Forgione, Marco
[Laboratoire Ampère UMR CNRS 5005, Ecole Centrale de Lyon, Ecully, France]
Birpoutsoukis, Georgios
[UCL]
Bombois, Xavier
[Laboratoire Ampère UMR CNRS 5005, Ecole Centrale de Lyon, Ecully, France]
Mesbah, Ali
[Department of Chemical and Biomolecular Engineering, University of California, Berkeley, USA]
Daudey, Peter J.
[Chemical Engineering Department, Delft University of Technology, The Netherlands]
Van den Hof, Paul M.J.
[Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands]
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.
| Bibliographic reference |
Forgione, Marco ; Birpoutsoukis, Georgios ; Bombois, Xavier ; Mesbah, Ali ; Daudey, Peter J. ; et. al. Batch-to-batch model improvement for cooling crystallization. In: Control Engineering Practice, Vol. 41, p. 72-82 (2015) |
| Permanent URL |
http://hdl.handle.net/2078.1/196569 |
