshape-induced segregation in DEM-based studies. Past
studies have demonstrated that the difference in particle
shape itself can induce segregation (Mandal and Khakhar,

Fig. 8 Different combinations of spherical and non-spherical particles.
Reproduced with permission from Ref. (Mandal and Khakhar, 2019).
Copyright 2019, AIP Publishing.

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2019; Zhao and Chew, 2020a). However, it is unclear how
particle shape induces segregation and specifically, whichshape characteristics are more significant for segregation.
Therefore, further work needs to be done to shed light on
shape-induced segregation. Challenging herein are thecomputational expense of irregularly shaped particles andthe fact that particle shape is not independent of particle
size. This is very important since if only the segregationinduced by particle shape is of interest, the “effective”
particle size should be the same to avoid misinterpretation(Shekhar et al., 2023). Therefore, we second that attempts
should be made to establish the criteria for finding the “effective” size of non-spherical particles for segregation (Yuet al., 2020).

4.1.4 Combinations of particle size, density andshape

While the preceding subsections were focused on studies
examining variations in a single particle property (such assize, density, or shape), a limited number of research works
investigated mixtures with multiple properties that vary simultaneously. To systematically discuss them, these studies
are grouped below using the symbols in Fig. 5 and Table 7:(A+B). Most of the studies varied the particle size anddensity at the same time. Two different scenarios can be
distinguished: 1. a mixture of (small/heavy) and (large/
light) particles, in which the percolation and buoyancy
mechanisms work together, or, 2. a mixture composed ofsmall/light and large/heavy particles where percolation andbuoyancy mechanisms oppose each other. Fig. 10 illustrates these two scenarios. As can be seen in Fig. 10(a), in
the first scenario, both percolation and buoyancy mechanisms act together and (small/heavy) particles sink into the
mixture. However, in the second scenario as in Fig. 10(b),the dominant segregation mechanism is not known a priori
and should be studied case by case. Past studies indicatedthat in the first scenario, reducing the size ratio and/or
density ratio leads to less segregation and could be used asa way to suppress segregation (Terui et al., 2017; Xu et al.,2017, 2018a). For the second scenario, Ketterhagen et al.

(a)

(b)

(d)

(e)

(f)

(g)

(c)

Fig. 9 Different particle shapes modelled. Reproduced with permission from Ref. (Zhao and Chew, 2020a). Copyright 2020, John Wileyand Sons.

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107(a)Review Paper(b)

Fig. 10 Two scenarios for particles differing in both size and density (solid black colour indicates heavier particles), (a) first scenario; a mixture of(small/heavy) and (large/light) particles, and (b) second scenario; a mixture of (small/light) and (large/heavy) particles.

(2008) varied the density ratio from 0.33 to 3.0 for a size
ratio of 4.3, and no significant segregation was detectedduring discharge from a wedge-shaped hopper. Similarly,
Tao et al. (2013) increased the density ratio from 0.5 to 2.0
for a mixture with a size ratio of 4.0 and observed that
segregation was negligible.
(A+C). In attempts to explore the simultaneous effect ofparticle size and shape on segregation, several studies varied the particle shape of binary-sized mixtures, i.e. mixtures in the form of 1C[(2S/2Sh)]. Mandal and Khakhar(2019) studied the flow of mixtures of non-spherical particles where the two particle types differ in both volume (i.e.,size) and shape, and observed that the segregation extent
increases for larger geometric mean diameter ratios. Tao etal. (2013) simulated binary-sized mixtures with different
particle shapes including spherical, corn-shaped, cylindrical and ellipsoidal particles. It was observed that particle
shape has a significant effect on the segregation extent withthe most segregated system for ellipsoidal and the least for
spherical particles.
(A+B+C). A few studies examined the segregation in
mixtures differing in particle size, density and shape. Liaoet al. (2023) investigated the segregation behaviour oflump ore and pellets that differed in size and density. To
examine the impact of particle shape on segregation, they
simulated three different shapes of lump ore particles,
namely spheres, cylinders, and schistous particles andconcluded that particle shape has a significant effect onsegregation, with spheres showing the least amount ofsegregation. In the mixture of sinter and briquette, Kim etal. (2020) changed both the shape and mass ratio of thebriquette and observed that while the shape had little effecton segregation, a mass ratio higher than 20 % led to significant segregation.
Despite previous efforts to investigate the influence ofvarious material properties on segregation, there remains asignificant knowledge gap. While the majority of naturaland industrial mixtures are multi-component, i.e. (A+B+C)
in Table 7, most studies have concentrated on binary or
ternary-sized mixtures consisting of spheres. Therefore, amore comprehensive and rigorous approach towards material properties that takes various combinations of size,
density and shape into account is required to accurately

predict segregation patterns and occurrences.

