Modeling and prediction of ventilation methane emissions of U.S.

longwall mines using supervised artificial neural networks

C. Özgen Karacan

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

Received 28 June 2007; received in revised form 8 September 2007; accepted 13 September 2007

Available online 22 October 2007

Abstract

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is

producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of

ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and

utilize the methane for energy production.

Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and

operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach

to predict the ventilation methane emission rates of U.S. longwall mines.

Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined

with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database

resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced

ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the

data set and network structures were used for network training and testing to achieve minimum mean square errors and high

correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to

multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with

high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall

ventilation emission rates.

Published by Elsevier B.V.

Keywords: Longwall mining; Ventilation; Methane emissions; Principle component analysis; Artificial neural networks

1. Introduction

Longwall mining is an underground mining method

that can maximize coal production in coalbeds that

contain few geological discontinuities. In these operations, a mechanical shearer progressively mines a large

block of coal, called a panel, which is outlined with

development entries or gate roads. This is a continuous

process in an extensive area, where the roof is supported

only temporarily during mining with hydraulic supports

that protect the workers and the face equipment (Fig. 1).

As the coal is extracted, the supports automatically

advance and the roof strata are allowed to cave behind

the supports (Karacan et al., 2007a).

Methane emissions can adversely affect the safety of

underground coal miners. During longwall mining,

methane emissions can originate from three major

E-mail address: cok6@cdc.gov. sources. These sources are: 1) gas emissions from the

0166-5162/$ - see front matter. Published by Elsevier B.V.

doi:10.1016/j.coal.2007.09.003
ribs surrounding the bleeder ventilation system, 2) gas

emissions from the active longwall face and mined coal

on the conveyor belts, and 3) gas emissions from

subsided strata (Mucho et al., 2000).

The first gas source originates from the unmined

coalbed adjacent to the development entries of the

bleeder system and from the solid coal ribs. Although this

emission tends to decrease over time, its total may

become a significant contributor of gas to the bleeder

ventilation system over time (Mucho et al., 2000). The

second source is the combination of the gas content from

the mined coal itself, the methane being emitted from the

fresh face on the longwall, and the methane emitted from

the coal transported out of the mine by the conveyer belts.

Methane emissions from the face, ribs, and conveyor belt

are directly discharged into the mine ventilation air;

therefore, the ventilation system must have sufficient

capacity to maintain methane levels within statutory

limits. The third source is the fractured and caved rock in

the subsided strata (gob) overlying the extracted panel as

the longwall face advances. The subsided strata are

generally characterized as being made up of the caved

zone, fractured zone, and the bending zone. The caved

zone is characterized as a fragmented rock mass

(completely caved) overlain by strata with extensive

fractures (partially caved). The height of the caved zone

is about 3 to 6 times the thickness of the mined coalbed

(Singh and Kendorski, 1981; Palchik, 2003). The stress

relief due to caving causes the overburden strata above

the caved zone, including gas-bearing coalbeds, to

fracture both vertically and horizontally. The thickness

of the fractured zone can vary up to 100 times the height

of the mined coalbed, depending on the size of the panel,

the geology, and the geomechanical properties of the

layers (Palchik, 2003). The methane from this source is

usually controlled by vertical boreholes drilled into the

fractured zone to reduce the migration of methane from

the fractured zone to the caved zone and finally to the

ventilation system.

Accurate prediction of longwall methane emissions is

important so that adequate ventilation air is supplied to

dilute and render harmless high gas levels that threaten

mine safety. It is also important to identify opportunities

to utilize mine ventilation air for energy production.

Underground coal mines worldwide liberate an estimated 1–1.5 × 1012 scf (29–41 × 109 m3

) of methane

annually, of which less than 2.3 × 109 m3 (81 × 109 scf)

are used for fuel (Bibler et al., 1998). Thus, in order to

provide a safer mining environment to underground

miners and to make investment decisions into methane

utilization technologies, ventilation emissions of the

longwall mines must be predicted accurately.

Accurate prediction of the rate of methane flow into the

working areas and eventually into the ventilation system

is complex due to the large number of variables involved

with these potential emission sources. Some of the

variables reported by Lunarzewski (1998) are lithologyand stratigraphy-related and include coal and rock

properties in the floor and roof strata, depth of the

mined coalbed, reservoir properties of coalbed, gas

content of mined coal seams, coal rank, and strength of

the overlying strata. Variables originating from the mining

process include longwall face width, face advance rates,

methane drainage, conveyor speeds, and thickness of the

mined seam, as well as time-dependent changes in these

parameters.

The complexity of the longwall mining process and

its associated methane sources have been modeled by

several researchers. Schatzel et al. (2006) and Krog et al.

(2006) empirically determined the increase in emissions

for wider longwall faces using a graphical method and

by including the effects of various methane contributors,

respectively. Also, by the premise that gas emissions in

underground mine environments can be linked to

different stages in the life of a coal mine, the following

empirical model was suggested (Lunarzewski, 1998):

Q yð Þ1⁄4 g

CA

X

yþ1

0

C

 !m

þ1 X

y

0

C

m

þ1

 ! ð1Þ

where: Q(y) is the average methane emission in a year

‘y’ of the mine's existence (m3

), CA is the coal

production in the most recent year only (tonnes), C is

the coal production for the life of the mine up to year ‘y’

(tonnes), and g and m are the site-specific coefficients

dependent upon geological and mining conditions. In

other words, this approach summarizes methane emissions as a function of coal production only and lumps

other variables into empirical constants. A similar,

lumped-parameter approach for estimating the instantaneous volume of gas released from all potential sources

is given by Lunarzewski (1998):

R 1⁄4 a ffiffiffiffiffiffiCP p þ b ð2Þ

In this equation, R is the total methane emission rate

(liters per second), CP is the daily coal production rate

(tonnes), and a and b are empirical constants related to

coal production levels and number of working days per

week.

Although simple to use, the above approaches are

site-specific and require a long period of data collection

before a correlation can be established for predictions. Also, they lump the variables into two empirical

372 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
constants which limits their applicability for other mines

with similar coal production levels but different mining

or geological conditions.

