Dynamic Stand-Alone Gas Detection System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1330
Dynamic Stand-Alone Gas Detection System
R. G. Dhokte1, Dr. M. H. Nerkar2
1R. G. Dhokte, Electronics and Telecommunication Engineering (Digital System), Government College of
Engineering, Jalgaon, Maharashtra, India
2Professor, Electronics and Telecommunication Engineering (Digital System), Government College of Engineering,
Jalgaon, Maharashtra, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Growing industries are need of 21st century,
but these growing industries are also responsible for
growing pollution. Not only the industries but also need of
transportation is also increasing which leads to increase in
concentration of carbon dioxide, carbon monoxide, etc.
gases. So, detection and concentration monitoring
(mapping) of these gases is very important issue. Currently
various static systems are located at key locations. But these
systems are not flexible for operating at different
applications. Therefore this dynamic system is designed.
This system is designed by using microcontroller as well as
GUI for flexible operation of hardware. This system uses
chemo resistive (MOS) sensors for detection of carbon
monoxide, LPG and methane gas. For controlling of system
AVR ATmega 16 is used. Also GSM module is used for
communication purpose. This system is cost effective; also
the results of sensors are approximately equal to the
standard system. Preheat time required for the result is 16-
22 minutes.
Key Words: CO, LPG, parts per million (ppm), MOS,
Graphics User Interface (GUI), Pollution under Control
(PUC), Methane, Permissible Exposure Limit (PEL).
1. INTRODUCTION
In the past few years, earth’s atmosphere is
becoming more and more unstable due to growing
pollution. Major cause of pollution is industries and
increase in number of transportation vehicles. Result of
this is depletion of ozone layer and adverse effects on
human health. Thus monitoring of these gases is very
necessary.
Earth’s atmosphere contains various gases in
specific concentration which keeps earth’s atmosphere as
per required by human body. Major components of earth’s
atmosphere in homosphere (80-100 km from surface) are
nitrogen, oxygen and water vapors. Amongst these gases
some are permanent gases while some are variable. Table-
1 gives percentage concentration of permanent gases and
table-2 gives percentage concentration of variable gases. It
is also given in ppm i.e. parts per million.
Table -1: Percentage Compositions of Permanent Gases
Gas Symbol % Concentration
Nitrogen N2 78.084
Oxygen O2 20.946
Argon Ar 0.9340
Neon Ne 0.001818
Helium He 0.000524
Hydrogen H2 0.000055
Xenon Xe 0.000009
Table -2: Concentration of Gases in % and ppm
Gas Symb
ol
% Conc-
entration
Concentrati
on in ppm
Water Vapor H2O 0 to 4 -
Carbon Dioxide CO2 0.038 380
Methane CH4 0.00017 1.7
Nitrous Oxide N2O 0.00003 0.3
Ozone O3 0.000004 0.04
Particles (dust
etc.)
- 0.000001 0.01-0.15
Chlorofluorocarb
on
CFCs 0.000000
02
0.0002
Carbon
Monoxide
CO - 35
Sulfur Dioxide SO2 - 5
The causes of pollution may be different such as
man-made and natural. Man-made causes includes
industrial pollution, pollution from vehicles, pollution
from plastic wests, etc., while natural pollution occurs due
to forest fire, volcano etc. Measurement of gases is
important aspect according to pollution control as well as
toxic gas detection and their source localization. In
industries or at gas station there is possibility of toxic or
flammable gas leakage. Most of the gases are colorless and
odorless which makes them difficult to detect by human
sensory organs. These gases can cause fatal accidents such
as fire, suffocation, etc. Therefore detection and GSL of
these gases is very important.
Occupational Safety and Health Administration
(OSHA) and National Institute for Occupational Safety and

Health (NIOSH) give the maximum permissible exposure
limit for different toxic and combustible gases. PEL for
toxic gases is given by ppm whereas for combustible gases
it is given by Lower Explosive Limit (LEL) and Upper
Explosive Limit (UEL). According to OSHA and NIOSH, in
the industrial area with 8 hours of work shift maximum
PEL for CO2 is 5000 ppm, for CO it is 35 ppm, for CH4 there
is no specific PEL, for LPG it is 1000ppm. The combustible
gases such as LPG and Methane are also represented in
terms of LEL and UEL. For LPG, LEL is 1.8% and UEL is
8.4% whereas for methane, LEL is 5% and UEL is 15%.
