minerals

Review
Jigging: A Review of Fundamentals and
Future Directions
Weslei M. Ambrós
Mineral Processing Laboratory, Federal University of Rio Grande do Sul, 9500 Bento Gonçalves Avenue,
Porto Alegre 91501-970, Brazil; weslei.ambros@ufrgs.br




Received: 19 October 2020; Accepted: 9 November 2020; Published: 10 November 2020


Abstract: For centuries, jigging has been a workhorse of the mineral processing industry. Recently,
it has also found its way into the recycling industry, and the increasing concerns related to water
usage has led to a renewed interest in dry jigging. However, the current scenario of increasing
ore complexity and the advent of smart sensor technologies, such as sensor-based sorting (SBS),
has established increasingly challenging levels for traditional concentration methods, such as jigging.
Against this background, the current review attempts to summarize and refresh the key aspects and
concepts about jigging available in the literature. The configuration, operational features, applications,
types, and theoretical models of jigging are comprehensively reviewed. Three promising paths for
future research are presented: (1) using and adapting concepts from granular physics in fundamental
studies about the stratification phenomena in jigs; (2) implementing advanced control functions by
using machine vision and multivariate data analysis and; (3) further studies to unlock the potential of
dry jigs. Pursuing these and other innovations are becoming increasingly essential to keep the role of
jigging as a valuable tool in future industry.

Keywords: jigging; gravity concentration; stratification; mineral processing; recycling

1. Introduction
Jigging is one of the oldest methods of ore treatment and remains one of the workhorses of the
mining industry. Until recently, it was, together with dense medium separation (DMS), the main
(when not the only) choice for pre-concentration and concentration of coarse-sized ores and coals.
Jigging has also currently exceeded the limits of mineral processing, having found applications in
different recycling industries, and growing concern related to water usage has led to a renewed interest
in the use of dry jigging.
On the other hand, the current scenario of decreasing ore grades and the recent developments in
sensor-based sorting (SBS) technologies have established increasingly challenging levels of operational
efficiency. With compact installation units, dry operation, and the ability to deal with ores of complex
mineralogy, like rare earth bearing minerals [1], SBS technology has the potential to replace jigs in
many of its traditional applications, particularly those involving coarse particle treatment. The scenario
is in some ways analogous to the beginning of the 20th century when the advent of magnetic
separation and froth flotation partially replaced gravity concentration processes. Breakthroughs and
innovations in the understanding, design, and optimization of jigs should be sought in order to keep
the technique competitive.
The present paper provides an up to date review of the fundamentals of jigging operation and
outlines some avenues for future research and developments. The configuration, operational principles,
and main applications of different jig types have been comprehensively reviewed. A description of the
main theoretical approaches used is presented, highlighting their strengths and weaknesses. Finally,
suggestions for upgrading jigging technology through new conceptual approaches are made.

Minerals 2020, 10, 998; doi:10.3390/min10110998 www.mdpi.com/journal/minerals

Minerals 2020, 10, 998 2 of 31

Paper Structure
Although the current paper does not consist of a systematic review, some ordinary criteria were
considered when gathering and evaluating the available literature. Many of the referenced articles
have been obtained after an extensive search in the databases Scopus® , Web of Science® , and Google
Scholar® by using the keywords “jigging” or “jig”. Some publications, especially older textbooks and
proceedings, were available only in their print versions. The majority of the literature covered is in
English, with some few exceptions.
The paper is structured as follows: Section 1 outlines the purpose of the review and describes
its structure and approach. Section 2 provides a comprehensive overview of jigging principles,
basic configurations, types, and applications; in this section, by considering the previous review by
Lyman [2] as a landmark, preference has been given to data published in the last three decades. Section 3
reviews the main theoretical approaches used to describe the complex mechanisms of pulsation and
stratification in jigs; purely empirical models, such as statistical models, have not been included here.
Section 4 discusses the various operational aspects affecting jigging. In Section 5, some research gaps,
potential upgrades, and new conceptual frameworks are identified and discussed. Finally, Section 6
synthesizes the main conclusions and future possibilities.

2. Jigging: Concepts and Development

Gravity concentration methods aim to create conditions in which particles of different densities,
sizes, and shapes may move relative to each other when under the action of gravity or centrifugal forces,
originating multiple bands containing the light and dense materials [3,4]. When the relative interparticle
movement manifests itself in the form of vertical expansions and contractions of a particle bed caused
by a pulsing fluid, the gravity concentration operation in question is known as jigging. Although its
origin possibly dates back as far as Classical Antiquity [2,5], the first remarkable description of jigging
appeared only in the 16th century in De Re Metallica, the well-known seminal work by Agricola [6].
In this, the first jigs are depicted as perforated baskets (the “jigging sieve”) containing the mixed ores
that were manually and repeatedly dipped into a water tank, after which stratified layers of ore were
removed by hand. Until the advent of the industrial revolution, modifications in jigging devices were
limited to a little more than the inclusion of levers for using larger baskets [7]. The emergence of
hydraulic systems like the piston pump and the plunger pump, with or without seals, are regarded as
decisive for the introduction of mechanically pulsated jigs [3], giving rise to the basic configuration of
modern jigs.

2.1. Hydraulic Jigs—Configuration and Types

In modern water jigs, the original basket was replaced by a compartmentalized vessel equipped
with a sieve (or screen) to support the particle bed, mechanical pulsation devices were introduced,
and operation became continuous. The general scheme of most industrial jigs consists of a container
divided into two compartments, one of them corresponding to the separation chamber where feed
particles are located on a supporting sieve and through which the water performs its oscillatory motion.
The other compartment contains the mechanism that drives fluid pulsation responsible for moving
the bed during its passage by the jig (Figure 1). The pulse wave can be produced either mechanically
through a plunger or by the pulsation of water or air that is intermittently fed into the jig vessel by
using a special valve. In some types of jig, the relative motion between particles and water is obtained
through the vertical displacement of the supporting sieve.
Minerals 2020, 10, 998 3 of 31

Figure 1. General scheme of a jig (front view).

In the case of continuous operation, particles of varied composition (like non-beneficiated ores)
are fed at one end of the jig tank and distributed over the screen, which is slightly inclined towards the
outlet end (see Figure 2). As particles pass through the equipment, they are subject to successive cycles
of expansion and compaction that promote the stratification action. When reaching the discharge end,
the particle bed must be separated into two distinct zones: a layer of light material, located in the upper
portion of the bed; and a layer of dense particles concentrated in the lower fraction. The target content
and yield of the desired product will define the height (“cut point” or “cut height”) in which the stratified
bed should be split at the discharge end. In hydraulic jigs, most of the water is withdrawn from the jig
with the products, so that replacement water (“hutch water”) is regularly pumped into the jig vessel.

Figure 2. General scheme of a jig (side view).

The differences among the various types of industrial jig are associated with a plethora of factors,
including vessel geometry, pulsation mechanism, bed transport, discharge system, and separation
control scheme. In this sense, Sampaio and Tavares [8] have proposed a broad classification of jigging
devices based on three main aspects: (a) condition of the jig sieve (fixed or mobile), (b) method of
extraction of the heavy product, and (c) bed pulsation mechanism.
Minerals 2020, 10, 998 4 of 31

In the most common fixed-sieve jigs, the water moves through a sieve that remains immobile
inside the separation chamber, as previously mentioned. In some jigs, however, the bed motion is
produced through the mechanical displacement of the jig sieve within a stationary fluid. Versions of
this type of jig were developed in the 19th century, as exemplified by the already obsolete Hancock and
James jigs [9]. Current jig models in this category include the ROMJIG [10] and the InLine Pressure
Jig [11]. In both, the jig screen is cyclically moved up and down by a hydraulic servo system connected
to the screen.
While the light product is invariably removed by overflowing, the dense product leaves the jig in
two distinct manners: “through the screen” and “over the screen”. In the first case, used for finer-sized
feeds, heavier particles pass through the screen to be drawn off as the dense product, collected into
the bottom of the jig compartment and removed through a spigot. In these jigs, a layer of heavy,
coarse material (called “ragging”) is placed on the screen onto which the feed is introduced. Thus only
particles of high density can penetrate through the ragging and then reach the jig screen.
When jigging “over the screen” is used, a discharge device equipped with sensor systems controls
the withdrawal of the heavy product. The most utilized methods of discharge include the regulation of
opening of a mobile gate or the adjustment of the rotation speed of a rotary discharge valve. In order to
maintain an accurate cut-point, the thickness of the heavy material layer is continuously measured by
sensors, and the immersed float method is still used. In this, a float calibrated with weights to exhibit
an apparent density equal to the separation density is used to adjust the height in which the bed will be
split [2]. Electromagnetic or ultrasonic displacement sensors measure the position of the float, which is
the input of proportional–integral–derivative PID controllers that drive the discharge device. Floats are
eventually subject to inaccuracies due to their invasive nature and the harsh environment inside a
jig bed. Radiometric density sensors have been pointed out as a more accurate option by allowing
tracking changes in density over the bed pulsation cycle and have been recently used to validate a
dynamic model of discharge in a coal jig [12].
Jigs can also be divided concerning the pulse generation mechanism into piston-type jigs,
diaphragm-type jigs, air-pulsated jigs, and mobile-sieve jigs (described above). One of the first
mechanical jigs is the Harz jig [3]. This consists of a two-chamber tank, as illustrated in Figure 1,
equipped with a piston linked to a connecting rod and crank system, thus resulting in a harmonic
movement. Although it has simple mechanical design and operation, water leakage is a recurring
concern when operating this jig due to the difficulty of maintaining the seal between the piston and the
housing walls.
A solution to prevent water leakage through the flanks of the plunger is to replace the piston with
a rubber diaphragm connected to an eccentric vertical shaft, such as in the Denver jig [4]. With a layout
nearly similar to the Harz jig, the Denver jig has a rotary valve operating in synchrony with the plunger,
which avoids the input of hutch water during the suction stroke and allows more precise control of the
jigging cycle. Variations in tank design and the position of the plunger in the chamber have originated
many other models of diaphragm-pulsated jigs, such as the Bendelari jig, the Pan-American jig, and the
Yuba jig [8]. Figure 3 depicts a basic scheme of these types of equipment.
Minerals 2020, 10, 998 5 of 31

Figure 3. Scheme of some piston and diaphragm-type jigs (adapted from Sampaio and Tavares [8]).

