A new methane control and prediction software suite for longwall mines$

$Code available from: http://www.cdc.gov/niosh/mining/products/product180.htm.

Heather N. Dougherty n,

n Corresponding author.

E-mail address: igq0@cdc.gov (H.N. Dougherty).

C. O ̈ zgen Karacan

National Institute for Occupational Safety and Health, Office of Mine Safety and Health Research, Pittsburgh, PA 15236, USA

abstract

This paper presents technical and application aspects of a new software suite, MCP (Methane Control and

Prediction), developed for addressing some of the methane and methane control issues in longwall coal

mines. The software suite consists of dynamic link library (DLL) extensions to MS-AccessTM, written in C++.

In order to create the DLLs, various statistical, mathematical approaches, prediction and classification

artificial neural network (ANN) methods were used.

The current version of MCP suite (version 1.3) discussed in this paper has four separate modules that

(a) predict the dynamic elastic properties of coal-measure rocks, (b) predict ventilation emissions from

longwall mines, (c) determine the type of degasification system that needs to be utilized for given

situations and (d) assess the production performance of gob gas ventholes that are used to extract

methane from longwall gobs. These modules can be used with the data from basic logs, mining, longwall

panel, productivity, and coal bed characteristics. The applications of these modules separately or in

combination for methane capture and control related problems will help improve the safety of mines.

The software suite’s version 1.3 is discussed in this paper. Currently, it’s new version 2.0 is available

and can be downloaded from http://www.cdc.gov/niosh/mining/products/product180.htm free of

charge. The models discussed in this paper can be found under ‘‘ancillary models’’ and under ‘‘methane

prediction models’’ for specific U.S. conditions in the new version.

1. Introduction

Longwall mining is an underground mining method that maximizes coal production from coal beds that contain few geological

discontinuities, such as faults, folds, and pinchouts. In longwall

operations, a mechanical shearer progressively mines a large block

of coal, called a panel, which is outlined with development entries or

gate roads. The location of mining along the panel is known as the

mining face. A schematic representation of a longwall panel is given

in Fig. 1. Longwall mining is a continuous process in an extensive

area, where the roof is supported temporarily with hydraulic

supports, called shields, which protect the workers and the face

equipment. As the coal is extracted, the supports automatically

advance and the roof strata are allowed to cave behind the supports.

The caving of immediate roof strata results in a stress relief in

overlying formations, which fracture horizontally and vertically

based on their mechanical strength (Karacan et al., 2007a). This

caved or fractured zone is called the gob. Singh and Kendorski(1981)

and Palchik (2003) analyzed the nature of strata disturbances due to

longwall mining and predicted that the gob created by the collapse

of immediate roof rocks can reach four to eleven times the thickness

of the mining height depending on the strength and porosity of the

overlying rocks (Fig. 1).

The gob created by longwall mining allows methane that

was once confined within the overlying strata (or reservoir) to

release after fracturing and find a path through gob to flow into

the mine environment. Emissions of methane into the mine atmosphere and accumulation in a working area in the mine may cause a

dangerous mixture of methane and air, which could lead to an

explosion. Therefore, it is critical to be able to predict the magnitude of methane emissions and act with accurate and appropriate methane control measures before the problems become

severe.

Due to the large number of variables affecting potential emission sources, accurate prediction of the rate of methane flow into

the working areas and eventually into the ventilation system is a

complex problem. Current prediction models depend on accurate

representation of the gob and the fractured strata that act as a gas

reservoir in order to predict methane emissions, the amount of

mine-air leakage into the gob, and effective control methods. The

reservoir properties of the gob are extremely important, but

difficult to compile and interpret (Lunarzewski, 1998; Karacan

et al., 2007a). This behavior is a product of the challenges and

unknowns related to the gob environment and its inaccessibility

for direct measurements and inhomogeneity of the overburden.

In this regard, knowledge of the dynamic elastic properties of roof

rock and the overlying coal-measure formations is necessary to

assess the size and thickness of the gob and fractured interval
and to estimate methane flow in these zones (Whittles et al.,

2006, 2007).

Gob gas ventholes (GGVs) are commonly used to control

methane emissions from the fractured strata (Fig. 1).

Shields 

Shearer 

Shearer Shearer 

Shields Flow of 

Tailgate 

entries 

methane 

taker 

Air 

leakage Longwall gob 

area 

Headgate 

Gob gas 

venthole 

Fig. 1. Schematic representations of a longwall mine and the gob created behind the face (modified from Karacan, 2008). Arrows show the direction of air leakage and the

methane paths in the gob. Side view shows the location of a gob gas venthole above the mined coal seam.

