Electrical Impedance Spectroscopy

Machine Learning in Crystallization Processes

Autor/innen

  • Nicholas Karsch Ruhruniversität Bochum

DOI:

https://doi.org/10.17560/atp.v63i08.2556

Schlagworte:

Electrical impedance spectroscopy, crystallization, supervised learning

Abstract

The electrical impedance measurement of a suspension is a valid method to monitor crystallization processes. As 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.

Veröffentlicht

21.09.2021

Ausgabe

Rubrik

Hauptbeitrag / Peer-Review