Electrical impedance spectroscopy
Machine learning in crystallization processes
The electrical impedance measurement of a suspension is a valid method to monitor crystallization processes. Since it allows measurement of conductivity and permittivity it enables the characterization of non-conductive suspensions. The results obtained show that the concentration of an organic compound of interest can be determined by evaluating its electrical and thermal properties. As the analytical analysis of independent process parameters is a challenging task, a machine learning approach is investigated to extract essential parameter dependency for automated process control purposes.
| Authors | Nicholas Karsch, Stephan Westerdick, Thomas Musch, Ruhr University Bochum; Lars Kaufhold, Marc Dittmann, Merck; Malte Mallach, Jan Tebrügge, Jan Förster, Michael Vogt, Krohne |
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| Publishing Date | 20 Aug 2021 |
| Format | |
| Publisher | Vulkan-Verlag GmbH |
| Language | German |
| Pages | 9 |
| Title | Electrical impedance spectroscopy |
| Subtitle | Machine learning in crystallization processes |
| Description | The electrical impedance measurement of a suspension is a valid method to monitor crystallization processes. Since it allows measurement of conductivity and permittivity it enables the characterization of non-conductive suspensions. The results obtained show that the concentration of an organic compound of interest can be determined by evaluating its electrical and thermal properties. As the analytical analysis of independent process parameters is a challenging task, a machine learning approach is investigated to extract essential parameter dependency for automated process control purposes. |