4.2 System configurations

In addition to the material properties, system parameters
play a key role in the segregation phenomena occurring inparticulate systems because they influence the motion of
the particles. In gravity-driven handling processes, twomain pieces of equipment can be distinguished: hopper/siloand chute/inclined surfaces. A review of these systems in
the context of segregation is presented in the following
paragraphs.

4.2.1 Hopper

Segregation during hopper discharge has been studied
since the early ’80s when most studies were experimental.
Ketterhagen et al. (2007) were one of the first to model
segregation during cylindrical hopper discharge using
DEM in 2007. Since then, a number of DEM studies haveinvestigated the influence of geometric properties on theextent of segregation by varying the outlet width, the slopeof the bottom wall and the smoothness of the walls.
Two main flow regimes are distinguished during hopper
discharge: mass flow, where the entire body of mass inside
the hopper is in motion during discharge, and funnel flow
(Jenike, 1967; Saleh et al., 2018; Schulze et al., 2008);
funnel flow is less desirable as it imposes asymmetric pressure distribution and causes significant segregation which
can be harmful to both hopper structural stability and particulate homogeneity (Saleh et al., 2018). The flow mode ismainly dependent on the bottom wall angle (θc) and wall
friction angle (ϕx). Smoother and steeper walls (i.e., low θc)most likely induce a mass flow regime and are less likely to
induce segregation. This was also confirmed in several
DEM studies (Ketterhagen et al., 2009, 2008; Saleh et al.,2018; Yu and Saxén, 2010).
Several studies used DEM to gain a deeper insight into
segregation during hopper discharge. T.F. Zhang et al.
(2020) varied the angle of the hopper wall from 15° to 90°
and observed that larger angles (i.e., flatter walls) lead tofaster percolation of small particles, increasing their tendency to gather at side walls (cf. Fig. 11(a)). Similarly, Z.
Zhang et al. (2020) showed that the increase in the hopper
angle promotes segregation in the bottom regions (red95Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

regions in Fig. 11(b)). However, they found that with larger
angles, the percolation near the wall region decreases. Thisdisagreement might be because of the different size ratio,wall condition (i.e. boundary walls in T.F. Zhang et al.
(2020)), or a different method of assessing segregation (cf.
Section 3.2). Also, it is remarkable that in T.F. Zhang et al.
(2020), the hopper outlet size of 3dl (where dl is the diameter of the large particles) is used, which does not satisfy
fundamental hopper design principles (Schulze et al.,2008) and can affect the segregation results.
Stating that the bottom angle of the hopper is a decisive
factor in the flow mode, Huang et al. (2022) proposed an
optimised design which increases the mass flow zone andas a result, achieves less size segregation (cf. Fig. 12).
In addition to the hopper wall angle, the outlet size can

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also affect segregation during hopper discharge.
Ketterhagen et al. (2008) found that the increase in outlet
width reduces the segregation of mixtures discharging fromwedge-shaped hoppers. Although Z. Zhang et al. (2020)
confirmed that the percolation in the bottom part of thehopper reduces in the case of a bigger outlet, a higher segregation degree near hopper walls was observed which canbe related to wall effects. Taking the effect of both the
hopper outlet and wall angle into account, Ketterhagen etal. (2008) claimed that segregation conditions remain thesame for hoppers with the same aspect ratio of hopper
height to hopper width.
To investigate the effect of flow-correcting inserts, Cliffet al. (2021) compared the segregation behaviour ofbinary-sized mixtures during discharge from a hopper with

(a)

(b)

Fig. 11 The effect of hopper wall angle on size segregation in two studies: (a) Reproduced with permission from Ref. (T.F. Zhang et al., 2020). Copyright 2020, Elsevier. and (b) Reproduced from Ref. Z. Zhang et al. (2020), used under Creative Commons CC-BY-NC-ND License.

Fig. 12 The effect of different hopper design on segregation during hopper discharge at different discharging fractions (md /mt). Reproduced withpermission from Ref. (Huang et al., 2022). Copyright 2022, Elsevier.

