DocumentCode
1080907
Title
Combustion efficiency optimization and virtual testing: a data-mining approach
Author
Kusiak, Andrew ; Song, Zhe
Author_Institution
Intelligent Syst. Lab., Iowa Univ., Iowa City, IA
Volume
2
Issue
3
fYear
2006
Firstpage
176
Lastpage
184
Abstract
In this paper, a data-mining approach is applied to optimize combustion efficiency of a coal-fired boiler. The combustion process is complex, nonlinear, and nonstationary. A virtual testing procedure is developed to validate the results produced by the optimization methods. The developed procedure quantifies improvements in the combustion efficiency without performing live testing, which is expensive and time consuming. The ideas introduced in this paper are illustrated with an industrial case study
Keywords
boilers; combustion synthesis; control engineering computing; data mining; machine testing; coal-fired boiler; combustion efficiency optimization; data mining; process control; virtual testing; Analytical models; Boilers; Combustion; Data mining; Evolutionary computation; Fuel processing industries; Neural networks; Optimization methods; Pressure control; Testing; Combustion efficiency; data mining; nonstationary process; process control; temporal data mining;
fLanguage
English
Journal_Title
Industrial Informatics, IEEE Transactions on
Publisher
ieee
ISSN
1551-3203
Type
jour
DOI
10.1109/TII.2006.873598
Filename
1668076
Link To Document