Artificial neural networks to determine ventilation emissions and 

optimum degasification strategies for longwall mines 

C.Ö. Karacan 

NIOSH Pittsburgh Research Laboratory, Pittsburgh, Pennsylvania, USA 

G.V.R. Goodman 

NIOSH Pittsburgh Research Laboratory, Pittsburgh, Pennsylvania, USA 

ABSTRACT: In longwall mining, premining prediction of methane emission rate depends on a number of 

geological factors, geographical factors, and operational factors. These same factors also can impact the selection 

of a specific degasification system. This study proposes a principle component analysis (PCA) and artificial 

neural network (ANN) approach to predict the ventilation methane emission rates of U.S. longwall mines and the 

optimum combination of degasification boreholes based on the given characteristics of the mine. Artificial neural 

networks, more than conventional computer models, are adaptable systems that can solve problems such as nonpolynomial or very complex relationships that are difficult to describe mathematically. Data on ventilation 

emissions and degasification system design were obtained from 63 longwall mines in 10 states and were 

combined with corresponding geological, geographical, and operational parameters for the period 1985-2005. 

PCA was used to determine the variables that most influence ventilation emissions and degasification system 

design. Different combinations of variables in the data set and network structures were used for network training, 

cross-validation, and testing. The resultant ANN models predicted ventilation methane emissions with a 90-95% 

accuracy and were superior to multi-linear and second order non-linear models. The ANN-based expert 

classification system was able to classify the design of the degasification system (no degasification, gob vent 

borehole, horizontal and gob vent borehole, and horizontal, vertical and gob vent borehole) with more than 80% 

accuracy. The models can be used as prediction and decision tools for the ventilation emissions and degasification 

system selection for specific site and mine-design conditions. 

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). 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. 

Gob vent boreholes 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. These ventholes generally become productive 

after the mining-induced fractures propagate under the well 

(Diamond, 1994; Karacan et al., 2007a). They are equipped 

with exhausters to capture gas from long distances and 

reduce methane migration from the fractured zone into the 

mine. 

Degasification of the coalbed prior to mining is also 

effective in reducing face emissions. The most commonly 

applied methane control technique, especially in high inplace gas content coalbeds, is drilling drainage boreholes 

into the panel area prior to longwall mining to reduce the 

1 Introduction 

Methane emissions can adversely affect the safety of 

underground coal miners. During longwall mining, 

methane emissions can originate from three major sources: 

1) gas emissions from the 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 also 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 thickness of the fractured 
 

 

 

 

 

 

 

 

 

 

 

 

 

methane content of the coalbed. These boreholes can be 

vertical boreholes drilled from the surface or in-seam 

horizontal boreholes drilled from the gateroad entries or 

from the surface. Horizontal drilling and its applications to 

degasify coal seams are well-documented in the literature. 

Vertical wells drilled from the surface to drain methane 

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. 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. 

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. This task is complex due to the large number 

of variables involved with these potential emission sources. 

The design of an effective degasification system to 

augment ventilation is also 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 emission rates and 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. 

The complexity of the longwall mining process and its 

associated methane sources have been modeled by several 

researchers (Schatzel et al., 2006; Krog et al., 2006; 

Lunarzewski, 1998) who empirically determined the 

increase in emissions for wider longwall faces as a result of 

increased coal production. Although simple to use, these 

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 constants which limit 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. 

Numerical techniques can also be used to predict the 

emissions for different mining settings and conditions 

(Lunarzewski, 1998; Karacan et al., 2005, 2007b). 

These models require user expertise and, in most cases, 

specialized and expensive software packages. A 

comprehensive mine simulator combining mining 

operation, coalbed reservoir, and methane production 

parameters does not exist, and creating a model 

representing every process contributing to mine emissions 

and integrated degasification would be very difficult. 

Instead, artificial neural networks can be used to predict 

mine ventilation system emissions and select potential 

degasification systems for more detailed modeling. The 

key advantages of neural networks are their abilities to 

learn, to recognize patterns between input and output 

space, and to generalize solutions. 

