Keywords
Instituto de Investigación Tecnológica (IIT), Water , Chemicals , Power generation , Input variables , Predictive models , Power system modeling , Neural networks , Artificial neural networks , Fault detection , Equations
Prediction of water chemical properties in the cycle of a coal power plant using artificial neural networks
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.
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.
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.
Información detallada
Materias, derechos, colecciones e identificadores