1 INTRODUCTION 

The discrimination of mine fires from nuisance 

signatures such as emissions from diesel equipment, 

flame cutting and welding operations, and battery 

charging operations is an ongoing problem for early 

and reliable mine fire detection. Currently most inmine fire detection systems use thermal or CO sensors. Thermal sensors are useful in the proximity of 

flaming combustion. CO sensors are subject to false 

signals from CO diesel equipment nuisance emissions and H2 cross-interference from battery charging operations. Multiple type fire sensors and statistical methods, including neural network, have been 

used for mine fire detection and the elimination of 

nuisance emissions in non-mining applications (Ishii 

et al., 1994, JiJi et al., 2003). As discussed previously (Edwards et al , 2002, Friel and Edwards, 

2002), a set of three sensors and a neural network 

was determined to predict the occurrence of a mine 

fire in the presence of diesel emissions. These sensors were: a carbon monoxide sensor; an optical path 

smoke sensor; and a MOS sensor with bimodal response to products-of-combustion (POC) from mine 

fires and NOx from diesel equipment. These evaluations were based upon coal and conveyor belt combustion experiments in the presence of diesel emissions. Subsequent to that research, additional 

experiments included wood combustion. A new 

neural network (NN) was constructed based upon an 

optimum choice of sensors. These included the previous three sensors and a second MOS sensor. The 

new NN uses directly the CO sensor, optical smoke 

sensor, and bimodal MOS sensor output, the product 

of the CO sensor and optical smoke sensor outputs, 

and the product of the outputs from the bimodal 

MOS sensor, labeled 2105, and a MOS sensor, labeled 2600, which is more responsive to H2 than 

CO. The utility of the program can only be successful for underground mining applications if predictions can be made accessible to the mine personnel 

in real-time. This required the implementation of a 

real-time sensor data interpretation program for the 

NN executable file. The objective of this research 

was to demonstrate how this could be accomplished. 

2 SENSOR TYPES 

For mine fire detection the available sensor types 

include gas, smoke, and thermal sensors. Although 

thermal sensors are widely used, their range is limited to the immediate proximity to a fire in its flaming combustion stage. A sensor commonly deployed 

when belt entry air is used at the mine working face 

is a CO sensor. An advantage of a CO sensor is its 

capability to quantify a fire in terms of the molecular 

concentration of a product gas species. A CO sensor 

is especially useful for early detection of a deep 

seated spontaneous coal heating where smoke concentrations are low. Disadvantages are its response 

to emissions from diesel equipment, flame cutting, 

Real-time neural network application to mine fire – nuisance emissions 

discrimination 

J.C. Edwards, R.A. Franks, G.F. Friel, C.P. Lazzara, & J.J. Opferman 

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

ABSTRACT: : The National Institute for Occupational Safety and Health (NIOSH) implemented a real-time 

neural network system which can discriminate mine fires from nuisance diesel emissions as part of an atmospheric mine monitoring system in NIOSH’s Safety Research Coal Mine. The real-time response of a neural 

network to fire sensor outputs was demonstrated for coal and belt combustion in the presence of diesel emissions. The fire sensors consisted of an optical path smoke sensor, a carbon monoxide (CO) sensor, and two 

types of metal oxide semiconductor (MOS) sensors. The real time neural network was trained with coal, 

wood, and belt fire experiments with and without diesel emissions background. The trained neural network 

successfully predicted mine fires with these combustibles in the smoldering stage prior to the onset of flames.
welding, and its susceptibility to interfering gases, 

such as H2 from battery charging operations. Smoke 

sensors, which are either optical or ionization, are 

responsive, stable, and easy to check. The ionization smoke sensors are usually based upon the decay 

of an alpha emitter such as Am241 or a beta emitter 

such as Kr85. A disadvantage of an ionization smoke 

sensor is the requirement for a license for the radioactive source dependent upon the strength of the 