They are a

form of supplemental controlthat removes some methane from the

gob, preventing it from entering the mine and increasing the load

on the mine ventilation system. These ventholes are drilled from

the surface to a depth that places them above the caved zone, so

that they usually do not directly interact with the ventilation

system. They are cased, and the bottom section of the casing is

slotted and placed adjacent to the expected gas production zone

(Palchik, 2005). GGVs can be equipped with exhausters, which

provide suction to capture sufficient gas from the fractured zone

before it migrates into the mine. They are drilled prior to undermining, from as little as a week or two to many months beforehand,

and generally become productive after the mining-induced fractures propagate under the well (Diamond, 1994; Karacan et al.,

2007b). To adequately design and locate GGVs, it is important to be

able to predict the performance of potential GGVs with a variety of

borehole and operational parameters. Despite improvements in

analytical and numerical modeling approaches, it is still difficult to

accurately predict methane production for GGVs. Predictions may

underestimate actual gob gas venthole production by at least a

factor oftwo. The key factor in this underestimation is the difficulty

in incorporating a predictive approach with the many factors that

affect venthole performance (Zuber, 1998).

The main mine ventilation system must handle methane from a

variety of sources, including methane that cannot be captured by

supplemental methane controls like GGVs, and emissions due to

the gassiness of the coal bed. The specific methane emissions from

the coal bed vary based on operating parameters. Ventilation air

thatis provided to the mine must be increased for situations of high

methane emissions to dilute them to safe levels before dangerous

situations arise. Generally, it is economically feasible to handle

specific emissions (total gas emission per unit amount of coal

mined) up to 1000 ft3

/ton with a well-designed ventilation system.

At higher specific emission rates, however, it is difficult to stay

within statutory methane limits using ventilation alone (Thakur,

2006). This condition can adversely affect the safety of the underground workforce.
In case of high specific emissions, other supplemental methane

controlmeasures, such as degasification ofthe coal bed using surface

vertical or horizontal wells, in-seam boreholes, or combinations of

methods, are needed to mine the coal safely. It is advantageous, and

economically more feasible, to determine the need for and the most

effective type of degasification well ahead of mining by considering

the various geological, coal bed, and mining parameters. The

preferred methane recovery method depends on the mining parameters and the gassiness of the coal seam. In most cases, a

combination of different drainage methods leads to the highest

recovery ofmethane from the coal bed and its overlying strata before

mining. Since drilling an array of vertical and horizontal boreholes is

costly, a technical assessment prior to mine development is generally needed. Such an assessment requires both empirical and

theoretical approaches. Since a comprehensive mine simulator that

combines mining operation, coal bed reservoir, and methane

production parameters does not currently exist, a technique that

can identify the optimal degasification system for a given set of

mining and geological parameters could be useful as a pre-planning

tool prior to mining.

This paper presents technical methodologies that provide proxy

solutions to the above-mentioned challenges and demonstrates

applications of a new software suite that can help mining operations control and capture methane from longwall mines.

This software suite contains four main modules:

•	 Coal measure rock properties—predicts dynamic elastic properties of coal-measure rock for better roof support and methane

control.

•	 Mine ventilation emission prediction—predicts ventilation air

methane (VAM) emissions from longwall mines.

•	 Degasification system selection—recommends the best degasification choice.

•	 Gob gas venthole production performance prediction—predicts

performance of gob gas ventholes, both methane percent and

production.

2. Methane control and prediction (MCP) tool kit for

longwall mines

The current version (version 1.3, which is soon to be updated

with version 2.0) of the methane control and prediction (MCP)

software was developed with four modules under the MS AccessTM

shell environment. Dynamic link libraries (DLLs) written in C++ are

used to extend the analysis capabilities of MS AccessTM.

The models were developed using a database of information about

mines, including basic well log, mining, longwall panel, productivity,

and coal bed characteristics. The resulting models take the same types

of information as input parameters. In order to obtain good predictions, MCP users need to enter the input parameters as realistically as

possible for their situation. The modules are most accurate within the

given min–max range, but there is some extrapolation flexibility in

the calculation methods, allowing themodules to performpredictions

beyond the minimum and maximum limits.When the entered data is

beyond the limits, the program provides a warning and asks the user

whether he/she would like to continue with the calculations. If the

user approves, the program continues with its calculations.

A mean value is also provided for input parameters. The mean

values are not the arithmetic averages of minimum and maximum

values, but are the statistically determined mean values from the

database employed in developing the models. If the user does not

have a particular input parameter available to them, the mean

value can be inputted as an approximate representative condition

for that parameter.

