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
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;
Journal_Title :
Industrial Informatics, IEEE Transactions on
DOI :
10.1109/TII.2006.873598