source. Optical smoke sensors can be either a point 

or a path type. The point type sensor is based upon 

optical scattering, and the path type is based upon 

optical obscuration. An advantage of the path sensor is its ability to probe the cross section of an entry. A disadvantage for a path optical smoke sensor 

is the optical path distance which can create a problem for its installation in a mine. Nuisance emissions which can affect smoke sensors include dust, 

water vapor, and particulate emissions from diesel 

equipment and flame cutting, and for the case of an 

optical path sensor the potential blockage by personnel and equipment. Optical smoke sensors, 

which are generally in the infrared optical range, are 

very responsive to larger smoke particulates produced by smoldering combustion, and ionization 

smoke sensors are generally more responsive to 

smaller smoke particulates produced by flaming 

combustion. The demarcation size is about 0.3 micrometer. Because of the optical sensor’s response 

to larger particulates, it will be less responsive to 

smaller diesel particulate emissions. 

 Another class of sensors which respond to mine 

fire gases are MOS sensors. These sensors are very 

responsive, but not very selective in their response. 

Their responsiveness can be ordered in terms of their 

resistance change to various oxidizing and reducing 

gases. Methods to vary their selectivity include the 

addition of a catalyst, sensor element grain size selection, heating of the sensor, and filters. Another 

advantage of the MOS sensor is its compact size. Its 

cylindrical structure has a height and diameter of 

about 1.3 cm. A disadvantage of a MOS sensor is 

the high temperature, 250 to 380 o C, of the sensing 

element. A MOS sensor which is sensitive to NOx

has an increase in its electrical resistance associated 

with adsorption of oxygen due to electron transfer 

from the surface element to the oxygen and the 

buildup of a positive space charge on the element 

surface. The presence of deoxidizing POC gases 

will remove the adsorbed oxygen and result in reduced resistance. To be useful, the MOS’s sensitivity to NOx must be sufficient to overcome its sensitivity to CO in diesel emissions, otherwise the 

discriminating capability of the sensor will be compromised. Generally there are gas concentration 

ranges over which the sensor’s resistance element 

will respond with a power dependence upon the adsorbed target gas concentration. For an extended 

range of concentrations this nonlinear logarithmic 

dependence makes the prediction of the net effect 

problematic, and direct experimental verification is 

required. 

In a refueling area where rapid flaming combustion most likely will occur, optical spectral emission 

sensors could be useful. In this case the distance between the source fire and sensor will be relatively 

short, and infrared signal attenuation by smoke will 

initially be minimal. 

The ordinary use of fire sensors depends upon defined alarm values. Fire sensors for in-mine use 

have established alarm values (Code of Federal 

Regulations, 2001). These include a CO alert value 

of 5 ppm above ambient, a CO alarm value of 10 

ppm above ambient, a smoke optical density of 

0.022 m-1, and a temperature of 165 ° F. 

3 MINE FIRE DETECTION METHODS 

There are three approaches for utilization of sensors for mine fire detection in the presence of nuisance emissions. One approach is to increase the 

sensor alarm and alert values to compensate for nuisance emissions. Without an exact knowledge of the 

nuisance emissions’ gaseous and particulate concentrations, which will be variable in response to the 

operation of the emissions producing equipment under variable power loads, early and reliable fire detection will be compromised. For example, this approach would be expected to miss early smoldering 

stages of a mine fire. A second approach is rule 

making. This approach would rely upon an understanding of the generation of emissions from nuisance producing sources and combustion sources. 

For example, this approach could examine rates of 

change in a measurable emission, such as CO, so as 

to differentiate CO emissions from a diesel and from 

a mine combustible. However, the rate of change of 

a fluctuating time variable quantity will fluctuate 

considerably and can produce indistinguishable 

events. Another example is to rely upon the measured historical relationship between two emission 

components, such as NO and CO from a diesel engine. This approach can depend upon the growth 

rate of the fire (Edwards et al., 1999). A third approach is information processing. This approach is 

useful when multiple type sensors with nonlinear responses to various POC are used. In this approach a 

relationship between the classification of an event 

and the sensors’ inputs can be established with a 
method which recognizes patterns for known data 

sets. An advantage is that a number of mine firenuisance emission experiments can be conducted to 

establish the functional relationships. One such information processing approach is a NN approach. 