One of the advantages of these software modules is the ability to

perform sensitivity studies by varying the values of input parameters. Sensitivity studies were prepared in this paper and shown in

Figs. 5, 8, 10, 11, and 13. These study figures were not created in MCP

but with a separate program to better visually analyze the data.

MCP runs with any version of MS AccessTM. Fig. 2 shows the

modules that are available to the user in the MCP model selection

window.

Fig. 2. Model selection window of the methane control tool kit software.

The following sections of the paper will be dedicated to

describe each model individually and their applications for the
interest of the geosciences and methane control/production community. The technical information related specifically to the

development of each model will be kept at minimum and will

be presented in the next section. Interested readers are referred to

Karacan (2008, 2009a, 2009b, 2009c)for more detailed information

on the development of these techniques, which utilize various

statistical, mathematical approaches, prediction and classification

artificial neural network (ANN) methods (Davis, 1986; Grima,

2000; Grima et al., 2000; Hardy and Beier, 1994; Krishnamoorty,

2006; Maier and Dandy, 2000).

3. Brief technical information and applications of the

prediction modules

3.1. Module: coal measure rock mechanical properties prediction

This module predicts the dynamic elastic properties of coalmeasure rock, which can help mining operations understand

properties of the gob and make decisions about both roof support

and methane control.

The module calculates shear and elastic (Young’s) moduli of a

formation using the information from basic gamma ray (GR) and

density (DL) well logs. The shear modulus is the material’s response

to shearing stresses, such as friction, while Young’s modulus is a

material’s response to linear stresses and strains. Young’s modulus

can also be used as an indicator of the stiffness of the material and

of its yield strength. Higher values for Young’s modulus typically

represent stiffer materials.

Dynamic elastic properties canbe directlymeasured by employing

full wave sonic logs in the boreholes and by determining the transit

times for primary and secondary waves in a full wave train. However,

obtaining sonic logs from boreholes requires that the borehole be

uncased and filled with water (Takahashi et al., 2006). Sonic logs may

not be available in all situations, or may be too costly to obtain.

Gamma ray and density logs are commonly run on exploration holes

and are simple to conduct with few special conditions; therefore, this

information is usually readily available. The presented approach and

the associated software evaluates the ‘‘in-situ’’ rock elastic properties

of coal-measure rocks for prediction purposes with reasonable

accuracy (Table 1).

Table 1

Accuracy of performance measure for dynamic elastic properties.

Performance measure Shear-modulus Young-modulus

MSE

Nominal MSE

Mean Abs error (GPa)

Min Abs error (GPa)

Max Abs error (GPa)

R

0.0894

0.3089

0.2209

0.0001

1.0228

0.8337

0.2906

0.1592

0.4213

0.0100

2.0058

0.9183

These outputs are used for strata and fracturing

and gob caving assessments for gas control and roof support. A

statistical analysis was preformed including mean square error (MSE),

Absolute error (Abs error) and R.

The prediction method developed for the dynamic elastic

properties of rocks is based on processing the GR and DL logs by

Fourier transforms, fractal statistics (fGn-fractional Gaussian noise

and fBm-fractional Brownian motion), and then modeling using

radial basis functions (RBF). The development detail ofthis model is

given in Karacan (2009a).

3.1.1. Use and application of coal measure rock mechanical properties

prediction module

Fig. 3 shows the input screen for the coal measure rock

mechanical property calculation model. The model uses the data

from gamma ray and density logs as inputs and calculates shear

modulus and Young’s modulus for the depth or strata of interest.

Fig. 3. Input screen for coal measure rock mechanical properties prediction model.

The gamma ray and density log values from exploration boreholes

are used to determine coal quality but are also used to assess the

strength and possible geological abnormalities of the ore and the

surrounding strata. Fig. 4 shows the output table obtained after

using this model.

Fig. 4. Output screen for coal measure rock mechanical properties prediction

model.

Although not explicitly programmed in this software, the shear

and elastic (Young’s) moduli can then be used to determine the

bulk modulus and Poisson’s ratio using the following relations:

E 1⁄4 2Gð1þsÞ

E K 1⁄4 3ð1-2sÞ

where E is the Young’s modulus (GPa), G is the shear modulus (GPa),

K is the bulk modulus (GPa) and s is the Poisson’s ratio (psi/psi).

