Degasification system selection for US longwall mines using

an expert classification system

* C. O ̈ zgen Karacan

* Tel.: +1412 386 4008; fax: +1412 386 6595.
 

E-mail address: cok6@cdc.gov
 

National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory, Pittsburgh, PA 15236, USA

abstract

Methane emissions from the active face areas and from the fractured formations

overlying the mined coalbed can affect safety and productivity in longwall mines. Since

ventilation alone may not be sufficient to control the methane levels on a longwall

operation, gob vent boreholes (GVB), horizontal and vertical drainage boreholes, and

their combinations are drilled and used as supplementary methane control measures in

many mines. However, in most cases, the types of degasification wellbores chosen are

decided based on previous experiences without analyzing the different factors that may

affect this decision.

This study describes the development of an expert classification system used as a

decision tool. It was built using a multilayer perceptron (MLP) type artificial neural

network (ANN) structure. The ANN was trained using different geographical locations,

longwall operation parameters, and coalbed characteristics as input and was tested to

classify the output into four different selections, which are actual degasification designs

that US longwall mines utilize. The ANN network selected no degasification, GVB,

horizontal and GVB, and horizontal, vertical and GVB options with high accuracy. The

results suggest that the model can be used as a decision tool for degasification system

selection using site- and mine-specific conditions. Such a model can also be used as a

screening tool to decide which degasification design should be investigated in detail

with more complex numerical techniques.

1. Introduction

Methane emissions can adversely affect both the

safety and the productivity of underground coal mines.

Generally, it is economically feasible to handle specific

emissions (total gas emission per unit amount of coal

mined) up to 1000 ft3

/ton (28.3 m3/ton) with a welldesigned ventilation system. At higher specific emission

rates, however, it is difficult to stay within statutory

methane limits using ventilation alone (Thakur, 2006).

Thus, supplementary methane control measures, such

as degasification of the coalbed, are needed prior to or

during mining. Fig. 1 shows a schematic representation of

the longwall environment and the different types of

degasification boreholes commonly used in US longwall

mines.

During longwall mining, methane emissions can

originate from the unmined coalbed adjacent to the

development entries (ribs) surrounding the ventilation

system (Fig. 1), from the active longwall face and mined

coal on the conveyor belts, and from subsided strata

(Mucho et al., 2000). Emissions from the face are a

combination of the methane emitted from the fresh face

on the longwall and from the coal on the conveyer belts.

Methane emissions from the face, ribs, and conveyor belt

are directly discharged into the mining environment. The

methane from the fractured and caved rock in the

subsided strata (gob) overlying the extracted panel is a
big contributor to emissions if overlying strata contain

coalbeds and methane-rich sandstones. The emissions from

these sources must be controlled effectively, either by the

ventilation system or by the degasification boreholes.

Gob vent boreholes (GVB) are commonly used to

control the methane emissions from the fractured zone

and are drilled from the surface to a depth that places

them above the caved zone so that they do not directly

interact with the ventilation system (Fig. 1A). The bottom

section of the pipe is slotted and placed adjacent to the

expected gas production zone. These ventholes generally

become productive after the mining-induced fractures

propagate under the well (Diamond, 1994; Karacan et al.,

2007b). They are equipped with exhausters to capture gas

from long distances and reduce methane migration from

the fractured zone into the mine.

Fig. 1. Schematic representation of longwall mining environment and commonly used degasification borehole types.

The most commonly applied methane control technique, especially in high in-place gas content coalbeds,

is drilling drainage boreholes into the panel area prior

to longwall mining to reduce the methane content of

the coalbed and to reduce face emissions during mining.

These boreholes can be vertical boreholes drilled from

the surface or in-seam horizontal boreholes (Fig. 1A and B)

drilled from the gateroad entries (Diamond, 1994;

Noack, 1998; Karacan et al., 2007a; Karacan, 2008). Inseam horizontal methane drainage boreholes are usually

completed open-hole and they are logistically difficult

to drill due to the limited work space. Vertical methane

drainage boreholes on the other hand have the distinct

advantage of not being conducted in the restrictive

underground environment. They are mostly suited

for highly gassy, deep, low-permeability coal seams where
it takes a long time prior to mining to adequately

degasify the coalbed (Thakur, 2006). However, the

drainage area of vertical wells is limited compared to that

of horizontal boreholes. Thus, in most circumstances

vertical wells need to be stimulated by hydraulic fracturing (Fig. 1A).

Vertical wells are often used in the San Juan Basin in

New Mexico and in the Black Warrior Basin in Alabama

since degasification of the coalbeds is started with

vertical wells a long time ahead of mining and also

since commercial coalbed methane production started

in these regions earlier than many other regions. They

are also used to degasify coalbeds in the Central

Appalachian Basin of Virginia and West Virginia (Diamond,

1994). Although stimulated boreholes have been shown

to be effective in draining methane from virgin coalbeds,

the technology has not been universally accepted by the

US mining industry due to concerns that hydraulic

fracturing might adversely affect the integrity of the weak

roof material, especially in the Northern Appalachian

Basin.

