Title :
Prediction of water chemical properties in the cycle of a coal power plant using artificial neural networks
Author :
Saez, Doris ; Sanz-Bobi, Miguel A. ; Cipriano, Aldo
Author_Institution :
Dept. de Ingenieria Electr., Pontificia Univ. Catolica de Chile, Santiago, Chile
Abstract :
Describes a systematic methodology based on artificial neural networks for model identification and its application to the prediction of water chemical properties under normal operation conditions in a power plant. The model obtained allows detection of incipient anomalies by comparison between the real and predicted values
Keywords :
autoregressive moving average processes; chemical analysis; identification; modelling; neural nets; power engineering computing; power plants; steam power stations; coal power plant; incipient anomalies; model identification; normal operation conditions; predicted values; real values; water chemical properties; Artificial neural networks; Chemicals; Equations; Fault detection; Input variables; Neural networks; Power generation; Power system modeling; Predictive models; Water;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687163
