A New Embedded E-Nose System to Identify Smell

of Smoke Salahedin Sadeghifard

Instrument Maintenance Department South Pars Gas Complex Assaloye, Boushehr, Iran

s.sadeghifard@yahoo.com

Abstract - This work examines the important

applications of modern electronic noses and focus on

fire detection system due to advantages over classical

method of detections. The three components of an

electronic nose consist of sample handling; detection

and data processing system are designed. These

devices are typically array of sensors used to detect

and distinguish odors precisely in complex samples

and at low cost and capable of classiijring smoke

based on neural networks. The potential advantages

of such an approach include, the ability to

characterize complex mixtures without the need to

identity and quantity individual components, Five

commercial gas sensors (Figaro) with interesting

cross sensitivity and low power consumption are used

in sensor array; a micro-controller equipped with a

compact flash memory assures data acquisition,

analyzing procedures in real time. Signals trom this

sensor array have unique pattern and applied to the

embedded system as inputs. The proposed method in

this paper has 97.2% efficiency In smoke

classification.

Keywords: Neural network, Electronic nose, Fire

detection, Gas sensor.

1 Introduction

One of the most important problems of human

life is damages from fire, so improving the reliability

of fire alarm systems is very important. House fire is

a catastrophic phenomenon which kills thousands of

people and injures more annually worldwide. In 2003,

there were 388; 500 reported house fires in the United

States, resulting in 3; 145 deaths, 13; 650 injuries and

$5;9 billion in direct property damage [1]. Therefore,

existence of a customized house fire alarm system

with capability of classifying burning materials is

very important for choosing a proper solution for

suppression of fires. Existing fire alarm systems

detect high temperature fire and its smoke and

hydrocarbon gases. They monitor various locations to

activate alarm signals as needed. Fire bums when

Leili Esmaeilani Instrument Maintenance Department

South Pars Gas Complex Assaloye, Boushehr, Iran I.esmaeilani@gmail.com

temperature exceeds a threshold and lead to chemical

activation. Results are temperature, flame, light,

smoke, monoxide carbonic, and other components.

Depending on burning materials, different

components are released in the space. Therefore, if a

system senses the raised components and analysis

them to find type of burning materials, it can use a

proper solution for suppression of fires. Our work

concerns the development of a portable system able

to detect and identify fire in early stage by using

artificial electronic nose system based on neural

networks and it can active proper extinguishing

system according to NFPA.[I] In this E-nose system,

for measuring and classifying odor of fire, signals

from sensors are collected and analyzed by ANN. The

empirical results show high reliability and accuracy in

early stage.

2 Artificial electronic nose system

The electronic nose is a relatively new analytical

technology, and well-known as efficient analytic

devices that are widely used for many applications

including the food industry [2], perfumery,

biotechnology, medicine [3], chemistry, and

environmental sciences. This technology is based on

the principle of mimicking the human odor

recognition mechanism. Artificial electronic nose

systems are used to detect and analyze the odor of

materials. The main idea of these systems comes from

human olfaction. E-nose has three main parts: sample

handling; detection and data processing system.

I Odo.mnl III -

Identifier

INeurail Network -...

�--�-T�L ____ ��

Figure I. Basic structure of E-Nose

3 Metal oxide gas sensor array as • Interface circuits and display

odor sensor

Metal oxide gas sensors are widely used in

various applications. The main part of the MOGS is

the metal oxide element on the surface of the sensor.

When this element is heated at a certain high

temperature, the oxygen is absorbed on the crystal

surface with the negative charge. The

reaction between the negative charge of the metal

oxide surface and deoxidizing gas makes the

resistance of the sensor vary as the partial pressure of

oxygen changes [4]. Based on this characteristic, we

can measure the net voltage changes while the

sensors absorb the tested odor. Every sensor has a

unique response to different gases and the ability of

identifying the odor will be improved if sensors are

arranged together in an array. The sensing element of

Figaro gas sensors is a tin dioxide (Sn02)

semiconductor which has low conductivity in clean

air.

In the presence of a detectable gas, the sensor's

conductivity increases depending on the gas

concentration in the air. A simple electrical circuit

can convert the change in conductivity to an output

signal which corresponds to the gas concentration. A

major issue with gas sensors is their sensitivity to

humidity. It is well-documented that water vapor

affects measurements by electronic noses and

manufacturers of these instruments have been forced

to issue specific operating procedures but in this

project it will be solved. Metal oxide gas sensors used

in this work are listed in table 1.

