and dynamic failure associated with coal mines has been a longstanding topic of
research. Coal mine bursts though reported over a century ago, are prevalent even today. The coal burst
research started after first accidents took place in 1920’s in the US. Many coal bursts accidents have occurred world over and are one of the main causes of fatalities
underground. Many technical issues raised long ago are still being debated.
Experience of earlier researchers suggests
it is difficult to define the exact mechanism responsible for coal bursts
because of its complex nature. Researchers have developed several methods to
predict or mitigate coal bursts, but, recent coal bursts in China and Australia
again raises the question, how much do we know about coal bursts? With the
continuous increase in production targets and the introduction of new methods of coal extraction, increasing
complexities of stress regime due to the increasing depth of working, gas pressure, etc., the research on coal burst is
continuing and will continue in future.

this project work, empirical, experimental, numerical simulation and risk
assessment approaches will be undertaken to predict, prevent and mitigate the
occurrence of coal bursts and gas outbursts. Empirical equations would be
assessed for identifying causative factors. Experiments would be carried out in
the laboratory to analyse the physico-mechanical properties of coal and coal measure
rocks occurring in different coalfields of Europe. Probabilistic rock burst
numerical modelling would be undertaken to parametrically vary different
causative factors and analyse their influence on the occurrence of coal bursts and gas outbursts. Risk ranking will be
done using multiparameter hierarchical analysis by assigning suitable weight to each causative factor and a generic risk assessment
methodology would then be developed. It is aimed to develop a generic rock
burst prediction methodology for prediction, prevention and mitigation of rock
bursts and gas outbursts which will enhance the safety of mine operators and
improve the life of machines working

1.      Introduction

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Fast depletion of shallow coal deposits by
opencast mining methods necessitates the need to extract coal from the higher depth of cover. Underground
coal mining at the higher depth of cover encounters many challenges, such as pre-existing in-situ stress, gas pressure and gas emission
management, leading to possibilities of coal bumps/rock
bursts (Campoli et al., 1987, Haramy and McDonnell, 1988). Rockbursts
can cause serious damage to man and machines, with over 30,000 events reported
worldwide (Calleja and Nemcik, 2016, Si et al., 2015, Lama and
Bodziony, 1998). Rockbursts
are a common phenomenon in deep coal
mines which may occur due to unfavourable local stress state, gas content,
physico-mechanical properties of the coal seams, etc. (Yin et al., 2016).

Researchers have tried to enlist the factors
responsible for coal burst occurrences in underground mines. The factors can be
grouped into regional factors and local factors (Calleja and Porter, 2016).
Regional factors includes pre-existing in situ stresses (varying with depth of
cover), presence of tectonic disturbances (Iannacchione and Zelanko, 1995a),
mechanical properties of coal (including strength and post peak behaviour),
geological conditions of strata (including thickness, rigidity, massiveness,
stiffness, strength of immediate roof and floor), presence of geological
features (like faults/displacement faults, shatter zones, cleavage, pyrite
veins, cylindrical and smooth fractures (Iannacchione and DeMarco, 1992),
fracture zones, seam dips, rapid topographic changes (Calleja and Porter, 2016),
slips, massive sandstone paleochannels scours, rolls, trough beds, cross beds,
coal stringers, clay bands (Iannacchione and Zelanko, 1995a), etc.
Local factors include depth of cover,
seam thickness, the method of mining
(including extraction sequences, mine layouts affecting stress redistributions,
multiple seam mining interactions, etc.) (Holland and Thomas, 1954). Of
all the factors, the method of mining is controllable while other factors are
beyond human control which makes it difficult to prevent the occurrence of coal
burst and gas outbursts.

Understanding the mechanism of rock burst
occurrence is the first step in predicting and mitigating the rock bursts.
Based on the mechanism of occurrence,
Rice classified bumps as excessive pressure and shock bumps as cited in (Iannacchione and DeMarco, 1992). (Babcock and Bickel, 1984) extended
the classification proposed by Rice by including loss of confinement. (Board et al., 1992)
categorized rock bursts into two types, viz. Crush type and Shear type. Rock
bursts occurring due to faulty mining layouts causing improper stress
redistributions and stress concentrations across mine opening were categorized
as Crush type rock bursts. Rock bursts occurring due to sudden
inelastic movement of rock mass across geological discontinuities or faults
resulting in seismic waves (Calleja and Nemcik, 2016) can
be grouped into Shear type rock bursts (Board et al., 1992).
(Brady and Brown, 2005) used
different terminology and categorized the rockbursts into Type I (Fault slip)
and Type II (Overstressed rock mass). (Ortlepp and Stacey, 1994, Iannacchione and Tadolini,
2015) found
that seismically triggered (Shear type) rock bursts are more destructive as
compared to Crush type rock burst because they cause significant dynamic stresses
in the coal resulting in their dynamic failure.