Some of the limitations of empirical models can be

addressed by calibrating the models and realistically

representing additional variables. Lunarzewski (1998)

used boundary element and sequential bed separation to

model floor and roof strata relaxation and immediate

roof bending separation. “Floorgas” and “Roofgas”

simulation programs were developed to characterize the

strata relaxation zones, gas emission boundaries, and

parameters for gas emission prediction. Tomita et al.

(2003) developed a three-dimensional (3-D) finite

element model (FEM) to predict the volume of methane

gas emitted from surrounding coal and rock layers based

on stress distribution and permeability changes. Karacan

et al. (2005, 2007b) developed a 3-D reservoir model of

a longwall mining district to simulate the effects of

increasing longwall panel widths on methane emissions,

the performances of gob gas ventholes, and degasification planning. This work predicted an increase in

emissions due to increasing panel width and the optimal

gob gas completion and placement strategies used to

prevent the gas from entering the underground workplace. However, numerical models require expertise in

users and, in most cases, specialized and expensive

software packages. Also, it is not possible or feasible to

numerically represent every process that contributes to

mine emissions with these models.

Artificial neural networks (ANNs), on the other hand,

are adaptable systems that can determine relationships

between different sets of data. ANNs have been

developed to solve problems where conventional

computer models are inefficient. These problems are

either the non-polynomial type, having no polynomial

relationship, or very complex problems that are difficult

to describe mathematically. The key advantages of

neural networks are their abilities to learn, to recognize

patterns between input and output space, and to

generalize solutions. Statistical techniques such as

multiple regression analysis have been used widely for

these kinds of problems, but they often fail in accuracy of

prediction, especially in the face of highly non-linear

relationships.

ANNs, due to their flexibility, have found their way

into many fields of application. Patel et al. (2007) used

artificial neural networks for estimation of gross

calorific values of coals. Bagherieh et al. (in press)

used the technique to study the relationships between

petrography and grindability of Kentucky coals. Yılmaz

et al. (2002) used ANN and fractal geostatistics to solve

the optimum bit selection problem during drilling in a

carbonate field. Sousa et al. (2007) performed a study to

predict atmospheric ozone concentrations using principle component analysis and ANN. Karacan (in press)

integrated reservoir simulations during development

mining with ANN to predict methane emissions into

roadways and to optimize ventilation air requirements.

Maier and Dandy (2000) provided an overview of

modeling issues and application of ANN for forecasting

water resources.

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

methodology to predict ventilation emissions from

longwall mines for mine safety applications and to

identify opportunities for utilizing ventilation methane.

Ventilation emission data obtained from different U.S.

mining regions were combined with corresponding

coalbed properties, geographical information, longwall

operation parameters, and productivities. The database

was analyzed using PCA to reduce complexity and to

determine the variables to be considered for ANN

modeling. 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 and high correlations between measurements and

predictions.

2. Description and analysis of database

2.1. Sources of input data for ventilation emission

modeling

In developing the database for modeling ventilation

emissions, 63 longwall mines in 10 states operating

between 1985 and 2005 were analyzed. Available data

sources were searched and evaluated carefully for these

mines to extract important parameters relevant to

ventilation emissions. These sources provided gas

contents of the mined coalbeds, annual coal productions

and emissions, longwall operational parameters, and

mine characteristics.

Gas contents of the mined coalbeds, based on their

geographical location at the mining depths, were

compiled from a report published by Diamond et al.

(1986). The publication contains approximately 1500

coal samples taken during various drilling operations

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. The lost gas was

determined by a graphical method which estimated how

much gas was lost during core recovery. Desorbed gas

was determined using the direct method determination

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 373
of gas content (Diamond and Schatzel, 1998) and

residual gas was determined after crushing the coal

samples using a ball mill. The total gas content was

reported as the sum of all three components.

Annual coal production and ventilation emission data

of longwall mines included in this study were taken

from the U.S. Environmental Protection Agency (EPA)

reports (1994, 1997, 2005) published to identify coal

mine methane production opportunities in U.S. coal

mines. These reports list various general mine information including geographical data, availability of degasification systems, sulphur content, and heating value of

mined coals along with annual coal productions and

emissions from ventilation systems. The reports contain

information for both large room-and-pillar and longwall

operations. (However, in this study, only longwall mines

were considered for analysis.) In the EPA reports, most

of the profiled mines are large operations with coal

productions exceeding one million tons per year. Coal

production data reported in EPA reports were primarily

based on the U.S. Energy Information Administration

(EIA) database, which is supplemented with data from

coal producing states and from the U.S. Mine Safety and

Health Administration (MSHA). The data for 2004 and

2005 were taken directly from MSHA's data retrieval

system (MSHA, 2007). Emissions data from the

ventilation systems were collected by MSHA personnel,

who measured methane emission rates at each coal mine

on a quarterly basis. Measurements were performed

underground at the same locations each time. The

reported ventilation emissions for a given year are

averages of the four quarterly tests.

Fig. 1. Schematic representations of a panel surrounded with a three-entry gateroad system (A). B is a vertical cross-section along the X–XIprofile in

A. C is a detailed view of shearer and cutting depth while mining coal.

374 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
Mining parameters and mine characteristics affect how

much and how fast methane will be generated as a result

of coal production. These factors also affect how much

strata gas will be released and how much of it will enter

the ventilation system as a result of caving and fracturing

of the overlying strata. The annual data representing mine

characteristics and mining parameters were compiled

from 1985–2005 issues of Coal Age (Mining Media Inc.),

which publishes longwall census data annually. The

tabulated data for each operation in the corresponding

year includes seam height, cutting height, cutting depth,

overburden thickness, longwall panel width, longwall

panel length, number of development entries, face

conveyer speed, and stage loader speed.

Table 1 shows the number of longwall operations in

each state. Table 2 lists variables and their units. There

were some important considerations when preparing the

final database. One of them was the need to form a

complete set of input data using each source for a

particular mine and year. For instance, if the production

and emission data were available for a particular mine and

year, but the census data were not, then that year's entry

was excluded from the database. Also, the operating status

and, sometimes, the names of the mines had changed over

the 20-year period. Some of the mines producing coal in

1980s were abandoned, while new ones were started in

later years. After evaluation of all data, the database used

for ANN modeling consisted of 538 data entries with 24

variables spanning a 20-year period (Table 2).

