Elman neural network in the soft sensor modelling for the unburned carbon in fly ash from utility boilers

Author

Jin, Xiu-zhang ; Li, Lin

Author_Institution

Dept. of Autom., North China Electr. Power Univ., Baoding, China

Volume

2

fYear

2012

fDate

15-17 July 2012

Firstpage

444

Lastpage

447

Abstract

Unburned carbon in fly ash is an important parameter affecting combustion efficiency of coal-fired boiler. In view of the deficiency of feed-forward neural network soft sensor modeling on unburned carbon in fly ash from the power plant, in this paper, we make use of recurrent Elman neural network to realize dynamic modeling of the boiler combustion process. A set of operating data from a 300MW power plant boiler is used here to train and validate the soft sensor model. Then this is compared with the results of BP network. The results after comparing show that Elman network can better achieve soft sensor modeling for unburned carbon in fly ash.

Keywords

backpropagation; boilers; feedforward neural nets; fly ash; power engineering computing; recurrent neural nets; sensors; steam power stations; BP network; boiler combustion process; coal-fIred boiler; feed-forward neural network soft sensor modeling; fly ash; power plant boiler; recurrent Elman neural network; unburned carbon; utility boilers; Abstracts; DH-HEMTs; Fly ash; Powders; Elman dynamic neural network; Soft sensor; Unburned carbon in fly ash;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6358964

Filename

6358964