The aim of this paper is to develop ANN-based 

methodologies to predict ventilation emissions from 

longwall mines and to develop an expert classification 

system to identify the need and the type of degasification 

system for a longwall operation. Ventilation emission data 

and the type of degasification system utilized 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. Outputs of this model were 

ventilation system methane emissions and identification of 

the optimal degasification option (N: None, G: Gob vent 

borehole, HG: Horizontal boreholes and gob vent 

boreholes, VHG: Vertical, horizontal boreholes and gob 

vent boreholes). This model can be used as a prediction 

tool for emissions and also as a decision-making tool for 

screening the available systems before designing 

degasification methods using more detailed numerical 

modeling approaches. 

2	 Description of Input Data and Database 

Construction for Predictive 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 and degasification system utilization. 

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 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 
 

 

 

 

 

3 Model Complexity Reduction for ANN 

Development: Principle Component Analysis 

(PCA) 

methane production opportunities in U.S. coal mines. 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). 

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. After 

evaluation of all data, the database used for ANN modeling 

consisted of 538 data entries with 24 variables spanning a 

20-year period. Of these 538 data entries, 93 (17%) had the 

G-type degasification method, 136 (25%) had the HG-type 

system, 153 (28%) had the VHG-type system, and 156 

(30%) did not have any degasification system installed (N 

type). 

A general discussion on database parameters for their 

potential effects on ventilation emissions and selection on 

degasification system can be found in Karacan (2007c). 

Those discussions indicate that complex relations exist 

between different factors and the resultant emission rates 

from longwall operations and the use and type of 

degasification system. 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. 

As with any other prediction models, the number and 

selection of appropriate input parameters are very 

important in ANN modeling. 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. 

Table 1. The number of mines and the mine names in 

each state included in the methane emission database. 

In this study, PCA was used for selecting the most 

appropriate input variables from all those presented in 

Table 1. 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 

State No. of 

Mines 

Mine Names 

Alabama 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 4 Deserado, Dutch Creek, Golden 

Eagle, West Elk 

Illinois 7 Galatia, Old Ben 24, Old Ben 25, 

Old Ben 26, Orient No.6, Monterey 

No.1, Rend Lake 

Kentucky 4 Wolf Creek No. 4, Wheatcroft No. 9, 

Camp No:11, Baker 

Maryland 1 Mettiki 

Ohio 4 Meigs No. 2, Meigs No. 31, 

Powhatan No.4, Powhatan No.6 

Pennsylvania. 10 

Bailey, Eigthy Four, Enlow Fork, 

Maple Creek, Cambria, Cumberland, 

Warwick, Homer City, Dilworth, 

Emerald 

Utah 4 Aberdeen, Sunnyside No. 1, Dugout 

Canyon, West Ridge 

Virginia 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 

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 

uncorrelated to each other. Most of the variance in the data 

set is retained in the first components that contribute to 

variance. 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). 

Before performing PCA for emission and 

degasification system selection, all the variables in Table 2 

were evaluated. The variables of city, county, mined 

coalbed, calorific value (Btu) of the produced coals, ash 

contents, and sulfur contents were eliminated. This was 

done because these variables were more related to 

combustion efficiency rather than their methane emission 

potentials, or they were represented by other parameters in 

the data set. The variable “year” was categorical 

information and could not be used for future projects or for 

new operation conditions to predict emissions. Elimination 

of these factors reduced the number of variables for PCA. 

Initial principle component analysis showed that 

approximately 80% of the total variance in the data could 

be represented by the first five PCs for emission and 

degasification system selection models. Thus, the first five 

PCs were selected for the principle component matrix. 
 

 

 

 

 

j 

e 

n 

l 

c 

m 

g 

o 

k 

d 

f 

i 

h 

b a 

-1 

-0.75 

-0.5 

-0.25 

0 

0.25 

0.5 

0.75 

1 

PC 2 

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

PC 1 

a: Basin, b: State, c: Residual Gas, d: Panel Length, e: Coal 

Production, f: Panel Width, g: Cut Depth, h: Seam Height, 

i: Mining (Cut) Height, j: Methane emission from 

ventilation system, k: Rank, l: Lost + Desorbed Gas, m: 

Total Gas, n: Overburden, o: Number of Entries 

1 

Table 2. The variables and their units in the database for 

each mine compiled from different sources. (Variables in 

bold were excluded from further analysis.) 

Variable Unit Variable Unit 

State Ash % 

Basin Coal prod. 106

 tons/y. 

