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Article Dans Une Revue Control Engineering Practice Année : 2005

Neural network-based software sensor: Data set design and application to a continuous pulp digester

Résumé

A neural network based strategy for detection of feedstock variations in a continuous pulp digester is presented. A feedforward two-layer perceptron network is trained to detect and isolate unmeasured variations in the feedstock. Training and validation data sets are generated using a rigorous first principles model. The most important issue discussed here is the design of the data set required to train the artificial neural network. Efficiency and limitation of such an approach are demonstrated using simulations.
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Dates et versions

hal-00351646 , version 1 (09-01-2009)
hal-00351646 , version 2 (21-01-2009)

Identifiants

Citer

Pascal Dufour, Sharad Bhartiya, Prasad S. Dhurjati, Francis J. Doyle Iii. Neural network-based software sensor: Data set design and application to a continuous pulp digester. Control Engineering Practice, 2005, 13 (2), pp.135-143. ⟨10.1016/j.conengprac.2004.02.013⟩. ⟨hal-00351646v2⟩

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