Despite different drainage methods are used in most

US longwall operations, some operations do not use any

degasification if the gas emission levels can be effectively

controlled with ventilation. In most cases, a combination

of different drainage methods leads to the highest

recovery of methane from the coalbed and its overlying

strata. The preferred methane recovery method depends

on the mining parameters and the gassiness of the coal

seam. Table 1 provides a summary of the degasification

methods used by US longwall mines and the approximate

efficiencies of these methods (EPA, 2005).

Table 1

Common degasification systems used and their drainage efficiencies, calculated based on percentage of methane recovered that would otherwise

be emitted

Method Description Gas quality Drainage

efficiency

Current use in

US coal mines

Vertical pre-mine Drilled from surface to coalbed months or years in Nearly pure methane Up to 70% 48 mines

wells advance of mining

Gob vent Drilled from surface to a pre-determined depth to Methane contaminated with mine Up to 50% 417 mines

boreholes the top of the coalbed air, especially later in production life

Horizontal Drilled from inside the mine to degasify the coalbed Nearly pure methane Up to 50% 418 mines

boreholes and to shield the entries

Cross-measure Drilled from inside the mine to degasify the Methane, sometimes contaminated Up to 20% Not used

boreholes surrounding strata with mine air widely

1.1. Motivation and objective of the study

Predicting the rate of methane emissions into working

areas and the design of an effective degasification system

are complex due to the number of factors involved.

Modifications to an existing methane drainage system,

especially after the methane problem has already become

severe, may result in higher costs and may create safety

problems. Thus, it may be advantageous to determine the

need for and the most effective type of degasification

before mining starts by considering the various geological,

coalbed, and mining parameters. This type of assessment

prior to mine development generally requires both

empirical and theoretical approaches.

Insight into the selection and configuration of effective

methane drainage systems can be gained from reservoir

simulations (King and Ertekin, 1989; King et al., 1986;

Remner et al., 1986; Brunner et al., 1997; Diamond et al.,

1989; Kelafant et al., 1988; Karacan et al., 2007a).

However, a comprehensive mine simulator combining

mining operation, coalbed reservoir, and methane production parameters does not exist (Diamond, 1994).

A technique that can identify the optimal degasification

system for a given set of mining and geological parameters could be used as a screening tool prior to

employing other numerical modeling techniques.

Artificial neural networks (ANNs) may fill the gap where

model-based diagnosis systems are not present or are not

adequate for making fast but reliable decisions. ANNs are

typically used for complex problems that are difficult to

describe mathematically. The pattern classification application can be regarded as a specific case of function approximation where the mapping is done from the input space to

a finite number of output classes (Hong et al., 2003).

Classification problems using ANNs have been successfully

applied to other fields of science (An et al., 1995; Brown et

al., 1998; Chen et al., 2002; Kılıc- et al., 2007).

The aim of this paper is to develop an ANN-based

expert classification system to identify the need and the

type of degasification system for a longwall operation. The

ANN model was built using a multilayer perceptron (MLP)

approach to map the inputs to four different degasification

options (N: none; G: gob vent borehole; HG: horizontal

boreholes and gob vent boreholes; VHG: vertical, horizontal boreholes and gob vent boreholes). The model was

developed using a database built on the operational,

coalbed, and geologic parameters and on the degasification practices of US longwall mines. Such a model can be

used as a decision tool for selection of a degasification

system and can also be used as a screening model before

designing degasification systems using more detailed

numerical modeling methods.

2. Description and analysis of database

2.1. Sources of input data

In developing the database, information on 63 longwall

mines in 10 states operating from 1985 to 2005 was

collected from various data sources. These sources
provided gas contents of the mined coalbeds, annual coal

productions and emissions, longwall mining operation

parameters, mine characteristics, as well as the type of

degasification system utilized.

Gas contents of the mined coalbeds, based on their

geographical location and the overburden depths of the

mines, were compiled from a report published by

Diamond et al. (1986). This publication reports the gas

content data for approximately 1500 coal samples from

more than 250 coalbeds in 17 states. The components of

total gas content (lost, desorbed, and residual gas

contents) were reported along with sample location (state

and county), sample depth, coalbed name, coal rank, and

ash content for each sample. The lost gas was determined

by a graphical method and estimated how much gas was

lost during core recovery. Desorbed gas was determined

using the direct method determination of gas content

(Diamond and Schatzel, 1998), and residual gas was

determined after crushing the coal samples with a ball

mill. The total gas content was the sum of all components.

Annual coal productions, ventilation emission data,

and degasification systems utilized by the operations

were reported in US Environmental Protection Agency

(EPA) reports (1994, 2005). These reports listed geographical data, availability of degasification systems, type of

degasification system, sulfur content and calorific value of

mined coals, annual coal productions, and emissions from

ventilation systems.

Mining parameters and mine characteristics are important for degasification system selection since they can

affect methane emissions. The annual data representing

these two groups were complied from 1985 to 2005 issues

of Coal Age magazine, which annually publishes longwall

census data. The tabulated data for each operating mine in

the corresponding year includes seam height, cutting

height, cutting depth, overburden thickness, longwall

panel width, longwall panel length, number of gateroad

entries, face conveyer speed, and stage loader speed.

Tables 2 and 3 show the names and the number of

longwall mines operating in each state and the variables

compiled from the data sources, respectively. After

evaluation of all data, the database consisted of 538 data

entries with 24 variables spanning a 20-year period. Of

these 538 data entries, 93 (17%) had G-type degasification

method, 136 (25%) had HG-type system, 153 (28%) had

VHG-type system, and 156 (30%) did not have any

degasification system installed (N type).

