Classification and Identification of Industrial Gases Based on Electronic Nose Technology

Abstract

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

“…For the pattern recognition stage, Hui et al [65], used LDA to detect and identify four different industrial gases, which achieved high classification rate and satisfactory predictive ability.…”

An Odor Labeling Convolutional Encoder–Decoder for Odor Sensing in Machine Olfaction

“…Thirdly, e-noses have the capacity of extending out olfactory perception scopes since gas sensors are capable of detecting those chemicals which humans are unable to smell and sense [ 23 ]. Furthermore, e-noses can be used in some unpleasant environments [ 24 , 25 ].…”

Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids

“…Electronic olfactory systems, known as "E-nose", can be implemented to analyze various gas components based on a sensing method similar to the human olfactory system [18][19][20][21][22]. In the field of fire safety, Luo et al developed an E-nose that had successfully classified four industrial gas included CO 2 , CH 4 , NH 3 , VOCs [23]. Sun et al created an E-nose that could successfully identify methanol, ethanol and acetone samples [16].…”

“…Despite their advantages, those E-noses still have limitations when they are applied on a bus such as being affected by interference in complex environments. As an interference, the temperature could affect the response values of MOS sensors [24] which can be overcome in two common ways: constant temperature control [23,25] and temperature compensation [26][27][28][29][30]. As far as we know, no electronic nose has been reported for detecting flammable liquids on buses.…”