On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques

Li, Xinli and Yan, Yong and Liu, Shi and Wu, Mengjiao and Lu, Gang (2015) On-line identification of biomass fuels based on flame radical imaging and application of radical basis function neural network techniques. IET Renewable Power Generation, 9 (4). pp. 323-330. ISSN 1752-1416. (doi:https://doi.org/10.1049/iet-rpg.2013.0392) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://doi.org/10.1049/iet-rpg.2013.0392

Abstract

In biomass fired power plants a range of biomass fuels are used to generate electric power. It is desirable to identify the type of biomass fuels on-line continuously in order to achieve an improved combustion efficiency, and reduced pollutant emissions. This paper presents the recent investigations into the on-line identification of biomass fuels based on the combination of flame radical imaging and radical basis function (RBF) neural network (NN) techniques. The characteristic values of flame radicals (OH*, CN*, CH* and C2*), including the intensity ratio, intensity contour, mean intensity, area and eccentricity, are computed to reconstruct two types of RBF NN, that is, accurate and probabilistic RBF networks. Experimental results obtained for three types of biomass fuels (flour, willow sawdust and palm kernel shell) firing on a laboratory-scale combustion test rig are presented to demonstrate the effectiveness of the proposed method.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Tina Thompson
Date Deposited: 07 May 2015 09:24 UTC
Last Modified: 15 Jul 2015 11:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48286 (The current URI for this page, for reference purposes)
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