Skip to main content

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

Zhou, Hao, Li, Yuan, Tang, Qi, Lu, Gang, 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:10.1631/jzus.A1600454) (KAR id:63473)

PDF Author's Accepted Manuscript
Language: English
Download (453kB) Preview
[img]
Preview
PDF Publisher pdf
Language: English

Restricted to Repository staff only
Contact us about this Publication
[img]
Official URL
http://dx.doi.org/10.1631/jzus.A1600454

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
DOI/Identification number: 10.1631/jzus.A1600454
Uncontrolled keywords: Coal blends; Flame monitoring; Online identification; RelifF; Support vector machine (SVM); Similarity
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
Divisions: 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: 21 Feb 2020 04:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63473 (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
  • Depositors only (login required):

Downloads

Downloads per month over past year