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and without an insert (cf. Fig. 13(a)). They stated that in
the case of a hopper with an insert, lower velocity gradientsat the free surface lead to less segregation (cf. Fig. 13(b)).
While most of the works mentioned above studied concentric hoppers, Ketterhagen and Hancock (2010) compared the segregation of binary-sized mixtures in eccentricand concentric hoppers (cf. Fig. 14(a)). They found thatwhile the eccentric hopper had slightly less segregation, thesegregation profiles around the outlet were completely different (cf. Fig. 14(b)). Additionally, they modified the design of the eccentric hopper, resulting in improved
segregation.
In conclusion, designing for mass flow is beneficial to
avoid size segregation during hopper discharge. This canbe achieved by steeper and smoother walls. However, several studies observed insignificant effects of these factors
on segregation. The reason for the conflict between thesestudies is that besides geometric parameters, material properties and operational parameters can influence particle
velocities and segregation. This makes it challenging tocompare the findings of different studies and arrive at adefinitive conclusion. Therefore, a more robust approach is(a)Review Paper

needed to investigate the simultaneous effect of different
geometric factors on segregation and its dependence onmaterial properties. Moreover, the work found evaluatedonly single-component mixtures, and the effect of geometric design parameters in the case of multi-component segregation is missing.

4.2.2 Chutes

Chutes are often used to transfer materials from one
piece of equipment to another. They can have different
cross-sectional shapes (e.g., semicircular, rectangular),lengths, curvatures and wall roughness. While the chuteangle may be regarded as a system configuration parameterin certain cases, e.g., transfer chutes, it is considered an
operational parameter in dynamic systems such as blast
furnaces. Therefore, we will be discussing the chute angle
later in Section 4.3.2 of this paper.
Segregation can occur simultaneously in three directions: in the direction of the flow (longitudinal/stream-wise
segregation), from the chute to the free surface (vertical/
normal segregation) and from one side wall to the other(horizontal/cross-stream-wise segregation). During chute(b)

Fig. 13 (a) The cross-sectional view of the hopper with insert, (b) Difference in the velocity gradient between the hopper with (right) and without(left) insert. Reproduced with permission from Ref. (Cliff et al., 2021). Copyright 2021, The Royal Society.

(a)

(b)

Fig. 14 (a) Concentric and eccentric hopper, and (b) Segregation profile in the eccentric hopper. Reproduced with permission from Ref. (Ketterhagenand Hancock, 2010). Copyright 2010, Elsevier.

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flow, segregation can happen in two stages. Firstly whenthe material is inside the chute, gravitational forces, centrifugal and Coriolis forces, inter-particle friction and particlewall friction forces act on the particles (Zhang et al., 2014).
Mio et al. (2008b) found that particles segregate inside thechute with smaller particles at the bottom wall of the chuteand larger ones on top. In the second stage, the material
leaves the chute and trajectory segregation causes larger
particles to travel further (Mio et al., 2008b). The effect ofchute-related geometric parameters on segregation was researched by employing either a simple inclined plane(Zhao et al., 2022) (to minimise the effect of side walls) ora real 3D chute (Panda and Tan, 2020a).
With respect to the effect of the cross-sectional shape ofthe chute, Kou et al. (2019) investigated the effect of threedifferent shapes, namely semi-circular, trapezoid, and rectangular on the segregation of ferrous burden (i.e., pellet,
sinter and lump ore) forming a heap in the blast furnace
throat. They found that the chute cross-sectional shapesignificantly affects the radial size distribution of materials
and the effect is intensified with larger chute angles andhigher rotating speeds. Moreover, unlike trapezoid andrectangular chutes, the semi-circular chute was reported tomaintain a uniform radial size distribution regardless of thechute angle and rotating speed. Panda and Tan (2020a;
2020b) focused on the segregation inside the chute andcompared the segregation between rectangular andsemi-circular chutes. It was observed that the rectangularchannel resulted in slightly higher segregation in thestream-wise direction, while there were barely any differences in cross-stream and vertical directions.
In terms of the impact of chute length, the observations
revealed that since segregation occurs within the chute, anincrease in the chute length leads to a greater extent ofsegregation (Bhattacharya and McCarthy, 2014; Kou et al.,2013; Yu and Saxén, 2012; Zhang et al., 2014). Bhattacharya
and McCarthy (2014) determined the critical chute length
for trajectory segregation of ternary-sized mixtures discharging from a hopper on to the chute, using both theoretical equations and simulations which yielded 4.0 and
5.4 m, respectively. They claimed that with other parameters fixed, there is significant segregation in the samplingboxes located under the chute for the chute longer than thecritical length. For the radial segregation of multicomponent mixtures of sinter, pellet and ore, Hong et al.
(2021) observed that the effect of chute length is negligible.
Mantravadi and Tan (2020) examined the effect of bend
angle (i.e. curvature) in periodic flow inversions (seeFig. 15)—an idea initially introduced by Shi et al. (2007)—
on reducing the size segregation of binary-sized mixturesand observed that a bend angle between 120 and 150 degrees leads to the minimum extent of segregation.
Zhou et al. (2016) varied the wall roughness by using thewall friction coefficient and found that a higher base rough-98