Based upon these relationships classifications of unknown events similar to those for which the relationships are developed can be made. These classifications can be quantified with a probabilistic 

interpretation. 

4 NN PROGRAM 

A NN program named NeuroSolutions developed 

by NeuroDimension, Inc1 was applied to fire prediction. The trained perceptron NN which was implemented was composed of thirty eight processing 

elements that produced three output classifications 

in response to five inputs. The NN program had 

two-hidden-layers with 20 processing elements (PE) 

in the first layer and 10 PE in the second layer. A 

hyperbolic tangent function was used as the activation function which operated on the linear combination of inputs to the PEs in the hidden layers. The 

output classification is a result of the application of a 

softmax function to the inputs to the PEs in the output layer. The softmax function is a probability 

function based upon exponential weights. Applications of the NN method require the determination of 

the internal weights between the input layer of sensor data and the first hidden layer, between the sequential hidden layers, and between the last hidden 

layer and the output layer. A backpropagation 

method for the errors associated with the feedforward of the input training is used to determine the 

weights for the PEs during the training stage. The 

testing stage or predicting stage uses a forward 

propagation method. The five inputs consisted partially of the responses of the CO sensor, the optical 

path smoke sensor, and the bimodal MOS sensor, all 

normalized by their ambient values. In addition two 

product functions of sensors were included. These 

were the product of the responses of the CO and optical smoke sensor, and the product of the bimodal 

MOS sensor and a MOS sensor responsive to H2 and 

CO. A trained NN executable file was constructed 

by iterating on the training data set with known output classifications. Based upon a training data set 

which consists of six in-mine combustion experiments in the presence and absence of diesel emissions, an optimum set of sensor type inputs was 

1

 Mention of any company or product does not constitute endorsement by NIOSH. 

determined which would detect the test fires and 

yield the minimum number of false alarms. The 

combustion fuel sources were coal, conveyor belt, 

and wood. 

5 DATA EVALUATION 

Each sensor’s analog output voltage is converted 

into a digital signal by an accessor card for processing by the mine monitoring system. The range of 

the analog signal is subdivided into 255 subdivisions 

associated with the 8 bits of electronic information 

available for data representation. The subdivision 

represents the minimal signal variation that can be 

processed. For example, if the sensor’s output signal range is 0 to 5 volts, then the minimum signal 

change which can be processed is 19.6 mV. As part 

of the mine monitoring system, an applications 

computer program was prepared which managed the 

real-time sensor data for inclusion into the NN executable file. To process the input data for the NN, 

the background average is computed for each sensor’s response based upon a specified time increment. This time was generally selected to be about 

5 minutes prior to start of the experiment. After the 

average values were determined for the sensors, the 

program proceeded to normalize the subsequent input by the average values. For all sensors exclusive 

of the CO sensor, this data normalization was accomplished by a division of the sensor output by the 

sensor’s average ambient value. For the CO sensor, 

the ambient value was subtracted. The normalized 

values were found to yield more consistent input 

data for the NN than the actual signal values, since 

changes relative to ambient are important for characterization of the event. The program also developed 

the appropriate multiplicative combinations from the 

sensor data as inputs for the NN. The NN output 

was displayed on a monitor as columns of probability values for clear air, diesel emissions, and fire 

POC. When a fire was identified the monitor displayed a box with a fire alert message. This system 

was successfully evaluated not only with previously 

acquired data, but also in real-time while a mine fire 

detection experiment was in progress. 

