To design a smart gas leakage detection that has the capability to make decision and recognise
the gas leakage early by using decision making process, that is earlier feed to the model for
pattern recognition purpose. To reduce false alann rate and for reliability, accuracy and
stability, it is ought to use hybrid optimisation Grey wolf along with Particle Swann
optimisation and accompanied by the Hybrid ML model such as Random Forest and XG Boost.
This ensures that the output that makes decision intelligently even when the environmental
parameter changes constantly and gives notifications via twilio, tinkercad and MIT. And thus
makes sure it could be implemented and gives the best decision even under high sensor noises
and trigger the alert
The proposed solution here integrates the lOT as well as the Hybrid machine learning to
compute and do the monitoring of the particular environment, where the setup is kept for better
gas leakage mechanism. This reduces false alarm rates and is more reliable with effective cost
(with affordability). This ensures there is monitoring continuously and it never fails to generate
and trigger the alert mechanism, if something goes not well and seems to be abnormal. Fast
response is ensured since server processing is used. There is no dependency on the latency of
the cloud and hence there is processing done faster, effectively. This way it is effective and
hopefully effective to use.
To design this model, MQ-2, MQ-6, DHT11, MAX 9814 and Ultrasonic sensor is used. MQ-
6, MQ-2 arc the sensors used to monitor the gas level, DHTll is used for monitoring the
temperature and humidity. Similarly theM AX9814 is used for monitoring any abnom1al hisses
sound that emerge during the leakage and the sensor ultrasonic is used to find, if any humans
are standing nearby. LED alerts are also set up there for indication and severity. ESP8266 is
used so that data could be transmitted wirelessly to the server and then it could send the data
to trigger and pass out the notifications to twilio, tinkercad and MIT. The gas level is monitored
constantly and is displayed on the OLED as well. It is reliable and cost effective since our
system used Arduino UNO microcontroller board. There is.no cloud dependency and therefore
the response is faster and convenient. Buzzer is used to buzz in case of high danger and the
led to glow continuously. and the notification would be sent immediately with no delay within
three seconds, this way the safety is enhanced and made sure Smart Gas Leakage Monitoring for Mining Safety using Machine Learning assisted
optimization is used to monitor whether any gas is leaked in industrial or the mining sites and
notifies by its severity.
2. Smart Gas Leakage Monitoring for Mining Safety using Machine Learning assisted
optimization is used to monitor whether any gas is leaked in industrial or the mining sites and
notifies by its severity of claim 1, wherein said that this paper alert the concerned smart phone
users.
3. Smart Gas Leakage Monitoring for Mining Safety usmg Machine Learning assisted
optimization is used to monitor whether any gas is leaked in industrial or the mining sites and
notifies by its severity of claim 1, wherein said that this paper save the employees valuable life.
4. Smart Gas Leakage Monitoring for Mining Safety using Machine Learning assisted
optimization is used to monitor whethe.r any gas is leaked in industrial or the mining sites and
notifies by its severity of claim 1, wherein said that this paper used to reduce the false alarm
rates and is more reliable with stability.
Summary of the Invention:
This design of smart gas leakage detection is for the environments such as mining as well as
Industries. For this, multiple sensors such as MQ-2 and MQ-6, along with other sensors gets
incorporated and activated for reliable use, effectively. Even the sound level are captured for
the identification of the hissing noise that would get produced during it's leakage. For decision
making and finding of the solution and to find the severity of the gas, GWO-PSO hybrid
optimization is used for feature classification and then to identify properly Random Forest and
XGBoost ML model is used, for accurate prediction of the gases. For on-site warnings, LED
;md buzzer is used to make sound and alert the people nearby. Using twilio communication
service, notifications of alert gets was sent to the concerned person, even the visual alert such
as the thinkspeak and MIT gets updated and could be used for visualization in the changes due
to the gas during the leakage period. Our solution helps to ensure there is faster response time
and no false alarm rate with accuracy and effectiveness. Here Arduino uno microcontroller is
used, this would process the collected data's from the sensors and moreover ESP8266 is used
for the transmission processes of the data. Here the leakage are classified based on severity not
just any leak. Overall this invention is cost effective, affordable, scalable and efficient to
achieve the correctness and accuracy of gas leakage Detailed Description of the Invention:
1. Hardware Configuration:
Here system consists of Arduino Uno microcontroller, it acts as the central processing unit.
Then MQ 2 and MQ 6, gas sensors are used for the detection of the gases, and to know its level
if it gets leaked. Then, DHT 11 sensor is used to monitor the temperature as well as the humidity
of the environment, where the sensor is present. To detect the presence of human being nearby
to the dangerous area ultrasonic sensors is used and then to find out the noises when there is
gas is leaked MAX 9814 sensor is used. For all the wireless transmission of the data and for
notification purpose ESP 8266 is used. A buzzer is also used along with LED in the model, so
that at the on-site people gets alert that there is something wrong. OLED is·setup to display the
reading of the gas level, temperature and humidity. All the hardware components are connected and synced with each other to work in an integrated fashion and to respond as per the condition
of the environment, without any error.
2. Software and Data Processing:
Here the software of the system is developed using Arduino IDE for real time acquisition of
the data. The PC act as the server with ML model in it with decision making based. The ML
model is trained well. Here in ML, GWO-PSO hybrid as well as hybrid Random Forest and
XGBoost is used. The concentration of gas, temperature, humidity and the intensity of the
sound during the leakage is noted. And then the sensor data is pre-processed properly, there
would be filtering of noises and even nom1alisation for doing the proper analysis. Then the
data's are fed into trained model, so that it does great job in predicting the data, whether it is
safe, warning of leakage, or there would be any severe leakage within 1 hour as such. This kind
of severity based logic helps to reduce false alarm rates. After the result is processed, if there
is leakage there would be triggering of alerts. Using twilio and by wireless in WhatsApp, .
notification is sent properly and right on time. And then visualization of the gas level whether
it is raised or lower could be seen in think speak as well as MIT app.
3. Advantages of the Invention:
The proposed system offer various advantages in many areas and in many aspects. In
environments such as mines it plays a crucial role in doing the monitoring of gases. There
would be most dangerous gases that would leak in mines due to the severe process they undergo
to extract the minerals, that gas could be propane, butane or LPG as well. All these gases would
be easily detected. By detecting the presence of the worker it could ensure to make sure the
human is safe around. It gives early warning since, decision making is used by ML, it could
predict easily. lt also ensures that there is cloud assisted SMS notifications sent via twilio to
whatsapp. Therefore there is no need to use GSM modules for the purpose of alert based things.
It supports human and assure that all the workers around that environment are safe. It is scalable
system and also affordable because of the simpler and available hardware components. It could
be integrated with mobile and any kind of web based monitoring applications that are available.
Not only it is suitable in mining environment but anywhere, where there would be leakage of
gas, we could use it, we could use it even in the chemical industries or laboratories etc.