Concentration of these gases beyond given PEL for more
than 8 hour work shift can cause adverse effects to human
health.
1.1 Objectives of the System
Degradation of ozone layer is biggest issue of 21st
century. Pollution is increasing continuously resulting in
different types of diseases. Some toxic and combustible
gases are hard to identify by humans as they are colorless
or odorless. The leakage of these gases can cause a
catastrophe. To avoid such catastrophe this system is
designed. Gas detection is the recommended system and it
is required by the law for people’s safety. It is needed for
pollution control by concentration measurement as well as
for detection of combustible and toxic gases which are
difficult to detect by human. The system is required for
safety of life and property which must provide early
warning of hazardous condition with safety measure such
as notification, ventilation, water sprinkler, etc. by GSL and
gas detection mapping.
Main purpose behind designing this system is to
detect different toxic and combustible gases and
monitoring their concentrations. The standard gas
concentrations are given by health organizations NIOSH
and OSHA. If the gas concentration reaches to the
threshold (which is designed keeping standard gas
concentrations in mind) proper measures should be taken.
Here comes second objective of the system that is disaster
management. This system should take readings at various
locations near the site where the gas was detected. Among
these readings the reading with higher gas concentration
will be the gas leak source. After determining leak source
we can communicate to monitoring person about gas leak.
2. LITERATURE SURVEY
Gas detection is very important issue and various
methods are available from manual inspection using
trained dogs to advanced satellite using multiple spectral
imaging. Gas detection methods are divided into two
categories as optical and non-optical methods of gas
detection. Optical gas detection methods are again
classified as active and passive optical techniques. Active
gas detection technique consists of laser or any optical
source whereas in passive optical gas detection no source
is used. This will reduce the cost required for the source
but the detectors used must require higher sensitivity for
proper detection of gases which also increases the cost of
the system.
The two major types of passive optical systems
used for monitoring leaks from natural gas pipelines are
thermal imaging [12][3] and multi-wavelength imaging
[1][12]. Thermal imaging detects natural gas leaks from
pipelines due to the differences in temperature between
the natural gas and the immediate surroundings. This
method can be used from moving vehicles, helicopters or
portable systems and is able to cover several miles or
hundreds of miles of pipeline per day. Usually, expensive
thermal imagers are required to pick up the small
temperature differential between the leaking natural gas
and the surroundings. In addition, thermal imaging will
not be effective if the temperature of the natural gas is not
different from that of the surroundings.
Multi-wavelength or hyper spectral imaging can
be accomplished either in absorption mode or in emission
mode. For obtaining gas concentrations utilizing multi-
wavelength emission, the gas temperatures have to be
much higher than the surrounding air. Multiwavelength
emission measurements have been typically used in the
past to obtain single point concentrations in hot
combustion products [12]. Multi-wavelength absorption
imaging utilizes the absorption of background radiation at
multiple wavelengths to directly image the gas
concentration, even in the absence of temperature
gradients between the gas and the surrounding air. This
technique has been used to monitor natural gas leaks in
industrial settings very successfully. However, multi-
wavelength or hyper spectral imaging typically utilizes
very sensitive and expensive imagers. The biggest
advantage of passive techniques is that they can be used
from ground, vehicle, aircraft, and even satellite platforms.
Therefore, long sections of pipelines can be monitored for
natural gas leaks relatively easily. In addition, multi-
wavelength passive systems are relatively immune to false
alarms, and can be utilized for remote monitoring without
being constantly watched over. The similar hyper spectral
imaging system was designed where cost effective
multispectral scanner was designed for natural gas
detection. The system reliably detected small leaks at 30-
50 feet height but source localization was little challenging
which required moving vehicle mounted scanner.