2.2. Discussion of database parameters for their

potential effects on ventilation emissions

This section summarizes the possible effects on ventilation emissions of the variables presented in Table 2.

Table 1

The number of mines and the mine names in each state included in the

methane emission database

State No. of

mines

Mine names

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

Table 2

The variables and their units in the database for each mine compiled

from different sources

Variable Unit Minimum Maximum Mean Std. dev.

State –

Basin –

County –

City –

Degasification Yes/No

Mined

coalbed

–

Sulphur

amount

%

Coal calorific

value

BTU/lb

Lost + desorbed

gas

scf/ton 6.4 542.1 176.5 144.0

Residual gas scf/ton 3.2 97.4 47.8 29.3

Total gas scf/ton 9.6 585.9 224.3 135.7

Rank –

Ash content %

Coal

production

106 short

ton/y

0.1 11.1 3.3 2.1

Seam height inch 39.0 276.0 80.2 34.3

Cut height inch 43.0 156.0 77.7 18.7

Panel width ft 400.0 1100.0 823.6 149.5

Panel length ft 2200.0 13000.0 7679.3 2361.8

Overburden ft 300.0 2700.0 994.3 509.0

Number of

development

entries

– 2.0 5.0 3.5 0.6

Cut depth inch 27.0 44.0 34.7 4.4

Face conveyor

speed

ft/min 187.0 450.0 278.1 41.5

Stage loader

speed

ft/min 220.0 600.0 353.7 64.3

Year –

Variables in bold were excluded from further analysis. Table also

shows the ranges of numeric input data included in the PCA analysis.

Alphanumeric data were represented in the PCA analysis by assigning

codes. (SI conversions: 1 inch = 0.0254 m, 1 ft = 0.3048 m,

1 ft3= 0.02832 m3

).

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 375
Fig. 1 provides schematic representation of some of the

variables discussed in the following sections. Fig. 1A

shows the graphical representation of a panel surrounded with a three-entry gateroad system, the gob and

caving behind the shields, and the face area, conveyors,

and unmined section of the coal seam. Fig. 1B is a

vertical cross-section along the X–XI profile in Fig. 1A.

This figure shows how overlying strata are supported by

shields above the personnel and shearer, the caving and

fracturing of the overlying strata after the longwall

supports advance, the coal seam, and mining heights.

Fig. 1C is a detailed view of shearer and cutting depth

while mining coal.

2.2.1. Parameters related to gas content of the coal

seam

Gas content is among the key data included in

coalbed methane resource estimations (Boyer and

Qingzhao, 1998). The gas content data, when combined

with geologic and engineering data, can be used as a

basis for an initial estimate of methane emissions and

ventilation requirements (Diamond et al., 1986; Noack,

1998; Karacan and Diamond, 2006).

Gas content has three major components: lost gas,

desorbed gas, and residual gas. The combination of all

of these gives the total gas content of the coalbed.

During mining, all three components potentially contribute to methane emissions into the mine atmosphere,

and these emissions can increase as the gas content of

the coal increases. Fig. 2 shows the change in ventilation

emissions from the mines in the database versus lost

plus desorbed and total gas contents of the mined coal

seams. The figure shows that there is a general positive

correlation and that emissions tend to increase with

increasing gas contents.

Rank represents the level of maturation reached in a

coal seam and usually increases with increasing depth.

Most longwalls, as well as most commercial coalbed

methane projects, operate in bituminous coalbeds. The

coals of sub-bituminous to low-volatile bituminous range

usually provide high gas content and natural permeability

(Steidl, 1996). Mining coals of this rank, particularly

medium- to low-volatile bituminous coals, potentially

liberates high amounts of gas into the ventilation system.

Depth of the mined coal seam, or its overburden

(Fig. 1B), impacts methane emissions in two ways. First,

for coals of the same rank, gas content generally increases

with increasing depth (Kim, 1977). The second impact of

overburden is on the disturbance created in the overlying

strata. This effect can be relative to the width of the

extracted panel. If panel width (Fig. 1A) is greater than

overburden depth, the panel is referred as a “supercritical”

panel (Mucho et al., 2000). This designation implies that,

for the same type of roof material and stratigraphic

sequence, the caving will be more complete after mining

compared to a situation where the panel width is less that

the overburden depth. Besides its ground control

implications, this situation may potentially affect the

methane reservoir and permeability pathways in the

overlying strata and may impact the emissions into the

mine and into the ventilation system (Karacan et al., 2005,

2007a). Fig. 3 shows ventilation emissions of the mines in

the database of this study versus overburden thickness of

the mined coal seams. The plot shows that there is a

general positive correlation between methane emissions

and overburden depth.

2.2.2. Panel size, coal productivity, and longwall

operational parameters

Panel dimensions influence ventilation emissions

by impacting the dimensions of the subsided strata

Fig. 2. Methane emissions from ventilation system of the mines from

1985 to 2005 as a function of total and lost plus desorbed gas contents of

the coalbeds they operated (1 ft3

= 0.02832 m3, 1 MMscf= 28,320 m3).

Fig. 3. Methane emissions from ventilation system as a function of

overburden thicknesses from 1985 to 2005 (1 ft = 0.3048 m,

1 MMscf = 28,320 m3

).

376 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
overlying the extracted panel and the emissions from the

longwall face. Current panels are typically 305 m

(1000 ft) in width (with a continual trend towards even

wider panels) and usually over 3000 m (10,000 ft) in

length. The increasing size of longwall panels, while

helping to increase coal production, may also increase

methane emissions due to the exposure of the mining

environment to a larger area of fractured, gas-bearing

strata. If not captured effectively, additional gas

emissions from bigger, fractured formations (Fig. 1B)

enter the mining environment and increase ventilation

emissions (Karacan et al., 2005). The increase in panel

width (Fig. 1A) also increases face emission rates during

mining operations. This gas inevitably enters the

ventilation system (Schatzel et al., 2006; Krog et al.,

2006).