6 EXPERIMENTAL METHOD 

Figure (1) shows a plan view of the section of the 

Safety Research Coal Mine (SRCM) in which the 

experiments were conducted. To establish a sensor 

atmosphere which contained diesel emissions, a diesel locomotive was positioned in B-Butt either up-
wind of the split formed by 10-Room, or at the entrance to 10-Room. In the latter case a tube was

Figure 1. Plan view of the SRCM section 

connected to the locomotive’s exhaust port to direct 

the emissions directly into 10-Room. Identical fire 

sensor types were located at S1 and S2, which were 

18 m and 148 m downwind of the fire zone. The 

average height and width of 10-Room were 2.0 m 

and 3.9 m, and the average height and width of FButt were 1.9 m and 4.5 m. The average air flows 

for the experiments were 3.0 m3

/s in 10-Room and 

4.4 m3/s in F-Butt. Table 1 lists the measured airflows for the experiments. The flow ratio refers to 

the airflow increase in F-Butt compared to 10-

Room. The higher airflow in F-Butt was due to the 

leakage around brattices which block the crosscuts 

from parallel airways into F-Butt. The dual sensor 

stations with gas and smoke signature dilution between stations S1 and S2 provided a method to define two experiments for one mine fire combustion 

event. There was an average 47 pct increase in the 

ventilation between the two sensor stations. 

The fires were contained in a 0.61-m square tray. 

In order to provide a slow heating of the solid fuels 

which would transition through a smoldering stage 

prior to a flaming combustion stage, a controllable 

heating source was maintained with the use of electrically powered strip heaters. For the coal fires the 

heater elements were embedded in approximately 14 

kg of Pittsburgh Seam Coal with a diameter less 

than 5 cm. About 1 kg of coal fines was distributed 

over the coal. For the conveyor belt combustion experiments about 0.5 m square samples of belt 1.1 cm 

thick with a 3 kg mass were attached to a steel plate 

which was heated from below by the electrical strip 

heaters. For one combined coal and belt combustion 

experiment a belt sample about 0.3 m by 0.5 m was 

positioned on top of the coal. The wood combustion 

experiments utilized twenty eight oak sticks, 0.46 m 

long and approximately 1.6 cm square cross section, 

cut from mine support timber arranged in a twolayer crib set with an approximate total mass of 2 

kg. The wood crib was placed upon five electrical 

strip heaters. The identification of the experiments 

is provided in table 1. 

7 RESULTS 

The training data set for the NN consisted of experiments T71S2, T75S1, T79S1, T81S1, T82S1, 

and T85S1. This data set includes coal, conveyor 

belt, and wood fires in the presence and absence of 

diesel emissions. The trained neural network was 

used to evaluate the experiments previously conducted, and used in a real-time mode for fire prediction in experiments T87S1, T88S1, and T89S1. A 

conservative fire alert was marked by the probability 

exceeding 0.5 even though a probability of a fire 

near 1/3 could exceed the probabilities of the other 

two events. 

Figure 2 shows the response of the sensors and 

NN probability at station S1 to a developing primary 

coal fire and secondary belt fire in the presence of 

diesel emissions as part of experiment T88S1. The 

power supplied to the heater elements varied from 

0

0.4

0.8

1.2

1.6

2

9:45 AM 10:08 AM 10:32 AM 10:55 AM 11:19 AM

TIME

0

10

20

30

40

50

60

CO, PPM

START DIESEL

HEATER ON

SMOKE

COAL FLAMES

BELT FLAMES

FIRE OUT

FIRE 

PROBABILITY

CO

2600

OPTICAL

2105

SENSORS, FIRE PROBABILITY

Figure 2. Sensor responses at station S1 and NN fire probability for coal and belt combustion in presence of diesel emissions. 