Active monitoring of natural gas leaks from
pipelines has been achieved with Lidar systems, [12][7],
diode laser absorption [4], Millimeter Wave Radar systems
[12], backscatter imaging [12], broad band absorption
[11][12], and evanescent sensing. Lidar systems typically
use a pulsed laser as the illuminating source. The
absorption of the energy of the laser along a long path

length is monitored using a detector. According to the
Lidar gas detection system pulse of laser radiation of
wavelength λ0 is transmitted by Lidar trans-receiver, laser
waves are scattered according to Raman scattering
principle. Due to special properties of the gases the
wavelength of transmitted signal gets shifted to λi and
according to the deviation of wavelength gas is detected.
Diode laser absorption uses the same technology
with the crucial difference being that diode lasers are used
instead of the more expensive pulsed lasers. If only a
single wavelength is used, the system can be prone to false
alarms since the laser can be absorbed equally well by
dust particles.
Broad band absorption systems utilize low cost
lamps as the source, significantly reducing the cost of the
active system. In addition, monitoring is achieved at
multiple wavelengths so that the system is less prone to
false alarms. For evanescent sensing, an optical fiber is
buried along with the pipe. When natural gas escapes, the
local changes in pressure or concentration causes a change
in the transmission character of the optical fiber. This
change in the transmission characteristics is monitored
using lasers and optical detectors.
Millimeter wave radar systems obtain a radar
signature above the natural gas pipelines. Since methane is
much lighter than air, the density difference provides a
signature that can be used as an indicator of a potential
leak. Backscatter imaging utilizes a carbon-dioxide laser to
illuminate the area above the pipeline. The natural gas
scatters the laser light very strongly. This scattered
signature is imaged using an infrared imager or an
infrared detector in conjunction with a scanner.
All the active systems described above use a
source and obtain either transmitted or scattered images
to determine the presence of methane. These systems are
can be mounted on moving vehicles, aircraft or on
location. The advantages of these systems include
capability to monitor over an extended range and ability to
monitor leaks even in the absence of temperature
differences between the gas and the surroundings. In
addition, these techniques have high spatial resolution and
sensitivity under specific conditions. The two
disadvantages of the method are the high cost of
implementation and the high incidences of false alarms.
Typically, these systems also require a skilled operator,
and cannot be used for unsupervised monitoring due to
the safety issues involved with the operation of powerful
lasers.
The primary non-optical methods include acoustic
monitoring [12]; gas sampling [9], soil monitoring [12],
flow monitoring [11][10][12], and software based
dynamic modeling [9][12]. Acoustic monitoring
techniques typically utilize acoustic emission sensors to
detect leaks based on changes in the background noise
pattern. The advantages of the system include detection of
the location of the leaks as well as non-interference with
the operation of the pipelines. In addition, they are easily
ported to various sizes of pipes. However, a large number
of acoustic sensors are required to monitor an extended
range of pipelines. The technology is also unable to detect
small leaks that do not produce acoustic emissions at
levels substantially higher than the background noise.
Attempts to detect small leaks can result in many false
alarms.
Gas sampling methods typically use a flame
ionization detector housed in a hand held or vehicle
mounted probe to detect methane or ethane. The primary
advantage of gas sampling methods is that they are very
sensitive to very small concentrations of gases. Therefore,
even very tiny leaks can be detected using gas sampling
methods. The technique is also immune to false alarms.
The disadvantages of the technology are that detection is
very slow and limited to the local area from which the gas
is drawn into the probe for analysis. Therefore the cost of
monitoring long pipelines using gas sampling methods is
very high.
In soil monitoring methods, the pipeline is first
inoculated with a small amount of tracer chemical. This
tracer chemical will seep out of the pipe in the event of a
leak. This is detected by dragging an instrument along the
surface above the pipeline. The advantages of the method
include very low false alarms, and high sensitivity.
However, the method is very expensive for monitoring
since trace chemicals have to be continuously added to the
natural gas. In addition, it cannot be used for detecting
leaks from pipelines that are exposed.
Flow monitoring devices measure the rate of
change of pressure or the mass flow at different sections
of the pipeline. If the rate of change of pressure or the
mass flow at two locations in the pipe differs significantly,
it could indicate a potential leak. The major advantages of
the system include the low cost of the system as well as
non-interference with the operation of the pipeline. The
two disadvantages of the system include the inability to
pinpoint the leak location, and the high rate of false
alarms.