Table 2

Number of mines and mine names in each state included into database for modeling

State No. of Mine names

mines

Alabama AL 8 Blue Creek No. 3, Blue Creek No. 4, Blue Creek No. 5, Blue Creek No. 7, North River, Mary Lee, Oak Grove,

Shoal Creek

Colorado CO 4 Deserado, Dutch Creek, Golden Eagle, West Elk

Illinois IL 7 Galatia, Old Ben 24, Old Ben 25, Old Ben 26, Orient No. 6, Monterey No. 1, Rend Lake

Kentucky KY 4 Wolf Creek No. 4, Wheatcroft No. 9, Camp No. 11, Baker

Maryland MD 1 Mettiki

Ohio OH 4 Meigs No. 2, Meigs No. 31, Powhatan No. 4, Powhatan No. 6

Pennsylvania PA 10 Bailey, Eighty Four, Enlow Fork, Maple Creek, Cambria, Cumberland, Warwick, Homer City, Dilworth,

Emerald

Utah UT 4 Aberdeen, Sunnyside No. 1, Dugout Canyon, West Ridge

Virginia VA 7 Buchanan, V.P. No. 8, V.P. No. 1, V.P. No. 6, V.P. No. 5, V.P. No. 3, McClure No. 1

West Virginia WV 14 Blacksville No. 2, Federal No. 2, Loveridge No. 22, McElroy, Pinnacle No. 50, Robinson Run No. 95, Ireland,

Osage No. 3, Shoemaker, Windsor, Shawnee, Arkwright, Blacksville No. 1, Humphrey No. 7

2.2. Discussion of database parameters for their potential

effects on selection of degasification systems

This section summarizes the possible effects of the

variables presented in Table 3 on ventilation emissions.

Table 3

Variables and their units in database for each mine compiled from

different sources

Variable Unit Variable Unit

State Ash content %

Basin Coal production mill. t/year

County Seam height Inch

City Cut height Inch

Ventilation emission MMscf/day Panel width ft

Mined coalbed Panel length ft

Sulphur amount % Overburden ft

Coal calorific value Btu/lb Number of entries

Lost+desorbed gas scf/ton Cut depth Inch

Residual gas scf/ton Face conveyor speed ft/min

Total gas scf/ton Stage loader speed ft/min

Rank Year

Variables in bold are not included into PCA.

Some of these variables are shown in Fig. 1, which also

provides schematic representation of various degasification borehole designs. Fig. 1B shows the graphical

representation of a panel surrounded by a three-entry

gateroad system, the gob behind the shields, the face area,

the transportation systems, and an unmined section of the

coal seam. Fig. 1A is a vertical cross-section along the

panel length in Fig. 1A. This figure shows the overlying

strata, hydraulic shields, longwall face area, and caving

and fracturing of the overlying strata.

2.2.1. Overburden, coal seam properties (gas content, rank,

seam height), and emission from ventilation system

Depth of the mined coal seam, or the overburden

(Fig. 1A), has two potential impacts on the selection of an
effective degasification system. The first one is its effect on

methane content. For coals of the same rank, gas content

generally increases with increasing depth (Kim, 1977).

Increasing gas content usually increases the gas emissions

in the mine and may prompt the use of vertical or

horizontal methane drainage boreholes for degasification

of the coalbed before or during mining. Fig. 2 shows the

change in total gas content (for various rank coals) with

overburden depth for the reported 1500 samples in

Diamond et al. (1986).

0.00 

100.00 

200.00 

300.00 

400.00 

500.00 

600.00 

700.00 

800.00 

38 

Total Gas (scf/ton) 

Total Gas 

Total Gas (Moving average trendline) 

40 95 99 771 1069 2214 

Depth (ft) 

Fig. 2. Total gas contents of the coals reported in Diamond et al. (1986) as a function of core depth.

The second impact of overburden depth is its effect on

the disturbances created in the overlying strata. If panel

width is greater than the overburden, the panel is called

‘‘supercritical’’ (Mucho et al., 2000), and thus the caving

will be more complete after mining compared to a

situation where the panel width is less than overburden

depth. This situation may potentially affect the methane

reservoir and permeability pathways in the overlying

strata (Karacan et al., 2005) and may promote reconsideration of some of the design parameters for gob vent

boreholes.

Fig. 3 shows the types of degasification systems used in

US longwall mines as a function of overburden depth. N 

500 1000 1500 2000 2500 3000 

Degasification Method 

VHG
 

0
 

Overburden (ft) 

Fig. 3. Types of degasification systems used in US longwall operations as

a function of overburden depth (N: none; G: gob vent borehole; HG:

horizontal borehole and gob vent borehole; VHG: vertical, horizontal and

gob vent borehole).

G 

HG 

The

graph suggests that overburden depths do not affect the

use of a particular degasification system. However, a

majority of the mines that do not use any of the

degasification systems operate between depths of 200

and 700 ft (61 and 213 m). These are fairly shallow mines

and therefore methane emissions likely can be controlled

by the ventilation system alone. The majority of the mines

that use the VHG system, on the other hand, operate at

depths between 500 and 2100 ft (152 and 640 m). Gob

vent boreholes, as the sole degasification system, are used

by mines that operate at depths between 500 and 1200 ft

(152 and 366 m), although there are deeper mines that use

only gob vent boreholes for degasification.

