Batch-to-batch model improvement for cooling crystallization

Forgione, Marco;Birpoutsoukis, Georgios;Bombois, Xavier;Mesbah, Ali;Van den Hof, Paul M.J.;et.al.
(2015) Control Engineering Practice — Vol. 41, p. 72-82 (2015)

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Authors
  • Forgione, MarcoLaboratoire Ampère UMR CNRS 5005, Ecole Centrale de Lyon, Ecully, France
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  • Birpoutsoukis, GeorgiosUCLouvain
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  • Bombois, XavierLaboratoire Ampère UMR CNRS 5005, Ecole Centrale de Lyon, Ecully, France
    Author
  • Mesbah, AliDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, USA
    Author
  • Van den Hof, Paul M.J.Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Abstract
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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Forgione, M., Birpoutsoukis, G., Bombois, X., Mesbah, A., Daudey, P. J., & Van den Hof, P. M. J. (2015). Batch-to-batch model improvement for cooling crystallization. Control Engineering Practice, 41, 72-82. https://doi.org/10.1016/j.conengprac.2015.04.011 (Original work published 2015)