Fig. 4 shows a plot of ventilation emissions of the

longwalls in the database as a function of their face

widths. The linear trendline passing through the data

shows that there is a slight positive correlation between

ventilation emissions and longwall face widths. However, since ventilation emission is affected by other

mining and geological related factors too, this relation is

far from being a strong linear one.

Cut depth (Fig. 1C) is the slice of coalbed that is

mined by the shearer during each pass. In general, the

greater the cutting depth, the more coal is produced per

rotation and the greater the emissions. However,

emissions may also be affected by the rotational speed

of the cutting drum and the loading rate of the shearer. If

the drum is rotating fast compared to haulage speed,

then a finer coal fraction is produced (Peng, 2006),

which potentially increases emissions.

The face conveyer and stage loader (Fig. 1A) are

parts of the coal transfer system. Their main roles are to

carry the cut coal from the face environment and to

transfer it to the belt conveyor, respectively. They

should have high capacity and should operate as fast as

is practical to ensure continued coal production. Their

speeds are important so that the coal is moved from the

face area as quickly as possible to minimize emissions

from the broken coal on the conveyor.

Most of the operational parameters contributing to

gas emissions also impact coal production. Practical

experience has shown that gas emissions are directly

related to coal production (Lunarzewski, 1998). The

more coal that is mined the higher the emission rates.

Fig. 5 shows the relationship between coal productivity

of the mines in the database and their methane emission

rates as measured in the ventilation system. The figure

shows that emissions are positively correlated with coal

production.

2.2.3. Seam and cut (mining) height

Although mining height is an operational parameter,

it is usually equal or related to the seam height. Usually,

mining height is equal to the seam height. In some thin

seam mines, the roof is also extracted, depending on the

rock material, to make room for equipment and

personnel. Seam and mining height impact ventilation

emissions because thick, permeable, and gassy coalbeds

potentially generate more gas from the longwall face

compared to their thinner counterparts. In addition,

caving height is proportional to mining height (Peng,

2006). Thus, the greater the mining height, the higher

the caving will be and the greater the potential for more

gas to flow into the gob from the overlying strata. If the

mining height is less than the coal seam height—for

example in thick seam mining by top coal caving (Ünver

and Yasıtlı, 2006) or where the operator leaves top or

bottom coal for ground control purposes—some of the

unmined coal seam stays in the gob and may generate

additional gas.

Fig. 4. Methane emissions from ventilation system as a function of

longwall panel widths of the analyzed mines between 1985 and 2005

(1 ft = 0.3048 m, 1 MMscf= 28,320 m3

).

Fig. 5. Methane emissions from ventilation system as a function of

annual coal productions of the analyzed mines between 1985 and 2005

(1 MMscf = 28,320 m3

).

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 377
2.2.4. Parameters related to gas removal

Gateroads are an integral part of longwall mining.

They are mainly used for ventilation and transportation

pathways in underground operations. In the U.S., some

of the early mines used five-entry gateroads. Today,

three-entries are preferred since they require less

development footage compared to four- or five-entry

systems. A four-entry system is used in coal seams of

low height and high gas content. These mines require

more air and thus more entries to minimize resistances.

Two-entry gateroads require a special variance and are

used in deep mines with a thick layer of strong overlying

strata, such as operations in Colorado and Utah (Peng,

2006). Thus, the increased number of entries is both an

indicator of the gassiness of the mine and a contributor

to emissions since it creates additional surfaces from

which methane can flow into the ventilation system.

Degasification of the coal seam and the overlying

strata prior to and during mining are effective ways of

controlling methane emissions during mining (Thakur

and Puondstone, 1980; Diamond, 1994; Karacan et al.,

2007b). An effective degasification process can reduce

emissions into the ventilation system. Depending on the

gassiness of the coal seam and the fractured strata,

horizontal boreholes, vertical boreholes, or gob gas

ventholes can be used. The number of boreholes, their

locations, and their degasification durations may change

based on site-specific factors (Diamond, 1994).

2.2.5. Geographical location (state and basin)

A strong relationship exists between gas emission

rates and geological factors, such as stratigraphy and the

gas contents and strengths of the overlying and

underlying strata. Pashin (1998) reported, based on

investigations in the Black Warrior Basin and in the San

Juan Basin, that regional variations in sequence

stratigraphy were useful for characterization of coalbed

methane reservoirs, in that they were affected by

regional sedimentology and tectonics. Although geographical location is not directly related to methane

emissions, it indirectly identifies the differences in

underground stratigraphy (schematically shown in Fig.

1B) between different locations. This variable also may

be used to identify geological differences in the absence

of direct geological data.

The discussions in this section show that complex

relations exist between different factors and the resultant

emission rates from longwall operations. In fact, these

relations are too complex to be explained by simple

polynomial relations or statistical methods, suggesting

that an ANN approach may be a good candidate for

modeling ventilation emissions.

3. Model complexity reduction for ANN development:

Principle component analysis (PCA)

As in any other prediction models, in ANN

modeling, the number and selection of appropriate

input parameters are very important (Faraway and

Chatfield, 1998). Thus, the key issue is to determine

and select the appropriate input parameters based on

knowledge of the causal variables and a familiarity with

the modeled system.

Hybrid ANN models integrated with various statistical techniques partially address the need for reducing

the number of input parameters by internally determining which model inputs are critical among all variables

for a modeling study. One of the disadvantages of hybrid

models is that presenting a large number of inputs to

ANN models, while relying on the network to determine

the critical model inputs, usually increases network size

and decreases processing speed (Maier and Dandy,

2000). Although easier to implement especially when

the modeled system is not well understood, these

models select variables automatically and thus eliminate

the knowledge and expertise of the user in selecting the

inputs. Therefore, if the relationship to be modeled is

well understood, there are distinct advantages in using

analytical techniques such as cross correlation analysis

or principle component analysis (PCA) to determine the

inputs for ANN models (Maier and Dandy, 2000).