Total gas content has three major components: lost gas,

desorbed gas, and residual gas. In a mining operation, all

of the components may contribute to emissions and may

affect the degasification system choice. The gas content

data, when combined with geologic and engineering data,

can provide an initial estimate of possible emissions into

the mine and an initial selection of a degasification

system design to control these emissions (Diamond

et al., 1986; Karacan and Diamond, 2006; Noack, 1998).

Fig. 4 shows the degasification methods as a function of

total gas content of the coalbed. The figure shows that as

the coalbed gets gassier, most mines tend to use HG- or

VHG-type systems. For instance, the majority of the mines

operating in coalbeds with gas contents between 350 and

550 scf/ton (9.9 and 15.6 m3

/ton) use a VHG-type system.

Coal rank represents the level of maturation of the coal

seam. Most longwalls operate in bituminous coalbeds.

The sub-bituminous to low-volatile (LV) bituminous

coals usually have high gas contents and permeabilities

(Steidl, 1996). Thus, mining coals of this range, particularly

medium-volatile (MV) to LV bituminous coals, can
potentially liberate high amounts of gas into the work

environment. Fig. 5 shows the variation of the degasification systems used in longwall mines in the dataset as a

function of coal rank. The figure shows that as the rank

varies from high-volatile (HV) to MV and LV bituminous, a

VHG-type system is preferred.

Seam thickness and mining height (Fig. 1A) impact

emissions in two ways. First, thick coal seams have higher

face and rib emissions (Karacan, 2008) compared to their

thinner counterparts due to increased surface area.

Second, the mining height impacts the thickness of the

fractured zone in the gob. This situation potentially can

result in more gas flowing from the overlying strata into

the mine environment. This situation affects gob vent

borehole drilling needs and design criteria.

Predicted emissions impact the use of the degasification scheme (Karacan, 2008). Degasification of the coal

seam and the fractured strata prior to and during mining

are effective ways of controlling methane emissions

during mining (Diamond, 1994; Karacan et al., 2007a).

Depending on the gassiness of the coal seam and the

fractured strata, horizontal boreholes, vertical boreholes,

or gob vent boreholes can be used. The number of

boreholes, their locations, and their degasification durations may change based on the site-specific factors. Fig. 6

shows the types of degasification systems used as a

function of daily methane emission rates from the

ventilation system. This figure shows that the mines with

the highest emissions tend to use VHG- and HG-type

degasification systems.

0 200 400 600 800 

Degasification Method

N 

G 

HG 

VHG 

Total Gas Content (scf/ton) 

Fig. 4. Types of degasification systems used in US longwall operations as

a function of total gas content of coalbed (N: none; G: gob vent borehole;

HG: horizontal borehole and gob vent borehole; VHG: vertical,

horizontal and gob vent borehole).

N 

Degasification Method

G 

HG 

VHG
 

HV-B HV-A MV LV
 

Rank 

Fig. 5. Types of degasification systems (N: 156, G: 93, HG: 136, VHG:

153) used in US longwall operations as a function of coal rank (HV-A:

high-volatile A; HV-B: high-volatile B; MV: medium-volatile; LV: lowvolatile).

Degasification Method

N
 

G
 

HG 

VHG 

0 5 10 15 20 25 

Ventilation Emission (MMscf/day) 

Fig. 6. Types of degasification systems used in US longwall operations as

a function of daily methane emission rates from ventilation system

(N: none; G: gob vent borehole; HG: horizontal borehole and gob vent

borehole; VHG: vertical, horizontal and gob vent borehole).

2.2.2. Operational, mine design, and coal productivity data

Practical experience has shown that emissions are

related to coal production rates. Thus, expected coal

production may affect degasification options for specific

mine conditions. Fig. 7 shows the type of degasification

system as a function of coal productivity. Degasification Method

N 

G 

HG

VHG
 

0 2 4 6 8 10 
 

Coal Production (x106 tons/year) 

Fig. 7. Types of degasification systems used in US longwall operations

as a function of annual coal production (N: none; G: gob vent borehole;

HG: horizontal borehole and gob vent borehole; VHG: vertical,

horizontal and gob vent borehole).

12 

This figure

shows that the coal productivity of those mines that use

only gob vent boreholes spans a wide range. This may be

due to the moderately gassy nature of these operations.

Productivity of these mines is followed by those that use

HG-type systems. The increasing size of longwall panels

(width and length) usually affects coal production and

methane emissions due to the exposure of the mining

environment to wider faces and to a larger area of

fractured strata. If not captured effectively, additional

gas emissions from a larger area of fractured formations

can enter the mining environment and may create unsafe

operating conditions (Karacan et al., 2005). Thus, as the

size of the panel increases, the expected increase in
emissions must be controlled by effective degasification

methods.

Cutting depth is the slice of coalbed that is mined by

the shearer during each pass. In general, greater cutting

depth produces more coal per rotation of the cutting drum

with an increase in emissions. However, shearer speed

and rotational speed of the cutting drum are also

important. Thus, cutting depth may not be a determining

factor by itself for selecting degasification options.