The use of compressed air instead of mechanical devices for pulsating the jig water appeared
at the end of the 19th century with the development of the Baum jig [13]. Since then, air-pulsated
jigs have been universally applied in mineral processing, especially coal beneficiation, due to their
high capacity and versatility compared to the piston and diaphragm-type jigs [14]. The decisive factor
for the success of air-pulsated jigs is the use of electronically controlled air valves of quick actuation
for controlling the water movement, thus allowing a wide variation of the pulse shape. The Baum
jig works by forcing air under pressure (up to 17 kPa) into an air chamber located on one side of a
U-shaped jig vessel (similar to that displayed in Figure 1) to pulsate the jig water [15]. Electrically
driven rotary-piston valves control the timing of air admission and exhaustion inside the jig chamber,
allowing precise control of pulse duration and intensity. When combined with automatic control
systems of discharge of the heavy product, Baum jigs results in a more flexible and robust option in
comparison to mechanically pulsated jigs.
To maintain a uniform distribution of water velocity across the bed is one major difficulty of
side-pulsed jigs like the Baum jig, which in turn limits the jig bed width (and so feed capacity).
Thus, the TACUB and then the BATAC jigs addressed this limitation by using multiple air chambers
positioned under the jig sieve equipped with individual electronic valves for controlling air input and
exhaust [16]. This new design provided a more uniform flow across the bed, enabling the construction
of larger jigs (up to 7 m wide along) able to achieve high separation densities [10]. Some BATAC and
Baum jigs have the longitudinal section of the vessel segmented into multiple compartments, each one
having independent control of the pulsating wave. For practical purposes, such equipment can be
considered as the conjugation of numerous jigs in series operation.
Table 1 summarizes the classification of different jigs according to the criteria mentioned previously.
It is worth noting that some types of jig, particularly diaphragm-pulsated types, are more likely to be
used for ores, in which the target is the denser constituent. The contrary occurs for coal, in which other
pulsating mechanisms and the “over the screen” discharge are most common. Also, the operating
flexibility of air-pulsated jigs makes them applicable for different uses. A more detailed discussion
about jigging applications is found in Section 2.3.
Minerals 2020, 10, 998 6 of 31

Table 1. Types of jig according to the classification proposed by Sampaio and Tavares [8].

Screen Type Heavy Product Discharge Pulsation Mechanism Jig Equipment Common Applications
Piston Harz Coal
Jeffrey Coal
Over the screen Diaphragm
Bendelari Ores
Baum Coal and ores
Air-pulsated
Batac/Tacub Coal and ores
Fixed screen
Denver Ores
Wemco/Remer Ores
Diaphragm
Through the screen
Yuba Ores
Pan-American Ores
IHC radial jig Ores
Air-pulsated Baum/Batac Coal and ores
Over the screen Mechanic ROMJIG Coal
Mobile screen
Through the screen Mechanic InLine Pressure Jig Ores

2.2. Alternative Jigs

A few separators have been developed or adapted using the basic principle of jigging, i.e.,
bed stratification induced by fluid pulses, although one or more design and operating features may
significantly differ from those of conventional hydraulic jigs. The most distinguishing equipment in
this class includes dry jigs, centrifugal jigs, and a range of jigging machines exclusively developed for
plastics recycling.

2.2.1. Dry Jigs

Dry jigging (also called “pneumatic” or “air jigging”, though these designations may be mistaken
as air-pulsated water jigs) is becoming more and more relevant as the concern with slurry waste
disposal and water handling costs intensifies. Dry jigging was introduced in the coal industry during
the 1920s by applying the concepts involved in wet jigging technology to the design of pneumatic
cleaning machines [17]. Since then, significant improvements have been implemented in its conception
and operation.
Modern dry jigs consist of an inclined vibrating deck in which the feed distributed at one
end is subjected to the action of two distinct upward air streams (see Figure 4). One of them is
continuous, having the function to loosen the bed and so allow a uniform air distribution. A second
superimposed pulsed airflow promotes density stratification of the bed during its passage on the jig
deck. The combined effect of the two independent air streams allows precise control of stroke frequency
and amplitude [18]. Nuclear density sensors are installed near the discharge end of the deck to control
the bed level and the cut-height [19]. Stargate discharge valves are normally used for the withdrawal
of the products. The equipment is enclosed and a dust collector system handles the generated dust.
The evident advantage of dry jigging is its complete elimination of process water, which is
particularly desirable in places with limited or difficult access to water or if the jig feed is affected by
moisture. However, its separation efficiency is recognizably lower than that of hydraulic jigs since the
density of air is negligible compared to the density of water. Such low density is compensated for
by using high air stream velocities, thus increasing turbulence and remixing effects. Consequently,
dry jigs are applied to separate only close-sized material, normally larger than 2 mm [20], containing
relatively low content of near-gravity material (NGM).
Minerals 2020, 10, 998 7 of 31

Figure 4. Basic scheme of a dry jig.

2.2.2. Centrifugal Jigs

In centrifugal jigs, the jig screen consists of a rotating cylinder in which centrifugal forces dozens
of times higher than gravity are produced. Therefore, it is classified as one of the so-called “enhanced
gravity concentrators” or simply centrifugal concentrators, which also include the Knelson, the Falcon,
and the multi-gravity concentrators [4]. Currently, there exist two commercial models of centrifugal jigs:
the Kelsey jig [21] and the Altair jig [22]. In both versions, the rotating bowl is placed vertically inside a
casing having launders to collect the concentrate and tailings. The jig hutch is enclosed and equipped
with a side-pulsed mechanism consisting of rubber diaphragms linked to pulse arms. During operation,
feed slurry enters the jig through a pipe near the middle of the bowl and is distributed onto the jig
screen by the action of centrifugal forces. The screen is covered with a layer of coarse heavy material
(ragging) so that the discharge of the heavy product is of “through the screen” type. Pressurized water
is periodically injected to keep the bed dilated as well as to ensure that only fine, dense particles can
pass through the ragging. Low-density particles move upwardly onto the screen and are withdrawal
over the bowl top.
The ability to greatly increase the apparent gravitational field allows centrifugal jigs to separate
ultrafine particles (<40 µm), far below the minimum size limit of any other jig [21]. However,
special attention must be paid to feed classification to avoid blockage of the jig screen by excessively
coarse particles. Centrifugal jigs also figure among the gravity concentrators having the largest specific
water consumption (up to 15 m3 /ton of ore) [8].

2.2.3. Jigs for Plastic–Plastic Separation

In recent years, the growth in the application of jigging for solid wastes recycling has driven
the conception of some unique jig separators. One such case is the set of jig prototypes developed
exclusively for separating plastics. The first of these prototypes was the RETAC jig, a modified TACUB
jig in which water pulsation and jigging cycle were optimized to address the difficulties related to the
separation of materials with densities close to that of water, like plastics [23,24]. A further development
consisted of installing an aeration diffusion tube (air bubbler) under the jig screen of a RETAC jig,
giving rise to the so-called hybrid jig [25]. This equipment allowed the separation of plastics having
Minerals 2020, 10, 998 8 of 31

similar densities but different surface wettability after a pretreatment step. The issue of separating
plastics lighter than water was settled by conceiving of the Reverse jig prototype [26]. In this, a second
screen installed at the top of the RETAC jig container allows separating plastics based on differences in
levitation velocity. Improvements in the scheme of product extraction were subsequently made for
the RETAC [27] and hybrid [28] jigs. Recently, the separation of metal wires and plastics was tested
with relative success in the RETAC jig [29]. Although tailored to the recycling context, undiscovered
benefits could result from the adaptation of some concepts involved in the design and operation of
such jigs to the framework of mineral processing (e.g., the use of under-screen aeration to change the
apparent density of particles, as in the hybrid jig).

2.3. Applications
The various types of hydraulic jig find vast application in mineral processing, ranging from
minerals as dense as native gold (19 g/cm3 ) to vitrinite (up to 1.3 g/cm3 ) [8]. It is par excellence
a traditional item in coal processing plants [30–32] while typical examples of using jigging for ore
processing include beneficiation of iron ore [33–35], alluvial gold concentration [36], beneficiation of
chalcedonite [37], pre-concentration of tungsten ores [38], cleaning of phosphate ores [39], as well as the
concentration of tin and copper ores [4]. Dry jigs, otherwise, are mostly used in coal processing [40–42].
In recent decades, jigging has surpassed the limits of mineral processing, being currently utilized
in a variety of sectors. As noticed by Turner [43], jigs have found applications as peculiar as in the
separation of bones and cartilages for chewing gum production. Most notably, jigging has found
particularly fertile ground in recycling and waste processing. As mentioned earlier, recycling of plastics
like polyethylene, polycarbonate, and polyvinyl chloride PVC has even been boosting the development
of new jigging devices, particularly in Japan [25,27,28]. In the case of metals recycling, jigging has been
used to recover ferrous and non-ferrous metals from different sources, such as automobile scrap [44,45],
steelmaking slag [46], and electric cable wastes [47].
Dry jigs, in particular, have been described as a promising method for recycling and upgrading
the quality of coarse aggregates from construction and demolition wastes (CDW) [48,49]. The results
obtained by Sampaio et al. [20] showed it to be possible to obtain recycled aggregates of composition
that meet the minimum standards of quality of many countries. On the other hand, dry jigs showed a
relatively high unit energy consumption (of the same order of crushers) in the economic analysis of a
CDW plant performed by Coelho and Brito [50].

3. Theoretical Concepts of Jigging

As previously presented, the jigging process involves the stratification of a particle bed through
pulsations generated by a fluid. Thus, two interdependent physical phenomena coexist in a jig:
the repeated dispersion of the bed caused by the pulse strokes, and the stratification resulting
from the differential relative motion of particles through the dispersed bed. Understanding bed
dispersion during jig pulsation is a precondition for determining the way the particles may segregate.
Ultimately, modeling bed dispersion allows determination of the incremental variation of bed porosity,
which is decisive to numerical simulations of stratification in jigs. Two main theoretical approaches
have been used to date for describing the mechanisms underlying particle stratification in jigs.
The fluid-dynamic approach, based on the analysis of forces acting on individual particles during
jigging, having its ultimate expression in the advanced Computational Fluid Dynamic-Discrete Element
Method CFD-DEM coupled models; and the thermodynamic approach, which considers the lowering
in potential energy of the bed as the true driving force behind stratification. The development of each
of these concepts is presented in detail.

3.1. Bed Movement

The process of particle segregation in jigs is, by definition, linked to the vertical oscillatory motion
of the jigging bed caused by the pulsation strokes. Some authors have found it appropriate to consider
Minerals 2020, 10, 998 9 of 31

jigging as a process of repeated fluidization and de-fluidization of a bed in which the upward fluid
velocity continuously varies along the operating cycle [51,52]. Therefore, similar to what occurs in
fluidized beds, the upward fluid velocity (water or air, the last in the case of dry jigging) must reach a
minimum threshold to enable the elevation of the jigging bed. This minimum velocity is a function of
the flow regime and the pressure drop caused by the passage of the fluid through the particle bed.
The first can be obtained from the Reynolds number for packed beds [53]:

U f ρ f dp
Re = (1)
µ(1 − ε)

where U f , ρ f , and µ are the superficial velocity, the density, and the viscosity of the fluid, respectively;
ε is the bed porosity, and dp is the average diameter of the bed particles, for the case of spherical particles.
Equation (1) is valid for fluid flow through the jig bed at rest only (before fluidization). For Re < 10,
the flow is laminar and the pressure drop of the fluid can be predicted using the Carman–Kozeny
equation:
(−∆p) −180µ (1 − ε)2
= Uf (2)
L d2p ε3

where L is the bed thickness. The minimum fluidization velocity occurs when the drag force (represented
by the pressure drop) is equal to the apparent weight of the bed:
 
− ∆P = (1 − ε) ρs − ρ f Lg (3)

in which ρs and ρ f are the densities of the bed-forming solid and the fluid medium, respectively, and g
is the acceleration of gravity (g = 9.81 m/s2 ). The expression contained on the right side of the equation
represents the apparent force-weight per unit area of the bed (kN/m2 ). By replacing Equation (3)
in Equation (2) for the minimum fluidization velocity, the following is obtained:
 
ε ρs − p f gd2p
U f , mín = (4)
(1 − ε) 180µ

Correlations for the case of non-laminar flow regime, such as the Ergun correlation, can be found
in the specialized literature [53]. A close observation of Equation. (4) reveals that the minimum fluid
flow required to raise the bed (Qmín = Uf,mín x A, where A is the cross-sectional area of the jig bed)
decreases as the density and viscosity of the medium increases. Since liquids are considerably denser
and more viscous than gases under the same conditions, it is expected the upward fluid flow used to
raise the bed will be much lower in hydraulic jigs than those used in dry jigging. Similarly, Uf,mín varies
with the square of the average diameter of the bed-forming particles. As discussed later, both factors
have an important limiting effect on the maximum particle size range of the jig feed, particularly for the
case of dry jigging. Bed elevation constitutes only one of the phases of particle motion during jigging.
Due to the cyclic pulsating stroke, the jigging bed describes an oscillatory movement characterized by
pulses of amplitude A and frequency f (pulses or cycles per minute). A jigging cycle can be defined
based on how the bed moves when subjected to pulses of amplitude A and frequency f. Figure 5
illustrates the bed and fluid displacement patterns for the simplest jigging cycle, a sinusoidal harmonic
vertical motion, characteristic of piston-type jigs [3,8], that can be described by the following equation:

U (t) = π f A sin(2π f t) (5)

where U is the superficial velocity of water and Umax = π f A is the maximum velocity.
Minerals 2020, 10, 998 10 of 31

Figure 5. Illustration of (a) fluid velocity and (b) fluid and bed motion along a sinusoidal pulsation
cycle in a jig. The points A to F represent the behavior of the jig bed at different moments within the
cycle (see Figure 6).

