Hazard Classification of Toluene, Methane and Carbon Dioxide for Bomb Detection Using Fuzzy Logic

Macasaet, Dailyne; Illahi, Ana Antoniette C.; Dadios, Elmer P.; Lauguico, Sandy

doi:10.1109/hnicem48295.2019.9073559

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Methane is not harmful and is not expected to harm human or animal health and productivity in normally ventilated buildings, but it contributes to global warming [13]. According to [14], Methane in the range of 0 -500 ppm does not affect a person's body, but according to [15], the highest concentration in cow dung is methane gas of 65.7 kcal/m 2 .…”

Section: ) Mq-4 Sensormentioning

confidence: 99%

Design of an E-Nose Detector for Contaminated Gas in Cow Farming Waste

Rusdianto,

Amilia,

Malik

2023

IJ-FANRES

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Cattle farm waste is organic waste produced by the livestock industry in the form of feces and can emit gases that cause air pollution. Cow dung contamination can cause environmental problems, disturbing humans and livestock. Hazardous gases produced from the decomposition of livestock manure, namely methane (CH4), sulfur dioxide (SO2), ammonia (NH3), and hydrogen sulfide (H2S), can cause respiratory diseases, deplete ozone and contribute to the greenhouse effect. Based on these problems, measuring gas levels using an Electronic Nose (E-Nose) tool capable of detecting harmful gases in livestock pens is necessary. The E-nose is a device that mimics the human nose and consists of several gas sensors that can detect environmental odors. The design of the detection and measurement of toxic gases using an electronic nose in this study adopts the Internet of Things (IoT) concept using the Arduino Uno and ESP8266 microcontrollers and MQ-4, MQ-136, and MQ-137 gas sensors to read levels of pollutant gas in units of ppm with an average of 46.25 ppm for the MQ-4 sensor, 1.86 ppm for the MQ-136 sensor, and 4.97 ppm for the MQ-137. The sensors were subjected to functional tests consisting of validity testing, resulting in a calculated r value of 1, meaning that each gas sensor performs its measurement function correctly or validly. The reliability test of the MQ-4 sensor is 0.00, the MQ-136 sensor is 2.442× 10-15, and the MQ-137 sensor is 1.554× 10-15, which means that the data reading from each gas sensor is unstable, less accurate, or unreliable compared to secondary data. However, the designed E-Nose tool can work well and improve farmers' performance in their daily work.

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Section: ) Mq-4 Sensormentioning

confidence: 99%

Design of an E-Nose Detector for Contaminated Gas in Cow Farming Waste

Rusdianto,

Amilia,

Malik

2023

IJ-FANRES

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Development of an Electronic Nose for Smell Categorization Using Artificial Neural Network

Macasaet¹,

Illahi²,

Dadios³

et al. 2021

JAIT

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BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

Illahi¹,

Dadios²,

Concepcion³

et al. 2022

JACIII

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The safety and security of an individual is important in our society. Bombing attacks can cause significant destruction and death. Energy efficient and compact bomb removal robots are challenging to develop because these typically involved a large array of sensors individually acquiring gas data. This study addresses this challenge by developing a multiple bomb-related gas prediction model using machine learning and the electronic nose sensor substitution technique. Three models can predict gasses such as ammonia, ethanol, and isobutylene using only carbon monoxide, toluene, and methane sensors. The feedforward artificial neural network (FFNN) with three hidden layers was optimized for the regression of each target gas. Consequently, ammonia, ethanol, and isobutylene predictions achieved R2 values of 1, 1, and 1 as well as MSE values of 0.35696, 0.052995, and 0.0022953, respectively. This study demonstrates that the sensor substitution model (BombNose) is highly reliable and appropriately sensitive in the field of bomb detection.

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Hazard Classification of Toluene, Methane and Carbon Dioxide for Bomb Detection Using Fuzzy Logic

Cited by 4 publications

References 4 publications

Design of an E-Nose Detector for Contaminated Gas in Cow Farming Waste

Design of an E-Nose Detector for Contaminated Gas in Cow Farming Waste

Development of an Electronic Nose for Smell Categorization Using Artificial Neural Network

BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique

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