Gateroad entries (Fig. 1B) surrounding the panels are

mainly used for ventilation and transportation pathways.

In the US, some of the early longwalls used five-entry

gateroads. However, three entries are preferred since this

requires less development footage compared to four- or

five-entry systems. Four-entry systems are used in coal

seams of low height or high gas content, where air flow

resistances and air flow requirements are high. Two

entries require a special permit and are used in deep

mines with a thick layer of strong overlying strata (Peng,

2006). Thus, the number of entries may be an indicator of

the gassiness of the mine.

2.2.3. Geographical location (state and basin)

A strong relationship exists between the gas emission

rates and the geological factors of a particular site, such as

the stratigraphy, the gas contents, and the strengths of the

overlying and underlying strata. Pashin (1998) reported,

based on the investigations in the Black Warrior Basin and

in San Juan Basin, that regional variations in sequence

stratigraphy are useful for characterization of coalbed

methane reservoirs since they are affected by regional

sedimentology and tectonics. Although geographical location is not directly related to the choice of the degasification system, it indirectly identifies the differences

between underground stratigraphy and emissions due to

local variations in reservoir characteristics. Thus, geographical location may be used to include these parameters in decisions regarding design of the degasification

system.

3. Model complexity reduction using principal component

analysis (PCA) and evaluation of database variables

In this study, PCA was used for selecting the most

appropriate input variables from all the variables presented in Table 3 for developing an ANN-based classification system. Identifying principal components (PCs) and

eliminating those that do not contribute to the variance of

the data decreases the dimension of the data set, while

retaining most of the variance and revealing information

on correlations between variables (Grima, 2000).

Before performing PCA, city, county, calorific value

(Btu), ash content, sulfur content, face conveyor speed,

stage loader speed, coalbed, and year information in

Table 3 were eliminated from the analysis, since they

either were irrelevant for the purpose or they could be

better represented by other variables in the data set. With

elimination of these nine variables, Table 3 was reduced to

15 variables. The variables represented by alphanumeric

information were designated by numeric information

(such as state) for PCA. For instance, numeric codes of

states were used for identification.

Table 4 lists the results of the PCA performed on the

remaining 15 variables in Table 3.

Table 4

Eigenvalues and variances explained by principal components extracted

using 538 observations of 15 variables shown in Table 3

PC Eigenvalue Variance (%) Cumulative %

1 4.88 32.56 32.56

2 2.75 18.31 50.87

3 2.35 15.64 66.51

4 1.40 9.36 75.87

5 0.87 5.79 81.66

6 0.66 4.41 86.07

7 0.54 3.57 89.64

8 0.42 2.83 92.47

9 0.40 2.69 95.16

10 0.27 1.82 96.98

11 0.22 1.49 98.47

12 0.12 0.77 99.24

13 0.08 0.57 99.80

14 0.03 0.20 100.00

It can be seen that all

variance in the data is represented by 14 PCs. However,

approximately 80% of the variance can be represented by

the first five PCs. The individual contributions of the

remaining 9 PCs are small and their total contribution is

only 20% of the total variance. The eigenvalues, which are

the proportion by which an eigenvector’s magnitude is

changed, is also shown in Table 4. Table 4 shows that the

largest eigenvalues (strongest correlations in the data set)

are also in the first five PCs. Thus, five PCs, whose factor

loadings are shown in Table 5, were selected as the

PC matrix.

Table 5

Loadings of variables in principal component matrix for first five

principal components

Variables PC 1 PC 2 PC 3 PC 4 PC 5

Basin -0.720 0.238 -0.404 0.423 -0.170
 

State -0.492 0.371 -0.589 0.444 -0.153
 

Seam height 0.244 0.095 0.693 0.581 0.132
 

Cut height 0.303 -0.057 0.715 0.518 0.137
 

Panel width -0.024 0.752 0.255 -0.237 0.004
 

Panel length -0.339 0.627 0.118 -0.355 0.034
 

Overburden 0.797 0.149 -0.191 0.152 -0.054
 

Number of entries 0.338 -0.026 -0.535 -0.015 0.675
 

Cut depth 0.019 0.709 0.296 -0.097 -0.214
 

Lost+desorbed gas 0.896 0.192 -0.178 0.102 -0.240
 

Residual gas -0.528 0.470 -0.299 0.379 0.276
 

Total gas 0.837 0.305 -0.253 0.190 -0.195
 

Rank 0.865 0.223 -0.332 -0.016 -0.051
 

Coal production -0.308 0.689 0.241 -0.073 0.176
 

Ventilation emission 0.694 0.423 -0.026 -0.108 0.262
 

Figs. 8A and B show the correlation circles for the

factor loadings of PC1 and PC2 and PC1 and PC3,

respectively. These figures show the degree of correlation

between variables and the PCs. In these plots, any two

variables that are far from the center and close to each

other are significantly and positively correlated. On the
other hand, variables on the opposite sides of the center

are negatively correlated. Variables close to the center are

not represented well by those axes and their information

may be hidden in other PCs and in the properties that they

represent. Fig. 8A shows that total gas content, rank of the

coal, and ventilation emissions are correlated with overburden thickness and that they are significant for the first

PC. Likewise, panel length is correlated with coal production and these same variables are significant for the

second PC. On the other hand, seam height, cut height,

and number of gateroad entries are close to the center,

suggesting that their information can be better represented by other PC axes. Fig. 8B is a similar diagram for

PC1 and PC3 which shows that total gas content, coal rank,

overburden depth, and gas emission data are still

correlated and significant on PC1. Seam and cut (mining)

heights show significant correlation with each other and

with PC3. On the other hand, the lengths of panel

dimensions and coal production get smaller since these

variables are significant on PC2, not on PC3.