Figure 6. Jig bed motion over a pulsation cycle. The displacement of the particle colored in black
represents the variation in the position of a dense particle within a bed of light particles.

An extensive analysis of the bed motion during jigging was performed by Kirchberg and
Hentzschel [51], who used a diaphragm jig with harmonic pulsations containing a bed of spherical
particles, widely varying bed properties and pulsation conditions. Initially, the basic bed displacement
was carefully recorded for the simplest possible case, i.e., for a bed formed by particles of the same
composition and uniform size (Figure 6).
In the beginning, the bed is at rest and the velocity of the pulsating water is zero (A). Then, just after
the upward flow starts, the drag force exerted by the fluid equals the apparent weight of the bed,
raising it as a rigid body (A to B). Soon after, a high porosity zone is formed below the bed as the
particles start settling (B to C). As the upward water flow passes through the bed, the bed porosity
increases when a dispersion wave propagates from the base of the bed. As the fluid flow approaches
its maximum value, the dispersion layer expands to a point where it covers the entire bed volume
(C to D). As the upward water velocity decreases, conditions for sedimentation of the particles begin to
Minerals 2020, 10, 998 11 of 31

prevail as the dispersion wave dissipates through the bed (D to E). Finally, the bed is once again static
on the jig sieve while the suction phase begins.
Later studies, such as those of Kawashima et al. [54], Jinnouchi et al. [55], and Rong and Lyman [56],
recognized that the porosity of the jigging bed (or, more precisely, the loosening of the bed) is a key
parameter for describing stratification during jigging. In this sense, the work of Witteveen [57]
provided great insights by modeling the porosity distribution as a function of time and height in a
jig bed composed of uniform particles (same density, shape and size). Two of its interesting findings
include: (a) loosening of the bed takes place during both parts of the stroke (upward and downward),
and (b) the higher the position of the particle in the bed, the shorter the time it remains fluidized.
More recent studies, such as those conducted by Xia et al. [58] and Viduka et al. [59], have applied the
principles of computational fluid dynamics (CFD) to calculate the fluid motion during pulsation by
directly solving the Navier–Stokes equations. The fluid flow and porosity distribution can thus be
simulated in detail, allowing the capture of detailed features of the bed motion.

3.2. Stratification: Fluid-Dynamic Approach

The essence of fluid-dynamic models lies in the balance of forces acting on a single particle in
an infinite fluid medium (Figure 7a). For the case of unidimensional vertical motion, the particle
displacement relative to the fluid is driven by the gravitational force. The buoyancy force and especially
the drag force are the main forces resisting the particle’s motion. The centrifugal force also plays a key
role in the case of centrifugal jigs [8], but this specific case will not be considered here.
The magnitude and direction of particle motion result from the balance between the forces acting
on it, given by [60]:
dUp
mp = Fa + Fe − F g (6)
dt
where mp and Up are the particle mass and velocity and Fa , Fe , and F g are the drag force, the buoyancy
force and the gravity force, respectively. The combined effect of the later results in the apparent weight
force:  
F g − Fe = Vp ρs − ρ f g (7)

where Vp is the volume of the particle and equal to Vp = (1/6)πd3p for a spherical particle. Unlike
dense medium separation, in jigging the particles are denser than the working fluid (i.e., ρs > ρf) for the
vast majority of cases, being the main exception the use of reverse jigging for plastic separation [26].
The drag force exerted on the particle depends on several factors, including its size, shape, inclination,
as well as the flow conditions. All these elements are embodied in a drag coefficient, CD , from which
the drag force can be determined according to the expression:

1 2
Fa = CD ρ f U∞ Ap (8)
2

where Ap = (1/4)πd2p is the surface area of a sphere.

Minerals 2020, 10, 998 12 of 31

Figure 7. (a) Forces acting on a spherical particle in a fluid; (b) drag curve for motion of a sphere in a
fluid. The curve was plotted based on the model of Haider and Levenspiel [61], see Equation (9).

The drag coefficient is related to the Reynolds number, Re = U f dp ρ f /µ, which represents the
ratio of inertial forces to viscous forces within the medium, in the form of standard drag curves,
as illustrated in Figure 7b. Four zones are identified according to the flow regime [53,60]: the so-called
Stokes’ law region (Re < 0.3), where viscous forces are largely dominant; Newton’s law zone (500 < Re
< 2 x 105) in which inertial forces are predominant; an intermediary region between the Stokes and
Newton regions (0.3 < Re < 500); and a turbulent region (Re > 2 x 105) in which the domain of inertial
forces is such that the flow becomes unstable and phenomena like boundary layer detachment take
place. The most common jigging conditions occur in the intermediate and Newton flow regime [8].
The determination of CD in the intermediary region is usually undertaken by empirical correlation
since there is no analytical solution of the Navier–Stokes equations in this range. Some correlations
have been proposed for the entire range of Re, such as that of Haider and Levenspiel [61]:
 
24  0.6459
  0.4251 
CD = 1 + 0.1806 Re + 
  (9)
Re 1 + 6880.95

Re

which was the expression used to plot the curve shown in Figure 7b.
dUp
A condition of interest occurs when the particle acceleration is zero ( dt = 0 in Equation (6)) and a
force balance is achieved so that the relative velocity of the particle is maximum. This velocity is known
as the terminal velocity. By substituting Equations (7) and (8) in Equation (6), rearranging the terms,
and considering Newton’s law region, the terminal velocity is expressed as follows:

  1
 3gdp ρs − ρ f  2
U∞ =  (10)
 
ρf

 

Equation (10) reveals that the terminal velocity depends on the particle size and density as well as
the density of the fluid medium. To evaluate the influence of each of these quantities, Rittinger [62]
considered the situation of a binary mixture composed of spherical particles of a light material,
Minerals 2020, 10, 998 13 of 31

with diameter dl and density ρl , with a dense material with diameter dd and density ρd . The condition
in which both particles have the same terminal velocity is given by:

  1   1
 3gdl ρl − ρ f  2  3gdd ρd − ρ f  2
 =  (11)
   
ρf ρf
 
   

which after a rearranging gives:

ρd − ρ f
!q
dl
= (12)
dd ρl − ρ f
Equation (12) indicates the settling ratio [62] between the sizes of particles of different densities
that have the same terminal velocity. This ratio is valid for all flow regimes, being represented by the
quotient q, where q = 12 for the Stoke’s regime, q = 1 for the Newtons’ regime, and 12 < q < 1 for the
intermediary regime. By integrating the combined effect of particle size and density, together with the
density of the fluid medium, Taggart [9] raised the settling ratio to the level of an index for evaluating
a priori the applicability of gravity separation methods, naming it as the concentration criterion (CC).
The higher the value of CC, the wider is the size range feasible to be fed in a jig, and the easier is the
separation by density. It also indicates that the density of the fluid medium is also a determining factor
influencing separation since the value of CC increases as fluid density gets close to the density of the
light constituent. Therefore, it can be inferred that using air as the separation medium like in dry
jigging should be less efficient than using water given that the density of air is many times smaller
than the density of water.
In practice, free-settling conditions do not occur due to particle–particle and particle–wall
interactions, as well as variations in pulp viscosity. Thus, Rittinger [62] and later Richards [63]
found it more useful to describe the process considering hindered-settling conditions. More specifically,
they hypothesize that the differential hindered-settling of particles in a system was the key mechanism
behind particle stratification in jigs (and in other gravity concentration devices). The free-settling ratio
can be turned into the hindered-settling ratio by changing ρ f by ρa in Equation (12), where ρa is the
pulp density, which is a function of the percentage of solids to liquid in the system.
Although useful for a basic understanding of jigging principles, the differential hindered-settling
analysis is not able to describe, even qualitatively, the phenomenon of particle stratification in jigs.
Issues such as the occurrence of reverse classification, i.e., the concentration of smaller, denser particles
in the same band, remained unaddressed. The practice also shows that it has been possible to feasibly
operate jigs in size ranges wider than those predicted by CC (Equation (12)). In order to address
these issues, Gaudin [13] proposed the existence of two other mechanisms besides the differential
hindered-settling: differential initial acceleration and consolidation trickling. These three mechanisms
compose the well-known hydrodynamic theory of jigging.
The mechanism of differential initial acceleration bases on the fact that particles composing a
jigging bed spend most of their time in alternate states of acceleration and deceleration. Amidst these
states, there are instances in which particles’ velocity is zero or close to zero so that the fluid-exerted
drag force can be considered negligible. At this condition and considering that the particle mass is
mp = ρs Vp , Equation (6) is written as:

dUp ρf
!
= 1− g (13)
dt ρs

That is, the initial acceleration of particles depends only on the differential densities of solids
and the fluid and is independent of the size. Thus, during the brief moments in which particle
acceleration is null and the direction of movement changes (like at the very end of the expansion stroke),
the difference in densities becomes the only driven force controlling stratification. If these moments
occupy a significant portion over time, the final separation would be greater than that predicted based
Minerals 2020, 10, 998 14 of 31

uniquely on the hindered-settling mechanism. This mechanism may be intensified by using high
pulsing frequencies and low pulse amplitudes.
As discussed before, in a jigging bed composed of particles of different densities and sizes,
the larger ones will tend to sink faster than the smaller ones. However, whereas large particles
remain immobile on the jigging screen, fine particles may continue to move through the large particles’
interstices. Such a condition describes the mechanism of consolidation trickling. Unlike the other
two, the consolidation trickling mechanism engenders reverse segregation concerning the particle size,
since it induces fine, dense particles towards the bottom of the bed. It is particularly strengthened
by using long suction strokes in hydraulic jigs, which increase the mass yield of fine particles in the
heavy product.
From the qualitative point of view, the theory developed by Gaudin satisfactorily describes
some general phenomena occurring during stratification in jigs. More importantly, it allows the
establishment of a circumstantial relationship between stratification profiles and operating parameters
(e.g., increasing the concentration of fine material in the dense product by making the suction stroke
longer). Nevertheless, it does not provide sufficient means for a quantitative description of jigging.
Taking advantage of the ongoing advances in computing power, the use of discrete element
methods (DEM) is becoming increasingly accepted as an effective technique for addressing simulations
in jigging studies. DEM simulations allow tracking the individual motion of thousands of particles at
short time steps taking into account the influence of contact forces among particles in both normal
and shear directions. The initial studies by Beck and Holtham [64] and Mishra and Mehrotra [65]
used two-dimensional DEM models and considered an idealized fluid behavior. Although then
limited by computing requirements, simulation results enable them to found near-optimal pulsation
conditions as long as enough input data of the system (particle features, bed depth, etc.) were specified.
The subsequent work of Viduka et al. [59] performed a combination of CFD to simulate fluid flow and
DEM to simulate particle motion considering a sinusoidal jigging pulse. The results provided detailed
information about the patterns of motion of particles as well as the influence of segregation on the
overall bed movement. Finally, the study by Crespo [66] coupled a DEM model with the bed porosity
distribution model proposed by Witteveen [57], also numerically dividing the elements of the bed into
horizontal strata of finite volume. His model also incorporated stratification and dispersion indexes
which were directly related to the operational parameters of jigging, providing some valuable insights
into the stratification mechanisms.