In this study, PCA was used for selecting the most

appropriate input variables from all the variables

presented in Table 2. Identifying principle components

(PCs) reduces the dimensionality of a data set, while

retaining as much of the variance in the data set as

possible. This reduction is achieved by transforming the

original variables into PCs, which are orthogonal and

highly uncorrelated to each other. Most of the variance

in the data set is retained in the first components that

contribute to variance to a greater degree. Elimination of

PCs that do not contribute to the variance of the data

decreases the dimension of the data set, while revealing

information on correlations between variables and their

weights in corresponding PCs (Davis, 1986; Grima,

2000; Grima et al., 2000).

3.1. Preliminary data evaluations and results of PCA

Before performing PCA using XLStat statistical

package (Addinsoft, 2007), all the variables in Table 2

were evaluated. The variables of city, county, coalbed

name, calorific value of the produced coals, ash

contents, and sulphur contents were eliminated. This

was done because these variables were more related to

378 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
combustion efficiency rather than their methane emission potentials, or they were represented by other

parameters in the data set. The variable “year” is

categorical information and cannot be used as a variable

for future projects or for new operation conditions to

predict emissions. Thus, this parameter was also

eliminated from the data set. With elimination of these

seven variables, Table 2 was reduced to 17 variables to

perform PCA. The variables represented by alphanumeric information in this table were designated by

numeric information for PCA. The presence or absence

of any degasification system in a particular mine was

labeled either 1 or 0, respectively, in order to be included

in the PCA. Table 2 also gives the ranges and mean and

standard deviation information for the inputs represented by numeric information.

Table 3 lists the results of the principle component

analysis performed on the remaining 17 variables in

Table 2. It can be seen that all variance in the data is

represented by 16 PCs. However, approximately 80% of

the total variance in the data can be represented by the

first five PCs. The individual contributions of the

remaining 11 PCs are small and their total contribution

is only 20% of the total variance. Table 3 also lists the

eigenvalues, which show the proportion by which an

eigenvector's magnitude is changed. The eigenvectors

with the largest eigenvalues represent the dimensions

with the strongest correlation in the data set. This data

set shows that the highest correlations are also in the first

five PCs. Thus, the first five PCs were selected for the

principle component matrix. Table 4 shows the loadings,

which indicates the influence of the variables in these

five PCs. Positive loadings indicate positive correlations, whereas negative loadings indicate negative

correlations. Also, loadings close to 1 indicate stronger

correlations.

Fig. 6 shows the correlation circle for the factor

loadings of the first two components. In this plot, if any

two variables are far from the center and if they are close

to each other, it indicates that they are significantly and

positively correlated. If any two variables are orthogonal, then there is no correlation between them. On the

other hand, if they are on the opposite sides of the center,

then they are negatively correlated. When the variables

are close to the center, it means that some information

may be carried over to other axes (Addinsoft, 2007).

Fig. 6 shows that gas content and rank of the coal are

correlated with overburden thickness and that they are

significant for the first PC. Likewise, mine operation

parameters and panel width are correlated and they are

significant for the second PC. On the other hand, seam

height and cut height are close to the center, suggesting

that their information can be better represented by other

axes.

Fig. 7 shows a two-dimensional map of the factor

scores of each mine on the first and second PC axes.

This plot enables analysis of the trends and groupings

between observations. There are two major clusters, one

formed by Virginia and Alabama mines and the other

one formed by the mines in the remaining eight states in

varying locations. Virginia and Alabama mines are

separated from the others because of their mining

depths, gassiness, and operational parameters. There are

Table 3

Eigenvalues and the variances explained by principle components

extracted using 538 observations of 17 variables shown in Table 2

PC Eigenvalue Variance (%) Cumulative %

1 4.62 27.18 27.18

2 3.88 22.84 50.02

3 2.42 14.26 64.28

4 1.49 8.77 73.05

5 0.92 5.41 78.46

6 0.64 3.75 82.21

7 0.62 3.65 85.86

8 0.53 3.11 88.97

9 0.43 2.56 91.52

10 0.41 2.43 93.95

11 0.31 1.81 95.76

12 0.27 1.58 97.34

13 0.19 1.10 98.45

14 0.13 0.76 99.20

15 0.11 0.65 99.85

16 0.03 0.15 100.00

The first five PCs represent ∼80% of the total variance.

Table 4

Loadings of the variables in the principle component matrix for the

first five principle components

Variables PC 1 PC 2 PC 3 PC 4 PC 5

Degasification 0.422 0.411 0.288 0.318 0.343

Basin −0.710 0.103 0.476 0.376 −0.285

State −0.479 0.227 0.683 0.371 −0.249

Seam height 0.251 0.223 −0.613 0.628 0.035

Cut height 0.316 0.058 −0.662 0.581 0.082

Panel width −0.071 0.755 −0.133 −0.214 0.011

Panel length −0.372 0.600 −0.015 −0.302 0.122

Overburden 0.790 0.099 0.217 0.101 −0.098

Number of entries 0.347 −0.102 0.521 −0.038 0.635

Cut depth 0.002 0.755 −0.151 −0.058 −0.065

Face conveyor speed −0.049 0.838 −0.104 −0.122 −0.172

Stage loader speed 0.073 0.800 −0.110 −0.166 0.116

Lost + desorbed gas 0.900 0.198 0.198 0.004 −0.246

Residual gas −0.520 0.359 0.436 0.408 0.245

Total gas 0.843 0.287 0.305 0.093 −0.209

Rank 0.853 0.186 0.349 −0.090 −0.068

Coal production −0.361 0.671 −0.098 0.035 0.088

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 379
also some scattered data points between these two main

clusters. These are due to operations having characteristics similar to those in either cluster. For instance, the

data points between the clusters located on the positive

side of the PC2 axis belong to Pinnacle No. 50 mine.

Although this mine is geographically located in West

Virginia, it operates in the Pocahontas No. 3 coalbed,

making it similar to the Virginia mines. Similarly, some

of the data on the negative side of the second PC axis

belong to North River mine of Alabama. However, its

operating depth is shallower [152 m–183 m (500–

600 ft)] and the gas content of this coalbed is less

compared to the other Alabama mines. Thus, it is

separated from the group and approaches those operations with which it shares similar characteristics.