Excessive pressure mechanism
proposed by Rice was extended by (Holland and Thomas, 1954). It analyses
the coal mine structure like coal pillars, barrier pillars and other remnants,
which act as stress concentrators and are subjected to excessive stresses that
occur due to large abutment pressures and improper stress redistribution. A
combination of factors like the massive immediate roof, pillars adjacent to
goaf, rib-crushing, abutment loading,
etc. can produce high stresses in the pillar core. (Holland and Thomas, 1954) made an analogy of coal pillar failure in
excessive pressure mechanism to the uniaxial compressive test done in the
laboratory. As the laboratory testing machine having post failure stiffness
less than the coal stiffness, drives the
coal to failure by suddenly releasing the elastic strain energy stored in the
platen due to extension and the sample
fails violently at the point of failure. Similarly, if the local mine stiffness
is less than the coal pillar stiffness, the large pillars (comparatively
stiffer than the smaller pillars) tend to gather higher load as compared to the
surrounding and may fail violently when extraction starts around or within the

Seismic shock burst mechanism
was introduced by Rice in late 1920’s as cited in (Iannacchione and DeMarco, 1992).
Seismic shock may get generated because of sudden failure of strata spanning a
goaf area. The impact of massive volume
of rock onto mine floor may generate shock waves which can travel through the
intervening strata and affect coal pillars in the vicinity. As the seismic
waves propagate through the mine, it
compresses and then subsequently extends the coal pillars. This causes an
increase in dynamic load on the coal pillars resulting in unstable stress state
in the pillar leading to violent failure.

Loss of confinement mechanism
of coal burst was proposed by (Babcock and Bickel, 1984),
based on laboratory experimental observations. They propounded that reducing
the confinement while maintaining the vertical stress state within a load
frame, results in violent failure. Taking an analogy from this observation,
they postulated, if a coal pillar loses its confinement rapidly between coal
and roof or coal and floor or experiences a reduction in the confinement by
mining into the elastic core of a pillar,
it can lead to the dynamic failure of the pillar.

The next step is to identify the bursting liability
of coal pillars. There are different factors known as prime factors used for
detection of coal mine burst (Zhao and Jiang, 2009). The
prime factors include mining depth, strong and stiff roof/floor, tectonic
stress concentration, mechanical properties of coal and geological structures.
Multivariable coupling factors are indices derived from the combination of different
prime factors which are used to assess the burst potential. Some of the multivariable coupling factors used for coal
burst detection includes Energy Release Rate (Salamon, 1984), Drilling yield test
 (Kidybinski, 1981, Haramy and McDonnell, 1988), Bump liability
index (Kidybinski, 1981), Tao discriminant
index (Ahmed et al., 2017), Rock deformation
measurement, Brittleness Coefficient (Ahmed et al., 2017), Burst Potential
Index (Mitri et al., 1999), Disturbed ratio (Zhao and Jiang, 2009), Coal pillar
stability index (Zhao and Jiang, 2009), etc. Advanced
methods like seismic monitoring, seismic tomography, electromagnetic radiation,
stress/strain monitoring and numerical modelling can also provide an estimate
regarding bursting liability (Calleja and Porter, 2016). Microseismic
monitoring can provide a prognosis of the vulnerable areas. Numerical modelling
can assist in optimizing mine design to minimize coal bursts occurrences based
on stress analysis.

Once the indices used for assessing burst potential
indicates liability of burst, suitable control technique should be adopted. The
approaches to control bursting can be broadly classified into predictive,
preventative and protective control measures (Calleja and Porter, 2016).

The predictive measures of
coal burst control involve identifying the coal burst risks prior to mining.
This gives the mine planners an insight into the likely ground control problems
which may be encountered and gives ample opportunity to make design changes to
minimize or eliminate the risk. Improving mining methods like adopting a straight line of retreat, maintaining small and
uniform pillars, sequential splitting may help in stress redistribution in a controlled manner away from the working face of
the mine (Iannacchione and DeMarco, 1992, Iannacchione and
Zelanko, 1995b). Risk
assessment is the most important tool for prediction of coal bursts. Risk
assessment, in case of coal burst, would
be a function of the geotechnical and mining
environment at the mine site and would be site-specific. In absence of
any universal quantitative risk rating scale, individual factors may be rated
as per their importance and dominance on a site-by-site
basis (Mark and Gauna, 2015).
Presence of bursting history in the mine is one of the important risk indicators. Risk assessment includes risk
identification, risk monitoring and application of risk management control
measures. Zoning and levelling method of coal burst prediction uses an
integrated approach taking inputs from regional risk assessment and combining
them with the real-time local and point
forecast obtained from traditional testing methods like Drill yield method, and
advanced approaches like microseismic monitoring, electromagnetic radiation (Dou et al., 2014). As
per (Calleja and Porter, 2016), no
risk assessment guidelines are available for coal bursts in Australia.