3.3. Stratification: Thermodynamic Approach

A thermodynamic view of the process of stratification in jigs was originally introduced by
Mayer [67,68], leading to a paradigm shift in the understanding of jigging away from the traditional
hydrodynamic theory. He considered the mixture of heterogeneous particles in a jig bed as a mechanical
system that tends to a lower potential energy state, which in turn corresponds to the stratified state.
Therefore, the difference in gravitational potential energy between the mixed and stratified states
would be the real driving force behind stratification, and the energy supplied by the fluid to raise the
bed would serve only for releasing the latent potential energy in the non-stratified bed.
The reduction in potential energy manifests in the form of a reduction in the center of mass of the
bed after stratification. Figure 8 illustrates this process considering two distinct states: (I) a perfectly
homogeneous binary mixture of particles with different densities and sizes; (II) a perfectly stratified
bed concerning density. The potential energy of the bed in the perfectly mixed state is given by:

H
E I = ( ml + md ) g (14)
2
where ml and md are the weight of the light and dense constituents, respectively, and H/2 is the vertical
position of the center of gravity SI in the mixed bed. Two distinct centers of gravity take place in the
Minerals 2020, 10, 998 15 of 31

stratified state: one for the light material layer and another for the dense material layer. In this state,
the potential energy of the bed is:

Hl H
   
EII = ml g + Hd + md g d (15)
2 2

Figure 8. Position of the center of gravity in a binary mixture of particles with different densities.
(I) homogeneous mixture, (II) perfect stratification, (III) lowering of the center of gravity. Adapted from
Mayer [68] and Sampaio and Tavares [8].

Therefore, the difference in potential energy between state I (mixed) and state II (stratified) is given by:

1
∆E = EI − EII = (m gH − ml gHd ) (16)
2 d l
which is always positive.
The first term in Equation (16) represents the energy of displacement of particles of the dense
material md along the distance Hl /2, that is, from the initial center of gravity SI to the final center of
gravity Sd . The second term represents the work of elevation of the light particles of mass ml by the
dense ones along the distance Hd /2, that is, from the initial center of gravity SI to the final center of
gravity Sl . The overall result is the lowering of the center of mass of the bed, which is expressed by:

∆E
∆S = (17)
(ml + md ) g

Mayer also noted that the lowering of the center of mass is related to the increasing degree of
packing of the bed as the stratification process progresses. Since packing density depends not only on
particle density but also on the size and shape of the particles, some stratification should also occur
due to differences in such properties. Thus, for instance, a tabular particle contained inside a mixture
of spherical particles would tend to continuously rise when pulsed until it is expelled from the bed
since the disturbance caused by its presence would generate a low packing zone within the bed.
Mayer [68], aiming at proposing a kinetic model to describe the stratification, assumed that the
potential energy of the non-stratified system is continuously converted into kinetic energy during the
bed rearrangement process. Such kinetic energy would be largely converted into particles’ movement,
whereas a fraction of it would dissipate as friction and heat. Thus, since the available energy decreases
Minerals 2020, 10, 998 16 of 31

as the bed segregates, the stratification rate also decreases progressively. This decrease was described
in terms of a first-order kinetic equation as a function of the jigging time:

d∆E
= −k∆E (18)
dt
in which the constant k represents the stratification evolution rate. By integrating Equation (18)
and replacing ∆E by ∆s , we obtain the following:

H (t) = ∆S exp(−kt) (19)

Equations (18) and (19) show that as t→∞, that is, for infinite jigging cycles, the potential energy of
the system will eventually reach a minimum value that corresponds to the perfect stratification of the
bed. Embedded in this assumption lies the main limitation in the potential energy theory as originally
proposed by Mayer since the practice proves never to be possible to obtain a perfect stratification.
A significant contribution to Mayer’s theory was made by King [69,70], who addressed the
problem of the impossibility of achieving a perfect separation. His model considers that the final
stratification profile after jigging results from a balance between the minimization of the system’s
potential energy, driving stratification, and a dispersive flow of diffusive nature that tends to remix the
stratified particles. Considering the case of a jigging bed composed of spheres of uniform size in which
one particle changes position (as previously depicted in Figure 2), the potential energy gradient of the
particle with density ρ in the bed of particles with mean density ρ is given by:

dE
= Vp g ( ρ − ρ ) (20)
dH

where H is the bed thickness measured from the jig sieve. The average density ρ of bed thickness H is
given by:
Z∞
ρ= ρCρ dρ (21)
0

Therefore, the potential energy gradient describes an upward or downward movement of the
particle depending on the sign of (ρ − ρ). The particle will move down if ρ > ρ or it will move up if
ρ < ρ. The flow of particles of density ρ caused by the potential energy gradient is given by:

dE
ns = −Cρ u = −Cρ uVp g(ρ − ρ) (22)
dH

where Cρ is the volume concentration of particles with density ρ in the bed with average density ρ,
and u is the velocity of penetration of the particle under the action of the potential energy gradient and
in the absence of any dispersive force. The negative sign means that each particle will tend to move in
order to minimize the potential energy of the system.
Opposed to the potential energy lowering flux, there is a diffusive flux caused by particle–particle
and fluid–particle interactions. This flux is described as a typical Fickian diffusion process of the type:

dCρ
nD = −D (23)
dH
in which the diffusion coefficient D depends on the particle size and shape and the bed expansion
mechanism. In this case, the negative sign indicates that particles will tend to move in order to
minimize differences in concentration over the bed height. A dynamic equilibrium is established when
nD = −ns , so that:
dCρ uVp g
=− Cρ (ρ − ρ) (24)
dH D
Minerals 2020, 10, 998 17 of 31

which can be rewritten in terms of the relative bed height h = H/Hb , where Hb is the total bed depth,
and in terms of a specific stratification index, given by:

uVp gHb
α= (25)
D
So that:
dCρ
= −αCρ [ρ − ρ(h)] (26)
dh
Thus, α is independent of the density of the particles and, consequently, of the bed composition.
It is a unique parameter used to describe the stratification process as a whole. This way, in a bed
consisting of spherical particles of uniform sizes, all particles have the same value of the stratification
index α. The solution of Equation (26) provides the vertical concentration profile of particles with
density ρ along the bed after reaching the equilibrium stratification. This solution should satisfy the
following conditions:
Z1
f
Cρ dh = Cρ f or all ρ (27)
0

Z∞
Cρ dρ = 1 0 ≤ h ≤ 1 (28)
0
f
where Cρis the density distribution by volume of the feed, which can be determined by sink-and-float
analysis. The different density ranges thus obtained can then be used to discretize Equation (26),
expressing it in the form:

dCi (h)
= −αCi (h)[ρi − ρ(h)] i = 1, 2, . . . , n (29)
dh
The system of equations thus obtained must be solved taking into account the following conditions:

n
X n
X Z1
f
ρ(h) = Ci (h)ρi , Ci (h) = 1 and Ci = Ci (h)dh (30)
i=1 i=1 0

An analytical solution for the particular case of binary mixtures tested in discontinuous jigs (n = 2)
was presented in the early work developed by King [69]. The use of the model for multi-component
systems, in which the analytical solution is not possible, was later addressed by Tavares and King [71],
together with the development of the model for the case of continuous jigs. In that study, the predictive
capacity of the model was successfully tested based on separation data measured for several Baum and
Batac jigs operating with different types of coal. A more recent model validation study was conducted
by Woollacott et al. [72], which demonstrated good accordance between experimental and simulated
data. However, the stratification index α was not necessarily independent of the density distribution
in the system as the ratio α/Hb varied, to some extent, with the number of constituents in the system,
although this finding has not been conclusive.
One of the main limitations of King’s model is that it does not account for systems containing
multi-sized or multi-shaped particles. In an attempt to extend the model applicability, Rao [73]
has incorporated the effect of multi-size systems by considering that α and the particle diameter di
could be related by a power law as follows:

α = A(di )b (31)
Minerals 2020, 10, 998 18 of 31

where A and b are parameters related to the stratification. Substituting this value of α in Equation (29)
we obtain:
dCij (h)
= −A(di )b Cij (h)[ρi − ρ(h)] i, j = 1, 2, . . . , n (32)
dh
where Cij (h) is the concentration profile of the species of density i and size j.
Unfortunately, despite some interesting results obtained by simulations (such as the lower degree
of density stratification for finer particles), his proposed extension of King’s model was not validated
with experimental data. More recently, the size dependency proposed by Rao was analyzed in detail
by Woollacott [74], who found that the stratification index seems to be not significantly dependent on
particle size. In particular, it has been proposed the existence of a “compositional region” where particle
size has little or no influence on the final stratification profile predicted by King’s model.
The contributions to the potential energy theory proposed by King [69,70] and Tavares and
King [71] are of great value since they allow estimation of the stratification profile at equilibrium and
thus predict the assay and recovery of products for a given cut point. An advantage in comparison
to empirical models based on partition curves is that one parameter only, the stratification index α,
provides an immediate indication about jigging efficiency. Notwithstanding this, similar to Mayer’s
theory it is limited only at predicting the equilibrium stratification and does not constitute a kinetic
model of jigging. Also, it seems unable to establish a link with operating parameters of jigging, such as
bed pulsation frequency and amplitude, which undoubtedly influence the final stratification extent.
Finally, the very fundamental basis in which the thermodynamic models of jigging rely was
recently challenged. By using X-ray computing tomography to measure the packing density after
size segregation in a laboratory jig, Woollacott [75] demonstrated some compelling evidence that
contradicts the potential energy theory. Reported results showed an unexpected decrease in packing
density near the bed bottom after stratification, whereas the most packed zone was detected near an
intermediate mixed zone. Despite being limited only to size segregation at quite limited conditions,
such outcomes reinforce the fact that alternative approaches may need to be considered to obtain an
integral description of the mechanisms behind stratification in jigs.

4. Operational Aspects Influencing Jigging

As previously discussed, diverse factors control the stratification extent during jigging, as well as
the yield and content of the products obtained. These factors can be arranged into three distinct
categories: (a) specific design features of the jig, (b) jigging cycle and pulsation parameters,
and (c) feed characteristics. The first depends basically on the jig type used and involves constructive
and operational details that were briefly discussed in Section 2.1. The other two categories are
discussed below.

