NDLI: Research on methods of forecasting unburned carbon content in the fly ash from coal-fired power plant

Content Provider	IEEE Xplore Digital Library
Author	Ya-qing Zhu Feng-ping Pan Shi-he Chen Xiao-wei Peng Yan-fen Liao Xiao-qian Ma
Copyright Year	2013
Description	Author affiliation: Electr. Power Res. Inst., Guangdong Power Grid Corp., Guangzhou, China (Ya-qing Zhu; Feng-ping Pan; Shi-he Chen) \|\| Sch. of Electr. Power, South China Univ. of Technol., Guangzhou, China (Xiao-wei Peng; Yan-fen Liao; Xiao-qian Ma)
Abstract	This paper proposed a new algorithm technologies for forecasting the unburned carbon content in the fly ash from coal-fired utility boilers by combination improved artificial bee colony algorithm with support vector machine ABC-SVM, for comparative purpose, back propagation neural network (BP) was also presented, comparing the pros and cons of both in the field of the predictive ability. Applied to a 1000MW coal-fired utility boiler, the ABC-SVM model which had been trained forecasted the unburned carbon in the fly ash in the test samples set, and got the mean square root error and the mean relative error of 1.25%, and 1.79%, respectively, which are 33.75% and 46.63% of BP neural network. These results show that ABC-SVM method is more accurate than the BP neural network, and can satisfy the forecasting demand well.
Sponsorship	IEEE Hong Kong Sect., IE/PE/IA/PEL Joint Chapt.
Starting Page	1
Ending Page	4
File Size	247470
Page Count	4
File Format	PDF
ISBN	9781479925223
DOI	10.1109/APPEEC.2013.6837281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fly ash Unburned carbon content Furnaces Coal Predictive models Back propagation Boilers Energy efficiency Support vector machine Utility boiler Carbon
Content Type	Text
Resource Type	Article

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