Fig. 5 shows the results of a sensitivity study in which gamma ray

and density log values were used to obtain different values of Young’s

and shear moduli. The figure shows that as the gamma ray reading

increases, both Young’s modulus and shear modulus decrease,

indicating the presence of clays and higher natural radioactivity. This

data is consistent with weaker rocks, such as shales. Stronger rocks, on

the other hand, exhibit higher values ofYoung’s modulus, density, and

shear modulus and a lower gamma ray value, which is consistent with

sandstones and limestones (Karacan, 2009d). These different kinds of

overlying rockwillimpartdifferent characteristics to the gobandhave

implications for methane control measures. Also, knowing the types

of rocks and their elastic properties will improve the understanding of

caving behavior of overlying formations and the basic requirements

for better roof-support design.
Fig. 5. Surface plot created from a graphics program showing shear modulus (GPa)

and Young’s modulus (GPa) outputs obtained using different density (g/cc) and

gamma ray (cps) input values. The data points are shown under the surface as dots.

3.2. Module: mine ventilation emissions prediction

This module uses an artificial neural network (ANN)-based

methodology to predict ventilation emissions from longwall mines.

Ventilation emissions data obtained from different U.S. mining

regions were combined with corresponding coalbed properties,

geographical information, longwall operation parameters, and productivities to create a database. The database was analyzed using

principle component analysis (PCA) to reduce complexity and to

determine the most influencing variables for ANN modeling (Grima,

2000). Table 2 shows the results of PCA after Kaiser’s varimax

rotation and the most influencing parameters for ANN modeling.

Table 2

Factor loadings of the variables after rotating the principle component (PCR) matrix

using Kaiser’s varimax rotation. Bold entries show the most influential variables in

each PCR.

Variables PCR 1 PCR 2 PCR 3 PCR 4 PCR 5

Degasification 0.472 0.221 0.163 0.245 0.538

Basin -0.287 -0.007 0.917 -0.136 -0.145

State 0.002 0.049 0.951 -0.196 0.002

Seam height 0.064 0.113 -0.093 0.925 -0.063

Cut height 0.048 -0.027 -0.225 0.911 -0.043

Panel width 0.036 0.798 -0.006 0.004 -0.029

Panel length -0.248 0.701 0.093 -0.202 0.052

Overburden 0.808 -0.075 -0.129 0.108 0.121

Number of entries 0.271 -0.178 -0.045 -0.224 0.805

Cut depth 0.125 0.745 0.056 0.142 -0.076

Face conveyor speed 0.145 0.834 0.116 0.056 -0.167

Stage loader speed 0.147 0.811 -0.048 0.070 0.105

Lost+desorbed gas 0.954 0.024 -0.187 0.065 -0.011

Residual gas -0.244 0.237 0.748 0.032 0.372

Total gas 0.960 0.077 -0.036 0.076 0.068

Rank 0.907 0.031 -0.174 -0.091 0.186

Coal production -0.221 0.688 0.251 0.114 0.036

Table 2 gives not only the loadings of each variable in rotated

components (PCR), but also shows how the variables are separated

between columns according to their characteristics or to the

properties that they represent. The table shows that the first PCR

is mostly related to gas content of the mined coalbed with both

overburden and rank positively correlated with total and lost plus

desorbed gas contents. The highestloading is from total gas content

(0.960), followed by lost plus desorbed gas (0.954), rank (0.907),

and overburden thickness (0.808). The second PCR represents

longwall panel dimensions, coal productivity, and underground

coal transportation. In this group, face and stage loader conveyer

speeds have the highest loadings (0.834 and 0.811, respectively),

followed by longwall panel width, cut depth, and panel length. The

loading of coal production is less. Based on the results of PCA and

comparative evaluation of various input parameters for their influence on the results, total gas content, panel width, face conveyor

speed, coal production, state, seam height, cut height, stage loader

speed, and number of entries were selected as the input variables for

the ventilation emission model.

The ANN model was built using a multilayer perceptron (MLP)

approach and was trained and tested using the database to achieve

minimum mean square error (MSE) and high correlations (R)

between measurements and predictions. Table 3 shows the performance parameters obtained from the testing stage of the developed

network. For detailed information, readers are requested to refer to

Karacan (2008).

Table 3

Testing performance ofthe final ANN model used in the software after optimizing its

network parameters.

Performance parameter

MSE 1.613

NMSE 0.086

Min. error (MMscf/day) 0.003

Max. error (MMscf/day) 3.087

R 0.956

3.2.1. The use and application of mine ventilation emissions

prediction model

This module calculates the predicted ventilation methane

emissions from a longwall mine located in one of a pre-determined

list of coal producing states (or in basins that are characteristically

similar in geology to one of the states). The prediction of methane

emissions is based on the location of the mine and a set of mining

and geological conditions that most affectthe ventilation emissions

as determined by PCA. The pre-determined list of states is available

in a pull-down menu on the input screen (Fig. 6), along with other

required inputs.

