Support vector machine based online coal identification through advanced flame monitoring

Zhou, Hao and Tang, Qi and Yang, Linbin and Yan, Yong and Lu, Gang and Cen, Ke-fa (2014) Support vector machine based online coal identification through advanced flame monitoring. Fuel, 117 (B). pp. 944-951. ISSN 0016-2361. (doi:https://doi.org/10.1016/j.fuel.2013.10.041) (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://dx.doi.org/10.1016/j.fuel.2013.10.041

Abstract

This paper presents a new on-line coal identification system based on support vector machine (SVM) to achieve on-line coal identification under variable combustion conditions. Four different coals were burnt in a 0.3 MW coal combustion furnace with different coal feed rates, total air flow rates and flow rate ratios of primary air and secondary air. The flame monitoring system was installed at the exit of the burner to acquire the coal flame images and light intensity signals. Spatial and temporal flame features were extracted for coal identification. The averaged prediction accuracy is 99.1%. The mean value of the infrared signal has the most significant influence on prediction accuracy. For “unstudied” operation cases, the prediction accuracy is 94.7%.

Item Type: Article
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: Tina Thompson
Date Deposited: 25 Apr 2014 15:12 UTC
Last Modified: 16 Jul 2014 08:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/40855 (The current URI for this page, for reference purposes)
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