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Fig. 15 The periodic flow inversion with a bend angle of θ. Reproduced with permission from Ref. (Mantravadi and Tan, 2020). Copyright 2020, Elsevier.

ness significantly accelerates segregation. However, in thecase that base friction was larger than inter-particle friction,no effect on the flow regime and segregation was observed.
Shirsath et al. (2015) studied the effect of chute wall roughness on the segregation of binary-density mixtures, i.e.,2C[(1S/1Sh) + (1S/1Sh)]. They implemented the roughness
artificially by modelling a number of particles at chutewalls, attributing the ratio of the diameter between surface
particles and flowing particles (ranging from 0.5 to 2.0) to
the degree of roughness. They reported that wall roughness
has a considerable effect on segregation as it is negligible
for the smooth (but frictional) chute wall. While it was observed that the degree of segregation increases with wallroughness, this increase plateaus when the diameter of wallparticles exceeds the flowing particle size. However, it isunclear to what extent the method of modelling wall roughness in this work is representative for roughness at micro
scale.
Concluding, the geometric aspects of chutes including
cross-section shape, length, curvature and wall roughness
can significantly influence segregation. Generally, it has
been found that longer chutes with rougher walls promotesegregation.

4.3 Operational parameters

Besides the material properties and system configurations discussed above, operational parameters are also
among the influencing factors on segregation. In order togain a deeper insight, related studies are reviewed in thefollowing subsections under different categories based on
the system type, namely hopper, chute and other systems.

4.3.1 Hopper filling operations

Various hopper-related operational parameters, including filling method, filling angle, and filling position havebeen studied in the past. Several studies investigated theAhmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

influence of various hopper filling methods, i.e., different
components/sizes being charged in separate layers or in a
pre-mixed state, on the segregation after hopper discharge(Chibwe et al., 2020; Ketterhagen et al., 2008, 2007; Mio etal., 2010; Yu and Saxén, 2010). Although Ketterhagen et al.
(2008), Yu and Saxén (2010) and Chibwe et al. (2020) usedslightly different setups and filling patterns, their conclusion was that the initial filling method significantly affects
the segregation.
Apart from the filling method, the filling position was
found to significantly influence segregation. Wu et al.
(2013) investigated the effect of burden apex (i.e., the highest point on the hill formed when materials are charged into
the hopper) on the segregation during hopper charging anddischarging. It was found that the burden apex mostly influences the particle size distribution during charging but has
little effect during discharging. In the case of a Paul-Wurthhopper, Zhang et al. (2021) also observed that the filling
position has a significant effect on the segregation and there
was strong segregation in the case of the right and left filling positions (cf. Fig. 16(a)). Moreover, they observed thata filling angle closer to the vertical position (cf. Fig. 16(b))

Review Paperand a larger charging outlet (cf. Fig. 16(c)) reduced theextent of segregation.
Cliff et al. (2021) showed that a continuous filling operation, as opposed to batch processing, significantly reduces
the amount of segregation because segregation mostly occurs in the last 25 % of hopper discharge.
Concluding, the filling method, filling position and filling angle significantly affect segregation downstream (i.e.,during hopper discharge). This means that all the handlingsteps should be accurately modelled in order to achieve a
reliable assessment of segregation.

4.3.2 Chute operations

With respect to chute-related operational parameters,
several studies have investigated the effect of chute angle(θ) (Jing et al., 2015; Kou et al., 2019, 2018, 2013; Mio etal., 2009; Nakano et al., 2012; Tripathi and Khakhar, 2011;
Xu et al., 2018b; You et al., 2016; Zhang et al., 2014), tilting direction (Terui et al., 2017), rotating speed (ω) (Kouet al., 2019, 2013; You et al., 2016), fill level (Izard et al.,2021; Panda and Tan, 2020a), the number of chute rotations(Mio et al., 2012) and installation of a damper (Mio et al.,

(a)

(b)

(c)

Fig. 16 The effect of (a) filling position, (b) filling angle and (c) hopper outlet on segregation during hopper charging. Reproduced with permission
from Ref. (Zhang et al., 2021). Copyright 2021, Elsevier.

