Estimation of State Variables in Semiautogenous Mills by Means of a Neural Moving Horizon State Estimator

Carvajal, Karina; Gonzalo Acuña

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Estimation of State Variables in Semiautogenous Mills by Means of a Neural Moving Horizon State Estimator

Artículo de Revista

URI persistente:: https://hdl.handle.net/20.500.12740/15730
Autor:: Carvajal, Karina - Gonzalo Acuña
Fecha de publicación:: 2007
Unidad:: Informática
Datos de publicación:: International Symposium on Neural Networks, 2007. Part of the Lecture Notes in Computer Science book series (LNCS, volume 4491)
Enlace:: http://link.springer.com/chapter/10.1007%2F978-3-540-72383-7_146
Palabras Claves:: ROOT MEAN SQUARE
RECURRENT NEURAL NETWORK
RESIDUAL STANDARD DEVIATION
SOFTWARE SENSOR
FILLING LEVEL
Base de datos:: Scopus

Descripción:

A method of moving horizon state estimation (MHSE) including a recurrent neural network as the dynamic model is used as an estimator of the filling level of the mill for a semiautogenous ore grinding process. The results are compared to those of a simple neural network acting as an estimator. They show the advantages of the Neural-MHSE, especially concerning robustness under large perturbations of the state variables (index of agreement > 0.9), which would favor its application to industrial scale processes.

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