Detecting crystals in suspensions: convolutional neural networks vs. gravity-based approach for size distribution detection

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dc.contributor.authorNeuendorf, Laura
dc.contributor.authorHöving, Stefan
dc.contributor.authorBennemann, Lennard
dc.contributor.authorKockmann, Norbert
dc.date.accessioned2025-01-23T12:03:18Z
dc.date.available2025-01-23T12:03:18Z
dc.date.issued2023-05-09
dc.description.abstractThe majority of fine chemical and pharmaceutical processes includes some form of crystallization steps. For process optimization and control of further downstream steps, the crystal size distribution of the product is a crucial factor. To identify characteristic particle size classes from a large number of measurements, each individual probe has to be separated from the mother liquor and manually analyzed. In this contribution a deep learning-based method is presented using microscopic images as input for crystal size analysis. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. A high segmentation accuracy of the crystals was achieved with 93.02 %. To evaluate the classification performed by the presented convolutional neural network (CNN), it is tested on two sets of images, containing a previously determined particle fraction. With the classifications of the CNN, a Q3 distribution is calculated. To validate the developed approach in terms of its accuracy it is compared to two other methods as well.en
dc.identifier.urihttp://hdl.handle.net/2003/43381
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25213
dc.language.isoen
dc.relation.ispartofseriesChemie - Ingenieur - Technik; 95(7)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComputer visionen
dc.subjectContinuous cooling crystallizationen
dc.subjectConvolutional neural networksen
dc.subjectDraft tube baffle crystallizeren
dc.subjectParticle size distributionen
dc.subject.ddc660
dc.titleDetecting crystals in suspensions: convolutional neural networks vs. gravity-based approach for size distribution detectionen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationNeuendorf, L., Höving, S., Bennemann, L. and Kockmann, N. (2023), Detecting Crystals in Suspensions: Convolutional Neural Networks vs. Gravity-Based Approach for Size Distribution Detection. Chemie Ingenieur Technik, 95: 1146-1153. https://doi.org/10.1002/cite.202200235
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202200235

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