3.2. Analysis of rotated principle component matrix

In order to improve interpretability, PCA was

continued with a new component matrix that was created

by using Kaiser's varimax rotation method, in which PC

axes are rotated to a position in which the sum of the

variances of the loadings is a maximum (Grima, 2000;

Grima et al., 2000). Table 5 shows the rotated matrix for

the five components given in Table 4 and the factor

loadings for each variable after rotation. It can be seen

that the rotated table not only shows the loadings of each

variable in new 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 highest loading 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

Fig. 7. A two-dimensional distribution of factor scores of each mine

( Table 1) on the first two principle components determined by PCA

for 1985–2005. The plot shows the groupings of mines of similar

characteristics (based on 17 variables).

Fig. 6. A correlation circle showing the magnitude and directions of the variable loadings in the first two principle components.

380 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
coal transportation. In this group, face and stage

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. However, coal production for a given

year is a function of other parameters in this column,

such as cut depth and panel dimensions. Therefore, it is

incorrect to say that coal production has less importance

in determining ventilation emissions.

The third PCR in Table 5 is related to the geographical location of the mine determined by state and

coal basin. These variables have high loadings in this

component, 0.951 and 0.917, respectively. They are the

only variables in the database that may be linked to the

impacts of underground geology, overlying strata, and

reservoir properties of the coalbeds on ventilation

emissions. However, since a single coal basin can be

present in more than one state, and underground

geology may change based on geographical location,

the state variable seems to be a better identifier to signify

a local area. The fourth PCR represents the coalbed and

mining height in the longwall environment. These

parameters affect potential emissions from the thickness

of the fractured strata and the methane emissions from

face and ribs. Also, the difference between seam height

and cut height may indicate that the unmined portion of

the seam is going to be left in the caved zone and that its

methane emissions will contribute to the ventilation

emissions. The fifth PCR represents the ventilation

system components. The only parameters that represent

ventilation system components in the database are the

number of gateroad entries and the presence or absence

of any degasification system. In most cases, if the

methane content of the coalbed or strata is high, vertical

gob gas ventholes and vertical and horizontal degasification boreholes are drilled to remove gas before it

enters the ventilation system (Karacan et al., 2007b).

It should be noted that the groupings of the variables

determined by PCA are in accordance with the groupings of the variables used in discussing their effects on

emissions (Section 2.2). Since the relationships between

most of the parameters and the ventilation emissions are

non-linear, the results of PCA need to be evaluated with

care. However, it seems reasonable to include input

variables from each of the five PCs into the ANN model

development in order to capture roughly 80% of

variance in the data and to represent those aspects

impacting ventilation emissions.

4. Development of ANN for predicting ventilation

emissions from longwall mines

4.1. Basic considerations of building an ANN

A neural network simulates a highly interconnected,

parallel computational structure of the brain with many

relatively simple individual processing elements, called

neurons (Eberhart and Dobbins, 1990). The input–output

flow of a network is determined by the connections and

the operation of the neurons. The operation of a single

neuron is dependent on the weighted sum of the incoming

signals and a bias term, fed through an activation function,

resulting in an output. This process can be shown as

(Grima, 2000):

yi 1⁄4 f Xn

r1⁄41

wirf Xm

j1⁄41

vrjuj þ br

 ! þ ci ! i 1⁄4 1; N ; l

ð3Þ

where u is the m× 1 input vector, y the l× 1 output vector,

n the number of neurons in the hidden layer, v and w are

the weight factors, f(.) is the activation function, and br

and ci are the bias values of the neurons in the hidden and

output layers. Neurons are networked (topology) in a

number of ways depending on the problem type and

complexity. One of the most widely used topologies is the

multilayer perceptron (MLP) because it can be applied in

almost every kind of modeling, general classification, and

regression. One of the critical issues in using MLPs is the

choice of the number of neurons, and thus weights, in the

hidden layers (Hornik et al., 1989; Flood and Kartam,

1994; Maier and Dandy, 2000).

Table 5

Factor loadings of the variables after rotating the principle component

(PCR) matrix using Kaiser's varimax rotation

Variables PCR1 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

Bold entries show the most influential variables in each PCR ( Section 3.2).

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 381
Within the hidden layer, the inputs are summed and

processed by a non-linear function, called a transfer

function, or axon. While there are various transfer functions, the hyperbolic tangent is generally the preferred

transfer function. The non-linear nature of this function

plays an important role in the performance of a neural

network.

The process of finding a suitable set of weights is

called “training.” Training is one of the most important

steps in the development of the neural network, since the

weights and the network characteristics will be used later

in testing data sets and making subsequent predictions.

The most common way of training the networks is via the

supervised training algorithms, which require repeated

showings (epochs) of both input vectors and the expected

outputs of the training set to the network to allow it to

learn the relations. The number of epochs needed to give

an acceptable average error and the initial weights before

training are important considerations.

The neural network computes its output at each

epoch and compares it with the expected output (target)

of each input vector in order to calculate the error. An

error is defined for a given pattern (input–output) and

summed over all output neurons over the entire epoch.

Then, the error is summed over all neurons giving a

grand total for all neurons and all patterns. Finally, the

grand total is divided by the number of patterns to give

an average mean squared error (MSE) (Eberhart and

Dobbins, 1990). Minimizing this error is the goal of the

training process. The most widely used technique is to

propagate the error back and adjust the weights in the

network, a process called back-propagation.

During error minimization, it is preferable to find the

global optimum rather than local optima. This situation

is helped by applying a momentum factor between 0 and

1. Momentum increases the effective step size in shallow

regions of error surface (Hassoun, 1995) and helps speed

up the training process.

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 the ANN will perform

when it is confronted with data it has not yet seen. It is

important that the validation data is not used as part of

training (Maier and Dandy, 2000) since this will

deteriorate the results of the validation.

4.2. Preliminary models and the search for effective

input parameters

Development of an ANN uses parameter estimation

and structure identification as fundamental steps. Using

PCA as described in the previous sections gives a good

idea as to which input parameters can potentially be

included in a longwall ventilation emission model.

