*Corresponding author: jce9@cdc.gov

Proceedings of the 2000 Technical Meeting of the Central States Section of the Combustion Institute

207

Mine Fire Source Discrimination Using

Fire Sensors and Neural Network Analysis

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

National Institute for Occupational Safety and Health

Pittsburgh Research Laboratory

Pittsburgh, PA 15236

Abstract

Fire experiments were conducted in the Safety Research Coal Mine (SRCM) at the National Institute

for Occupational Safety and Health, Pittsburgh Research Laboratory, with coal, diesel-fuel, electricalcable, conveyor-belt, and metal-cutting fire sources to determine the response of fire sensors to productsof-combustion (POC). Metal oxide semiconductor (MOS) and smoke fire sensors demonstrated an

earlier fire detection capability than a CO sensor. This capability was of particular significance for a

conveyor-belt fire in which the optical visibility was reduced to 1.52 m with an increase in CO of less

than 2 ppm at a distance of 148 m from the fire. Application of a neural-network program to the sensor

responses from each type of fire source resulted in correct classifications of coal, diesel-fuel, cable, belt,

and metal-cutting combustion with a mean of 96% of the test data correctly classified.

Introduction

Fire detection in underground coal mines is important for

early fire location and safe miner evacuation. Fire detection

is aided by the in-mine ventilation which transports the fire

products-of-combustion (POC) from the fire source to fire

sensors, and it is impeded by the diluting effects of the

ventilation which reduces the measurable signal. Early mine

fire detection experiments in both normally ventilated and

near zero airflow mine entries have been previously

investigated experimentally [1, 2]. The results of that

research showed the improved performance of ionization

and optical smoke fire sensors over CO sensors. The next

advancement beyond early detection, in addition to

determination of the fire location, is the determination of the

material burning, the mode of combustion, and the extent of

fire growth. This knowledge can be applied to the

determination of appropriate actions to be taken to

extinguish a mine fire and to initiate escape and rescue

procedures for miners. Various in-mine materials can

provide the initial fuel for a mine fire. The common fire

source materials considered in this program were coal,

diesel fuel, electrical cable, conveyor belt, and acetylene gas

used for metal cutting. The solid material combustion was

advanced using heaters through a smoldering combustion

stage to produce a slowly increasing range of measurable

POC. The POC include CO and other oxidizable gases and

smoke particles with submicron diameters. 

It is the types of combustion products and their rate of

change which will indicate the combustion material, mode,

and growth rate. One method which can be deployed to

make these determinations is the use of multiple fire sensors

to discriminate the POC. The rapid analysis of mine fire

products with a neural network program has been reported

elsewhere [3, 4]. These approaches utilize temperature and

gaseous POC from combustion of home materials [3] and

laboratory heating of coal [4]. CO and smoke sensors are

used for the early detection of underground coal mine fires.

While CO sensors, in the absence of cross-interference from

other gases, respond to the concentration of CO, the

response of smoke sensors in mining applications is

presented in terms of the smoke optical density. Ultimately,

the optical density depends upon the smoke mass

concentration, smoke particle diameter, and the dielectric

constant of the smoke particles. Ionization smoke sensors

are more responsive to smoke from flaming combustion, and

optical smoke sensors are more responsive to smoke from

smoldering combustion. The intense turbulent combustion

during the flaming stage reduces the average particle size

associated with smoldering combustion.

In addition to these fire sensors, there are MOS sensors
208

Figure 1. Plan view of mine section.

which respond to oxidizable gases. These MOS sensors

operate on the principle that oxygen (O2) is adsorbed on the

surface at grain boundaries which increases the electrical

resistance across the surface. The oxidation of POC gases

removes O2 from the surface and reduces the electrical

resistance across the surface. The measurable change in

surface resistance is a measure of the POC concentration.