1 

e f 0.75 g 

d c 0.5 j

b l

n 0.25 a

h m k 0 

PC 3 PC 2

o 

i 

-0.25 

-0.5 

-0.75 

-1 

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 

PC 1 

1 

h i 0.75 

0.5 

g e 0.25 f

d j 0 

k l 

-0.25 m

c a n 

-0.5 

o b 

-0.75 

-1
 

-1 -0.8 -0.5 -0.3 0 0.25 0.5 0.75 1 

PC 1 

Fig. 8. Correlation circles showing magnitude and directions of variable

loadings in PC1–PC2 (8A) and PC1–PC3 (8B).

The factor scores of each observation on each PC can be

plotted to investigate the trends and groupings. Figs. 9A

and B show a two-dimensional map of the factor scores of

each mine on axes PC1 and PC2 and on axes PC1 and PC3,

respectively. These plots enable the analyses of the trends

and groupings between mines based on the variables they

represent on each PC. Figs. 9A shows that there are two

major clusters: The cluster on the right side is formed by

Virginia and Alabama mines while the one on the left is

formed by the mines in the remaining eight states.

Observations (axes PC 1 and PC 2) 

6 WV, PA, CO, UT, 

IL, KY, OH, MD VA mines Pinnacle

4 mines No: 50 

2 

PC 3 PC 2

0 

-2

AL mines North River -4 

-6 -4 -2 0 2 4 6 8 

PC 1 

Observations (axes PC 1 and PC 3) 

6

West 

Elk mine CO 4 mines 

WV, PA, UT, IL, AL mines

2 KY, OH, MD

mines 

0

-2

B Pinnacle VA

No: 50 mines -4
 

-8 -6 -4 -2 0 2 4 6 

PC 1 

Fig. 9. A two-dimensional distribution of factor scores of each mine on

PC1–PC2 (Fig. 9A) and PC1–PC3 (Fig. 9B) determined by PCA. Plot shows

groupings of mines based on different characteristics.

Virginia and Alabama mines are separated from the others

because of the mining depths, gassiness, and the evaluated operation parameters. The scattered data points

between these two main clusters are due to these

operations having characteristics of the mines in both

states. For instance, the data points located on the positive
side of PC2 belong to Pinnacle No. 50 mine. Although this

mine is geographically located in West Virginia, it is

operating in the Pocahontas No. 3 coalbed which makes it

similar to the Virginia mines operating in this coalbed.

Some of the data on the negative side of axis PC2 belong to

the North River mine in Alabama, where the overburden

(500–600 ft) and gas content of the coalbed are less than

those at other Alabama mines.

Fig. 9B shows the factor scores of the mines on the first

and third PC axes. Plotting these axes resulted in splitting

some of the data points, mainly for Colorado mines, from

the clusters (Fig. 9B). Since the mines in Colorado operate

mostly in thick coal seams with mining heights exceeding

100 inches (2.5 m), they form a separate group scattered

between the Alabama mines and the multi-state cluster

on the left. However, the West Elk mine is separate from

all the groups due to its seam thickness and mining

height, 276 inches and 144 inches (7.0 and 3.7 m), respectively, which are high compared to the other mines in the

analysis. The Pinnacle No. 50 mine is also separated from

the other West Virginia operations and approaches the

Virginia mines due to differences in gas contents of the

coal seams.

In order to improve interpretability of results, the

selected PC matrix (Table 5) was used to create a new

component matrix by using Kaiser’s varimax rotation

method. In varimax rotation, PC axes are rotated to a

position in which the sum of the variances of the loadings

is the maximum (Grima, 2000). Table 6 shows the rotated

matrix for five components (PCR) and the factor loadings

for each variable. This table also shows how the variables

are separated between columns according to their

characteristics.

Table 6 shows that the first PCR is mostly related to gas

content of the mined coalbed, overburden rank, and the

methane emissions.

Table 6

Factor loadings of variables after rotating principal component matrix

using Kaiser’s varimax rotation

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

Bold entries show most influential variables in each rotated principal

component (PCR).

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 of the

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 highest

loadings (0.820 and 0.755), followed by longwall panel

length (0.751) and cut depth (0.721). The third PCR in

Table 6 represents the coalbed and mining heights, where

their loadings are 0.939 and 0.917, respectively. The fourth

PCR in Table 6 is related to geographical location of

the mine determined by state and coal basin. These

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 is more localized and seems to be a better

identifier for this purpose. The fifth PCR represents the

number of gateroad entries.

4. Development of a classification ANN to identify

longwall degasification systems

ANN simulates the human brain with many relatively

simple individual processing elements, called neurons

(Eberhart and Dobbins, 1990). The neuron layers are

interconnected in particular structures to receive input

and output. One of the most widely used structures is the

MLP due to its applicability to general classifications and

regressions.