However, it does not tell how many and exactly which

parameters should be included in the model to cope with

the complexity, non-linearity, and multivariable nature

of the problem. PCA simply reduces the complexity and

allows for a more targeted search.

This section presents the results of initial modeling

attempts to find the appropriate input parameters for ANN

modeling using NeuroSolutions 5.0 (NeuroDimension,

2006). This attempt was undertaken using a heuristic

approach. The strategy began with an ANN structure

common to all models, where the input parameters were

subsequently changed to find the model yielding the best

results. For this phase, 10 different models were

evaluated. In all models, the training and geometry

options were the same. By keeping them so, all models

and results had common properties which allowed for

suitable comparisons.

For the initial models, a two-hidden layer ANN was

constructed with 50 and 30 processing elements for the

first and second layers, respectively. A hyperbolic

tangent was selected as the transfer function for all

layers and a momentum factor of 0.7 was applied for

1500 training epochs. The step size was 1 between the

input and first hidden layer and 0.01 between the second

hidden and output layer.

Table 6

Different models tested in ANN for their efficiency in predicting

ventilation emissions and the variables included

Model

number

Input variables ⁎

1 OB, L + D gas, panel W, face C.S., coal P., state, seam H.

2 OB, L + D gas, panel W, face C.S., coal P., state, seam

H., panel L.

3 OB, L + D gas, panel W, stage L.S., coal P., state, seam

H.

4 OB, total gas, panel W, face C.S., coal P., state, seam H.

5 Total gas, panel W, face C.S., coal P., state, seam H.,

cut H.

6 Total gas, panel W, face C.S., coal P., state, seam H.,

cut H., stage L.S.

7 Total gas, panel W, face C.S., coal P., state, seam H.,

cut H., stage L.S., rank

8 Total gas, panel W, face C.S., coal P., state, seam H.,

cut H., stage L.S., entries

9 Total gas, panel W, face C.S., coal P., state, seam H.,

cut H., stage L.S., cut D.

10 Total gas, face C.S., coal P., state, seam H., entries

⁎ OB: Overburden; L +D gas: lost + desorbed gas; panel w: panel

width; face C.S.: face conveyor speed; stage L.S.: stage loader speed;

panel L.: panel length; seam H.: seam height; cut H.: cut height; cut D.:

cut depth; coal P.: coal production.

382 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
For pre-processing of the data, the whole dataset

(exemplars) was first randomized and then separated into

three sections for use in the training, cross validation, and

testing phases. Randomization was used to prevent the

biases and to make different sections 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 a cross

validation data set, and 81 exemplars (15%) were saved

for testing the trained network.

Table 6 summarizes the variables used in the

prospective models. In order to evaluate the performance

of these models, minimum mean squared errors (MSE)

were noted for training and cross validation phases. The

values of nominal mean squared error (NMSE),

regression coefficient (R), and minimum and maximum

absolute errors, as well as MSE, were noted for testing.

Tables 7 and 8 show the performances of the models in

the training and cross validation phase and in the testing

phase, respectively. The data show that almost all models

produce low training and cross validation errors. However,

testing errors which can be evaluated by MSE, NMSE, R,

and minimum and maximum absolute errors are lower for

“model 8” than for the other models. In testing of “model

8,” an MSE of 1.817 and an NMSE of 0.097 with

regression coefficient to testing data of 0.95 are achieved.

Also, the least maximum absolute error [95.8× 103 m3

/day

(3.383 MMscf/day)] is achieved with this model. Thus,

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.

4.3. Determination of final network parameters

After selecting the “model 8” input parameters, the

optimum network parameters were searched to improve

the predictive performance of the ventilation emission

model. For this purpose, various combinations of parameters were tested. The number of hidden layers was

chosen as two and the hyperbolic tangent transfer function

was used as it was in the base model. The number of

processing elements in hidden layers and the value of

momentum were varied to improve the model predictions.

Values of minimum and final mean squared error (MSE)

were noted for training and cross validation of the ANN.

Also, values of nominal mean squared error (NMSE),

regression coefficient, and absolute error were noted for

testing the ANN.

First, the numbers of processing elements in the first

hidden layer were varied between 44 and 62, while

keeping the number of processing elements in the

second layer at 30, keeping momentum at 0.7, and

running the network for 1500 epochs. The results

showed that 56 processing elements in the first hidden

Table 9

Testing performance of the final ANN model using the variables of

“model 8” in Table 6 after optimizing network parameters

Performance parameter

MSE 1.613

NMSE 0.086

Min. error m3

/day 84.96

(MMscf/day) (0.003)

Max. error m3

/day (× 103) 87.42

(MMscf/day) (3.087)

R 0.956

Table 8

Performance of the tested models given in Table 6 with “testing” data set

Model number 1 2 3 4 5 6 7 8 9 10

MSE 2.528 2.682 2.582 2.523 1.821 1.968 1.924 1.817 1.971 2.275

NMSE 0.135 0.143 0.137 0.134 0.097 0.105 0.102 0.097 0.105 0.121

Min. error m3

/day 623.04 339.84 453.12 509.76 56.64 84.96 113.28 368.16 28.32 877.92

(MMscf/day) (0.022) (0.012) (0.016) (0.018) (0.002) (0.003) (0.004) (0.013) (0.001) (0.031)

Max. error m3

/day (× 103) 119.31 150.49 129.08 136.50 113.39 100.79 108.81 95.81 131.55 141.66

(MMscf/day) 4.213 5.314 4.558 4.820 4.004 3.559 3.842 3.383 4.645 5.002

R 0.931 0.925 0.931 0.931 0.950 0.946 0.947 0.950 0.946 0.938

Table 7

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

tested model

Model number Training MSE Cross validation MSE

1 0.00652 0.00766

2 0.00562 0.00816

3 0.00721 0.00869

4 0.00651 0.00729

5 0.00701 0.00639

6 0.00533 0.00812

7 0.00621 0.00725

8 0.00670 0.00854

9 0.00529 0.00799

10 0.00867 0.01284

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 383
layer produced the best MSE values of 0.00456 and

0.00823 for training and cross validation, respectively.