4.1. Jigging Cycle and Pulsation Parameters

As described in Section 3.1, the bed motion that characterizes the jigging operation results from
the vertical pulses produced by a fluid stream. The simplest pulse shape constitutes a sinusoidal
harmonic vertical motion in which the duration and intensity of the impulsion and suction strokes are
similar, as illustrated in Figure 9a. The sinusoidal oscillation waveform is characteristic of piston-type
and some diaphragm-type jigs such as the Harz, the Denver, and the Bendelari jigs [8,76]. However,
other pulsation profiles can be obtained depending on the stratification mechanism emphasized by
manufacturers and operators. Regardless of the type, the pulse profile used should allow particles
with different densities to move and reach different positions in the bed.
Minerals 2020, 10, 998 19 of 31

Figure 9. Typical pulsation diagrams of different jigs. (a) sinusoidal, (b) trapezoidal, (c) “saw-tooth”
with rapid upward, and (d) “saw-tooth” with rapid downward.

In air-pulsated jigs, like the Baum and Batac jigs, the cycle can be widely varied by controlling
the opening and exhausting period of the air valves connected to the jig hutch [35]. In these models,
the trapezoidal shape cycle (Figure 9b) is quite used, especially in coal processing. Its purpose is to
maintain the bed “opened” as long as possible to maximize hindered settling conditions, which is
desirable for concentrating coarse, dense particles.
Saw-tooth shape pulses are typical of jigs with movable screens. Figure 9c illustrates the saw-tooth
pulse typical of an InLine Pressure Jig [11]. In this, the fast upward stroke avoids the loss of fine,
dense product to the light product, whereas the slow downward stroke facilitates its percolation
trickling through the bed, giving them more time to be recovered in the heavy product. Thus, this pulse
shape is more appropriate for “through the screen” jigging, and when the target product is in finer size
ranges than the light gangue (up to 60 µm), as in the recovery of free gold, diamonds, and sulfides [4].
Conversely, the saw-tooth pulse with a rapid downward stroke and slow upward stroke shown in
Figure 9d is the pulse shape regularly used in the ROMJIG [10]. In this case, the feed consists of
coarse-sized material (350–40 mm) that tends to stick to the jig screen if the lowering of the hydraulic
arm (that moves the bed) is not rapid enough. Hence, a sudden downward stroke must be employed
to ensure a sufficient detachment of the jig bed.
Besides the pulse waveform, the amplitude and frequency of the pulses are key factors on
stratification. Its influence on bed dispersion was studied in-depth by Kirchberg and Hentzschel [51].
As a result, it was found that while low amplitudes (or low water superficial velocities) may not allow
sufficient dispersion of the bed, too high amplitudes suffer excessive interference from the counterflow
of water produced before the end of the upward motion of particles. Similarly, low frequencies are
generally associated with low water superficial velocities, whereas too high frequencies interfere in
the dispersion wave propagation throughout the bed due to the rapid change in the flow direction.
From this, it follows that larger particles require higher pulse amplitudes while finer particles require
higher pulse frequencies for a successful stratification. Similar trends were observed in the subsequent
studies of Mukherjee and Mishra [52] and Hori et al. [23].
The residence time of particles inside the jig (or the number of pulsing cycles applied under fixed
jigging conditions) has a cumulative effect on stratification: the longer the jigging time, the closer the
Minerals 2020, 10, 998 20 of 31

bed comes to the equilibrium stratification profile. Rong and Lyman [56] observed that the stratification
of density tracers (1.3 <ρs < 2.0 g/cm3 ) in Baum jigs evolved rapidly until an operating time of 180 s.
From this point on, the stratification rate progressed more slowly to t = 300 s, when a state closer to the
equilibrium was reached. Longer times were necessary to achieve the equilibrium stratification when
particles finer than 8 mm were tested, suggesting that segregation kinetics is slower for finer particles.
Similar behavior for the stratification of aggregates in a dry jig was reported by Sampaio et al. [20]
but with even faster kinetics. In this case, most of the stratification occurred when t < 30 s whereas
no significant variation was observed for t > 120 s. As highlighted by the authors, stratification is
expected to be faster in dry jigs since particles move more rapidly in the air than in water.

4.2. Feed Characteristics

The main feed characteristics influencing stratification in jigs are particles’ density, size, and shape;
and feed rate and bed depth. These aspects are discussed below.

4.2.1. Particle Density

Segregation by density is the primary objective of jigging and depends on the differences in density
between light and dense constituents. For small differences, the stratification profile is characterized
by a lower stratified layer rich in the dense material, an upper layer concentrating the light material,
and an intermediary layer between the two in which different components are mixed. For significant
differences in density, a detachment of the layers of distinct densities can occur after stratification,
producing an out of phase oscillation during the pulsation stroke due to the differential influence of
fluid drag force. The occurrence of this behavior was reinforced by recent simulations studies, such as
that by Viduka et al. [59].
The proportion of near gravity material (NGM), commonly defined as the fraction of material
within a range of +0.1 g/cm3 of the separation density, is an important factor affecting the separation
performance in jigs. The larger the content of NGM particles, the higher is the tendency to occur
misplacements between light and dense particles in the products. The reference NGM values proposed
by Bird [77] for the case of coal are still quite used as a guide to determine the more suitable gravity
separators for treating a given feed. In the case of jigs, the maximum threshold recommended ranges
from 7% to 15% NGM in weight.
The extent of differences in densities of particles is decisive for the time required for stratification
and processing capacity. Lill and Smith [78] measured the penetration velocities of intrusive particles
of different densities (up to ρs ≈ 10 g/cm3 ) positioned on the surface of a homogeneous bed of glass
spheres (ρs = 2.54 g/cm3 ). It was found that the penetration velocity increased linearly with the
increase of intruder density. In practice, this means that fewer pulsations are required for separating
materials with large differences in density, resulting in shorter feed residence times and thus higher
processing rates.

4.2.2. Particle Size

The influence of particle size on density stratification in hydraulic jigs has been extensively
studied in the literature. The general trend is similar to that observed in other gravity concentration
equipment: the finer the feed size, the less precise is the separation as a whole [52,79–81]. It is also
reported that stratification efficiency increases when reducing the size range of the feed or when using
mono-sized beds [52,66]. However, though valid, caution is necessary when generalizing these results.
Since the overlap between density and size segregation is ubiquitous, the use of wider size ranges
can be eventually beneficial for density separation depending on the specific size distribution of the
light and dense materials. Also, segregation by size is less straightforward than segregation by density
due to its non-monotonic nature. Since it involves complex geometrical interactions among particles,
changes in the size ratios between larger and finer particles may result in different stratification patterns.
Aiming to address these issues, the study of Woollacoot and Silwamba [82] analyzed the effect of
Minerals 2020, 10, 998 21 of 31

varying the size ratio on size stratification in a batch hydraulic jig. The results revealed the occurrence
of four distinct stratification pattern types, thus suggesting the degree of complexity that can be
involved if the combined effect of density segregation is considered.
Studies on the effect of particle size and size distribution on stratification for the case of dry jigging
are much less numerous. By analyzing historical and current data of dry jigs’ performance in coal
beneficiation plants, Weinstein and Snoby [17] observed that the ones operating with finer feed particle
sizes showed a lower performance. On the other hand, a recent study by the author [83] indicated
that the stratification of aggregates (concrete, brick, and gypsum) in a pilot-scale dry jig was enhanced
when using finer sizes or wider size distributions (within the limit of 19.1 to 4.75 mm). However, there
is still no complete evidence to identify general patterns regarding the effect of particle size variations
on dry jigging, since the reported studies considered only specific, context-based processes and did not
isolate the influence of other properties as particle shape and pulsating conditions.

4.2.3. Particle Shape

Few studies have addressed the role of differences in particle shape on jigging. It is accepted that
the segregation pattern due to differences in particle shape is oriented to the densification (increasing
of packing density) of the particle bed, as envisioned by the potential energy theory (Section 3.3.).
Some previous studies seem to confirm such a pattern for the case of dry jigging [48,83]. It is
also the typical segregation pattern observed in particulate systems subjected to vertical external
oscillations [84,85]. In fluidized beds, lamellar particles suffer a more intense drag due to
the normal component (perpendicular to the particle surface) of the drag force exerted by the
fluid [53]. This phenomenon was supported by results obtained by Pita and Castilho [81], which tested
the separation of plastics in a Denver jig. Separation precision was higher when the light component
was richer in lamellar particles, facilitating its displacement towards the upper layer of the bed. On the
other hand, a loss of efficiency was observed when the dense fraction was enriched with lamellar
shape particles.

4.2.4. Other Factors

Bed depth, feed rate, and feed solid content are relevant aspects to be considered during jigging.
The transport velocity of the bed during its passage on the deck of a conventional hydraulic jig varies
with depth. The average longitudinal velocity at the top of the bed can be up to 10 times greater than
in the bottom zone [86]. Thus, if bed thickness is excessively large or if the jig screen is too inclined,
some particles (particularly fine ones) may not have enough time to dislocate to its equilibrium position.
The recent study by Kumar and Venugopal [87] have highlighted the importance of bed thickness,
being this parameter which most affected the yield and content of coal cleaning during tests in a
laboratory Denver jig.
The suitable bed thickness is normally defined based on the feed top size, being, for example, in
the order of 30 cm for the case of coal of 19 mm top size [8] (Sampaio and Tavares, 2005). Feil et al. [88]
also observed that using shallow beds can be advantageous when the target product is the light
constituent in a system containing a larger proportion of heavy material. Since thicker beds need
higher water velocities to be lifted, its use can increase the contamination of the light product with
heavy particles.
The capacity of jigs is related to the surface area of the jig screen. Typical jigs capacity ranges from
7 ton/h.m2 for some diaphragm jigs to 24 ton/h.m2 for air-pulsated jigs [8]. The nominal feed rate
should consider the minimum residence time necessary to achieve a proper separation. Excessively
high feed rates can hamper the stratification process since particles (particularly those that overflow
the jig) leave the jig before reaching the desired segregation. Finally, few jigs are relatively affected by
the solid content of the feed if compared to other gravity concentration equipment. Notwithstanding
this, excessive dilution (e.g., lower than 30% in mass) can harm segregation as the horizontal velocity
of water becomes too high [3].
Minerals 2020, 10, 998 22 of 31

5. Challenges and Future Directions

Under the current background of decreasing ore grades and more complex mineralogy, the mining
industry has witnessed an unprecedented growth of activities using artificial intelligence solutions for
advanced control and supervision of unit operations. Sensor-based sorting, in particular, is finding its
place in the future and can be set to replace some traditional methods in processes involving coarse-size
ores concentration. Gravity concentration techniques, on the other hand, have been slower in adopting
sophisticated control technologies, and the use of the novel machine learning tools remains very poorly
explored in the framework of jigging. Notwithstanding this, jigs can benefit from the novel gains
in sensor technology and simulation tools, as well as from a further understanding of the complex
mechanisms behind particle stratification.
The current scenario is also of increasing constraints placed on the mining industry in terms of
environmental performance. The dozens of dam failure disasters that have occurred in the last few
years have raised important issues regarding the use of water in mineral processing and ignoring it
in new plant design and operation is no longer an option. Even though optimizing water treatment
and using closed-loop water circuits may address this issue to a great extent, the appeal for using
dry-processing techniques tends to increase. In this sense, dry jigging may gain growing importance
among water-free processes in the near future.
On the other hand, jigging technology has started to find its way into the recycling sector,
which has its own challenges. Not by chance, the latest novelties in the design and operation of jigs
have been driven by its increasing use in the recycling sector, as exemplified by the array of equipment
developed for recycling plastics (Section 2.2.3), whereas the mineral processing industry remains pretty
much limited to old formulas. Solutions addressing these issues involve the development of adapted
separation control systems until changes in design features, as in the case of jigs used for plastics.
Taking into account these challenges, the following sections discuss some promising paths that could
be explored in future studies as starting points for future advances related to the jigging process.