1.6 kW to 2.9 kW over a 20-min time period. The 

presence of diesel emissions results in a significant 

response of the two MOS sensors, an increase of the 

CO concentration above the 5-ppm alert concentration, and an insignificant response of the optical path 

smoke sensor prior to heating the combustibles. Associated with the visual observation of smoke results 

was a rapid increase in the 2600 sensor’s response, 

and less rapid rates of response for the CO, optical 

smoke, and 2105 sensors. The onset of flaming coal 
combustion resulted in visually obscuring smoke 

which saturated the optical smoke sensor’s response. 

The identification of a fire with the NN probability 

curve occurred 5 min after the visual observation of 

smoke, and 16 min prior to coal flaming combustion. The flaming belt combustion occurred 10 min 

after the flaming coal combustion. When the NN 

identified the fire, the optical density was 0.012 m-1, 

which is less than the 0.022 m-1 smoke optical density alarm value specified in 30CFR75.344 and 

30CFR75.340 (Code of Federal Regulations, 2001). 

The maximum temperature measured at the roof 

above the fire was 98 ° F, which occurred during the 

belt flaming stage. This temperature is less than the 

thermal alarm temperature of 165 ° F. In this particular example, the clear advantage of the NN approach is its ability to discriminate the false CO alert 

value associated with the diesel emissions, and identify the incipient smoldering coal combustion stage 

prior to its flaming combustion, and the subsequent 

flaming belt combustion. 

Figure 3 shows the response of the sensors at station S1 and the NN probability curve to a belt fire in 

the presence of diesel emissions for experiment 

T78S1. In this case the ventilation was adjusted to 

0

0.4

0.8

1.2

1.6

2

9:00 9:16 9:33 9:50 10:06 10:23

TIME

0

10

20

30

40

50

60

CO, PPM

START DIESEL

SMOKE

FLAME

HEATER ON

CO

2600

2105 OPTICAL FIRE 

PROBABILITY

SENSORS, FIRE PROBABILITY

Figure 3. Sensor responses at station S1 and NN fire probability for belt fire in presence of diesel emissions. 

produce CO concentrations from the diesel exhaust 

in excess of 30 ppm prior to heating of the belt. 

This exceeds the 30CFR75 defined 10 ppm alarm 

value. Smoke emissions were observed when the 

temperature of the belt surface in contact with the 

heated plate reached approximately 560 oF, and 

flaming combustion commenced when the temperature reached approximately 870 oF. The electrical 

power applied to the heaters varied from 1.1 kW to 

3.3 kW over a 40-min time period. The NN probability curve identified the fire 7 min after the visual 

observation of smoke, and 19 min prior to belt flaming combustion. At the NN identification of the fire 

the optical density was 0.0061 m-1. A comparison of 

the maximum CO concentration for the experiments 

in figures 2 and 3 prior to heating of the fuel show 

measured CO values of 8 and 36 ppm respectively. 

In figure 2 a CO concentration of 36 ppm, which 

was an average of two CO sensors at the station, was 

not reached until flaming coal combustion occurred. 

This illustrates the uncertainty in increasing a fire 

sensor’s alarm alert and alarm value. Although 

there will be a scaling of the CO concentration with 

ventilation change, this scaling provides one more 

parameter which would need to be known with certainty prior to identification of a fire produced CO 

alarm concentration. 

To evaluate the effectiveness of the NN for identification of a variable quantity nuisance emissions 

source, the trained NN was used to identify a nuisance event which consisted of a diesel locomotive 

moving along B-Butt past the air split of 10-Room. 

Figure 4 shows a minimal deviation of the optical 

smoke sensor to the diesel emissions, and the relatively sharp responses of the CO and 2105 sensor to 

0

1

2

3

4

5

9:15 AM 9:38 AM 10:01 AM 10:25 AM 10:48 AM

TIME

CO, PPM

0

0.2

0.4

0.6

0.8

1

1.2

OPTICAL

2105

CO

DIESEL PROBABILITY

FIRE PROBABILITY

2105, BEAM, FIRE PROBABILITY

Figure 4. Sensor responses at station S1 and NN fire probabilities for diesel locomotive variable emissions 

the emissions. The NN probability curve correctly 

identifies the diesel emissions signature. Its response is coincident with the CO and 2105 responses. The fire probability curve shows that in no 

case is there a significant indication of a fire. The 

advantage of a probability display is the presentation 

in real-time of a continuous source of information 

which mine personnel can use to make decisions. 