County Seam height inch 

City Cut height inch 

Degas. Yes/No Panel width ft 

Mined 

coalbed Panel length ft 

Sulphur % Overburden ft 

Coal cal. Number of dev. 

value BTU/lb entries 

Lost+des. 

gas scf/ton Cut depth inch 

Face conv. 

Residual gas scf/ton speed ft/min 

Stage load. 

Total gas scf/ton speed ft/min 

Rank Year 

Vent. emiss. scf/day Type of degas. 

system 

Figures 1 and 2 show the correlation circles for the 

factor loadings of the first two components of the PCA 

performed on the variables of emission and degasification 

system selections, respectively. 

In these plots, if any two variables are both far from the 

center and close to each other, this 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, which is not 

seen on the graphs. Figure 1 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. Figure 2 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. 

 

 

Variables (axes PC1 and PC2) 

e 

r 

n 

c 

o 

g j 

q 

m 

d 

f 

l p b 

a 

k 

-1 

-0.75 

-0.5 

-0.25 

0 

0.25 

0.5 

0.75 

1 

PC 2 

h 

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

PC 1 

a: Basin, b: State, c: Residual Gas, d: Panel Length, e: Coal 

Production, f: Panel Width, g: Face Conveyor Speed, h:Cut 

Depth, j: Stage Loader Speed, k: Degasification, l: Seam 

Height, m: Overburden, n: Total Gas, o: Lost + Desorbed 

Gas, p: Mining (Cut) Height, q: Number of Entries, r: Coal 

Rank 

1 

Figure 1. A correlation circle showing the magnitude and 

directions of the variable loadings in the first two principle 

components (emission modeling). 

Figure 2. Correlation circle showing the magnitude and 

directions of the variable loadings in PC1-PC2 after PCA 

in degasification system selection parameters. 
Table 3. Factor loadings of the variables after rotating the 

principle component (PCR) matrix using Kaiser’s varimax 

rotation. Bold entries show the most influential variables in 

each PCR for ventilation emission modeling. 

Variables PCR1 PCR 2 PCR 3 PCR 4 PCR 5 

Degas. 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 0.271 -0.178 -0.045 -0.224 0.805 

ent. 

Cut depth 0.125 0.745 0.056 0.142 -0.076

Face conv. 0.145 0.834 0.116 0.056 -0.167 

speed 

Stage load. 0.147 0.811 -0.048 0.070 0.105 

speed 

Lost+des 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 prod. -0.221 0.688 0.251 0.114 0.036

Figures 3 and 4 show a two-dimensional map of the 

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

for emission and degasification system selections, 

respectively. 

Observations (axes F1 and F2) 

6 

Pinnacle VA mines 4 WV, PA, CO, No: 50 

UT, IL, KY, 2 OH, MD mines 

0 

-2 

-6 

-4 

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

PC 1 

North River AL mines 

PC 2 

Figure 3. A two-dimensional distribution of factor scores 

of each mine on the first two principle components 

determined by PCA for 1985-2005. The plot shows the 

groupings of mines of with similar characteristics in terms 

of parameters affecting ventilation emissions. 

Observations (axes PC 1 and PC 2) 

6 

WV, PA, CO, UT, IL, 

4 KY, OH, MD mines VA mines Pinnacle 

No: 50 

2

PC 2 

0 

-2 

-4 

-6 -4 -2 0 2 

PC 1 

4 6 

AL mines 

8 

Figure 4. A two-dimensional distribution of factor scores 

of each mine on PC1-PC2 determined by PCA for the 

parameters influencing degasification system selection. 

The plot shows the groupings of mines based on different 

characteristics. 

These plots enable analyses of the trends and groupings 

between observations. In both Figures, 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, emissions, and operational parameters. 

There are 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 in Figure 3 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 in Figure 3 belong to the North River mine 

in Alabama. However, its operating depth is shallower 

[150-180ft (500-600m)] 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. 

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). Tables 3 and 4 show the rotated matrix 

for the five components for each model discussed in this 

paper (Table 3 for emission, Table 4 for degasification 

system selection). 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. 

Table 3 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 second PCR represents longwall panel 

dimensions, coal productivity, and underground coal 

transportation. The third PCR in Table 3 is related to 

geographical location of the mine determined by state and 

coal basin. The fourth PCR represents the coalbed and 

mining height in the longwall environment. 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. Table 4 

can be interpreted similarly. 