Table I.Figaro sensors used in this work

Sensor Type Detectable gases

TGS-2602 Air Contaminant

2 TGS-S22 Organic Solvent vapor

3 TGS-S25 Hydrogen Sulfide

4 TGS-SI3 Combustible gas

5 TGS-SSO Cooking Vapor

6 SHT71 Humidity and

4 Configuration of fire alarm system

Schematic of intelligent fire alarm system,

shown in Figure 3 consists of three parts:

• Sensing unit

• Signal processing and control unit

4.1 Sensing unit

Each odorant presented to the sensing system

produces a characteristic pattern of the odorant. By

presenting a mass of sundry odorants to this system a

database of patterns is built up. It is used then to

construct the odor recognition system. In this work

sensing unit consists of 5 cheap and applicable MOG

sensors, a temperature and humidity sensor. All the

sensors are placed on an electrical board, providing

also the electrical interface for the gas sensor

temperature control and the sensor measurement

conditioning. These Figaro type sensors require to be

heated continuously at approximately 300°C in order

to get the chemical operating point.

Electrical conduction of these sensors changes

when they are exposed to gases and this conduction

can be measured by circuit shown in Figure

2.RL,VC,VH,VRL are load resistance, biasing voltage of

gas sensing element, biasing voltage of heating

element and output voltage respectively.

GND�------��----���

Figure 2.MOGS bias circuit

It is important to know that none of these

sensors can measure a specific mixture of gases

absolutely [2].In addition, each of them designed to

measure a special gas but actually they have some

responses to other gases [4].While some metal oxide

gas sensors are put together in an array, the ability of

detecting odor will be improved because odor is a

combination of different gases. Each smell (especially

gases made by fire) exposing to sensor array makes a

unique pattern that is a combination of different

sensor response to specific smell in gas combination.

4.2 Humidity Control

As discussed in section 3, most MOGS gas

sensors are sensitive to humidity. Therefore, if two

identical samples with a different humidity are

measured, the results can be different. In our work,

we solved the humidity problem is a software-based

approach to achieve maximum accuracy. A

mathematical model describing the resistance of each

gas sensor at different humidity level can be

calibrated to subtract the humidity signal from the

total signal. Samples other than the smoke of burning

material can also be used with this algorithm.

Consideration relative humidity [%RH] was varied

from 30% to 80%. Mathematical models for the

sensors' response to humidity can be fitted via the

following formulations [8], equations of TGS813,

TGS822, TGS880, TGS2602, and TGS825 are:

RS813 = 86682exp (-�:o�:l) + 55063.48 (1)

RS82Z = 24931.58exp (-��o::l) + 9054.41 (2)

RS880 = 90496.88exp (-�:�::l) + 55135.22 (3)

RSZ602 = 6958.22 + 129.172[%RH] - 0.9788[%RHF (4)

RS825 = 5646.63 + 103.26[%RH] - 1.34[%RHF (5)

4.3 Signal processing and control unit

Most of the electronic noses developed are

implemented in a Personal Computer (PC) based

platform which, due to cost, size and power

requirements, limits their use in day to day life. This

study discusses the development of an A VR family

microcontroller based embedded system and

implementation of ANN in the embedded system for

fire classification. The autonomous control part,

corresponding to the brain of the electronic nose

figure 3, 4 uses an 8 bits A VR microcontroller

(ATMEGA32). The microcontroller assures

measurement control, data acquisition, analyzing

treatments and data transfer or storage. In this unit,

signals coming from sensor array processed and

classified according to type of burning material and a

particular output signal set on to actuate extinguishing

system, matched with NFPA will be produced.

For accurate operation of neural network, it is

necessary to calculate weights and biases and use

them in the form of a program in the microcontroller.

For this purpose, signals from sensors are

simultaneously sent to PC via microcontroller. These

signals are sample in each second and these data

make a matrix with dimension 5 (number of sensors)

x number of sampling times, then mean value of data

in duration 20 to 40 second chose as proper data, the

criteria for choosing this duration is the time where

sensors are stabled and saturated. The materials that

selected and burnt for smoke sampling in this work

are listed in table 2.

Figure 3:Embedded E-Nose system

Figure 4.Hardware configuration

Table 2. The codes assigned to the materials.

