Deep Learning-based Sensor for Crystal Size and Contour Characterization
Document Type
Conference Proceeding
Publication Date
1-1-2019
Abstract
In the field of crystallization, practitioners do not have access to sensors that can make real-Time, accurate measurements of crystal size in high-density slurries. Several paths have been explored to solve this problem but most of them have come up short when applied to industrial settings. One path that has shown some promise is video microscopy and image analysis. In this approach, a probe is inserted into the crystal slurry and a microscope captures images. The images are sent to an imaging software, where the individual crystals are segmented using variations of a two-step algorithm: morphological treatment followed by particle segmentation. This approach works well when the particles are adequately separated but breaks down in dense slurries. Particles in dense slurries have their shapes distorted by attrition and experience significant overlap due to surface forces. This obscures the intra-particle boundaries and complicates the task of segmenting crystal particles. Detecting particles in such an environment requires a sensor that is robust to large variations in particle size, shape and visibility.
Publication Source (Journal or Book title)
Industry 4.0 Topical Conference 2019 - Topical Conference at the 2019 AIChE Spring Meeting and 15th Global Congress on Process Safety
First Page
140
Recommended Citation
Manee, V., Zhu, W., & Romagnoli, J. (2019). Deep Learning-based Sensor for Crystal Size and Contour Characterization. Industry 4.0 Topical Conference 2019 - Topical Conference at the 2019 AIChE Spring Meeting and 15th Global Congress on Process Safety, 140. Retrieved from https://repository.lsu.edu/chem_engineering_pubs/504