Fig. 6. Input screen for mine ventilation emissions prediction model.
The output screen gives the values ofthe input parameters and the

predicted methane emissions in millions of cubic feet of methane per

day (MMsfc/day). To run the model again with different conditions,

the user can select the ‘‘re-run model’’ option (Fig. 7).

Fig. 7. Output screen for mine ventilation emissions prediction model.

Fig. 8 shows information from application of the mine ventilation emissions prediction model conducted for mines operating in

different states with different mining and geological conditions.

Fig. 8. 3-D graph showing the predicted mine emissions (MMscf/day)from mines in

various states as a function of coal seam gas content (scf/ton) and coal production

(MMtons/year).

Mines in West Virginia, Alabama, and Virginia have the highest

gas content, and therefore a higher emissions rate from their

respective ventilation systems. These mines have lower yearly coal

production and a higher ventilation air flow to lower the concentration of methane in the ventilation air. The gas content of the coal

seams mined in Pennsylvania, Ohio, Maryland, Kentucky, Illinois,

and Colorado are relatively lower, and thus the mines in these

states have lower ventilation emissions and higher production

compared to the West Virginia, Alabama, and Virginia mines.

The output of this module, total predicted ventilation emissions

in millions of cubic feet per day, can be helpful in determining the

needs of multiple projects in the mine. For example, having a good

estimate of the total methane output can provide the operator with

information to size a ventilation fan or to determine a ventilation

strategy.

3.3. Module: degasification system selection

Since ventilation alone may not be sufficient to control the

methane levels on a longwall operation, gob gas ventholes (GGV, or

G), horizontal (H) and vertical drainage boreholes (V), or combinations of these systems (HG or VHG) are drilled and used as

supplementary methane control measures. In most cases, mining

operations base their choice of degasification system on previous

experiences, sometimes without analyzing the different factors

that may have affected these decisions.

Table 4 shows the rotated matrix for five components (PCR) and

the factor loadings for each variable during PCA. This table also

shows how the variables are separated between columns according

to their characteristics. Table 4 shows that the first PCR is mostly

related to gas content of the mined coalbed, overburden rank, and

the methane emissions.

Table 4

Factor loadings of the variables after rotating the principle component matrix using

Kaiser’s varimax rotation. Bold entries show the most influential variables in each

rotated principle component (PCR).

Variables PCR 1 PCR 2 PCR 3 PCR 4 PCR 5

Basin -0.296 0.010 -0.137 0.907 -0.133

State -0.004 0.037 -0.198 0.952 -0.020

Seam height 0.070 0.076 0.939 -0.078 -0.070

Cut height 0.054 -0.036 0.917 -0.211 -0.067

Panel width 0.100 0.820 0.035 0.011 -0.059

Panel length -0.187 0.751 -0.192 0.105 -0.035

Overburden 0.817 -0.083 0.112 -0.124 0.131

Number of entries 0.276 -0.158 -0.168 0.022 0.853

Cut depth 0.186 0.721 0.118 0.064 -0.272

Lost+desorbed gas 0.949 -0.054 0.067 -0.173 -0.031

Residual gas -0.212 0.279 0.042 0.777 0.285

Total gas 0.962 0.002 0.080 -0.016 0.029

Rank 0.917 -0.011 -0.082 -0.163 0.193

Coal production -0.173 0.755 0.121 0.213 0.061

Ventilation emission 0.646 0.339 0.104 -0.251 0.369

The highest loading is from total gas

content (0.962), followed by lost plus desorbed gas (0.949), rank

(0.917) and overburden thickness (0.817). Emissions measured

from the ventilation system have a loading of 0.646. Selection of a

degasification system is a direct consequence of emissions or the

capacity ofthe ventilation system. Thus, it should be included in any

model. The second PCR is weighted by longwall panel dimensions,

cut depth, and coal production. In this group, panel width and coal

production have the highestloadings (0.820 and 0.755),followed by

longwall panel length (0.751), and cut depth (0.721). The third PCR

in Table 4 represents the coalbed and mining heights, where their

loadings are 0.939 and 0.917, respectively. The fourth PCR in Table 4

is related to geographical location of the mine determined by state

and coal basin. They are the only variables in the database that may

be linked to the impact of underground geology on emissions from

overlying strata. Their loadings in this PCR are 0.952 and 0.907,

respectively. However, since a coal basin can be present in more

than one state and underground geology may change based on

geographical location,the state variable ismore localized and seems

to be a better identifier for this purpose. The fifth PCR represents the

number of gateroad entries.