Software based dynamic modeling monitors
various flow parameters at different locations along the
pipeline. These flow parameters are then included in a
model to determine the presence of natural gas leaks in
the pipeline. The major advantages of the system include
its ability to monitor continuously, and non-interference
with pipeline operations. However, dynamic modeling
methods have a high rate of false alarms and are expensive
for monitoring large network of pipes.

Chemical sensor based method (chemo resistive,
electrochemical sensors) are most suited method as it can
be implemented on drones very easily as payload weight is
relatively less and sensitivity is good which gives proper
gas detection with source localization. Mcgonigle provided
an early demonstration using a small remotely piloted
helicopter flying into gas plumes from an active volcano to
measure the chemical concentrations of CO2 and SO2 as a
part of geophysical research [6]. Neumann demonstrated
the first rotary wing micro-UAV-based GDM and GSL. The
experiments were conducted in small outdoor
environment. The challenges in the system were reported
to be gas distribution changes due to drone rotors [5].
Malaver illustrated the efforts in greenhouse gas
monitoring through the use of a fixed wing, solar-powered
UAV system with an integrated MOX sensor to measure
CO2 and CH4 [5]. Johnson presented chemical vapor
detection sensor with two types of micro-UAV i.e. fixed
wing and rotary wing. The tests were performed in an
enclosed environment but the work did not progress to
the real time system [12][2].
3. SYSTEM DESIGN
The proposed system mainly uses MOS sensors
which will detect different gases such as CO, methane, and
LPG. These sensors detect the concentration of the gases, if
the gas concentration is above PEL, the predefined user
will be notified with the help of GSM system with the
location of gas leakage. Here LCD display is also attached
which will display the real time gas concentration reading.
Sensing is mainly done by the microcontroller. Here GUI is
also designed using Matlab which will help in monitoring
the gas concentrations continuously with the locations.
Also GUI provides greater flexibility to the system such as
by using different conversion formulas single sensor can
be used to measure concentrations of different gases. Also
by using GUI we can contact to the different disaster
management services such as fire brigade, ambulance, etc.
where we can inform them about the gas leakage, its
concentration with location of gas leak source.
3.1 Block Diagram
Hardware of this system consists of AVR ATMega16
Microcontroller, MQ2 sensor, MQ7 sensor, MQ4 sensor,
GSM module, Display unit, Power supply and RS232 to
USB converter IC. Figure 1 shows block diagram of system.
Fig -1: Block Diagram of Proposed System
Block diagram consists of 3 MOS sensors, a
microcontroller, a LCD display, power supply unit and
RS323 to USB converter for easy interface between
hardware and GUI which is designed using matlab tool in
PC. Similarly GSM module is also connected to PC through
RS232 to USB converter. GSM module requires supply of
12V which can be supplied from system power supply as
we are using 12V, 1A adapter for supply voltage with
IC7805. Working principle of sensors is explained in
preceding sections with configuration of GUI and
hardware units.
Interfacing of GSM module with PC is shown in
figure 2 which is shown below. Here RS232 to USB
converter IC is used as an interface between GSM module
and PC i.e. GUI. TXD, RXD and GND are pins used for the
interface between GSM module and RS232.
Fig -2: Interfacing of GSM Module with PC
3.2 Mathematical Model
Development of system mainly incudes
mathematical aspects and they must be taken into
consideration. Such aspects include power requirements
for each unit, ADC conversion and digital to ppm
conversion of readings.
Power
Supply
To PC
(RS232-
USB)
LCDMQ2
Sensor
MQ7
Sensor
MQ4
Sensor
AVR
ATMega16
Controller
PC
with
GUI
GSM
Module
GND
TXD
RXD
TXD
RS232 to
USB Con-
verter IC

Power Calculation
This system comprises of AVR ATMega16
microcontroller, three MOS sensors namely MQ2, MQ4 and
MQ7 with LCD display. All these units require battery
powered supply and hence constant DC power supply is
used to provide required power.
a) AVR ATMega16 microcontroller requires supply of 5V
DC with current requirement of 1.1mA. Thus Power
consumption for microcontroller is 5.5mW.
b) MQ2 sensor requires 900mW of power with supply
voltage of 5V DC.
c) MQ7 sensor requires 350mW of power with supply
voltage of 5V DC.
d) MQ4 sensor requires 950mW of power with supply
voltage of 5V DC.
e) LCD works on 3-5V DC with current requirement of
1.2-1.5mA.
