On-line Fuel Tracking by Combining Principal Component Analysis and Neural Network Techniques

Xu, Lijun and Yan, Yong and Cornwell, Steve and Riley, Gerry (2005) On-line Fuel Tracking by Combining Principal Component Analysis and Neural Network Techniques. IEEE Transactions on Instrumentation and Measurement, 54 (4). pp. 1640-1645. ISSN 0018-9456. (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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This paper presents a novel approach to the online tracking of pulverized fuel during combustion. A specially designed flame detector containing three photodiodes is used to derive multiple signals covering a wide spectrum of flame radiation from the infrared to ultraviolet regions through the visible band. Various flame features are extracted from the time and frequency domains. A back-propagation neural network is deployed to map the flame features to an individual type of fuel. The neural network has incorporated principal component analysis to reduce the complexity of the network and hence its training time. Experimental tests were conducted on a 0.5 MWth combustion test facility using eight different types of coal. Results obtained demonstrate that the approach is effective for the online identification of the type of fuel being fired under steady combustion conditions, and the average success rate is 93.4%.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Yiqing Liang
Date Deposited: 18 Nov 2008 14:39
Last Modified: 30 Jun 2014 11:16
Resource URI: https://kar.kent.ac.uk/id/eprint/8928 (The current URI for this page, for reference purposes)
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