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2008b). Some of the parameters are schematically illustrated in Fig. 17.
It should be noted that in a rotating chute, particles are
subjected not only to gravitational forces but also to Coriolis and centrifugal forces (Shirsath et al., 2015). The relative impact of inertial and Coriolis forces is quantified by
the Rossby number (Eqn. (3)):

Ro 

νa
2ωL cos θ(3)where νa, ω, L, and θ denote the flow velocity, chute rotation rate, chute length and chute angle, respectively. In the(3)of Ro ≫ 1, the effect of the rotation rate can be necase
glected. For instance, Shirsath et al. (2015) varied the chuterotation rate from 4 to 16 rpm and observed Ro larger than1, concluding that the flow was gravity driven. Also, theFroude number (Eqn. (4)) can be used to quantify the relative significance of the centrifugal force compared to thegravitational force (Shirsath et al., 2015):
Fr 

ω 2 L cos θg(4)

In the case of Fr ≪ 1, the gravitational force is dominant,preventing the particles from escaping out of the chute.
(4)
The chute angle is an important flow control parameteras it is used to increase or decrease the flow velocity. Past
DEM studies have investigated the effect of chute angle on
the segregation occurring either inside the chute or whenparticles flow out and form a heap. Regarding the segregation happening inside the chute, Tripathi and Khakhar(2011) observed that the shear rate and extent of segregation in a vertical direction increased with a lower angle.
Similarly, Jing et al. (2015) found that segregation occurs
faster as the angle decreases, which was expected since therate of acceleration increases as the chute is steeper. However, the final (converged) segregation degree was similar

Review Paper

for different chute angles and, surprisingly, the converged
state was reached at approximately the same time for different angles.
For segregation occurring after discharging from chutes,
several studies have found that reducing the chute angle
intensifies the segregation in different systems, namely
blast furnace throat (Kou et al., 2019; Mio et al., 2009; Xuet al., 2018b; Zhang et al., 2014), feed bed of sinteringmachine (Nakano et al., 2012), COREX melter gasifier(Kou et al., 2015, 2013; You et al., 2016). However, Zhang
et al. (2014) varied the chute angle from 21° to 37° andobserved that the radial segregation index (RSI) would notnecessarily decrease (cf. Fig. 18). Also, Kou et al. (2013)
observed that increasing the chute angle from 10° to 30°
first leads to a decrease of segregation of large particles andthen to an increase.
In modern blast furnaces equipped with the Paul-Wurth
Bell-Less Top® charging system, as well as in the COREX
process, a rotating chute is used to distribute materials in
the circumferential directions of the furnace throat(Cameron et al., 2019; Kou et al., 2013). In addition to thepreviously discussed chute angle, other operational parameters such as the rotating speed, tilting direction (i.e. changing the angle from low to high or vice versa as shown in
Fig. 17), and the number of rotations have been studied
with respect to segregation. Terui et al. (2017) modelled amixture of coke and sinter and two types of tilting directions, namely conventional tilting (i.e., from wall to centre
direction) and reverse tilting (i.e., from centre to the wall
direction). They found that with reverse tilting, coke particles are less segregated as shown in Fig. 19. The rotating
speed of the chute was found to have a negligible effect onsegregation compared to the chute angle (Kou et al., 2019,2013; You et al., 2016). Mio et al. (2012) observed that anincrease in the number of chute rotations intensifies
segregation, leading to a larger mean particle size near theθReverse tilting
Conventional tilting

Fig. 17 Schematic view of a rotating chute and related operational parameters: chute angle (θ), rotating speed (ω).

100

Fig. 18 Change in radial segregation index (RSI) with different chuteangle. Reproduced with permission from Ref. (Zhang et al., 2014).
Copyright 2014, Elsevier.