By considering above requirements of each unit DC power
supply of 5V, 1A is designed using 12V, 1A adapter and
IC7805.
ADC Conversion
Sensor module gives the output in the form of
voltage. This analog voltage is converted to digital using
10-bit ADC which is on chip ADC in AVR ATMega16
microcontroller. As ADC is 10 bit the values are as follows
Total digital values =210 = 1024
The values therefore changes from 0 to 1023. The
maximum output voltage from sensor is 5V. So the
conversion of voltage change to digital value is shown
below
5V/1024= 0.004883V=4.883mV
For each 4.883mV of change in sensor voltage will result
in change of digital value by 1.
Digital to ppm Conversion
Conversion of digital reading to ppm is very
important task, because by using different sensitivity
curve, different gas can be measured for same digital
reading. According to datasheet of MQ2 sensor, sensitivity
curve for LPG is different from sensitivity curve of smoke
or CO. By using first point of curve, last point of curve and
slope of curve digital readings are converted to ppm. The
formula for this conversion is as shown below
Ppm reading =
3.3 Development of System
Development of system mainly includes the
hardware development and software development of the
system. Hardware development gives the circuit diagram
of the hardware which is designed for gas detection of CO,
LPG and methane. Circuit diagram is simulated in proteus
software. Figure 3 shows circuit diagram of hardware
showing interfacing with sensors and LCD. As shown in
circuit diagram three sensors are connected to pin number
38, 39 and 40. MQ7 sensor is connected to pin number 39
which ADC1 pin. MQ2 sensor is connected to pin number
40 i.e. ADC0 pin and MQ4 sensor is connected to pin
number 38. LCD is connected to port C from PC0 to PC5 i.e.
pin number 22-27. Here supply voltage of 5V DC is applied
to circuit.
Fig -3: Circuit Simulation of the System
Software development of system mainly includes
the design of GUI. Here GUI is designed using matlab.
Figure 4 shows the designed GUI for dynamic stand-alone
gas detection system. GUI components used in this gas
detection system is push button, radio button, static box,
edit text and axes. In figure ‘Start Acquisition’ and ‘Send’
are two push buttons used. Start acquisition is used to
enable acquisition of sensor data which is converted into
ppm and displayed in edit text box. These edit text box will
display numeric values of gas concentration in ppm. Thus
the name of corresponding gas whose value is to be
displayed in edit text box is given in static box. These static
boxes are named as ‘CO (ppm)’, ‘LPG’, and ‘Methane’. The
values acquired from sensor are also represented in
graphical form by using axes.
The ‘Connection’ is a radio button used for
enabling the connection between hardware and software.
This connection is done using COM port interface (RS232
to USB converter IC). Here hardware includes system

hardware and GSM module. The other COM port
connection is used for GSM module interface. If the
connection is successful ‘open’ message will be displayed
in static box. Static text with name ‘Number’ is used to
represent edit text box which is used to represent phone
number of the person to whom we want to send a
message. Message string to be sent is a standard string “CO
level at location 1 is x” where x represents the real time
value of CO gas concentration. String will be different for
different gases. If we want to change the message string
the provision is given by edit text box.
Fig -4: Development of GUI in Matlab
Lastly the three edit text boxes named as ‘CO
Threshold’, ‘LPG Threshold’ and ‘Methane Threshold’ are
programmed to take threshold values of gas concentration
from user. If the real time gas concentration is greater
than threshold defined the message will be send to the
person whose phone number is entered in GUI.
