Title :
Clustering-Based Performance Optimization of the Boiler–Turbine System
Author :
Kusiak, Andrew ; Song, Zhe
Author_Institution :
Dept. of Mech. & Ind. Eng., Univ. of Iowa, Iowa City, IA
fDate :
6/1/2008 12:00:00 AM
Abstract :
In this paper, two optimization models for improvement of the boiler-turbine system performance are formulated. The models are constructed using a data-mining approach. Historical process data is clustered and the discovered patterns are selected for performance improvement of the boiler-turbine system. The first model optimizes a widely used performance index, the unit heat rate. The second model minimizes the total fuel consumption while meeting the electricity demand. The strengths and weaknesses of the two models are discussed. An industrial case study illustrates the concepts presented in the paper.
Keywords :
boilers; data mining; power engineering computing; turbines; boiler-turbine system performance; clustering-based performance optimization; data-mining approach; performance index; Boiler–turbine system; Boiler--turbine system; clustering; data mining; performance optimization;
Journal_Title :
Energy Conversion, IEEE Transactions on
DOI :
10.1109/TEC.2007.914183
