Combining flame monitoring techniques and support vector machine for the online identification of coal blends

Zhou, Hao and Li, Yuan and Tang, Qi and Lu, Gang and Yan, Yong (2017) Combining flame monitoring techniques and support vector machine for the online identification of coal blends. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 18 (9). pp. 677-689. ISSN 1673-565X. E-ISSN 1862-1775. (doi:https://doi.org/10.1631/jzus.A1600454) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

The combustion behavior of two single coals and three coal blends in a 300 kW coal-fired furnace under variable operating conditions was monitored by a flame monitoring system based on image processing and spectral analysis. A similarity coefficient was defined to analyze the similarity of combustion behavior between two different coal types. A total of 20 flame features, extracted by the flame monitoring system, were ranked by weights of their importance estimated using ReliefF, a feature selection algorithm. The mean of the infrared signal was found to have by far the highest importance weight among the flame features. Support vector machine (SVM) was used to identify the coal types. The number of flame features used to build the SVM model was reduced from 20 to 12 by combining the methods of ReliefF and SVM, and computational precision was guaranteed simultaneously. A threshold was found for the relationship between the error rate and similarity coefficient, which were positively correlated. The success rate decreased with increasing similarity coefficient. The results obtained demonstrate that the system can achieve the online identification of coal blends in industry.

Item Type: Article
Uncontrolled keywords: Coal blends; Flame monitoring; Online identification; RelifF; Support vector machine (SVM); Similarity
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Gang Lu
Date Deposited: 18 Sep 2017 22:45 UTC
Last Modified: 08 Sep 2018 23:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63473 (The current URI for this page, for reference purposes)
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