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Article Dans Une Revue Control Engineering Practice Année : 2015

Batch-to-batch model improvement for cooling crystallization

Résumé

Two batch-to-batch model update strategies for model-based control of batch cooling crystallization are presented. In Iterative Learning Control, a nominal process model is adjusted by a non-parametric, additive correction term which depends on the difference between the measured output and the model prediction in the previous batch. In Iterative Identification Control, the uncertain model parameters are iteratively estimated using the measured batch data. Due to the different nature of the model update, the two algorithms have complementary advantages and disadvantages which are investigated in a simulation study and through experiments performed on a pilot-scale crystallizer.
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Dates et versions

hal-01152563 , version 1 (18-05-2015)

Identifiants

Citer

Marco Forgione, Georgios Birpoutsoukis, Xavier Bombois, Ali Mesbah, Peter Daudey, et al.. Batch-to-batch model improvement for cooling crystallization. Control Engineering Practice, 2015, pp.72-82. ⟨10.1016/j.conengprac.2015.04.011⟩. ⟨hal-01152563⟩
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