Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering

Li, Xinli and Li, Yijiao and Lu, Gang and Yan, Yong (2018) Biomass Fuel Identification Based on Flame Spectroscopy and Feature Engineering. Proceeding of the CSEE, 38 (15). pp. 4474-4481. (doi:https://doi.org/10.13334/j.0258-8013.pcsee.171984?) (Full text available)

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Abstract

Flame spectra contain useful information about combustion and hence the spectral features of flame radicals may be used to identify different biomass fuels. A technique for biomass fuel identification is proposed based on the spectral features of flame radicals, feature engineering and improved support vector machine. The spectral intensity signals of biomass flames and flame radicals (OH*(310.85nm), CN*(390.00nm), CH*(430.57nm) and C2*(515.23nm, 545.59nm)) were acquired using a spectrometer. Feature engineering was built, which can accurately reflect the characteristics of sample category, through feature extraction, feature selection based on Filter and feature learning based on dictionary learning. The support vector machine is used to build the identification model, where radial basis kernel parameter ? and error penalty factor C are optimized using an improved grid search algorithm. Experimental results from a laboratory-scale combustion rig show the effectiveness of the proposed method for the identification of biomass fuel.

Item Type: Article
Additional information: article in Chinese
Uncontrolled keywords: flame spectroscopy; flame radicals; biomass; fuel identification; feature engineering; dictionary learning; support vector machine
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Yong Yan
Date Deposited: 01 Nov 2018 17:01 UTC
Last Modified: 01 Nov 2018 17:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69883 (The current URI for this page, for reference purposes)
Yan, Yong: https://orcid.org/0000-0001-7135-5456
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