These sensors are very responsive but not very selective of

the target gas. They are also temperature and humidity

dependent. However, it is their extreme sensitivity to

various hydrocarbons that increases their potential for use as

mine fire sensors. The selection of a base set of multiple

sensors is a key decision for a neural network program to

successfully discriminate between hazardous mine-fire

combustion and normal mining combustion that may result

from sources such as diesel engines and cutting and welding

procedures.

Specific Objectives

This research has two objectives. One objective is to

evaluate the comparative time sequence of alarm values for

CO, smoke, and MOS fire sensors for in-mine combustible

source fires. The second objective is to determine a suitable

set of mine fire sensors and a neural network analysis

program which can be used to classify a mine fire

combustible source. 

Experimental Method

The plan view of the SRCM section in which the

experiments were conducted is shown in figure 1. Room 10,

in which the fire is located, has an average height and width

of 2.0 m and 3.9 m, respectively. F-Butt has an average

height and width of 1.9 m and 4.5 m, respectively. The

cloth brattice at Room 10 and B-Butt was adjusted to

regulate the airflow into Room 10 and F-Butt. Air quantity

measurements were made at the fire zone and near the end

of F-Butt, 7.6 m downwind from sensor station S2. For the

experiments conducted, the average air quantity at the fire

zone was 3.49 m3

/s and at the end of F-Butt was 5.17 m3/s.

The increase in air quantity downwind of the fire zone was

caused by air leakage into F-Butt around brattices shown

along the ribs in figure 1 blocking crosscuts connecting FButt and parallel airways. The fire sensors used for the

experiments are listed in table 1 below. 

Table 1. Fire sensor types used

Sensor Type

SA Optical Smoke

SB Ionization Smoke

CO Carbon Monoxide 

FA, FB MOS

Sensor SA is an optical path sensor which operates at an

infrared wavelength with a transmitter-receiver separation

path of 9.65 m. Sensors SB, CO, FA, and FB are point-type

sensors. Sensors FA and FB are MOS sensors which are

similar in their responses to various POC gases. These

sensors were located at station S2, as was an optical-path

light monitor to measure the optical density of the smoke.

At station S1, a CO sensor was located to calculate the

transport time of the CO component of the POC between

stations S1 and S2.

The heating of the coal, cable, and belt was conducted

with electrical heaters to which power was supplied slowly

such that the smoldering mode passed through a slow growth

phase with the emanation of POC prior to flaming

combustion in order to validate the sensitivity of the fire

sensors and to duplicate a slowly developing mine-fire

event. The heating time prior to flaming combustion for the

solid fuels varied between 42 and 109 min. To validate the

discriminating capability of a neural network program when

applied to a normal mine combustion source, an experiment

was conducted which consisted of cutting a rail section with

an acetylene torch 14 m upwind from sensor station S2. 

Neural Network Analysis

A neural network analysis was applied to the

classification of fire sensor responses to differentiate

between possible fire events. In this neural network,

temporal experimental data were compared to the nonlinear

approximations generated by the neural network until

adequate approximations for correct classifications were

obtained through corrective iterations. The input layer of

neurons contained the experimental sensor data along with

variables generated from the experimental data. The output

layer of neurons contained the fire source classifications

generated by the neural network. Between the input and

output layers were two hidden layers of neurons or process
209

0

20

40

60

80

100

500 1100 1700 2300 2900

TIM E, S

VISIBILITY, 

M

0

5

10

15

20

25

30

35

CO, PPM

C O

Visibility

Figure 2. Visibility and CO concentration at station S2 for

experiment No. 10.

elements (PEs). The inputs to the hidden layers of neurons

were multiplied by weights, summed, and processed through

a bounded, nonlinear activation function. In the training

phase of the neural network, the output classifications were

subtracted from the correct classifications and the

differences, or errors, were used by a backpropagation

method, which is a modification of the gradient-descent

search technique, to adjust the values of the weights until a

sum of the errors was less than a reasonable tolerance. For

the sensor data analysis considered here, the neural network

software package entitled NeuroSolutions for Excel** from

NeuroDimension, Inc. was used.