Table 4. Factor loadings of the variables after rotating the 

principle component matrix using Kaiser’s varimax 

rotation. Bold entries show the most influential variables in 

each rotated principle component (PCR) for degasification 

system selection model. 

Variables PCR1 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 0.276 -0.158 -0.168 0.022 0.853 

ent. 

Cut depth 0.186 0.721 0.118 0.064 -0.272 

Lost+des. 0.949 -0.054 0.067 -0.173 -0.031 

gas 

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 prod. -0.173 0.755 0.121 0.213 0.061 

Vent. emiss. 0.646 0.339 0.104 -0.251 0.369 

It should be noted that since the relationships between 

most of the parameters and the ventilation emissions are 

nonlinear, 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 the 

variance in the data and to represent those aspects 

impacting ventilation emissions. 

4 Development of ANN for Predicting Ventilation 

Emissions and Degasification System Types 

4.1 Basics 

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 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): 

⎛ n ⎛

∑

m ⎞ ⎞

y i = f ⎜∑wir f ⎜ 
 vrj u j + b ⎟ + c ⎟ = 1,..., ⎜ i i l (1) ⎜ r ⎟ ⎟ ⎝ r =1 ⎝ j=1 ⎠ ⎠

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 the 

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

the bias values of the neurons in the hidden and output 

layers. Neurons are represented by networking (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 and their activation properties (Maier and 

Dandy, 2000). 

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. 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. 

4.2 ANN Modeling of Ventilation Emissions from U.S. 

Longwall Mines 

This section presents the results of initial modeling 

attempts to find the appropriate input parameters for ANN 

modeling. 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 and with the model parameters given in Table 5. 

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. In testing of these models, “model 8” was 

most efficient with an MSE (mean square error) of 1.82, an 

NMSE (nominal mean square error) of 0.10, and a 

regression coefficient of 0.95. Also, the maximum absolute 

error was 3.4 MMscf/day (95.8 × 103 m3/day). This model 

used total gas content, panel width; face conveyor speed, 

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

speed, and number of entries as input variables for the 
 

 

ventilation emission model. 

Table 5. Different models tested in ANN for their 

efficiency in predicting ventilation emissions and the 

variables included. 

 

 

 

 

 

 

6 

7 

8 

9 

Model Input variables*
 

number 

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., 

* 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 

State, Seam H., Cut H. 

Total Gas, Panel W, Face C.S., Coal P., 

State, Seam H., Cut H., Stage L.S. 

Total Gas, Panel W, Face C.S., Coal P., 

State, Seam H., Cut H., Stage L.S., Rank 

Total Gas, Panel W, Face C.S., Coal P., 

State, Seam H., Cut H., Stage L.S., Entries 

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 

After selecting the “model 8” input parameters, the 

optimum network parameters were determined to improve 

the predictive performance of the ventilation emission 

model. Various combinations of network parameters were 

tested to optimize prediction capabilities of the model. The 

final model used 56 and 38 processing elements between 

the first and second layers, respectively, hyperbolic tangent 

as the transfer function, and a momentum of 0.6. The step 

size was 1 between the input and first hidden layer and 0.1 

between the second hidden and output layer. After 1500 

training epochs, the optimized model produced a MSE of 

1.61, a NMSE of 0.09, and an R-value of 0.96. The 

maximum and minimum errors were 3.1 and 0.003 MMscf, 

respectively. 

Figure 5 compares the target emission rates in the test 

data set with the predicted values using the network. The 

comparison of the target data with the predicted values 

shows that the performance of the network in predicting 

emissions is reasonably good despite the large variations in 

the data. The predictive capability of the ANN model is 

also compared with multilinear and second-order nonlinear statistical models in the form of: 

Emission = a0 + a1V1 + a2V2 +.... + a9V9 (2) 

Emission = a0 + a1V 2 1 + a2V2 +.... + a V9 + b 2 9 1V1 + b2V2 .... 

+ b9V 2 

9 (3) 

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. 