Based on the results given in Table 4, and comparison tests

performed with the ANN, total gas content, panel width, coal

production, state, seam height, cut height, overburden thickness

and ventilation emissions were selected as the input variables for

the degasification system identification model.

The ANN-based classification model was built using a multilayer perceptron (MLP) approach to map the inputs to four different

degasification options outputting the ‘‘best’’ option with the

highest probability. Such a model can be used as a decision tool
for selection of a degasification system and can also be used as a

screening or planning model. Based on the performance of the

network with different elements,the final network had two-hidden

layers with 48 and 28 processing elements, hyperbolic tangent and

softmax axon as the transfer functions, a momentum term of 0.7

and a training epoch of 1500. Table 5 gives the performance of the

classification network that was developed and implemented in

the software for degasification system identification.

Table 5

Testing performance of the final ANN model (Figure C-10) after optimizing network

parameters.

ANN Output HG (Horizontal

+GVB)

N (None) G (GVB) VHG (Vertical+

Horizontal+GVB)

HG

N

G

VHG

% Correct

21 (True)

0

1 (False)

0

95.5

3 (False)

29 (True)

0

0

90.6

1 (False)

2 (False)

12 (True)

0

80.0

0

0

0

12 (True)

100.0

The detailed

explanation of the development of this model is included in

Karacan (2009b).

Degasification systems have become necessary due to their

ability to remove most of the gas from the coal seam prior to

mining, allowing operations to safely extract the coal. Currently,

mines use different methods to find the most cost-effective way to

mine safely and efficiently. With a selection model now available to

design degasification plans, the mining operation can more precisely and efficiently choose methods that will maximize degasification and minimize methane-induced ventilation issues.

This module uses an ANN-based expert classification system to

identify the need and the type of degasification system best suited

for a particular longwall operation. The ANN-based classification

model was built using a multilayer perceptron (MLP) approach to

map the inputs to four different degasification options and output

the probabilities of each output options, as reviewed in the

application section given in 3.3.1. The model can be used as a

decision tool for selection of a degasification system and as a

screening or planning model. For a detailed explanation of the

development of this model, see Karacan (2009b).

3.3.1. The use and application of degasification system selection

model

This expert classification system identifies the need and the

type of degasification system for a longwall operation given minespecific inputs. The model maps the inputs to four different

degasification options:

J N: No degasification needed

J G: Gob gas venthole only

J HG: Horizontal boreholes and gob gas ventholes

J VHG: Vertical boreholes, horizontal boreholes, and gob gas

ventholes.

As in the emissions prediction module, the user must select the

state from the pull-down menu on the input screen, and enter

additional coal and mining characteristics. When the model is

executed, it calculates the choice probabilities for available degasification systems based on the inputs given, and maps the

probabilities to the four degasification systems.

The output screen for this module shows input information, the

calculated probabilities for each system, and whether the system is

recommended (Fig. 9).

Fig. 9. Output screen for degasification system selection model. This particular

scenario resulted in a ‘‘best’’ recommendation for a system using vertical and

horizontal boreholes plus GGVs.

The system with the highest positive

probability calculated is labeled ‘‘best’’ in the recommendation

column. Recommended ‘‘best’’ option in this module indicates the

degasification system choice with highest probability of successfully removing methane from the mine environment.

Fig. 10 shows thatthe mines operating in coal seams with higher

gas content and under mid to high overburden depth require a

more extensive degasification system comprised of vertical degasification holes, horizontal degasification holes, and gob gas ventholes (shown in red as VHG in this figure), while mines operating in

coal seams with less gas content and in shallower depths do not

need multiple degasification techniques (N: none needed or G:

GGVs only) to supplement their ventilation system. Mines operating in overburden depths from 750 to 1250 ft and in coal seams

with gas contents less than 200 scf/ton typically have ventilation

emissions from 1 to 10 MMscf/day.

Fig. 10. 3-D scatter diagram showing the distribution of the type of degasification

system (type of Deg) as a function of overburden depth (ft), coal seam gas content

(scf/ton), and ventilation system emissions (MMscf/day).

These mines are good candidates for degasification systems shown in both blue (G: GGVs only)

and green (HG: Horizontal boreholes and GGVs).

Fig. 11 shows the results of comparing the overburden (ft), panel

width (ft), and production (tonnes/year) to the type of degasification system recommended. It can be seen thatthe program predicts
that mines in the mid to high range of overburden depths should

have more extensive degasification systems, while mines in lower

overburden have minimal degasification, which indicates that the

coals have lower gas contents. This prediction is consistent with

mining experience: deeper coals tend to be gassier than shallower

coals. Fig. 11 also shows that mines with lower gas emissions have

higher coal production rates and do not always need degasification

systems.