Results and Discussion

Table 2 lists the fire experiments conducted with

reference to fuel type and the relative alarm times of the fire

sensors with respect to the sensor which alarmed first

(indicated by 0 s). The estimated arrival time of the POC at

station S2 from the flaming-stage, solid fuels, relative to the

first sensor alarm time, is listed as Tf

 and is based upon the

measured ventilation rate.

Table 2. Fire sensor relative alarm times

Exp. Fuel FA, s FB, s CO, s SA, s SB, s Tf

, s

1 Coal 80 0 3521 556 736 1579

2 Diesel 34 0 105 6 34 na1

1

3 Cable 219 59 2614 0 149 2388

4 Belt1 178 98 1486 38 0 1307

5 Coal 218 204 1590 0 242 881

6 Coal 174 0 3690 1736 2166 3242

7 Diesel na3

3 55 83 0 29 na11

8 Diesel 0 7 196 42 84 na1

1

9 Cable 149 109 2746 0 191 2518

10 Belt1 952 892 2709 0 294 2572

11 Belt2 14 0 2853 156 92 na2

2

12 Belt3 910 758 4735 406 0 4627

1

Symbol na1 -flame at initial heating time. 2

Symbol na2 - no flaming combustion. 3

Symbol na3 -alarm not achieved due to fuel vapors.

The alarm time for CO is based upon a 5-ppm rise above

the ambient concentration. For the smoke and MOS sensors,

the alarm time is based upon a ten-standard-deviation

change from the sensor ambient signal. A ten-standarddeviation change is less probable than a value at the mean in

a Gaussian distribution by a factor of about 1022. The diesel

fuel experiments required the calculation of a background

value for sensors FA and FB which excluded vapors from

heptane used to ignite the fuel. In the case of experiment

No. 7, the background concentration was too high to define

an alarm for FA. Table 2 shows that the smoke and MOS

fire sensors always alarmed before the solid fuel, flamingstage POC reached the sensor station at time Tf

, which

occurred before the CO sensor alarm time. For the diesel

fuel fires, the smoke and MOS fire sensors alarmed prior to

the CO sensor. Figure 2 shows for experiment No. 10 that

the optical visibility, which is calculated from the optical

density measured by the light obscuration monitor as defined

by a visibility optical density relationship [5], decreased to

1.52 m while the CO concentration increased to 1.1 ppm

above ambient concentration 1,900 s after the first alarm

and, based upon table 2 data, 948 s after the last of the nonCO, fire-sensor alarms. A visibility of 36.4 m corresponds

to an optical density of 0.022 m-1, which is the alarm value

for a mine-fire smoke sensor. The times plotted in figure 2

are from the first alarm time. The flaming mode POC did

not reach the sensor station until 2,572 s after the first sensor

alarmed. This flaming event is coincidental with the

significant increase in measurable CO seen in figure 2. The

in-mine hazard of severely reduced visibility without a CO

alarm, but with an alarm for each smoke sensor, reinforces

the importance of fire smoke sensors.

In order to use the neural network program, the data for

each experiment were prepared in files with the fire sensor

signals normalized to their ambient background signals. For

sensors FA and FB, the sensor electrical resistance value **Reference to a specific product does not imply

endorsement by NIOSH.
210

0

0.2

0.4

0.6

0.8

1

1.2

0 600 1200 1800 2400 3000 3600

TIME, S

FA

0

1

2

3

4

5

6

CO, PPM

CO

FA

Figure 3. Sensors FA and CO response to coal combustion

for experiment No. 6.

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0 800 1600 2400 3200

TIME, S

SA, V

-0.18

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

SB, V

SA

SB

Figure 4. Sensor SA and SB response to coal combustion

for experiment No. 6.

was normalized to unity under ambient conditions. Figures

3 and 4 show the data recorded from coal fire experiment

No. 6 for sensors FA, SA, SB, and the CO sensor relative to

the first alarm time. The responses of FA and FB were

nearly identical.