The input–output flow of a network is performed

through hidden layers, with the number of hidden layers

dependent on the problem (Flood and Kartam, 1994). The

number of neurons in the hidden layers needs to be

optimized to train the networks effectively (Maier and

Dandy, 2000). Using multiple showings (epochs) of the

expected outputs, an error is calculated and propagated

back to adjust the interconnection weights to minimize

the error. Minimizing the mean squared error (MSE) is the

goal of the training process. Minimization of error can be

done using a gradient descent method where momentum

provides the gradient descent with some inertia so that

the solution moves towards a global, rather than local,

optimum.

Within the hidden layer, the inputs from neurons are

multiplied by interconnection weights and summed and

processed by a nonlinear transfer function, or axon.

Hyperbolic tangent is one of the most widely used axons.

In the case of classification, the output for each class with

higher probability should be 1 and others should be 0.

This function is usually a ‘‘softmax’’ function in the output

layer that will scale the outputs between 0 and 1 and will

make the higher probability class stronger and the other

classes weaker.

Once the training phase is complete, the performance

of the network needs to be validated on an independent

data set. Cross-validation is a model evaluation method

that gives an indication of how well the ANN will do when

it is confronted with data it has not yet seen. It is
important that the validation data should not be used as

part of training (Maier and Dandy, 2000).

4.1. Preliminary models and the search for effective input

parameters for identification of degasification systems

This section presents the results of attempts to find the

appropriate input variables for ANN modeling. The

strategy was to start with an ANN structure common to

all models and to change the input variables to find that

model yielding the best results. For this phase, nine

different models were tested (Table 7).

Table 7

Different models and variables tested in ANN

Model number Input variablesa

1 Total gas, Panel W, Entries, State, Seam H.

2 Total gas, Panel W, Panel L., State, Seam H.

3 Total gas, Panel W, State, Seam H., Cut H.

4 Total gas, Panel W, State, Seam H., Rank

5 Total gas, Panel W, State, Seam H., Cut D.

6 Total gas, Panel W, Coal P., State, Seam H., Vent E.

7 Total gas, Panel W, Coal P., State, Seam H., Vent E.,

Cut H.

8 Total gas, Panel W, Coal P., State, Seam H., Vent E.,

Cut H., OB.

9 Total gas, Panel W, Coal P., State, Seam H., Vent E.,

Cut H., Entries

a OB: overburden; Panel W: panel width; Panel L.: panel length;

Seam H.: seam height; Cut H.: cut height; Cut D.: cut depth; Coal P.: coal

production; Vent E.: ventilation emission; Entries: number of entries.

In all models, the

ANN training and network options were the same so that

all models and results could be compared.

A two-hidden-layer ANN with 50 and 30 processing

elements for the first and second layers, respectively, was

constructed to test the model variables in Table 7. The

number of nodes in the output layer was four, each

corresponding to a degasification scheme, namely no

degasification (N), gob vent borehole (G), horizontal

boreholes and gob vent boreholes (HG), and vertical,

horizontal, and gob vent boreholes (VHG). Hyperbolic

tangent was selected as the transfer function for the

hidden layers and softmax function was selected for the

output layer. A gradient descent search algorithm was

utilized with step sizes varying between 1 and 0.01. For all

layers, a momentum factor of 0.7 was applied. Training

epochs were 1500 in each modeling attempt.

For pre-processing of the input data (exemplars), the

dataset was first randomized and then separated into

three sections for training, cross-validation, and testing

phases. Randomization prevented biases and made subsets of the dataset representative of the whole population.

In separating the dataset, 403 out of 538 exemplars (75%)

were saved as training data, 54 exemplars (10%) were

saved as cross-validation data sets, and 81 exemplars

(15%) were saved for testing the trained network.

Table 8 shows the performance data of preliminary

input variables (Table 7) in training and cross-validation

phases.

Table 8

Training and cross-validation mean squared errors (MSE) for each tested

model shown in Table 7

Model number Training MSE Cross-validation MSE

1 0.009672 0.016827

2 0.007019 0.090226

3 0.009130 0.017182

4 0.013726 0.015097

5 0.008895 0.088342

6 0.005677 0.011981

7 0.005156 0.005666

8 0.004355 0.007716

9 0.004455 0.012284

In order to evaluate the performance of these

variables, minimum MSEs were noted for training and

cross-validation. For testing the same models, identification accuracy was noted for the four different degasification schemes (Table 9).

Table 9

Performance of tested models (in % correct) given in Table 7 in

identifying degasification systems included in ‘‘testing’’ data set

Model number HG N G VHG

(Horizontal+ (None) (GVB) (Vertical+

GVB) Horizontal+

GVB)

1 86.4 96.9 60.0 100.0

2 90.9 25.0 26.7 58.3

3 77.3 87.5 80.5 100.0

4 77.3 90.6 86.7 75.0

5 81.8 50.0 66.7 58.3

6 90.9 93.8 66.7 100.0

7 81.8 90.6 73.3 91.7

8 86.4 93.8 80.0 91.7

9 86.4 93.8 66.7 91.7

The data in Table 8 show that

‘‘model 8’’ produced the least MSE training error

(0.004355). The cross-validation error was the second

lowest after ‘‘model 7.’’ The degasification system in the

testing set was more correctly identified using ‘‘model 8’’

than ‘‘model 7.’’ Testing of ‘‘model 8’’ produced 86.4%,

93.8%, 80%, and 91.7% correct identifications for HG, N, G,

and VHG classes, respectively (Table 9). Thus, the input

variables for ‘‘model 8,’’ namely, 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 (Table 7).