Fig. 11. 3-D Surface diagram showing the overburden (ft), coal production (MMtons/year), panel width (ft), and the type of degasification method suggested (type of degassif)

using the degasification system selection module. The data points are shown under the surface as dots.

3.4. Module: gob gas venthole production performance prediction

Gob gas ventholes are used to help control methane inflows into

a longwall operation by capturing it before it enters the ventilation

system. It is important to understand the effects of various factors,

such as drilling parameters, location of borehole, applied vacuum,

and mining/panel parameters to evaluate the performance of GGVs

and to predicttheir effectiveness in controlling methane emissions.

Until the development of MCP, a practical model for this purpose

did not exist. This module develops an ANN-based methodology to

predict GGV production rates and methane concentrations based

on venthole location, mining parameters, borehole location with

respect to panel and surface terrain, and exhauster pressure.

Detailed information is given in Karacan (2009c).

Various factors affect the performance of gob gas ventholes

producing from an active or completed mine. Table 6 shows the

sensitivity values of outputs to these various inputs, as well as the

total sensitivity in standard deviations about the mean due to

sensitivities of gas flow rate and methane percentage to inputs

when they are considered together.

Table 6

Sensitivity (in standard deviations) of performance parameters (gas flow rate and

percent methane) to individual input parameters used in this model and the total

sensitivity (third column) when both performance parameters are combined. Bold

values are the total sensitivity above the average sensitivity value (33.8) calculated

using all inputs.

Input variable Gas flow Percent Total

rate methane (Std. dev.)

Panel completed (Yes) 13.1 8.1 21.2

Panel completed (No) 28.2 3.6 31.9

Is face advancing? (No) 24.3 10.3 34.7

Is face advancing? (Yes) 18.7 0.3 19.0

% of panel mined 45.2 3.1 48.3

Linear advance rate 7.5 1.4 8.9

Surface elevation 34.4 6.6 41.0

Overburden 16.1 4.2 20.3

Casing diameter 81.2 4.4 85.6

Casing distance to coalbed 7.7 2.2 10.0

Distance to tailgate 67.7 1.7 69.5

Distance from panel start 52.3 12.2 64.5

Panel length 37.7 4.3 41.9

Panel width 5.1 1.4 6.5

Barometeric pressure 14.0 0.6 14.6

Average vacuum at wellhead 22.1 1.9 24.0

The average total sensitivity

was calculated as the arithmetic average of third data column in

Table 6, which gave a value of 33.8. For the sake of this analysis, the

total sensitivity values above this average of 33.8 were considered

as high sensitivities of gob gas venthole performance to the

corresponding input variables. According to this approach and

the data in Table 6,(a)face advance (whether itis advancing or not),

(b) percentage of the panel that has been mined, (c) surface

elevation of the venthole (above sea level), (d) casing diameter,

(e) distance of the venthole to the tailgate, (f) distance of venthole

to panel start, and (g) panel length are more influential for the

venthole performance.

It is important for methane control to have a practical model to

decide the design parameters of a GGV before drilling it, or to respond

to an emerging situation by changing some of the operating parameters. However, a practical model for these purposes currently does

not exist. The aim ofthis software module is to develop anANN-based

methodology to predict gob gas venthole production rates and
methane concentrations based on venthole location, mining parameters, borehole location with respect to panel and surface terrain,

and exhauster pressure.

For this purpose, a two-layer ANN model with 22 processing

elements in the first hidden layer and with 16 processing elements

in the second hidden layer was developed. Hyperbolic tangent

activation function, ’’Tanh,’’ between the layers with momentum

parameters of 0.7 and 2500 iterations were also used. Table 7 gives

the performance of the network model that was used and

programmed into this module to predict production performance

of the GGVs. Detailed information is given in Karacan (2009c).

Table 7

Predictive performance of the GGV performance prediction network obtained

during testing phase.

Performance indicator Total flow scf/day Percent methane

Nominal MSE

Mean Abs error

Min Abs error

Max Abs error

R

0.13569

1235.92

166.91

20,628.04

0.930

0.08595

3.52427

0.02904

24.2980

0.956

3.4.1. The use and application of GGV production performance

prediction model

This module calculates the total gas production rates and the

methane concentrations in the produced gas stream of the GGV

based on venthole location in the panel, mining parameters,

venthole location with respect to panel and surface terrain, and

applied exhauster pressure. The input screen requires the panel

status (active or completed), face status (advancing or idle), venthole location data, venthole completion parameters, and the

operational properties of its exhauster. The model output is GGV

total production in scfm and methane concentration percentage

(Fig. 12).

