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

Authors:
Karina Carvajal;Gonzalo Acuña
Affiliations:
Facultad de Ingeniería, Universidad de Atacama., Copayapu 485, Copiapó, Chile;Facultad de Ingeniería, Universidad de Santiago de Chile, USACH, Avda. Ecuador 3659, Santiago, Chile
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

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.