4.2. Determination of final network parameters and its

identification performance

In order to improve identification performance, the

form of the final model was optimized using various

combinations of network parameters. For this purpose,

two hidden layers were chosen, hyperbolic tangent

transfer function was used in the hidden layers, and

softmax function was used in the output layer. The

number of processing elements in the hidden layers, the

value of momentum, and the number of epochs were

varied.
The number of processing elements in the first hidden

layer was changed between 44 and 58 while maintaining

30 processing elements in the second layer, maintaining

the momentum at 0.7, and running the network for 1500

epochs. The results showed that 48 processing elements in

the first hidden layer produced the lowest MSEs of 0.003197

and 0.005485 for training and cross-validation, respectively.

Next, the number of processing elements in the second

hidden layer was varied between 24 and 38, while the

number of processing elements in the first hidden layer was

set to 48. The minimum MSE was 0.003462 and 0.004766

for training and cross-validation, respectively, with 28

processing elements in the second hidden layer.

Momentum was varied between 0.5 and 0.8 while

keeping the number of processing elements in the first

and second hidden layers to their optimal values of 48 and

28, respectively. The minimum MSEs of 0.003462 and

0.004766 for training and cross-validation were achieved

with a momentum of 0.7. The number of training epochs

was varied from 1000 to 2000. For 1000 and 2000 epochs,

the cross-validation errors increased to 0.008201 and

0.007087, respectively, as opposed to 0.004766 that was

achieved with 1500 epochs. Thus, 1500 was selected as the

number of epochs for training and cross-validation.

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.

Fig. 10 shows the topology and parameters of the final

ANN network used to design the degasification system.

State 

Total gas 


Overburden
 

Cut height 


Seam height 

Panel width 

Ventilation emission 

Coal production 

Epoch: 1500 

48 

Processing Processing 8 inputs elements elements

28 

N 

G 

HG 

VHG 

1st LAYER 

Hyperbolic tangent 

Momentum: 0.7 

Step size: 1 

2nd LAYER 

Hyperbolic tangent 

Momentum: 0.7 

Step size: 0.1 

OUTPUT 

Softmax. axon 

Momentum: 0.7 

Step size: 0.01 

Fig. 10. Inputs, topology, and parameters of proposed ANN model for identification of a suitable degasification system (N: none; G: gob vent boreholes;

HG: horizontal boreholes and gob vent boreholes; VHG: vertical, horizontal and gob vent ventholes) for US longwall mines.

Table 10 shows the performance of the optimized

(final) network with testing data set.

Table 10

Testing performance of final ANN model (Fig. 10) after optimizing network parameters

ANN output HG N G VHG

(Horizontal+GVB) (None) (GVB) (Vertical+Horizontal+GVB)

HG 21 (True) 3 (False) 1 (False) 0

N 0 29 (True) 2 (False) 0

G 1 (False) 0 12 (True) 0

VHG 0 0 0 12 (True)

% Correct 95.5 90.6 80.0 100.0

This table shows the

‘‘true’’ and ‘‘false’’ identifications for each class and the

accuracies achieved by the model. As can be seen from

this table, the network identified one case out of

22 samples falsely for the HG-type degasification system,

for an accuracy of 95%. The model correctly identified

29 mines without any degasification system (N) out of

32 samples. It identified three of them falsely and recommended an ‘‘HG’’ system instead. For gob vent borehole

identification (G), out of 15 samples the model identified

12 of them correctly and recommended one of them as HG

and two of them as N. The highest score was obtained for

the VHG system. The ANN identified all 12 cases correctly.

5. Summary and conclusions

The objective of this work was to develop an ANNbased recognition model for identifying the need and type

of degasification system for US longwall operations. The

variables of the dataset that likely have impact on

degasification system selection were analyzed with PCA.
Based on the results, a number of models were tested to

find the best combination of variables for selecting a

degasification system using the ANN approach. Finally, the

parameters of the ANN were optimized to improve the

classification performance of the model. The final model

had two hidden layers with 48 and 28 processing

elements in each layer.

Results showed that degasification systems that are

commonly used at US longwall mines can be classified

effectively using a few variables. This study showed that

using the variables of state, total gas content of coalbed,

seam and cut heights, panel width, coal productions,

overburden depths of the mines, and ventilation emissions

resulted in high accuracies for correctly identifying the use

of no degasification, gob vent boreholes (GVB), horizontal

and GVB, and horizontal, vertical, and GVB designs.

The approach and the results suggest that, by incorporating the critical stratigraphic features in place of

geographical information, this model can be applicable to

other mines in different locations. This model can also be

used as a screening tool prior to applying more detailed

simulation studies for optimizing recovery efficiencies of

the ANN-identified systems.

Acknowledgment

Barbora Jemelkova of the EPA is thanked for her help in

obtaining ventilation methane emission data. W.P. Diamond

is thanked for his help in obtaining gas content data.

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