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Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network
The efficiency of many chemical engineering applications depends on the surface/volume ratio of the dispersed phase. Knowledge of this particle size distribution is a key factor for better process control. The challenge of measurements acquired by optical imaging techniques is the segmentation of overlapping particles, especially in high phase fraction flows. In this work, a convolutional neural network is trained to segment droplets in images acquired by a shadowgraphic approach. The network is trained on artificial images and implemented into a droplet size algorithm. The results are compared to an OpenSource segmentation approach.
Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network
The efficiency of many chemical engineering applications depends on the surface/volume ratio of the dispersed phase. Knowledge of this particle size distribution is a key factor for better process control. The challenge of measurements acquired by optical imaging techniques is the segmentation of overlapping particles, especially in high phase fraction flows. In this work, a convolutional neural network is trained to segment droplets in images acquired by a shadowgraphic approach. The network is trained on artificial images and implemented into a droplet size algorithm. The results are compared to an OpenSource segmentation approach.
Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network
Schäfer, Jan (author) / Schmitt, Philipp (author) / Hlawitschka, Mark W. (author) / Bart, Hans‐Jörg (author)
Chemie Ingenieur Technik ; 91 ; 1688-1695
2019-11-01
8 pages
Article (Journal)
Electronic Resource
English
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