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Biomass Fuel Identification Using Flame Spectroscopy and Tree Model Algorithms

Ge, Hong, Li, Xinli, Li, Yijiao, Lu, Gang, Yan, Yong (2019) Biomass Fuel Identification Using Flame Spectroscopy and Tree Model Algorithms. Combustion Science and Technology, . ISSN 0010-2202. (doi:10.1080/00102202.2019.1680654) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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https://doi.org/10.1080/00102202.2019.1680654

Abstract

This paper presents an identification method for types of fuel such as biomass by combining flame spectroscopic monitoring and tree model algorithms. The features of the flame spectra are extracted, including the spectral intensity of flame radicals [OH* (310.85 nm),CN* (390.00 nm), CH* (430.57 nm) and C2* (515.23 nm, 545.59 nm)], flame radiation intensity and flame radiation energy (integration of spectral intensity). The identification models are built using four tree model algorithms, i.e., decision tree, random forest, extremely randomized trees and gradient boost decision tree. The different type biomass and spectra features of combustion flames are composed of sample pairs to train identification models. Experiments are carried out on a laboratory-scale biomass-air combustion test rig. Four different biomass fuels, including corncob, willow, peanut shell and wheat straw are burnt. The results demonstrate that the identification models proposed is capable of identifying types of biomass fuels correctly with the average identification success rate of 98% in ten trials.

Item Type: Article
DOI/Identification number: 10.1080/00102202.2019.1680654
Uncontrolled keywords: Fuel identification, Flame spectroscopy, Flame radicals, Tree model algorithm, Biomass
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Gang Lu
Date Deposited: 13 Oct 2019 22:19 UTC
Last Modified: 21 Feb 2020 04:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77399 (The current URI for this page, for reference purposes)
Lu, Gang: https://orcid.org/0000-0002-9093-6448
Yan, Yong: https://orcid.org/0000-0001-7135-5456
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