5.1. New Breakthroughs in the Understanding of Jigging

While the current existing theoretical models of jigging are useful to some extent, they do
not provide a full understanding of the intricate mechanisms involved in the stratification process.
On the one hand, thermodynamic type models have a difficult connection with the main operational
parameters of jigs and present shortcomings when applied to multi-sized systems [75]. On the other
hand, recent works on DEM and CFD-DEM simulations have shed some light on some key aspects of
bed motion and stratification [59,66], but studies on this are still scarce. More than that, some intriguing
phenomena remain unexplored in empirical studies and cannot be explained by the existing theories.
One of them, which will be briefly presented here, exhibits strong similarities with a phenomenon
widely studied in granular physics: granular convection.
Figure 10 shows pictures of the top view of sliced stratified beds after typical tests of aggregates
separation in pilot-scale Baum and dry jigs. A well-defined pattern of horizontal segregation can be
noticed, in which denser particles are more concentrated in the center zone of the strata while light
particles are distributed near the sidewalls. In the case of dry jigs, the extent of horizontal segregation
can be of the same order as that of vertical segregation [89]. The evidence suggests that, at least
for pilot-scale batch jigs, the constraints imposed on particles closed to the container sidewalls may
significantly affect the stratification process.
Minerals 2020, 10, 998 23 of 31

Figure 10. Observed horizontal segregation patterns after stratification tests with ternary mixtures of
aggregates (concrete, brick, and gypsum) in batch jigs. (a) Baum jig (top surface of the bed); (b) dry jig
(bottom layer).

To better understand the formation of the pattern described, a relatively simple test was outlined:
a 25 mm thin layer of gypsum (1.8 g/cm3 ) was positioned on the dry jig screen and covered with a
125 mm thick layer of concrete (2.4 g/cm3 ). This layout was defined to inspect the distribution of
gypsum particles over the top surface during its upward motion. Figure 11 shows video snapshots
capturing the progression of the test. As can be noted, the first gypsum particles erupted near the center
of the container and spread over the bed surface toward the outer edges. What is more interesting is
that gypsum particles filling the bed top were continually transported and pushed toward the sidewalls
by a circular motion maintained by the lower layer of concrete. This slow, orderly motion in which
particles move upward in the center and down along the sidewalls has been extensively reported in
the literature as a typical pattern defining the occurrence of granular convection [90–94]. In vertically
vibrated systems, its origin has been related to the frictional effects caused by lateral walls and its
intensity depends mainly on the amplitude and frequency in which the bed oscillates [95]. Not by
coincidence, the same factors characterizing the bed displacement in jigs. Most importantly, granular
convection influences stratification by density, inducing the concentration of dense particles in the
central portion of the bed while light particles concentrate nearer the sidewalls [96], a pattern similar
to that displayed in Figure 10. Tests similar to the previously mentioned were performed using tracers
in a batch Baum jig, which seems to reinforce the possibility of occurring convective patterns in jigs.

Figure 11. Snapshots of the top surface of a dry jig bed taken at different times during pulsation.
Minerals 2020, 10, 998 24 of 31

Although the relevance of granular convection in industrial jigs is a matter of discussion,

its preliminary identification allows acknowledging that ideas taken from the literature on granular
physics may benefit from a deeper understanding of the mechanisms behind jigging. The behavior of
vertically oscillated granular beds, a sub-field of granular physics, comprises important concepts like
that of granular temperature [97] and phase transition [98], which are fundamental for describing the
dynamics of segregation and re-mixing in many granular systems. Adapting such concepts, if and
when applicable, to the context of jigging, together with the powerful tools of CFD-DEM simulations,
may provide answers to some challenging questions that have impaired a full understanding of jigging.

5.2. Upgraded Strategies of Online Data Acquisition

Besides flowmeters and density gauges used for monitoring basic variables, floats and radiometric
density meters (Section 2.1) are typically used in jigs for controlling the bed cut-height, i.e., the thickness
of the lower layer to be discharged as the heavy product. However, advanced control functions such
as feature extraction and fault detection are difficult to implement using this standard instrumentation
only. New stochastic sensors, together with advanced strategies of multivariate image analysis,
have already been used with success in comminution and flotation circuits [99], but remains poorly
explored in the framework of jigging. In this sense, two interesting approaches for improving jigging
control and stability are briefly presented: the application of machine vision techniques and the use of
particle-tracing sensors (PTS).
Machine vision comprises a set of methods for the acquisition and extraction of relevant digital
image features for correlating them to process variables. Digital images at different wavelengths
are captured by using CCD (charge-coupled device) cameras positioned in strategic spots of a
process unit. Once captured, the next stage is image processing. Duchesne [100] has presented
a general machine vision framework used in multivariate digital image analysis, composed of
three steps: (a) image pre-processing, (b) feature extraction, and (c) feature reduction and analysis.
Image pre-processing involves using operations such as filtering and segmentation to facilitate the
subsequent feature extraction step. This one consists of computing key information such as color
variations and geometrical features for further analysis. Finally, in the third step, multivariate regression
techniques, like principal component analysis (PCA), are used to establish a relationship between the
extracted image features and the key variables of the process.
Figure 12 depicts the previously mentioned three steps for the case of using the edge detection
technique for monitoring the bed motion during the pulse stroke in a batch jig. Videos capturing the
bed motion under fixed conditions were recorded by a camera positioned in front of the container of a
batch jig. Video files were split into multiple sequential images (frames) that were segmented and
filtered considering the resting bed as the baseline (Figure 12a). Then, each frame was decomposed
into a matrix of vectors, in which each matrix element corresponded to a pixel. In each frame, the edges
of the bed were detected by identifying the vectors whose intensity changed abruptly (associated with
changes in image brightness) using principal component analysis (PCA) (Figure 12b). Once identified,
the variation of the detected contours frame-by-frame, the bed movement over the horizontal plane
could be analyzed. The technique allowed quantifying the differences between bed displacement in
the center zone and near the jig walls (Figure 12c).
Minerals 2020, 10, 998 25 of 31

Figure 12. Use of image analysis to evaluate the behavior of a jig bed during the pulse stroke in a batch
Baum jig. (a) Pre-processing; (b) Feature extraction; (c) Feature analysis.

Another potential application of machine vision in jigs has been illustrated in the study of Cazacliu
et al. [48]. The authors have used image processing (the type not specified in the study) for analyzing
the evolution of stratification of a target component (in this case, gypsum) based on images captured
through the transparent walls of a batch jig. One such benefit provided in comparison to conventional
sensors is the ability to monitor and analyze the whole vertical distribution of species along the jig bed,
allowing a comprehensive evaluation of the product yield and content for a given cut-height.
One limitation involved in using machine vision techniques in jigs is that only surface information
of the bed is available when using conventional sensors (like CCD cameras) which, considering the
aforementioned influence of jig walls on the process, may harm the acquired data accuracy. A relatively
simple idea to overcome such limitation could be combining the use of density tracers with the concept
of “spy particle” or particle tracing sensor [101]. In this, a micro-three-dimensional acceleration
sensor, a microprocessor, and a data communication interface (like a Bluetooth wireless transceiver)
are encapsulated in a small spherical shell of a few millimeters size [102]. The “spy object” can then be
mixed to the feed of a given unit to track the in situ movement inside the equipment, including its
translational and rotational velocities. In the case of jigging, one useful idea would be to embed sensors
into shells of varying densities, thus producing density tracers that can be tracked. The real-time
motion record could provide unprecedented detail about the influence of jigging operational factors
on the segregation of each density class. Given the relative simplicity and the fact that generating an
external field is not necessary for detection, the use of micro-acceleration sensors seems a safer and
substantially cheaper alternative in comparison to techniques such as X-ray tomography, magnetic
resonance imaging (MRI), and positron emission particle tracking (PEPT).

5.3. Exploring the Unique Features of Dry Jigging

Despite the renewed interest in dry-processing technologies, most of the basic and applied studies
on jigging have remained oriented to conventional hydraulic jigs. Dry jigs, however, display constructive
Minerals 2020, 10, 998 26 of 31

and operational features (Section 2.2.1) that lead to a singular and yet not well-understood stratification
dynamics. The suction stroke is not present and a continuous airflow keeps the bed sufficiently
opened for receiving the periodic pulse stroke. High air velocities must be used to compensate for
the low density of air, thus making the maintenance of a uniform air distribution over the bed a more
challenging task. The demand for high air flow rates may also imply energy and maintenance costs
higher than expected, as described by Coelho and Brito [50]. All these circumstances have contributed
to making the application of dry jigging rather limited so far.
On the other hand, the distinctive operation of dry jigs enables exploring potentialities not
available for hydraulic jigs. The blower of a commercial pilot-scale dry jig can produce air flows up to
around 170 m3 /min with outlet pressures in the order of 6 kPa [103]. Data obtained by Boylu et al. [40]
have shown that the pressure drop occasioned by the air passing through the jig bed has varied in the
range 0.4–1.4 kPa, depending on the magnitude of air velocity, pulsation frequency, and bed thickness.
This implies that a great part of the energy produced by the blower is not utilized and leaves the system
with the air flowing out. One possibility to make better use of this lost energy could be using thicker
beds, although, after a certain threshold, it can prejudice the separation process (Section 4.2.4). Another
possibility is shown in Figure 13a and consists of simply placing a second jigging bed in the path of the
passing upward airflow. From a practical standpoint, and considering that some degree of stratification
can be achieved in this second bed, it acts to enlarge the jig capacity at no additional energy costs.

Figure 13. (a) Placement of a second bed in the container of a batch dry jig. (b) Snapshot of testing the
removal of organic contaminants from a mixture of aggregates.

The peculiar pulsation conditions also enable dry jigs to work as pneumatic classifiers for the
separation of low-density materials such as wood, paper, and plastics, which is particularly useful
in recycling applications. Moreover, for cases in which these light constituents are mixed with much
denser materials (like rocks and metals), dry jigs have shown the ability to operate as a multi-component
separator, assuming the combined functions of an air classifier and a jig [104]. Figure 13b exemplifies
such a situation for the case of a mixture of aggregates (crushed rocks and bricks) contaminated with
plastics and paper. Once the pulsation begins, virtually all the plastic and paper are swept away by the
air stream while stratification of the stony fraction proceeds as habitual. Suitable adjustments in the jig
container design and the dust collection system, among others, could eventually allow implementing
this multi-separator configuration of jigs in recycling plants.
Minerals 2020, 10, 998 27 of 31

6. Conclusions
For centuries, jigs have been an invaluable tool for coal and ores processing. Nowadays, jigging has
found its way in areas as varied as recycling of printed circuit boards and chewing gum production.
However, the current scenario of increasing ore complexity, lowering of water usage, and rapid
development of sensor-based sorting (SBS) technologies has imposed increasingly challenges on the
traditional concentration operations. The path to a competitive future requires a continuous search for
enhancements and innovations in jigging technology.
In this context, the current review has attempted to summarize the key aspects and concepts
about jigging available in the literature. The design, operational features, applications, equipment
types, and theoretical models have been comprehensively described. Three issues have been identified
as priorities for future studies: (1) improving the existing models of particle separation in jigs by filling
the remaining gaps in the understanding of stratification mechanisms. Embracing some concepts of
granular physics may be valuable in this sense; (2) improving the accuracy and robustness of control
and supervisory systems in jigs by using advanced sensors; (3) exploring the full potentialities of
dry jigging. The more the understanding of the capabilities of jigging, the quicker one can arrive at
solutions to overcome the current challenges, maintaining the role of jigging as a valuable tool in
industry in future.