Material

Fresh Air(Normal)

paper

Wood

Cotton

Carpet

Plastic

Oil

Methane

Incense

Assigned Code

[00000000 I]

[0000000 I 0]

[000000 I 00]

[000001000]

[000010000]

[000100000]

[00 I 000000]

[0 I 0000000]

[100000000 ]

We should take into consideration that in lack of

smoke, air is the dominant gas, so we should add the

signal of air to our data, as the base line. According to

sensors structure, an internal heater should warm up

the sensor and it takes time to make sensor stable, this

time is about 50 to 60 seconds according to figure 5.

After sensor stabilization, it is time for sampling.

Each source of smoke was sampled and tested 20

times to achieve high repeatability of the system.

To form the response pattern, the mean value of

sensors output is calculated and considered as the

effective value of the sensor during 20 to 40 seconds.

figure 6 is an example pattern formed by sensor

signals from burning of paper.

5.0 -,-------------------, 4.0

'6 3.0 6

1.0

51

(TGS-813) Vc = VH = 5v RL = 5.6 kQ

101 151 201 251 Time(s)

Figure 5.Response of sensor "Figaro-TGS813" in free

air

For recognition the pattern and make relation

with the smoke of specific material, we need a pattern

recognition system. According to good background of

neural network (MLP) with back propagation

algorithm to specify the complicated relation between

the combination of different gases made from burning

different material [5] [6] [7] and the ability of

classifying pattern an MLP network is used. A neural

network which is designed to detect and classify fire

is able to adjust weights and biases to reach the best

response according to experiences achieved. This

system has high potential to classify the material

burning in the first stage.

·'1, - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ._ --L:I - - - - - - - - - - - -- - - - - - - - -- - - - - - - - -- - - - - - .--L5

·l·1

- - -- - - 1 - ·--

0_) I----'--�-----�---�-----�-'--�

Figure 6.Sensors response pattern for paper (y axis is

voltage)

MLP used in this ANN consists of a 5 input

layer (proportional to sensors number), a hidden layer

of 10 neurons and a 9 output layer (proportional to

burning tested material). For training this network,

efficient values of sensors in patterns formed from

free air and 8 smoke samples (result of burning

different material) are choose as input vector and

detected materials are chose as desired outputs.

Different transfer functions were tested in network

and "Logsig" function had the best response with the

least error while it is used in hidden and output layers

of network. According to behavior of "Logsig"

function in network data should be normalized

between 0 and 1 .It is done by equation (6).

(6)

After training network with samples, the weight

and bias of ANN was transfer to Microprocessor and

accurate operation of network was tested in Real time

operation of embedded device with smoke from

burning material and results are shown in table 3.

Table .3 Practical results

Material Test Correct Incorrect Validity Plastic 9 9 0 100% Wood 9 7 2 77.7% Paper 9 9 0 100%

Incense 9 9 0 100% Methane 9 9 0 100%

Oil 9 9 0 100% Carpet 9 9 0 100% Cotton 9 9 0 100%

We should take into consideration that it is very

important how sample of smoke, effect on different

sensors, depends on many parameters. After detecting

the kind of burning material, an output signal will

actuate fire extinguishing system according to NFPA.

In NFPA, paper, wood and cotton are classified

in extinguishing group "A" and methane is in group

"B" and materials such as incense just detected and

no output is activated.

5 Experiment

In this section, we report performance of the

system after final assembly. To do the experiments,

we tum on the system and combust the materials in

the environment and record the detections of the

system. We test implementations of the system using

ANN Table 3 shows confusion matrix of the

experiments with ANN pattern recognition algorithm.

It can be seen that, except a wood instance that is

classified as paper, all other instances are correctly

classified. So the accuracy of the system in these

experiments is more than 97 percent. For sending

information to PC and calculating proper biases and

weights, an interface circuit is used between

microcontroller and PC meanwhile a single display is

used to show data.

6 Conclusion

In this paper, we have presented the reliability

of a new EN system designed from various kinds of

MOGS as intelligent fire alarm and extinguishing

system. Electronic noses have been proposed as

fantastic instruments which could solve almost any

problem concerned with odor of fire, The EN has the

ability to identify various sources of burning material

in the early stage with more than 97.2% of accuracy

in the BP case. The operation of artificial electronic

nose was shown just by 5 sensors. It can be

concluded that the EN is suitable for detecting the

early stage of fire and we can improve accuracy,

reliability and ability of system by using more

sensors. High reliability of this sensors combination

can make our fire alarm system safe to false alarms.

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