Fig. 12. Output screen from the gob gas venthole production performance model,

showing the predicted total gas flow rate (scfm) and methane concentration (%)

from a GGV and the associated input data.

Scenarios with various input parameters were analyzed to test

the outputs and the sensitivity of the model. One scenario analyzed

the relationships between distance of the GGV from the start of the

panel, GGV total production rate, panel length, and linear face

advance (Fig. 13).

Fig. 13. Surface diagram showing the distance of the GGV from the start of the panel (ft), gob gas venthole (GGV) total production rate (scfm), panel length (ft) and linear face

advance (ft/day) obtained using the GGV production performance prediction module. The data points are shown under the surface as dots.

The analysis shows that the GGV has a higher

production rate when operating above a longwall with a length of

9500–12,000 ft and witha linear advance rate greater than 30 ft/day.

When varying the length of the longwall block and linear advance

rate,the variable ‘‘GGV distance from the start’’ did not seem to have

a great impact on the output values. MCP allows the user to keep the

other variables constant while changing the GGV distance from the

panel start, and therefore can show how this variable can influence

both GGV production and methane percentage.

Another scenario analyzed methane concentration (Fig. 14). The

highest predicted methane concentrations occurred atthe start and

end ofthe longwall panels. The largest concentration was predicted

at the beginning of the panel and with a linear advance rate

between 25 and 30 ft/day. This is not surprising, because the first

GGV in a panel has a larger reservoir of easily accessible methane

primarily due to abutment effects at the ends of the longwall panel

than GGVs in other locations along the panel. A higher predicted

concentration of gas and production rate is also visible at the end
of the panel further from the start. As with the first GGV, this

indicates thatthere is a larger gas reservoir from which the GGV can

produce.

Fig. 14. Surface diagram showing the distance of the GGV from the start of the panel (ft), gob gas venthole (GGV) total production rate (scfm), linear face advance (ft/day), and

methane concentration (meth conc) from GGV (%) obtained using the gob gas venthole production performance prediction module. The data points are shown under the

surface as dots.

Table 8

Conversion factors for converting English units to SI units

Name of unit Symbol Definition Relation to SI units

atmosphere (standard) atm =101 325 Pa

cubic foot cu ft =1 ft x 1 ft x 1 ft =0.028 316 846 592 m3

cubic foot per minute CFM =1 ft3/min 1⁄44.719474432 x 10-4 m3/s

foot (International) ft =(1/3) yd=0.3048 m=12 inches =0.3048 m

foot per minute fpm =1 ft/min 1⁄45.08 x 10-3 m/s

inch (International) in =1/36 yd=1/12 ft =0.0254 m

inch of mercury (conventional) inHg =13 595.1 kg/m3x 1 in x g E3.386 389 x 103 Pa

inch of water (39.2 1F) inH2O E 999.972 kg/m3 x 1 in x g E249.082 Pa

pound per square inch psi =1 lbf/in2 E6.894 757 x 103 Pa

ton, short sh tn =2 000 lb 1⁄4907.184 74 kg

4. Conclusions

In this paper, NIOSH’s new MCP (methane control and prediction) software has been introduced with brief details of its

development and with applications of methane control and

methane emissions predictions. The software modules, used

together or separately, can help mining operations predict and

control longwall methane drainage. MCP software can calculate the

elastic and shear moduli of coal-measure rock and the total

ventilation output of methane. Both can help to determine if the

methane can be captured and controlled safely and allow time to

develop control measures to more effectively ventilate working

areas. The degasification system selection module determines the

safest way to degasify a longwall mine with given characteristics

using proven techniques. Finally, MCP can predict the performance

of gob gas ventholes and accurately determine the impacts of

drilling/exhauster parameters, borehole location, and mining parameters on GGV performance.

Previously, there were no simple ways to estimate and plan for

methane drainage and emissions, other than from past experience

in a specific coal seam or at a certain operation. Numerical

modeling techniques were available, but could be very time

consuming and could require expertise to use them. The MCP

software is a practical, new way to predict methane drainage,

estimate ventilation emissions, plan methane drainage techniques,

and predict the effectiveness of GGVs. The applications of the MCP

software modules for methane control-related problems can help

improve the safety of mines and the underground workforce.

5. International unit conversions

See Table 8.

Disclaimer

The findings and conclusions in this report are those of the

author(s) and do not necessarily representthe views ofthe National

Institute for Occupational Safety and Health. Mention of any

company or product does not constitute endorsement by NIOSH.
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