Funding reference number: 13N15851 Funding reference 01IS21065D SOPAT will provide relevant information for process control with fast and process-related data evaluation based on image analysis of digital images

Funding reference 01IS21065D The aim of the project is to develop and validate methods for the synthesis of training data for Deep Learning-based particle measurement technology, exemplified by

— Authors: Maaß, S., Metz, F., Rehm, T. und Kraume, M.— Chemical Engineering Journal, 162 (2): 792 – 801 (link) Although investigations in the field of stirred liquid/liquid dispersions have

— Authors: Maaß, S., Wollny, S., Voigt, A. und Kraume, M.— Experiments in Fluids, 50(2): 259-269 (link) An online measurement technique for drop size distribution in stirred tank reactors

— Authors: Maaß, S., Rehm, T. und Kraume, M.— Chemical Engineering Journal, 168(2): 827–838 (link) Based on the successful predictions of transient drop sizes in the first part of

— Authors: Maaß, S., Buscher, S., Hermann, S. und Kraume, M. — Biotechnology Journal, 6(8): 979-992  Understanding of particle strain and drop breakage is relevant for various technical

— Authors: Maaß, S. und Kraume, M.— Chemical Engineering Science, 70: 146-164 (link) An evaluation of several breakage rates from the literature based on single drop experiments was carried

— Authors: Maaß, S., Paul, N. und Kraume, M. — Chemical Engineering Science, 76: 140-153 (link) The effect of the dispersed phase fraction on the evolving drop size distribution