Soft-sensing modeling of the carbon content in fly ash based on information fusion for thermal power plant

Author

Zhang, Guiwei ; Lin Bao

Author_Institution

Coll. of Econ. & Manage., Hebei Univ. of Eng., Handan, China

fYear

2009

fDate

9-12 Aug. 2009

Firstpage

3860

Lastpage

3865

Abstract

A new algorithm, which is based on information fusion and soft-sensing technique to modeling of the carbon content in fly ash for thermal power plant, is proposed. Firstly, adaptive weighted fusion and least square support vector machine (LSSVM) algorithms are designed. Secondly, for three nonlinear testing functions, BP neural network, LSSVM and LSSVM based on adaptive weighted fusion algorithms are used to modeling respectively. Finally, the algorithms of the LSSVM based on adaptive weighted fusion to modeling of the carbon content in fly ash for power plant are given.

Keywords

ash; backpropagation; carbon; neural nets; power engineering computing; sensor fusion; support vector machines; thermal power stations; BP neural network; C; adaptive weighted fusion algorithm; fly ash carbon content; information fusion; least square support vector machine algorithm; nonlinear testing functions; soft-sensing modeling; thermal power plant; Computational modeling; Computer networks; Computer simulation; Fly ash; Mathematical model; Middleware; Power generation; Protocols; Real time systems; Satellites; LSSVM; adaptive weighted fusion; carbon content in fly ash; information fusion; soft-sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2009. ICMA 2009. International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4244-2692-8

Electronic_ISBN

978-1-4244-2693-5

Type

conf

DOI

10.1109/ICMA.2009.5244905

Filename

5244905