Preventive measures of coal
burst control are based on destressing
the stressed coal at the active face. This is the most logical method to
prevent bursts and has been tried for a few decades now (Konicek et al., 2013). Over
the period, several other control techniques have also been tried with varied
successes. Common control techniques include Volley Firing (Haramy et al., 1985, Haramy and McDonnell, 1988), Thin pillar
mining (Iannacchione and DeMarco, 1992), Auger drilling,
preferred in excessive pressure or loss of confinement type of rock bursts (Haramy et al., 1985, Iannacchione and Zelanko, 1995a), Hydraulic fracturing or
hydrofracking, useful in seismic shock and excess pressure type rock bursts (Iannacchione and Zelanko, 1995a, Haramy and
McDonnell, 1988), water infusion, effective in
excessive pressure and loss of confinement type of rock bursts (Iannacchione and Zelanko, 1995a, Konicek et al.,
2013), Shot firing can be used in all
three type of burst mechanism (Iannacchione and Zelanko, 1995a), Provocative
blasting (Calleja and Porter, 2016), etc.

Protective control measures
include the use of flippers and rubber
mats on longwall shields, using blast-proof barriers on continuous miners, use
of dynamic support system which has a capacity to absorb a high level of kinetic energy and maintain
support loads while allowing large deformations (Calleja and Porter, 2016).


and/or Objectives

main aim of the research project is to develop a generic risk assessment
methodology for prediction, prevention and mitigation of coal bursts and gas
outbursts. Developing an index for assessing coal burst potential which will be
based on multiparameter hierarchical analysis by assigning different weight to
causative factors. Developing generic risk assessment methodology for coal bursts
prediction and management.

3.      Methodology

1: Literature review on coal burst and gas outburst mechanism

extensive literature review would be conducted identifying the research gaps.
The present methods of coal burst detection would be analysed for the factors
considered and their limitations. Analysis of past accident reports would help
in identifying the similarities in different coalfields where coal bursts have
previously occurred. The analysis of different causes and control measures adopted
in the past, their success or reasons for failure would be analysed to suggest
suitable risk management and control measures.

2: Laboratory experiments at Imperial College, London for determining
physico-mechanical properties of coal and coal measure rocks

has been emphasized that outburst-prone coal has low permeability, which
leads to gas pressure build-up increasing the chances of rockbursts and gas
outbursts. As per literature, coal having permeability >5mD is not liable to
burst (Si et al., 2015). Hence, permeability assessment of
representative coal samples from different coal mines would be undertaken using
Hassler type flow cell (Hassler, 1944) and triaxial testing machine. The
rock and coal samples would be tested in the triaxial testing machine, to
document the physico-mechanical
properties of rock samples which will be used as the input values for numerical
modelling and in development of generic risk
assessment methodology.

3: Numerical Modelling research on behaviour of mine wide model to dynamic
disturbances and seismic waves

3-dimensional finite difference model in FLAC3D will be simulated
and calibrated with geomechanical
properties of coal determined in the laboratory as the input parameters.
Different FLAC3D material modules would be used to design the mine
wide numerical models for rockburst
analysis. The dynamic analysis module of FLAC3D would be used to
observe the behaviour of the model to
dynamic disturbances caused by seismic waves and analyzing the seismic energy waveforms for potential rockbursts/gas
outburst occurrence. Probabilistic rockburst modelling
using dynamic analysis module of FLAC3D will facilitate in analyzing the effect of different causative
parameters and influence they have in rockburst and gas outburst occurrence.
The analysis will help in assigning weight to different causative parameters in
the multiparameter hierarchical analysis
for risk assessment.

4: Field investigations and validation of numerical models to identify critical
parameters for probabilistic risk assessment modelling

model will be validated at mine sites. The seismic events recorded in the field
will be analyzed for estimating values
above which rockbursts and gas outburst occurs. A
direct correlation exists between micro-seismic events and gas emission
rate which implies that gas emission reaches its peak when seismic energy
release increases. P-wave velocity increases with increasing stress, thus a high-frequency seismic waveform may be a
precursor of a rockburst or gas outburst (Si et al., 2015). The simulation
of dynamic instability caused by seismic waves
and the response of mining model to microseismic wave may serve as a
predictive tool for coal burst and gas outburst occurrence.

5: Development of generic risk assessment methodology based on input from above

data from laboratory experiments, analysis of
stochastic numerical models developed for key critical parameters, and field
monitoring data would be gainfully used as input for developing a probabilistic
risk assessment methodology. The stress and pressure variation would be
evaluated quantitatively for its effectiveness. Several control measures
regarding the risk reduction potential for selected scenarios relevant to the different type of mining method would be
investigated for prediction of events and implementation of effective
mitigation methods.


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