For example, the continuous fire probability curve 

can be subdivided into regimes of low, medium, and 

high fire alarm. The current application program 

provides a real-time visual display of the probability 

values for fire, diesel emissions, and clear air conditions. 

A listing of all the mine fire experiments by combustible, presence or absence of diesel emissions and 

airflows is shown in table 1. For the experiments 
conducted, the NN prediction of a fire is evaluated 

in terms of the prediction of the fire relative to the 

first visual observation of smoke and flames. Figure 

5 shows the NN prediction of a fire based upon the 

fire probability exceeding 0.5 as a lag time relative 

to the time of first observation of smoke. (Notation 

T in table 1. is omitted from experiment number on 

coordinate axis in figure 5.) 

-10

0

10

20

30

40

50

60

70

71S274S174S275S175S276S176S277S177S278S178S279S179S281S181S282S182S283S183S284S184S285S185S287S188S189S1

EXPERIMENT

TIME, MIN

FLAME RELATIVE 1ST SMOKE

FIRE PROBABILITY( =0.5 ) RELATIVE 1ST

SMOKE

Figure 5. NN prediction of fire relative to first observation of 

smoke, and flaming combustion relative to smoke for each experiment 

 Also shown in the figure is the lag time of the 

flaming combustion relative to first smoke. For example, for experiment T71S2 the fire probability is 

17 min after the first observation of smoke, and 

flaming combustion occurs 67 min after smoke production. Although for experiments T74S1, T74S2, 

T81S2, and T85S2 the fire is identified prior to the 

first observation of smoke, its occurrence is after the 

heating of the combustible was initiated. For 25 of 

the 26 experiments the NN successfully predicted 

the occurrence of a fire. Only for one experiment, 

T83S2, did the NN fail to identify the fire, and that 

was due to a relatively small wood fire with dilution 

of the POC at S2. For the experiments listed in table 

1, the average optical density at which the NN predicted a fire was 0.0059 m-1 with a standard deviation (SD) of 0.0050 m-1. This average value is more 

than three SD s less than the minimum smoke alarm 

value of 0.022 m-1 specified in 30CFR75. 

Table 1. Mine fire detection experiments listed by combustible, presence or absence of diesel emissions, and airflows. 

Test No.

And Sensor 

Station 

Combustible Diesel 

Emissions 

Flow, 

m3 / s 

Flow 

Ratio 

T71S2 Coal no 4.3 1.7 

T74S1 Coal yes 2.7 

T74S2 Coal yes 5.4 2.0 

T75S1 Coal yes 2.0 

T75S2 Coal yes 3.9 1.9 

T76S1 Coal yes 3.3 

T76S2 Coal yes 4.2 1.3 

T77S1 Belt yes 3.3 

T77S2 Belt yes 5.7 1.7 

T78S1 Belt yes 1.9 

T78S2 Belt yes 3.8 2.1 

T79S1 Belt yes 2.8 

T79S2 Belt yes 5.4 1.9 

T81S1 Wood no 1.7 

T81S2 Wood no 3.5 2.1 

T82S1 Wood yes 2.1 

T82S2 Wood yes 4.8 2.3 

T83S1 Wood yes 2.9 

T83S2 Wood yes 2.8 1.0 

T84S1 Wood yes 3.1 

T84S2 Wood yes 4.5 1.4 

T85S1 Belt no 3.1 

T85S2 Belt no 4.2 1.4 

T87S1 Coal yes 5.6 

T88S1 Coal\Belt yes 4.2 

T89S1 Coal no 3.7 

The most sensitive sensor for fire detection in the 

selected set of sensors is the optical path smoke sensor. This is illustrated with a simple test in which a 

rag was ignited upwind of sensor station S1 in the 

SRCM. 