Figures 5-7 show the plots of measured emissions 

versus predicted values using the ANN, linear, and nonlinear models, respectively. These Figures show that the 

ANN model is capable of predicting the data better than 

linear and 2nd order non-linear models. These data show 

that the ANN model is superior when compared to the 

statistical models in predicting ventilation emissions from 

longwall mines. A detailed discussion on this approach and 

the development details can be found in Karacan (2007c). 

ANN Model 

0 

2 

4 

6 

8 

10 

12 

14 

16 

18 

20 

0 2 4 6 8 10 12 14 16 18 20 

Measured Emission Rate (MMscf/day) 

Pred. Emission Rate (MMscf/day) 

R2

=0.92

Figure 5. A scatter plot of measured methane emissions 

and ANN predictions of the “testing” data set. 

Linear model 

0 

2 

4 

6 

8 

10 

12 

14 

16 

18 

20 

0 2 4 6 8 10 12 14 16 18 20 

Measured Emission Rate (MMscf/day) 

Pred. Emission Rate (MMscf/day) 

R2

=0.54 

Figure 6. A scatter plot of measured methane emissions 

and multilinear model (Eqn. 2) predictions of the “testing” 

data set. 

4.2 ANN Modeling of Degasification System Selection 

for U.S. Longwall Mines Using Classification 

Mapping. 

A similar initial model search was performed for 
Non-linear model 

0 

2 

4 

6 

8 

10 

12 

14 

16 

18 

20 

0 2 4 6 8 10 12 14 16 18 20 

Measured Emission Rate (MMscf/day) 

Pred. Emission Rate (MMscf/day) 

R2

=0.61 

 

 

 

 

 

Figure 7. A scatter plot of measured methane emissions 

and second-order non-linear model (Eqn. 3) predictions of 

the “testing” data set. 

degasification system selection. Again, the strategy was to 

start with an ANN structure common to all models and to 

change the input variables to find the model that would 

yield the best results. For this phase, 9 different models 

were tested (Table 6). 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 

model with 50 and 30 processing elements for the first and 

second layers, respectively, was constructed. 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). 

Table 6. Different models and variables tested in the ANN 

for degasification system selection for U.S. longwall 

mines. 

Model 

number 

Input variables*
 

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 

* 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 

The results of the initial input parameters search 

resulted in “model 8” of Table 6, producing the least MSE 

training error (0.004). 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%, 80.0% and 91.7% correct identifications for 

HG, N, G, and VHG classes, respectively. Thus, the input 

variables for “model 8,” namely total gas content, panel 

width, coal production, and state, seam height, cut height, 

overburden thickness, and ventilation emissions were 

selected as the input variables for the degasification system 

identification model. 

The optimization of network parameters for 

degasification system selection by changing various 

network parameters resulted in a two-hidden-layer 

network, with the best results given by 48 and 28 

processing elements, hyperbolic tangent and softmax axon 

as the transfer functions, a momentum term of 0.7, and 

1500 training epochs. Table 7 shows the performance of 

the network using testing data. As can be seen from these 

data, the network incorrectly identified 1 case out of 22 

samples 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 3 of them incorrectly 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 incorrectly as HG 

and two of them incorrectly as N. The highest score was 

obtained for the VHG system. The ANN identified all 12 

cases correctly. 

Table 7. Performance of the ANN network used 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 U.S. longwall mines. 

ANN 

Output 

HG 

(Hor+GVB 

) 

N 

(None) 

G 

(GVB) 

VHG 

(Vert+Hor 

.+GVB) 

HG 

N 

G 

VHG 

% 

Correct 

21 (True) 

0 

1 (False) 

0 

95.5 

3 (False) 

29 (True) 

0 

0 

90.6 

1 (False) 

2 (False) 

12 (True) 

0 

80.0 

0 

0 

0 

12 (True) 

100.0 

5 Summary and Conclusions 

The results of the PCA and initial ANN model search 

process showed that ventilation emissions and 

degasification system selection could be defined by a 

number of variables, each representing a particular 

emission contributor and degasification system selection 

criterion. 

In this study, PCA and preliminary test models 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. 

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 and generalized models to predict 

methane emissions from longwall mines. Based on the 

results, the best combination of variables for selecting a 

degasification system using the ANN approach was 

determined. 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 U.S. 

longwall mines can be classified effectively using only 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 may be applicable to other mines 

in different locations. 

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