Next, the processing elements in the second hidden

layer were varied between 26 and 40, while setting the

number of processing elements in the first hidden layer

to 56. The results showed that 38 processing elements in

the second hidden layer produced minimum MSEs of

0.00460 and 0.00660 for training and cross validation,

respectively.

Finally, the momentum value was varied between 0.5

and 0.8 while setting the first and second hidden layer

processing elements to 56 and 38, respectively. The best

MSE values of 0.00501 and 0.00719 were achieved for

training and cross validation, respectively, using a

momentum value of 0.6. Table 9 shows the performance

parameters of this network with the testing data set.

Optimization of the number of processing elements in the

hidden layers and the momentum value improved the

performance of the network in the face of new data.

Table 9 shows that MSE and NMSE values improved

from 1.817 to 1.613 and from 0.097 to 0.086, respectively.

The minimum and maximum absolute errors decreased to

7.6× 10−5 m3

/day (0.003 MMscf/day) and to 87.4×

103 m3

/day (3.087 MMscf/day), respectively. Similarly, R

improved from 0.95 to approximately 0.96. Fig. 8 shows

the topology and parameters of the final ANN network

proposed for predicting longwall ventilation emissions.

Fig. 9 compares the target emission rates in the test

data set with the predicted values using the model in

Fig. 8. The comparison of the target data with the

predicted values in Fig. 9 shows that the performance of

the network in predicting emissions is reasonably good

despite the large variations in the data. This comparison

also indicates the flexibility and interpolation/extrapolation performance of the network.

Fig. 9. Comparison of target methane emission rates (measured values

in the database) of the “testing” data set with the predicted rates from

the proposed ANN shown in Fig. 8 (1 MMscf = 28,320 m3

).

Fig. 10. A scatter plot of measured methane emissions and ANN

predictions of the “testing” data set (1 MMscf = 28,320 m3

).

Fig. 8. The inputs, topology and the parameters of the proposed ANN model for predicting ventilation methane emissions from longwall mines.

384 C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387
5. Comparison of ANN predictions with statistical

models

In this section, the predictive capability of the ANN

model shown in Fig. 9 is compared with multilinear and

second-order non-linear statistical models. For this

purpose, two statistical models having the same input

variables as the ANN model were developed. These

models were developed using the training data used to

“train” the ANN network. However, predictive capabilities of the statistical models were tested using the “test”

data used to evaluate the performance of ANN. This

approach enabled all the models to be developed and

compared on the same basis. The linear and non-linear

models are shown in Eqs. (4) and (5), respectively.

Emission 1⁄4 a0 þ a1V1 þ a2V2 þ N þ a9V9 ð4Þ

Emission 1⁄4 a0 þ a1V1 þ a2V2 þ N þ a9V9

þ b1V2

1 þ b2V22 N þ b9V29 ð5Þ

In these equations, Vn are the variables (n= 1 to 9), an

values are the coefficients of first degree variables, and

bn values are the coefficients of second degree variables.

Figs. 10–12 show the plots of measured emissions

versus predicted values using the ANN, linear, and nonlinear models, respectively. Fig. 10 shows that the ANN

model is capable of predicting the test data reasonably

well (R2= 0.92). The linear model (Fig. 11) and even

non-linear model (Fig. 12), on the other hand, are less

successful in predicting the new data using the same

variables. The R2 values in these cases are 0.54 and 0.61

for linear and non-linear models, respectively. These

data show that the ANN model is superior when compared to the statistical models in predicting ventilation

emissions from longwall mines. This is due to the highly

non-linear and variable nature of the data, for which the

ANN demonstrates more flexibility in capturing trends

and making generalizations compared to other models.

6. Summary and conclusions

Prediction of ventilation methane emissions from

longwall mines is complex due to the various factors

contributing to these emissions. There are few mathematical models in the literature that can be used to predict

methane emissions from longwall mines. The objective

of this work was to develop an artificial neural network

(ANN)-based model for the prediction of ventilation

methane emissions from U.S. longwall operations.

Ventilation methane emission data obtained from 63

U.S. longwall mines in 10 states were combined with

corresponding coalbed properties, geographical information, longwall operation parameters, and coal productions for the years 1985–2005. The variables in the

dataset were analyzed with principle component analysis (PCA) to determine their impacts on methane

emissions. A number of models were tested to find the

best combination of variables for the prediction of

ventilation emissions using the ANN approach. Finally,

the parameters of the ANN were optimized and the

predictive performance was compared with linear and

non-linear models.

The results of the above process showed that ventilation emissions could be defined by a number of

variables, each representing a particular emission contributor. In this study, PCA and preliminary test models

Fig. 11. A scatter plot of measured methane emissions and multilinear

model Eq. (4) predictions of the “testing” data set (1 MMscf= 28,320 m3

).

Fig. 12. A scatter plot of measured methane emissions and secondorder non-linear model Eqn. (5) predictions of the “testing” data set

(1 MMscf = 28,320 m3

).

C.Ö. Karacan / International Journal of Coal Geology 73 (2008) 371–387 385
showed that state, total gas content of coalbed, seam and

cut heights, panel width, face conveyor speed, stage

loader speed, number of entries, and coal productions

were the most effective parameters to predict ventilation

emissions. The ANN algorithm yielded good results for

predicting emissions. A two-hidden layer model with 56

and 38 processing elements in each layer was found to be

sufficient. Also, 1500 epochs were enough to train the

network for satisfactory results.

Comparison of the ANN model with the linear and

non-linear statistical models showed that the ANN model

performed better using the testing data due to its flexibility

and highly non-linear nature. A regression coefficient of

0.92 was obtained from the ANN as compared to 0.54 and

0.61 for linear and non-linear models, respectively.

The presented approach using PCA and ANN is one

of the more accurate models to predict methane

emissions from longwall mines. By incorporating the

critical stratigraphic features in place of geographical

information, the approach and the model can be applied

to other mines.

Acknowledgements

Barbora Jemelkova of the U.S. Environmental

Protection Agency 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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