Funding: This research received no external funding.

Conflicts of Interest: The author declares no conflict of interest.

References
1. Veras, M.M.; Young, A.S.; Born, C.R.; Szewczuk, A.; Neto, A.C.B.; Petter, C.O.; Sampaio, C.H. Affinity of dual
energy X-ray transmission sensors on minerals bearing heavy rare earth elements. Miner. Eng. 2020, 147, 106151.
[CrossRef]
2. Lyman, G.J. Review of Jigging Principles and Control. Coal Prep. 1992, 11, 145–165. [CrossRef]
3. Burt, R.O. Gravity Concentration Technology; Elsevier: Amsterdam, The Netherlands, 1984.
4. Wills, B.A.; Finch, J. Wills’ Mineral Processing Technology: An Introduction to the Practical Aspects of Ore Treatment
and Mineral Recovery; Butterworth-Heinemann: Oxford, UK, 2015.
5. Habashi, F. A Short History of Mineral Processing. In Proceedings of the XXIII International Mineral
Processing Congress, Istanbul, Turkey, 3–8 September 2006; pp. 3–8.
6. Hoover, H.C. Georgius Agricola De Re Metallica; Ripol Classic Publishing House: Moscow, Russia, 1950.
7. Henderson, J. On the methods generally adopted in cornwall, in dressing tin and copper ores (including
plate). In Minutes of the Proceedings of the Institution of Civil Engineers; Thomas Telford-ICE Virtual Library:
Cornwall, UK, 1858; pp. 195–212.
8. Sampaio, C.H.; Tavares, L.M.M. Beneficiamento Gravimétrico: Uma Introdução aos Processos de Concentração
Mineral e Reciclagem de Materiais por Densidade; Editora da UFRGS: Porto Alegre, Brazil, 2005.
9. Taggart, A.F. Handbook of Mineral Dressing; Wiley: Hoboken, NJ, USA, 1945; Volume 1.
10. Sanders, G.; Ziaja, D.; Kottmann, J. Cost-efficient beneficiation of coal by ROMJIGs and BATAC jigs. Coal Prep.
2002, 22, 181–197. [CrossRef]
11. Nesbitt, A.; Breytenbach, W.; Van der Plas, P. Characterisation of the pulse wave of an InLine Pressure Jig in
a near density application. Miner. Eng. 2005, 18, 1–7. [CrossRef]
12. Cierpisz, S. A dynamic model of coal products discharge in a jig. Miner. Eng. 2017, 105, 1–6. [CrossRef]
13. Gaudin, A.M. Principles of Mineral Dressing; ACS Publications: Washington, DC, USA, 1939.
14. Rao, D.S.; Gouricharan, T. Coal Processing and Utilization; CRC Press: Boca Raton, FL, USA, 2016.
15. Poling, B.E.; Thomson, G.H.; Friend, D.G.; Rowley, R.L.; Wilding, W.V. Perry’s Chemical Engineers’ Handbook;
Section 2; McGraw-Hill Publishing: New York, NY, USA, 2008.
16. Chen, W.L. Batac jig cleaning in five US plants. Min. Eng. 1980, 32, 1346–1350.
17. Weinstein, R.; Snoby, R. Advances in dry jigging improves coal quality. Min. Eng. 2007, 59, 29–34.
18. Snoby, R.; Thompson, K.; Mishra, S.; Snoby, B. Dry jigging coal: Case history performance. In Proceedings of
the SME Annual Meeting, Denver, CO, USA, 22–25 February 2009; pp. 09–052.
Minerals 2020, 10, 998 28 of 31

19. Kumar, D.; Kumar, D. Dry Cleaning Process. Sustain. Manag. Coal Prep. 2018, 115–130. [CrossRef]
20. Sampaio, C.H.; Cazacliu, B.G.; Miltzarek, G.L.; Huchet, F.; Le Guen, L.; Petter, C.O.; Paranhos, R.; Ambrós, W.M.;
Oliveira, M.L.S. Stratification in air jigs of concrete/brick/gypsum particles. Constr. Build. Mater. 2016, 109, 63–72.
[CrossRef]
21. Beniuk, V.; Vadeikis, C.; Enraght-Moony, J. Centrifugal jigging of gravity concentrate and tailing at Renison
Limited. Miner. Eng. 1994, 7, 577–589. [CrossRef]
22. Mohanty, M.; Honaker, R.; Patwardhan, A. Altair jig: An in-plant evaluation for fine coal cleaning. Miner. Eng.
2002, 15, 157–166. [CrossRef]
23. Hori, K.; Tsunekawa, M.; Hiroyoshi, N.; Ito, M. Optimum water pulsation of jig separation for crushed
plastic particles. Int. J. Miner. Process. 2009, 92, 103–108. [CrossRef]
24. Tsunekawa, M.; Naoi, B.; Ogawa, S.; Hori, K.; Hiroyoshi, N.; Ito, M.; Hirajima, T. Jig separation of plastics
from scrapped copy machines. Int. J. Miner. Process. 2005, 76, 67–74. [CrossRef]
25. Hori, K.; Tsunekawa, M.; Ueda, M.; Hiroyoshi, N.; Ito, M.; Okada, H. Development of a New Gravity
Separator for Plastics—A Hybrid-Jig—. Mater. Trans. 2009. [CrossRef]
26. Ito, M.; Tsunekawa, M.; Ishida, E.; Kawai, K.; Takahashi, T.; Abe, N.; Hiroyoshi, N. Reverse jig separation of
shredded floating plastics—Separation of polypropylene and high density polyethylene. Int. J. Miner. Process.
2010, 97, 96–99. [CrossRef]
27. Tsunekawa, M.; Kobayashi, R.; Hori, K.; Okada, H.; Abe, N.; Hiroyoshi, N.; Ito, M. Newly developed discharge
device for jig separation of plastics to recover higher grade bottom layer product. Int. J. Miner. Process.
2012, 114, 27–29. [CrossRef]
28. Ito, M.; Saito, A.; Murase, N.; Phengsaart, T.; Kimura, S.; Tabelin, C.B.; Hiroyoshi, N. Development of suitable
product recovery systems of continuous hybrid jig for plastic-plastic separation. Miner. Eng. 2019, 141, 105839.
[CrossRef]
29. Phengsaart, T.; Ito, M.; Hamaya, N.; Tabelin, C.B.; Hiroyoshi, N. Improvement of jig efficiency by shape
separation, and a novel method to estimate the separation efficiency of metal wires in crushed electronic
wastes using bending behavior and “entanglement factor”. Miner. Eng. 2018, 129, 54–62. [CrossRef]
30. Cierpisz, S.; Kryca, M.; Sobierajski, W. Control of coal separation in a jig using a radiometric meter. Miner. Eng.
2016, 95, 59–65. [CrossRef]
31. Rong, R. Analysis of jig behaviour in coal preparation plants in China. Bull. Proc. Australas. Inst. Min. Metall.
1986, 291, 59–65.
32. Tripathy, A.; Panda, L.; Sahoo, A.; Biswal, S.; Dwari, R.; Sahu, A. Statistical optimization study of jigging
process on beneficiation of fine size high ash Indian non-coking coal. Adv. Powder Technol. 2016, 27, 1219–1224.
[CrossRef]
33. Miller, D. Design and operating experience with the Goldsworthy Mining Limited BATAC Jig and spiral
separator iron ore beneficiation plant. Miner. Eng. 1991, 4, 411–435. [CrossRef]
34. Mukherjee, A.; Bhattacharjee, D.; Mishra, B. Role of water velocity for efficient jigging of iron ore. Miner. Eng.
2006, 19, 952–959. [CrossRef]
35. Naudé, N.; Lorenzen, L.; Kolesnikov, A.; Aldrich, C.; Auret, L. Observations on the separation of iron ore in a
prototype batch jig. Int. J. Miner. Process. 2013, 120, 43–47. [CrossRef]
36. Laplante, A.; Gray, S. Advances in gravity gold technology. Dev. Miner. Process. 2005, 15, 280–307.
37. Surowiak, A.; Gawenda, T.; Stempkowska, A.; Niedoba, T.; Nad, A. The Influence of Selected Properties
of Particles in the Jigging Process of Aggregates on an Example of Chalcedonite. Minerals 2020, 10, 600.
[CrossRef]
38. Srivastava, J.P.; Pathak, P. Pre-concentration: A necessary step for upgrading tungsten ore. Int. J. Miner. Process.
2000, 60, 1–8. [CrossRef]
39. Carlson, J.; Eisele, T.; Kawatra, S.K. Investigation of jigging as a method for removing dolomite from
high-MgO phosphate ores. Min. Metall. Explor. 2012, 29, 56–60. [CrossRef]
40. Boylu, F.; Talı, E.; Çetinel, T.; Çelik, M. Effect of fluidizing characteristics on upgrading of lignitic coals in
gravity based air jig. Int. J. Miner. Process. 2014, 129, 27–35. [CrossRef]
41. Sampaio, C.; Aliaga, W.; Pacheco, E.; Petter, E.; Wotruba, H. Coal beneficiation of Candiota mine by dry
jigging. Fuel Process. Technol. 2008, 89, 198–202. [CrossRef]
Minerals 2020, 10, 998 29 of 31

