Smoldering peat fire detection by time-resolved measurements of transient CO2 and CH4 emissions using a novel dual-gas optical sensor

Raza, Mohsin; Chen, Yuying; Trapp, Joshua; Sun, Haojia; Huang, Xinyan; Ren, Wei

doi:10.1016/j.fuel.2022.126750

Cited by 9 publications

(1 citation statement)

References 33 publications

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“…In order to effectively prevent such accidents, it is necessary to carry out rapid and accurate identification for CH 4 , CO, and their mixed gases. At present, the common detection methods are mass spectrometry, light spectrum, gas chromatography, and a chemical method, which can accurately identify the gas composition and concentration [ 1 , 2 , 3 , 4 ]. However, their disadvantages, such as high cost, huge equipment, complicated operations, and time-consuming analysis greatly limit their applications.…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose

Yin

Zhao

Peng

et al. 2023

Sensors

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The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.

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Section: Introductionmentioning

confidence: 99%