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

centre of the blast furnace.
The volume (or mass) flow rate through a chute is another operational parameter which is generally varied on an
on-demand basis. For a given chute design, increasing/decreasing the flow rate results in a higher/lower fill levelof the chute. Panda and Tan (2020a) found that the increasein the fill level causes the small and large particles to move
together, i.e., decreases the velocity difference, resulting inlower segregation in the cross-stream and vertical directions of the chute. Although it was observed that the filllevel does not affect velocities in a stream-wise direction,
segregation was decreased because of less available spacefor percolation due to denser packing. In a study on sintercooler charging systems, Izard et al. (2021) investigated theeffect of the fill level of the chute (i.e. 20 %, 50 % and
90 %) on the segregation pattern in the trolleys and foundthat while a filling ratio smaller than 50 % imposes an increasing size heterogeneity in the trolley, no change in the
segregation pattern was observed for a filling ratio larger
than 50 %.
In conclusion, the operational parameters for operating
moving chutes including chute angle, tilting direction, rotating speed and fill level, affect the segregation to someextent. Based on the studies reviewed here, it appears thatchute angle and fill level may be more significant than theother parameters. However, further research is needed toconfirm this hypothesis and to fully understand the complex nature of segregation in granular mixtures discharging
from moving chutes. Nevertheless, all the parameters havethe potential to be adjusted with the purpose of reducing or(a)Review Paper

controlling segregation.

4.3.3 Other systems

In addition to hoppers and chutes, other operationrelated factors have been investigated in the context of thegravity-driven segregation of cohesionless materials. This
includes the falling height in the stockpile (Kou et al.,2013; Zhang et al., 2017), the method of “discharge” froma v-blender (which is essentially a gravity-driven flow)(Pereira and Cleary, 2013), the bottom base shape of the
COREX furnace (cf. Fig. 20) (Kou et al., 2018) and feed
rate as well as rill plate angle in an iron ore sinter strand (Liet al., 2019). Hence, in every industrial system, there areseveral operational parameters that can affect segregation.
Detecting these parameters and studying their effect utilising DEM can pave the way to optimise the industrial processes with respect to segregation. This is highly
advantageous because, in industrial applications, it is notalways possible to control segregation by modifying material properties and system configurations.

5. Conclusion

This review paper presents an overview of the state of
the art in the DEM modelling of the segregation behaviourof complex multi-component mixtures in applications involving the gravity-driven flow of dry, cohesionless granular materials. First of all, a novel scientific notation has
been introduced to ensure consistency and accurately describe different types of mixtures. The main findings of thisreview can be summarised as follows:(b)Fig. 19 Cross-sectional view of the radial distribution of a coke-sinter mixture for (a) reverse tilting and (b) conventional tilting. Reproduced withpermission from Ref. (Terui et al., 2017), used under Creative Commons CC-BY-NC-ND License.

(a)Fig. 20

(b)

(c)

(d)

Different bottom base shape of the COREX furnace. Reproduced with permission from Ref. (Kou et al., 2018). Copyright 2018, Elsevier.

101Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107● Although most mixtures existing in nature and industrial
settings are considered multi-component (i.e., they contain particles differing simultaneously in size, density
and shape), relatively few studies have investigated thesegregation behaviour of such mixtures.
● Despite the fact that calibration is an essential part ofdeveloping a reliable DEM model, most of the past studies have omitted it. This is especially true when it comesto models of multi-component mixtures. For instance,
the effect of interaction parameters between components
has not yet been studied in detail. Furthermore, it is notclear whether calibrated parameter values obtained for acertain mixture composition are applicable to other compositions as well.
● The effect of size distribution and particle shape on segregation has not yet been fully understood. Considering
the fact that including small particles and irregular particle shapes in the model increases computational time
considerably, the effect of approximating real-world
mixtures with a finite number of particle sizes and spherical particles should be comprehensively studied.
● When quantifying segregation experimentally, noninvasive techniques such as image analysis are generally
more suitable than conventional invasive methods (i.e.,sampling and weighing), since non-invasive techniques
do not disturb the mixture structure and are applicable tomixtures differing in both size and density.
● Results of past DEM studies on segregation show thatthe effects of different parameters influencing segregation, i.e., material properties, system configurations andoperational parameters, should be considered together
since they are highly intertwined.
● Compared to material properties and system configurations, operational parameters are relatively easier tochange. Hence, to reduce and control segregation in industrial applications, operational parameters are preferential.

