Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network1. Issue 11 (2nd September 2019)

• Record Type: Journal Article
• Authors: Schäfer, Jan; Schmitt, Philipp; Hlawitschka, Mark W.; Bart, Hans‐Jörg
• Is Part Of: Chemie Ingenieur Technik. Volume 91:Issue 11(2019)
• Page Start: 1688
• Page End: 1695
• Publication Date: 2019-09-02
• Subjects: Convolutional neural networks -- Image analysis -- Multiphase flows -- Particle size distributions -- Shadowgraphic imaging; Chemical engineering -- Patents -- Periodicals; Chemical engineering -- Periodicals; Chemical industry -- Periodicals; Chemistry, Technical -- Periodicals; 660.05
• DOI: 10.1002/cite.201900099
• Languages: English
• ISSNs: 0009-286X

Abstract

Abstract: 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. Abstract : Knowledge of the particle size distribution is essential for understanding multiphase chemical engineering processes. Main problem in optical measurements is the segmentation of overlapping particles. A solution to this is a convolutional neural network trained with artificial images and used for particle segmentation at high holdups.