Automatic Harmful Gas Detection Using Electronic Nose Technology

Illahi, Ana Antoniette C.; Dadios, Elmer P.; Vicerra, Ryan Rhay P.; Sybingco, Edwin

doi:10.1109/hnicem54116.2021.9732049

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environ 2021

DOI: 10.1109/hnicem54116.2021.9732049

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Automatic Harmful Gas Detection Using Electronic Nose Technology

Ana Antoniette C. Illahi

Elmer P. Dadios

Ryan Rhay P. Vicerra

et al.

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Cited by 2 publications

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“…The artificial olfactory presented in the paper provides a promising strategy for future bio-inspired electronics research, especially in the field of mimicking biological sensory systems. The automatic detection of harmful gases by using the technology of E-nose was studied by [109]. The detection of these gases can be helpful in industrial areas because such harmful gases as ammonia can be very dangerous to human health.…”

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confidence: 99%

E-Nose-Driven Advancements in Ammonia Gas Detection: A Comprehensive Review from Traditional to Cutting-Edge Systems in Indoor to Outdoor Agriculture

Moshayedi

Khan

et al. 2023

Sustainability

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Ammonia (NH3) represents a perilous gas that poses a substantial hazard to both human well-being and the environment, particularly within agricultural regions. Agricultural activities constitute a primary source of ammonia emissions. Thus, effective monitoring and measurement of ammonia sources in agriculture are imperative for mitigating its adverse impact. However, not all existing ammonia detection methods are suitable for discerning the low concentrations typically encountered in agricultural ammonia volatilizing (ranging from 0.01 to 5 parts per million). Consequently, curtailing ammonia volatilization from farmland assumes paramount importance, with real-time monitoring serving as a crucial mechanism for assessing environmental contamination and minimizing agricultural ammonia losses. Deploying appropriate detection methodologies ensures that requisite measures are taken to safeguard human health and the environment from the deleterious repercussions of ammonia exposure. The present paper introduces a comprehensive approach to detecting and analyzing ammonia in agricultural settings. It elucidates the merits and demerits of conventional indoor and outdoor ammonia detection methods, juxtaposing them with the innovative technology of Electronic nose (E-nose). Within the paper, seven widely employed ammonia detection methods in farmland are scrutinized and compared against traditional techniques. Additionally, the constructional aspects and distinct components of E-nose are meticulously delineated and appraised. Ultimately, the paper culminates in a comprehensive comparative analysis encompassing all the aforementioned methodologies, elucidating the potential and limitations of E-nose in facilitating ammonia detection endeavors within agricultural contexts.

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mentioning

confidence: 99%

E-Nose-Driven Advancements in Ammonia Gas Detection: A Comprehensive Review from Traditional to Cutting-Edge Systems in Indoor to Outdoor Agriculture

Moshayedi

Khan

et al. 2023

Sustainability

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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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Automatic Harmful Gas Detection Using Electronic Nose Technology

Cited by 2 publications

References 4 publications

E-Nose-Driven Advancements in Ammonia Gas Detection: A Comprehensive Review from Traditional to Cutting-Edge Systems in Indoor to Outdoor Agriculture

E-Nose-Driven Advancements in Ammonia Gas Detection: A Comprehensive Review from Traditional to Cutting-Edge Systems in Indoor to Outdoor Agriculture

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

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