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Batch-to-batch learning for process control with application to cooling crystallization.

Project members:  M. Forgione, prof. P.M.J. Van den Hof, X.J.A. Bombois
Keywords:  Model-based control, Process technology, System identification, Learning and adaptive control
Sponsored by:  ISPT
An industrial crystallizer
An industrial crystallizer
Crystallization can be defined as phase change in which a solid product is obtained from a solution, melt or a gas. In the industrial practice crystallization is utilized as a separation and purification step in sectors such as pharmaceutical, food and fine chemicals. The process is often operated in batch mode. The solution is loaded into the vessel at the beginning of a batch and the solid product is removed at the end. The batch time is of the order of few hours. Multiple batches are performed in order to fulfil the production demand.
In many cases, stringent requirements on the quality of the final product have to be met. The control of a number of variables throughout the batch infuences the quality of the final product to a large extent. For this reason, the industries in the field are interested into the introduction of advanced automation technology.
Batch operations bring both challanges and opportunities for identification and control. From one hand, batch process system are operated over a wide dynamical range. For this reason, strategies based on linearization around a single operating point lead often to poor results. In some cases, first-principles models describing the full nonlinear dynamic behavior are available. Unfortunately, these models are often either not very accurate or not suitable for control purposes.
On the other hand, the repetitive nature of batch processing allows the use of iterative techniques based on the information collected during the previous runs.

In this project we are developing identification and control strategies suitable for industrial batch processes such as crystallization. We are particularly interested in
  • Batch-to batch Identification & Optimization
  • Iterative Learning Control
  • Experiment Design
We are collaborating with several industrial partners such as DSM, Albemarle, Frieslandcampina, MSD and DotX Control Solutions.

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Last modified: 31 October 2013, 7:30 UTC
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