3.4 Algorithm for System
This section gives algorithm of system which
explains step by step working of system. Sensor used here
is MOS type sensor which converts gas concentration into
equivalent resistance through chemical reaction. The
equivalent resistance is converted to voltage using sensor
module. These sensor readings are acquired by
microcontroller. As the voltage is analog quantity it has to
be converted to digital for further processing. ATMega16
microcontroller has inbuilt 10-bit ADC which converts
analog voltage readings to digital 10-bit data. Display
those readings on LCD. The LCD used here is 16x2 line
LCD which displays the digital readings with sensor name.
Graphical user interface which is abbreviated as
GUI is popular these days for flexible system design. The
GUI designed here converts the digital data from hardware
i.e. digital readings from sensor to ppm (parts per million).
It also displays real time readings of sensor (in ppm) in
numeric form as well as graphical form. It also interfaces
with GSM module which can be used for communication.
GUI compares calculated ppm readings with the
thresholds standardized by NIOSH and OSHA. If the sensor
readings are greater than respective threshold then
message will be sent to the monitoring unit. The phone
number of the monitoring unit can be easily changed to
another person or to the hospital in case of catastrophe.
The message will display gas name and its concentration.
GUI is designed using matlab as a tool. The microcontroller
interfaced with sensor and LCD is a hardware which needs
to be interfaced with PC (i.e. configuration with GUI). Same
goes for GSM module which needs to be interfaced with PC
in order to be configured with GUI. This interface is
achieved using RS232 to USH converter IC.
4. PERFORMANCE ANALYSIS
This section gives practical performance
characteristics of sensors and shows results of system at
various parameter changes such as time, distance from
source, and readings at different concentration of gases
etc. MQ7 readings were calibrated with PUC checking
device at RTO office, Jalgaon. Experimentation of methane
gas concentration measurement has been done only in
clean air. Whereas for LPG gas experimentation was done
using household gas stove. The results of system interface
with GSM through GUI are also shown in this section.
4.1 Output of System
Output of the system is shown on LCD (digital
readings) and GUI (ppm readings). Also text messages can
be seen on mobile. Figure 5 shows the digital readings
which are displayed on LCD with system hardware.
According to figure 5 sensor MQ7 shows reading 269
which is digital value given by 10-bit ADC (i.e. values
between 0 and 1023). Similar goes for MQ2 and MQ4.

Fig -5: System Hardware Showing Digital Readings on LCD
GUI was designed by using Matlab as a tool.
Configuration of hardware and GSM module with GUI is
important task. USB port is used for communication
between hardware and PC. So the appropriate COM port is
selected for interfacing system hardware and GSM module.
As soon the connection is open between hardware and PC
or GSM module and PC we can start to acquire the sensor
readings. Here the digital readings are converted to the
ppm value and displayed on GUI. Figure 6 shows the ppm
readings acquired from hardware.
Fig -6: Data Acquisition from Sensors using GUI
Figure 6 shows GUI acquiring readings of the
sensor. Here threshold can be given by user for different
applications. Also text message sent to the number is
shown beside. We can change the phone number as shown
in figure. String showing CO level at the bottom of the axis
is shown only when the concentration of gas is greater
than threshold.
4.2 Performance of System
Dynamic stand-alone gas detection system uses
three sensors which are MQ2, MQ7 and MQ4. Performance
of these sensors is analyzed in this section. As we know
that heating time is the important parameter for analyzing
sensor performance. As heating time increases the
readings of the sensors gets more and more stable. Table 3
shows the readings of MQ7 sensor at different heating
time in clean air and air with CO gas leak. Chart 1 shows
the line graph showing performance characteristics of
MQ7sensor with respect to heating time. These readings
were taken on smoke generated from matchstick.
Table -3: MQ7 Sensor Readings at Different Heating Time
Heating Time
(minutes)
Sensor Readings (in ppm)
In Clean Air Air with CO
0 1102 9695
4 116.19 1593
8 53.38 664.13
12 28.92 338.63
16 19.98 4023
Chart -1: Performance Characteristics of MQ7 Sensor w.r.t.
Heating Time
Please note that in above graph, variation in
readings in air with CO leak is due to gas concentration
changes. Here equation of line (line showing readings of
MQ7 sensor in clean air at different heating time) is
calculated by using linear and exponential trade line.