The training of the neural network was accomplished

with the five sets of sensor data from coal, diesel-fuel,

electrical-cable, and conveyor-belt fires, which are the fires

of experiment numbers 1 to 4 in table 2, and an acetylenetorch, metal-cutting experiment. Seven data inputs were

processed from the sensor data to classify the five fire types.

The inputs, which include time and multiplicative

combinations of the data from four of the sensors but

excluding sensor SB, were determined by trial-and-error to

be the most suitable inputs for accurate classifications. The

size of the training data sets ranged from 85 to 991

exemplars, or time samples, of the four sensor inputs and

two functions of the sensor inputs with the total size of the

training set being 2,988 exemplars. Time zero at the

beginning of each data set corresponded to the first sensor

alarm for each type of fire. Sampling by the sensors

occurred at two-second intervals.

Various neural network programs provided in the

package by the vendor were applied to the data in attempts

to successfully classify the fire types. A two-hidden-layered

perceptron network with momentum backpropagation of

error algorithm produced reproducible results. No

smoothing of the data or inclusion of rates of data change

was necessary for successful training and testing. The first

hidden layer consisted of 20 PEs and the second hidden

layer consisted of 10 PEs. It was discovered that the testing

results were reproducible even though the initial weights

between the PEs were assigned randomly. The activation

function used in the hidden layers was the hyperbolic

tangent function with the output layer using a softmax

classification function. One thousand epochs, or iterations,

through the samples were performed with error correction

after every epoch. The minimum squared error achieved

after 1,000 epochs was 0.0012.

For testing the neural network, seven data files were

presented to the trained network. These files included

experiment numbers 5 to10 in table 2 and 1 metal-cutting

experiment. The number of testing exemplars in each file

ranged from 121 to 1,854 with the total size of the testing set

being 4,255 exemplars. Two coal and two diesel-fuel fires

were included in the set of testing files. The percentage of

exemplars predicted correctly in each of the seven testing

data files is presented in table 3.

Table 3. Percentages of testing files predicted correctly

Exp. 5 6 7 8 9 10 Cutting

% Correct 100 86 90 99 94 100 100

The average correct classification of the seven tests in

table 3 is 96%. The minimum value of 86% for a single

experiment is not unreasonable. Evaluation of

experiments 11 and 12 could not be made with the neural

network program because experiments using materials

similar to those of BELT2 and BELT3 were not available to

include in the testing set.

Conclusions

The role for mine-fire smoke sensors and MOS sensors

was shown to be enhanced by their earlier alarm times

relative to a CO sensor. The low optical visibility in the

absence of significant CO for flammable material, BELT1,

further supports the role of smoke sensors for early mine-fire

detection. Data, recorded from an optical path smoke, a CO,
211

and MOS sensors placed in a multiple sensor arrangement

and inserted into a backpropagation neural network

program, enabled the program to correctly classify coal,

diesel-fuel, electrical-cable, and conveyor-belt test fires and

a metal-cutting procedure based upon a training set similar

to the testing set. This correct mine fire combustible source

classification is based upon an average 96% correct

classification of seven tests of the test data with the worst

case probability of a correct prediction being 86%.

Acknowledgments

The authors acknowledge the miners who maintain the

Safety Research Coal Mine at the Pittsburgh Research

Laboratory for their assistance in preparation of the

experiments.

References

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

and Opferman, J.J. 8th U.S. Mine Ventilation

Symposium, Univ. of Missouri-Rolla, pp. 295-301,

(June 1999).

2. Edwards, J.C., Friel, G.F., Franks, R.A., and Opferman,

J.J. 6th International Mine Ventilation Congress,

Pittsburgh, PA, pp. 331-336, (May 1997).

3. Ishii, H. and Ono, T. Fire Safety Science-Proceedings

of the 4th International Symposium, pp. 761-772,

(1994).

4. Brinn, M. and Bott, B. Mining Engineer (London),

v. 154, n. 396, pp. 71-74, (September 1994).

5. Rasbash, D.J. Fire International, 5, n. 4, pp. 30-49,

(1975).