A New Image Evaluation Method for Disperse Multiphase Processes Using Synthetic Training Data

Abstract

A new algorithm for the detection and evaluation of spherical particle objects (called SPODE) has been developed. It can be used to automatically analyze images of disperse flows (e.g., bubbles, droplets, sprays) obtained with a shadow graphic inline probe. The algorithm contains a convolutional neural network (CNN) trained with synthetically generated particles. The use of synthetic particles has the advantage that the exact size and position of the particles are known, and therefore the CNN can be validated. The synthetic images are generated using a physical model of light transport in the particle-loaded fluid (i.e., ray tracing). The algorithm was applied to real data of droplets in a pump-mixer and bubbles in an aerated stirred tank. Particles were detected, and their size distributions were determined. The results clearly show that the algorithm can automatically and reliably analyze images of disperse multiphase flow.