Acknowledgements

This research was carried out under project number
T18019 in the framework of the Research Program of theMaterials innovation institute (M2i) (www.m2i.nl) supported by the Dutch government. Also, this work was carried out as part of the “Industrial Dense Granular Flows”
project, which received funding from the Dutch ResearchCouncil (NWO) in the framework of the ENW PPP Fundfor the top sectors and from the Ministry of Economic Affairs in the framework of the “PPS-Toeslagregeling”.
The authors would like to extend their acknowledgements to dr.ir. Jan van der Stel and dr.ir. Allert Adema from
Tata Steel IJmuiden for their invaluable contributions tothis review paper.

102

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In Table 2, we summarized the segregation indices usedin the literature. These indices quantify to which degree thetracer particle is homogeneously distributed within the
mixture. For indices no. 1–8, the concentration of tracer

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particles is determined in each sub-domain, which can be
expressed in terms of the number, mass or volume fraction,
as indicated in Table A1.

Table A1 Equations for calculating the concentration of different types of particles (type-A or type-B) on number, mass and volume basis. Legend: N =number of particles, m = mass of particles, V = volume of particles.

Number-based
Fraction of particle type-A

NA
N
where N = NA + NB

N  A N

Mass-basedmm  A  A
mVolume-based

VA
V
where V = VA + VBV  A 

where m = mA + mB

mB
m

V

Fraction of particle type-B

N  B  B[11]Nm  B 
[12]

Total (number/mass/volume) fraction

ϕN = (ϕN)A + (ϕN)B = 1

ϕm = (ϕm)A + (ϕm)B = 1

ϕV = (ϕV)A + (ϕV)B = 1[15] m t i[16]  Vt i

Relative fraction of tracer particle in bin i[14] N  i


yti

t

N t iNwhere t = A or BN tRelative fraction of tracer particle in the entireyt  N t[17]
system
N

106


xti

 m  tmwhere t = A or B
xt 
[18]

m t m tmV  B  B
[13]
V

i

cti 

V
where t = A or B

V t
ct 
[19]

V

where t = A or B

where t = A or B

where t = A or B

[20]

[21]

[22]

Ahmed Hadi et al. / KONA Powder and Particle Journal No. 41 (2024) 78–107

Review Paper

Authors’ Short Biographies
Ahmed Hadi
Ahmed Hadi earned a BSc in Civil Engineering from Isfahan University of Technology in 2015.
He then pursued an MSc in Geotechnical Engineering at the University of Tehran, graduating in
2018. During his master’s degree, he specialised in the DEM modelling of shear banding in granularsoils. Currently, Ahmed Hadi is a PhD candidate at the GranChaMlab@TUDelft. His current research project focuses on the DEM modelling of multi-component segregation in the blast furnace
charging system.

Raïsa RoeplalRaïsa Roeplal pursued a master’s degree in Mechanical Engineering at the University of Twente
in 2016. During that time, she studied the mixing of cohesive particles in paddle mixers and became
fascinated with the segregation behaviour of granular materials. She joined the GranChaMlab@
TUDelft at Delft University of Technology in 2020, in pursuit of her PhD degree. She is currently
developing a DEM model to study the flow and packing behaviour of blast furnace mixtures during
furnace charging. Her main focus areas in this regard are: high-velocity dense granular flows, industrial granular flow modelling, multi-component segregation and mixture calibration.

Yusong Pang
Yusong Pang received his MSc degree in Electrical Engineering in 1996. In 2000 he started working at Practic B.V. and Seaview B.V., the Netherlands, for industrial production life cycle management. After his PhD research of intelligent belt conveyor monitoring and control in 2007, he was
employed by the Advisory Group Industrial Installations of Royal Haskoning, the Netherlands, as anexpert in material handling. In 2010 he was appointed Assistant Professor in the section of Transport
Engineering and Logistics, Delft University of Technology, the Netherlands. His research focuses on
the intelligent control for large-scale material handling systems and logistics processes.

Dingena Schott
Dingena Schott is a Full Professor at TU Delft on Machine Cargo Interaction Engineering. She
obtained her PhD in 2004 from TU Delft on the homogenisation of bulk materials in mammoth silos.
In 2007 she started the GranChaMlab@TUDelft to characterise, model, calibrate and validate granular materials for enabling the simulation-supported design of cargo handling equipment on an industrial scale. Since then, she has worked on developing calibration frameworks and modelling
particle-based systems in various design contexts; including terminal designs for particulate materials, as well as an award-winning new grab design. Her main research interests include: machine-cargointerfaces, simulation-supported design, biomass materials and energy-transition-driven handlingand logistics.

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