DocumentCode :
3092230
Title :
Coal quality analysis techniques using artificial neural network techniques
Author :
Salehfar, H.
Author_Institution :
Dept. of Electr. Eng., North Dakota Univ., Grand Forks, ND, USA
Volume :
2
fYear :
1999
fDate :
1999
Abstract :
Summary form only given. The paper describes the design and development of an online hybrid intelligent system from which coal quality can be determined interactively as an aid to steam power plant operators. The hybrid model combines neural networks with expert systems. The neural net module predicts the online performance of a boiler and its health for various coal blends and load conditions. The expert system deciphers the neural network model results and then diagnoses the situation. If coal quality conditions change, then the expert system alerts the operator and reports any impending consequences and performance degradation. The system then suggests one or more corrective actions to the steam power plant operator
Keywords :
boilers; coal; combustion; expert systems; neural nets; power station control; steam power stations; artificial neural network; boiler health; boiler performance; coal quality analysis techniques; corrective actions; expert systems; online hybrid intelligent system; performance degradation; steam power plant; Artificial neural networks; Ash; Availability; Boilers; Diagnostic expert systems; Hybrid intelligent systems; Impurities; Neodymium; Neural networks; Power generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering Society Summer Meeting, 1999. IEEE
Conference_Location :
Edmonton, Alta.
Print_ISBN :
0-7803-5569-5
Type :
conf
DOI :
10.1109/PESS.1999.787502
Filename :
787502
Link To Document :
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