These equations are given below
Equation of line using exponential trade line:
y = 545.42e-0.235x

Equation of line using linear trade line:
y = -56.283x + 714.36
Table 4 shows the readings of MQ2 sensor at
different heating time in clean air and air with LPG gas
leak. Chart 2 shows the line graph showing performance
characteristics of MQ2 sensor with respect to heating time.
This experimentation was done on household LPG gas
stove.
Heating Time
(in Minutes)
Sensor Readings (in ppm)
In Clean Air Air with LPG Leak
0 69.36 1114
4 3.75 466.33
8 2.76 1763
12 2.2 388.42
16 1.95 1861.9
Heating Time
Please note that in above graph, variation in
readings in air with LPG leak is due to gas concentration
changes. Here equation of line (line showing readings of
MQ2 sensor in clean air at different heating time) is
calculated by using linear trade line only as it covers
almost all points in straight line.
The equation for linear trade line:
y = -3.4093x + 43.278
Table 5 shows the readings of MQ4 sensor at
different heating time in clean air. Chart 3 shows the line
graph showing performance characteristics of MQ4 sensor
with respect to heating time.
Heating Time (in
Minutes)
Sensor Readings in
Clean Air
0 4.49
4 1.6
8 1.71
12 2.01
16 2.28
Chart -3: MQ4 Sensor Readings at Different Heating Time
We can see in all three graphs that sensor
readings are getting more and more stable as heating time
of heater coil is increasing. Variation in readings which is
unacceptable is because of change in gas concentration.
Here equation of line (line showing readings of MQ7
sensor in clean air at different heating time) is calculated
by using linear and polynomial trade line. These equations
are given below
Equation of line using polynomial trade line:
y = 0.0291x2 - 0.5653x + 4.15
y = -0.1003x + 3.22
Distance is also an important parameter for
sensor performance analysis. Using this parameter we can
achieve gas leak source. As we know that distance
between sensor and gas leak source decreases the gas
concentration increases resulting in high ppm values
which can be used to detect the gas leak source. For
analysis of this parameter, experimentation was done with
MQ2 gas sensor and household LPG gas was used as
source of gas leak. Table 6 shows readings of MQ2 sensor
taken at various locations near or far from gas leak source.
Chart 4 shows a line graph showing performance

characteristics of MQ2 sensor with respect to distance of
sensor from gas leak source.
Table -6: MQ2 Sensor Readings at Different Distance from
Leak Source
Distance of Sensor from Gas Leak Source
We can see in the figure above that slope of the
power line is high near origin, i.e. we can easily localize a
gas source as the gas concentration near a gas source
varies drastically as we go away from source. But at far
distances from source the variations in gas concentration
is low. Thus by using this experimentation we can easily
localize a gas leak source. Equation of power line showing
MQ2 gas sensor response is calculated using exponential
and linear trade line. These equations are shown below
Equation of line using exponential trade line:
y = 3321.6e-0.161x
y = -206.49x + 2948.6
Gas concentration is another parameter for
analyzing sensor performance. Some experimentation was
done on MQ7 sensor using different vehicles as gas leak
source. As we can tell each vehicle has different
concentration of CO leak, thus this parameter was also
analyzed to check performance of gas sensor. Table 7 gives
readings of MQ7 sensors with different gas leak source.
Chart 5 shows bar graph showing performance
characteristics of MQ7 sensor w.r.t different gas leak
sources.
Table -7: MQ7 Sensor Readings with Different Gas Leak
Sources
Type of Vehicle MQ7 SENSOR Readings (in ppm)
Pleasure Bike 24841
Wego Bike 17787
Auto 79484
Hero Honda Bike 34285
Different Gas Leak Sources
4.3 Calibration of Sensor
MQ7 sensor was calibrated with the PUC checking
machine at RTO, Jalgaon. Readings on PUC machine are
shown in table 8 and are compared with the readings
taken by MQ7 sensor. Source of gas leak used here was a
“Pleasure Scooter”.
