Prediction of water chemical properties in the cycle of a coal power plant using artificial neural networks

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.

URI

http://hdl.handle.net/11531/105475

Tipo de Actividad

Capítulos en libros

Materias/ categorías / ODS

Instituto de Investigación Tecnológica (IIT)

Palabras Clave

Water , Chemicals , Power generation , Input variables , Predictive models , Power system modeling , Neural networks , Artificial neural networks , Fault detection , Equations
Water , Chemicals , Power generation , Input variables , Predictive models , Power system modeling , Neural networks , Artificial neural networks , Fault detection , Equations

Colecciones

Artículos