Jump label

Service navigation

Main navigation

You are here:

Sub navigation

  • Staff+

Main content

Janine Lins

Janine Lins Photo of Janine Lins

(+49)231 755-2541

(+49)231 755-2341

Consultation hours
via Email


Room G2-R3.09a

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

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)

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. Image analysis can be used to characterize the products, e.g., the particle size distribution (PSD), the morphology and the type of crystal. Consequently, image analysis is a powerful tool for process optimization and control.

In this work, a new experimental set-up is developed and implemented, so that image analysis can also be used in continuous crystallization. The recorded high quality videos can be evaluated. Especially, it is possible to identify agglomerates – bounded primary particles by solid bridges. The aim is to obtain a deeper insight in the nucleation and agglomeration kinetics by characterizing the size and shape of agglomerates and the primary particles forming the agglomerates.




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


Oral Presentations


  • 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