0

0.5

1

1.5

2

2.5

3

9:58 AM 10:02 AM 10:05 AM 10:09 AM 10:12 AM 10:15 AM

TIME

2600

0

0.2

0.4

0.6

0.8

1

2600

PROBABILITY 

(OPTICAL)

PROBABILITY (NO 

OPTICAL)

FIRE PROBABILITY

Figure 6. NN prediction of rag burn with and without optical 

path smoke sensor 

 Figure 6 illustrates the response of the 2600 

MOS sensor to the burning of the rag. The duration 

of this sensor’s response defines clearly the combustion duration. The fire probabilities with and without the response of the optical path sensor are shown 

in figure 6. It is seen that the fire probability determination with the inclusion of the optical path sensor correctly envelopes the fire duration. The NN 

calculation which excludes the optical path sensor 

results in a much shorter indication of the fire dura-
tion. 

0

0.5

1

1.5

2

2.5

3

9:58 AM 10:02 AM 10:05 AM 10:09 AM 10:12 AM 10:15 AM

TIME

2105, 2600

0

0.2

0.4

0.6

0.8

1

2105

2600

PROBABILITY (NO 

OPTICAL)

FIRE PROBABILITY

Figure 7. Comparison of NN fire prediction with response of 

2105 and 2600 

Figure 7 illustrates the reason for this result. The 

2105 MOS sensor responds to the NOx compounds 

generated by the burning rag. There appears to be 

unknown nitrogen additives in the cloth. As the 

NOx concentration increases, the fire probability 

evaluation in the absence of the optical smoke sensor incorrectly identifies the combustion as a nuisance event. 

8 CONCLUSIONS 

A NN was trained to discriminate a mine fire 

from background diesel emissions based upon the 

response of a multiple sensor array consisting of a 

CO sensor, an optical path smoke sensor, and two 

MOS sensors, one of which has a bimodal response 

to NOx emissions and reducing gases. An interactive computer program was developed that can accept real-time data from the data acquisition system 

and evaluate with the NN the probability of a fire. It 

was demonstrated from applications to coal, conveyor belt, and wood combustion experiments in the 

presence and absence of diesel emissions that a 

smoldering fire prior to flaming combustion could 

be predicted for 25 of 26 fire detection experiments 

conducted. 

REFERENCES 

Code of Federal Regulations, 30 CFR, Parts 75.340, 75.344, 

and 75.351, 2001. Office of the Federal Register, National 

Archives and Standards Administration, U.S. Government 

Printing Office, Washington, D.C., July 1, 2001. 

Edwards, J.C., Franks, R.A., Friel, G.F., Lazzara, C.P., and J.J. 

Opferman. Mine Fire Detection in the Presence of Diesel 

Emissions. In J.C. Tien (ed), Proc. 8th U.S. Mine Ventilation Symposium, The University of Missouri-Rolla, June 

11-17, 1999, (University of Missouri-Rolla Press, Rolla, 

MO), pp. 295-301. 

Edwards, J.C., Franks, R.A., Friel, G.F., Lazzara, C.P., and 

Opferman, J.J. In-Mine Evaluation of discriminating Mine 

Fire Sensors. In E.DeSouza (ed), Proc. 1st North American 

& 9th U.S. Mine Ventilation Symposium, Ontario, Canada, 

June, 2002, ( A.A.Balkema, Lisse, 2002) pp. 527-532. 

Friel, G.F., and J.C. Edwards. Neural Network Application to 

Mine-Fire Diesel-Exhaust Discrimination. In E.DeSouza 

(ed), Proc. 1st North American & 9th U.S. Mine Ventilation 

Symposium, Ontario, Canada, June, 2002. (A.A.Balkema, 

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Ishii, H., Ono, T., Yamauchi, Y., and S. Ohtani. Fire Detection 

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