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Janine Lins

Janine Lins Photo of Janine Lins

Telephone
(+49)231 755-2339

Fax
(+49)231 755-2341

Address

Room G2-R3.09a

Contents
Born April 20th, 1994 in Bochum (Germany)
2004-2012 Graf-Engelbert Schule, Bochum (Germany)
2012-2016

B.Sc. Biochemical Engineering Studies, TU Dortmund University

Bachelor Thesis: Cost evaluation for the transesterification of racemic 1-phenylethanol in a stirred tank

2014 Semester abroad at University of Pennsylvania (USA)
2016-2018

M.Sc. Chemical Engineering Studies, TU Dortmund University

Master Thesis: Optimal shared resource allocation in uncertain networks

Since 2018

PhD at the Laboratory of Plant and Process Design, TU Dortmund University

Field of Research: Investigation of agglomeration processes during organic compound crystallization

Field of research

The demand for specific, tailor made crystalline products in the chemical, pharmaceutical and food industry is steadily increasing. Whenever high demands are placed on the product, the focus must already be on crystallization, because here the particulate properties are initially defined.

Agglomeration is a phenomenon in crystallization and in the subsequent solid liquid separation and drying which influences key characteristics of the crystals. Therefore, it is of vital importance to investigate and control agglomeration during crystallization. Nevertheless, it is still often considered as incidental phenomenon occurring alongside nucleation and growth and is not fully understood. A promising opportunity to enhance the fundamental understanding of agglomeration on the microscopic scale are imaging techniques. However, there are also some challenges related to image acquisition during the process and to image processing. In recent years, Convolutional Neural Networks (CNNs) have been increasingly used to make image processing more accurate and faster so that a step has been made towards real-time monitoring.

 

Publications

Paper

  • Janine Lins, Ute Ebeling, Kerstin Wohlgemuth
    Agglomeration Kernel Determination by Combining In-Process Image Analysis and Modeling
    Crystal Growth & Design (2022) accepted 

  • J. Lins, T. Harweg, F. Weichert, K. Wohlgemuth
    Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization
    Applied Sciences (2022) accepted

  • J. Lins, S.Heisel. K. Wohlgemuth
    Quantification of internal crystal defects using image analysis
    Powder Technology 377 (2021) 733-738

  • Wierschem, M.; Langen, A.; Lins, J.; Spitzer, R.; Skiborowski, M.
    Model validation for enzymatic reactive distillation to produce chiral compound
    Journal of Chemical Technology & Biotechnology 93 (2), (2018) 498–507

 

Oral Presentations

 

  • Janine Lins; Ute Ebeling; Hessam Ramezani; Kerstin Wohlgemuth
    On the choice of image analysis sensor for the study of phenomena during crystallization
    Jahrestreffen der ProcessNet Fachgruppe Kristallisation 2021 (digital)

  • J. Lins, C. Schwenk, T. Dahlmanns, F. Weichert, K. Wohlgemuth
    Artificial intelligence- the manifold opportunities using deep convolutional neural networks for image analysis during crystallization
    Jahrestreffen der ProcessNet Fachgruppe Kristallisation 2020 (digital)

 

  • J. Lins, F. Weichert, K. Wohlgemuth
    Particle classification during crystallization using deep convolutional neural networks
    Workshop Deep Learning- What is Deep Learning and where in the (Chemical) Process Industry can it be applied?, Dortmund (2020)

Poster Presentation

  • J.Lins, S.Heisel, S.Krause, K.Wohlgemuth
    Identification of primary particles in agglomerates
    Jahrestreffen der ProcessNet Fachgruppe Kristallisation 2019, Bamberg