||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, i.e. the solution is loaded into the vessel at the beginning of a batch and the solid crystalline product is removed at the end. The batch time is of the order of few hours. Multiple batches are performed in order to fulfill the production demand.
In many cases, stringent requirements on the quality of the crystals have to be met.
The control of a number of variables throughout the batch influences the quality of the crystals 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 challenges and opportunities for identification and control. From one hand, batch process systems are operated over a wide dynamical range.
For this reason, strategies based on linearization around a single operating point may lead to poor results. In some cases, first-principles models describing the full nonlinear dynamic behavior are available. However, these models are often rather inaccurate or unsuitable for control purposes. On the other hand, the repetitive nature of batch processing allows for the use of the information from previous batches, for instance by adpoting Iterative Learning Control (ILC). Furthermore, due to the fairly slow dynamics of crystallization, advanced on-line estimation and optimization strategies are computationally feasible.
The interested student will develop algorithms for identification and control of batch crystallization systems. Possible areas of research are ILC, Recursive Estimation, Batch-to-Batch Optimization, Design of Experiments for nonlinear systems.
An internship with DSM/Geleen or Albemarle/USA can also be arranged within this project.