42. Sampaio, C.H.; Ambrós, W.M.; Cazacliu, B.; Moncunill, J.O.; José, D.S.; Miltzarek, G.L.; Brum, I.A.S.D.;
Petter, C.O.; Fernandes, E.Z.; Oliveira, L.F.S. Destoning the Moatize Coal Seam, Mozambique, by Dry Jigging.
Minerals 2020, 10, 771. [CrossRef]
43. Turner, J. Gravity concentration, past, present and future. Miner. Eng. 1991, 4, 213–223. [CrossRef]
44. Kuwayama, Y.; Ito, M.; Hiroyoshi, N.; Tsunekawa, M. Jig separation of crushed automobile shredded residue
and its evaluation by float and sink analysis. J. Mater. Cycles Waste Manag. 2011, 13, 240–246. [CrossRef]
45. Mori, S.; Nonaka, M.; Matsufuji, R.; Fujita, T.; Futamata, M.; Hata, M. Recovery of Non-Ferrous Metals
from Car Scrap using the Jig Separator (ECHO Metal Jig, Type-SP). In Ecomaterials; Elsevier: Amsterdam,
The Netherlands, 1994; pp. 771–774.
46. Sripriya, R.; Murty, C.V. Recovery of metal from slag/mixed metal generated in ferroalloy plants—A case
study. Int. J. Miner. Process. 2005, 75, 123–134. [CrossRef]
47. Pita, F.; Castilho, A. Separation of copper from electric cable waste based on mineral processing methods:
A case study. Minerals 2018, 8, 517. [CrossRef]
48. Cazacliu, B.; Sampaio, C.H.; Miltzarek, G.; Petter, C.; Le Guen, L.; Paranhos, R.; Huchet, F.; Kirchheim, A.P.
The potential of using air jigging to sort recycled aggregates. J. Clean. Prod. 2014, 66, 46–53. [CrossRef]
49. Waskow, R.P.; dos Santos, V.L.; Ambrós, W.M.; Sampaio, C.H.; Passuello, A.; Tubino, R.M. Optimization and
dust emissions analysis of the air jigging technology applied to the recycling of construction and demolition
waste. J. Environ. Manag. 2020, 266, 110614. [CrossRef]
50. Coelho, A.; De Brito, J. Economic viability analysis of a construction and demolition waste recycling plant in
Portugal–part I: Location, materials, technology and economic analysis. J. Clean. Prod. 2013, 39, 338–352.
[CrossRef]
51. Kirchberg, H.; Hentzchel, W. A study of the behaviour of particles in jigging. Progress in Mineral Dressing.
Trans. Int. Min. Dress. Cong. Stockh. 1957, 193–213.
52. Mukherjee, A.; Mishra, B. An integral assessment of the role of critical process parameters on jigging.
Int. J. Miner. Process. 2006, 81, 187–200. [CrossRef]
53. Coulson, J.; Richardson, J. Chemical Engineering-Particle Technology and Separation Processes; RK Butterworth.
Heinemann: Oxford, UK, 1998; Volume 2.
54. Kawashima, S.; Jinnouchi, Y.; Hamada, L. On the dynamics of the air-pulsated jig washers. Trans. Jpn. Soc.
Mech. Eng. 1994, 40, 1309–1317. [CrossRef]
55. Jinnouchi, Y.; Kita, S.; Tanaka, M.; Sawada, Y. New trends in theory and technology of the air-pulsated jigs in
Japan. Min. Metall. Explor. 1984, 1, 76–81. [CrossRef]
56. Rong, R.X.; Lyman, G.J. The effect of jigging time and air cycle on bed stratification in a pilot scale Baum jig.
Fuel 1992, 71, 115–123. [CrossRef]
57. Witteveen, H.J. The Response of a Uniform Jig Bed in Terms of the Porosity Distribution; Universiteitsdrukkerij TU
Delft: Delft, The Netherlands, 1997.
58. Xia, Y.; Peng, F.F.; Wolfe, E. CFD simulation of fine coal segregation and stratification in jigs. Int. J. Miner. Process.
2007, 82, 164–176. [CrossRef]
59. Viduka, S.; Feng, Y.; Hapgood, K.; Schwarz, P. CFD–DEM investigation of particle separations using a
sinusoidal jigging profile. Adv. Powder Technol. 2013, 24, 473–481. [CrossRef]
60. Rhodes, M.J. Introduction to Particle Technology; John Wiley & Sons: Hoboken, NJ, USA, 2008.
61. Haider, A.; Levenspiel, O. Drag coefficient and terminal velocity of spherical and nonspherical particles.
Powder Technol. 1989, 58, 63–70. [CrossRef]
62. Ritter von Rittinger, P. Lehrbuch der Aufbereitungskunde in Ihrer Neuesten Entwicklung und Ausbildung Systematisch
Dargestellt; Ernst & Korn: Berlin, Germany, 1867.
63. Richards, R.H. Ore Dressing; McGraw-Hill: New York, NY, USA, 1908; Volume 2.
64. Beck, A.; Holtham, P. Computer simulation of particle stratification in a two-dimensional batch jig. Miner. Eng.
1993, 6, 523–532. [CrossRef]
65. Mishra, B.; Mehrotra, S. Modelling of particle stratification in jigs by the discrete element method. Miner. Eng.
1998, 11, 511–522. [CrossRef]
66. Crespo, E. Modeling segregation and dispersion in jigging beds in terms of the bed porosity distribution.
Miner. Eng. 2016, 85, 38–48. [CrossRef]
Minerals 2020, 10, 998 30 of 31

67. Mayer, F. A new theory concerning the mechanism of settling with its consequences for the rational shape of
the diagram of the washing stroke and development of the corresponding regulator of a non-plunger jig.
In Proceedings of the 1st First International Coal Preparation Conference, Paper A7. Paris, France; 1950;
pp. 316–322.
68. Mayer, F. Fundamentals of a potential theory of the jigging process. In Proceedings of the 7th International
Mineral Processing Congress, New York, NY, USA; 1964; pp. 75–86.
69. King, R. A quantitative model for gravity separation unit operations that rely on stratification. In APCOM
87: Proceedings of the Twentieth International Symposium on the Application of Computers and Mathematics in
the Mineral Industries, Johannesburg, South Africa, 19–23 October 1987; South African Institute of Mining and
Metallurgy: Johannesburg, South Africa, 1987; pp. 147–151.
70. King, R.P. Modeling and Simulation of Mineral Processing Systems; Elsevier: Amsterdam, The Netherlands, 2001.
71. Tavares, L.; King, R. A useful model for the calculation of the performance of batch and continuous jigs.
Coal Prep. 1995, 15, 99–128. [CrossRef]
72. Woollacott, L.; Bwalya, M.; Mabokela, L. A validation study of the King stratification model. J. S. Afr. Inst.
Min. Metall. 2015, 115, 93–101. [CrossRef]
73. Rao, B.V. Extension of particle stratification model to incorporate particle size effects. Int. J. Miner. Process.
2007, 85, 50–58. [CrossRef]
74. Woollacott, L.C. On the size dependence of the King stratification index. Miner. Eng. 2018, 124, 86–97.
[CrossRef]
75. Woollacott, L. The impact of size segregation on packing density in jig beds: An X-ray tomographic study.
Miner. Eng. 2019, 131, 98–110. [CrossRef]
76. Burt, R. The role of gravity concentration in modern processing plants. Miner. Eng. 1999, 12, 1291–1300.
[CrossRef]
77. Bird, B. Interpretation of Float-and-sink Data. In Proceedings of the Third International Conference on
Bituminous Coal, Pittsburgh, PA, USA, 16–21 November 1931; p. 722.
78. Lill, G.; Smith, H. A study of the motion of particles in a jig bed. In Proceedings International Congress of
Mineral Dressing; IMM London: London, UK, 1960; pp. 515–535.
79. Kowol, D.; Matusiak, P. Badania skuteczności osadzarkowego oczyszczania kruszywa z ziaren w˛eglanowych.
Min. Sci. 2015, 22, 83–92.
80. Olajide, O.; Cho, E. Study of the jigging process using a laboratory-scale Baum jig. Min. Metall. Explor.
1987, 4, 11–14. [CrossRef]
81. Pita, F.; Castilho, A. Influence of shape and size of the particles on jigging separation of plastics mixture.
Waste Manag. 2016, 48, 89–94. [CrossRef]
82. Woollacott, L.; Silwamba, M. An experimental study of size segregation in a batch jig. Miner. Eng.
2016, 94, 41–50. [CrossRef]
83. Ambrós, W.M.; Sampaio, C.H.; Cazacliu, B.G.; Conceição, P.N.; dos Reis, G.S. Some observations on
the influence of particle size and size distribution on stratification in pneumatic jigs. Powder Technol.
2019, 342, 594–606. [CrossRef]
84. Abreu, C.R.; Tavares, F.W.; Castier, M. Influence of particle shape on the packing and on the segregation of
spherocylinders via Monte Carlo simulations. Powder Technol. 2003, 134, 167–180. [CrossRef]
85. Escudie, R.; Epstein, N.; Grace, J.R.; Bi, H.T. Effect of particle shape on liquid-fluidized beds of binary
(and ternary) solids mixtures: Segregation vs. mixing. Chem. Eng. Sci. 2006, 61, 1528–1539. [CrossRef]
86. Osborne, D.G. Coal Preparation Technology; Springer: Dordrecht, The Netherlands, 1988.
87. Kumar, S.; Venugopal, R. Performance analysis of jig for coal cleaning using 3D response surface methodology.
Int. J. Min. Sci. Technol. 2017, 27, 333–337. [CrossRef]
88. Feil, N.F.; Sampaio, C.H.; Wotruba, H. Influence of jig frequency on the separation of coal from the Bonito
seam—Santa Catarina, Brazil. Fuel Process. Technol. 2012, 96, 22–26. [CrossRef]
89. Ambrós, W.M.; Cazacliu, B.G.; Sampaio, C.H. Wall effects on particle separation in air jigs. Powder Technol.
2016, 301, 369–378. [CrossRef]
90. Garcimartín, A.; Pastor, J.; Arevalo, R.; Maza, D. Convection in a vibrated granular layer. Eur. Phys. J. Spec. Top.
2007, 146, 331–340. [CrossRef]
91. Majid, M.; Walzel, P. Convection and segregation in vertically vibrated granular beds. Powder Technol.
2009, 192, 311–317. [CrossRef]
Minerals 2020, 10, 998 31 of 31

92. Rosato, A.D.; Blackmore, D.L.; Zhang, N.; Lan, Y. A perspective on vibration-induced size segregation of
granular materials. Chem. Eng. Sci. 2002, 57, 265–275. [CrossRef]
93. Xue, K.; Zheng, Y.; Fan, B.; Li, F.; Bai, C. The origin of granular convection in vertically vibrated particle beds:
The differential shear flow field. Eur. Phys. J. E 2013, 36, 8. [CrossRef]
94. Zhang, F.; Wang, L.; Liu, C.; Wu, P.; Zhan, S. Patterns of convective flow in a vertically vibrated granular bed.
Phys. Lett. A 2014, 378, 1303–1308. [CrossRef]
95. Evesque, P.; Rajchenbach, J. Instability in a sand heap. Phys. Rev. Lett. 1989, 62, 44. [CrossRef] [PubMed]
96. Yang, S. Density effect on mixing and segregation processes in a vibrated binary granular mixture. Powder
Technol. 2006, 164, 65–74. [CrossRef]
97. Goldhirsch, I. Introduction to granular temperature. Powder Technol. 2008, 182, 130–136. [CrossRef]
98. Fan, Y.; Hill, K.M. Phase transitions in shear-induced segregation of granular materials. Phys. Rev. Lett.
2011, 106, 218301. [CrossRef] [PubMed]
99. Sbárbaro, D.; Del Villar, R. Advanced Control and Supervision of Mineral Processing Plants; Springer Science &
Business Media: Berlin/Heidelberg, Germany, 2010.
100. Duchesne, C. Multivariate image analysis in mineral processing. In Advanced Control and Supervision of
Mineral Processing Plants; Springer: Berlin/Heidelberg, Germany, 2010; pp. 85–142.
101. Zhang, Q.; Huang, C.; Jiang, D.; Wei, X.; Qian, Z.; Wei, F. Particle measurement sensor for in situ determination
of phase structure of fluidized bed. Particuology 2009, 7, 175–182. [CrossRef]
102. Cai, R.; Qiu, J. Position and posture determination of a large dense object in a fluidized bed. Flow Meas. Instrum.
2016, 51, 40–48. [CrossRef]
103. Ambrós, W.M. Novos Aspectos da Estratifificação de Partículas em Jigues Descontínuos. Ph.D. Thesis,
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brasil, 2017.
104. Ambros, W.M.; Sampaio, C.H.; Cazacliu, B.G.; Miltzarek, G.L.; Miranda, L.R. Usage of air jigging for
multi-component separation of construction and demolition waste. Waste Manag. 2017, 60, 75–83. [CrossRef]

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