HEAD OF THE GROUP

Prof. Dr.-Ing. Kai Sundmacher
Prof. Dr.-Ing. Kai Sundmacher
Phone: +49 391 6110-351
Fax: +49 391 6110-353
Room: N. 309
Links: Publications

Team Leaders

Dr. Andreas Voigt (OvGU)
Dr. Andreas Voigt (OvGU)
Phone:+49 391 675-1435
Email:voigt@...

Researchers

M. Sc. Viktoria Wiedmeyer
M. Sc. Viktoria Wiedmeyer
Phone: +49 391 67 54636
Room: G25 - R317
Dr.-Ing. Holger Eisenschmidt
Phone: +49 391 6110-368
Room: S3.07

Chemical Production Systems

Multidimensional Crystallization Processes

Over 60% of all chemicals produced worldwide are solid products. Many of these solid products are formulated as crystals in the final stage or somewhere in between the initial form and the final product. Therefore, crystallization is an essential formulation step in numerous applications from food products, pharmaceuticals up to catalytic materials. Many important properties of crystalline products depend on the size and in particular on the shape of the crystals. As such, the control of both, crystal size and shape is an important task in designing crystallization processes. 

We focus our current attention in this research area on ways to control the crystal size and shape evolution in batch [3] and continuous crystallizers [8] with liquid solutions. Crystallizer models including the relevant kinetics are identified and parameterized, where the dominant phenomena are convective spatial transport, growth and aggregation. The evolution of the crystal size and shape distribution is tracked by means of flow-through microscopy [1] and image-based shape estimation [2] (Figure, left). This information is then in turn exploited to obtain the crystallization kinetics that are governing the crystallization process [2,3,7].

An aggregation dominated fluidized bed crystallizer and a growth dominated helically coiled flow tube crystallizer [8,9] are designed and investigated in experiments and in simulations. Fluidized bed crystallizers are state-of-the-art industrial crystallizers. Tubular crystallizers are advantageous when narrow residence times and, hence, narrow crystal size and shape distributions are desired. Experiments and flow field simulations [7] serve to parameterize a coupled population balance equation system. This equation system allows predicting the dynamic evolution of the crystal size and shape distribution.

Crystal agglomeration is a major phenomenon of crystal size enlargement. Our research concentrates on the understanding and modeling of this phenomenon. In order to describe the kinetics of the agglomeration process, we apply an inverse problem approach [5]. A size-dependent agglomeration kernel is determined purely based on a time series of measurements of the crystal size distribution. Insight into the relative orientation of primary crystals in aggregates is gained by means of µCT measurements [6].

Fig.: Left: Compact (top) and needle (bottom) shapes developed by cooling crystallization during one exemplary process with potassium dihydrogen phosphate. Middle: Glass helically coiled flow tube (HCT) crystallizer. Right: Measured crystal size xversus crystal residence time τ(color) with approximation line for τ(x) (black) for potash alum grown in the HCT crystallizer [8]. Zoom Image

Fig.: Left: Compact (top) and needle (bottom) shapes developed by cooling crystallization during one exemplary process with potassium dihydrogen phosphate. Middle: Glass helically coiled flow tube (HCT) crystallizer. Right: Measured crystal size xversus crystal residence time τ(color) with approximation line for τ(x) (black) for potash alum grown in the HCT crystallizer [8].

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Selected Publications

[1] Borchert, C. and Sundmacher, K. (2011). Crystal aggregation in a flow tube: Image-based observation. Chemical Engineering Technology34, 545-556.

[2] Borchert, C., Temmel, E., Eisenschmidt, H., Lorenz, H., Seidel-Morgenstern, A. and Sundmacher, K. (2015). Image-based in situ identification of face specific crystal growth rates from crystal populations. Crystal Growth & Design14, 952-971.

[3] Eisenschmidt, H., Voigt, A. and Sundmacher, K. (2015). Face-specific growth and dissolution kinetics of potassium dihydrogen phosphate crystals from batch crystallization experiments. Crystal Growth & Design15, 219-227.

[4] Eisenschmidt, H., Bajcinca, N. and Sundmacher, K. (2016). Optimal control of crystal shapes in batch crystallization experiments by growth-dissolution cycles. Crystal Growth & Design16, 3297-3306. 

[5] Eisenschmidt, H., Soumaya, M., Bajcinca, N., Le Borne, S. and Sundmacher, K.(2017). Estimation of aggregation kernels based on Laurent polynomial approximation. Computers & Chemical Engineering103, 210-217.

[6] Kovačević, T., Wiedmeyer, V., Schock, J., Voigt, A., Pfeiffer, F., Sundmacher, K. and Briesen, H.(2017). Disorientation angle distribution of primary particles in potash alum aggregates. Journal of Crystal Growth467, 93-106.

[7] Temmel, E., Eisenschmidt, H., Lorenz, H., Sundmacher, K. and Seidel-Morgenstern, A.(2016). A short-cut method for the quantification of crystallization kinetics. 1. Method development. Crystal Growth & Design16, 6743-6755.

[8] Wiedmeyer, V., Voigt, A. and Sundmacher, K.(2017). Crystal population growth in a continuous helically coiled flow tube crystallizer. Chemical Engineering & Technology40, 1584-1590.

[9] Wiedmeyer, V., Anker, F., Bartsch, C., Voigt, A., John, V. and Sundmacher, K.(2017). Continuous Crystallization in a Helically Coiled Flow Tube: Analysis of Flow Field, Residence Time Behavior, and Crystal Growth. Industrial & Engineering Chemistry Research56 (13), 3699-3712.

 
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