Table -8: Comparison of Readings of the System with
Standard Readings
Readings Taken by PUC
Machine
Readings Taken by MQ7
Sensor
2.096% (nearly after 2
min of heating time)
2.484% (After 16 min of
heating time)
1.690% (nearly after 2.30
min of heating time)
1.778% (After 22 min of
heating time)
Distance of Sensor from
Source (in cm)
MQ2 Sensor Response
(in ppm)
0 3446.3
3 2414.5
6 1013.9
9 716.01
12 476.28
15 332.5

By observing these readings we can say that after
heating a sensor for some time we can get the readings
which are approximately equal to standard readings. The
cost of PUC machine is 3.5 lakh rupees approx. whereas
the cost of this system is 1800-2000 rupees approx. Thus
this gas detection system is advantageous as it gives
nearly accurate readings in low cost, the only short come
is heating time is greater for the system.
5. CONCLUSIONS
Objectives of the system explained in first chapter
says the system should detect toxic gases and if the gas
concentration is greater than defined threshold then
monitoring system will be alerted with the message
showing gas concentration and location of gas leak.
According to the results shown in fourth chapter we can
say that the objective is achieved except for automatic gas
localization. Localizing the source of gas leak automatically
without any manual processing and sending information
regarding exact location of gas leak is important task and
which is partially achieved.
Dynamic stand-alone gas detection system is a system
which very low in cost with approximately equal results
compared to standard CO gas detection system used at
PUC centers except for the short come of heating time,
which is greater for the system than standard system.
According to results shown in table 3, 4, 5 and 6 we can
conclude that as heating time increases readings becomes
more and more stable. Also, the heating time of 30
minutes is more than sufficient for approximately accurate
readings. Preheat time of 48 hours which is a short come
of this system can be compromised if we compromise a bit
in accuracy. Results shown in table 6 concludes that gas
localization is very likely possible if we reduce a distance
between source and sensing device.
This system likely have some short comes but the
system is cost effective as well as gives approximately
equal results as compared to standard system. Thus, this
system can be used instead of PUC machines, for
household LPG gas leak or methane gas leak, for
measuring CO concentrations in industries where workers
works in 8 hours shift, etc. applications.
Gas localization using GPS system is totally
ineffective process as range of GPS localization is very
poor. Thus a system with camera mounted on it can be
effective for exact location identification of gas leak
source. The major scope of work is achieving higher
accuracy with minimum preheat time. If we succeed to
achieve higher accuracy in less preheat time then that
would be a revolution for low cost gas detection systems.
REFERENCES
[1] Bennett, C. L., Carter M. R., and Fields, D. J.,
“Hyperspectral imaging in the infrared using LIFTIRS,”
Optical Remote Sensing for Environmental and Process
Monitoring, Volume 55, pp. 267-275. 1995.
[2] Brandy J. Johnson, Anthony P. Malanoski, Jeffrey S.
Erickson, Ray Liu, Allison R. Remenapp, David A.
Stenger, and Martin H. Moore, “Reflectance-based
detection for long term environmental monitoring”,
Heliyon, Volume 3, Issue 6, June 2017.
[3] Cosofret B. R., Marinelli W. J., Ustun T., Gittins C. M.,
Boies M. T., Hinds M. F., Rossi D. C., Coxe R., Chang S.,
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[13] Figaro Engineering INC, Google,
www.figaro.co.jp/en/technicalinfo/principle/mos-
type.html
[14] Henwei Electronics Co., LTD, Pololu,
www.pololu.com/file/0J309/MQ2.pdf
[15] Henwei Electronics Co., LTD, Pololu,
www.pololu.com/file/0J313/MQ7.pdf
[16] Henwei Electronics Co., LTD, Sparkfun Electronics,
www.sparkfun.com/datasheets/sensors/Biometric/M
Q-7.pdf.
[17] Neru5.Ru,neru5.ru/index.php?route=product/produ
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[18] Wikipedia,
en.wikipedia.org/wiki/Atmosphere_of_Earth
[19] Zhengzhou Winsen Electronics Technology Co., LTD,
Sparkfun Electronics,
cdn.sparkfun.com/datasheets/sensor/Biometric/MQ-
4ver1.3-Manual.pdf

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