Insight into Microdispersion Flows with a Novel Video Deep Learning Method

Abstract

Microdispersion technology is an important part of the flow chemistry method in creating tiny bubbles or droplets for enhancing mass/heat transfer performance, in which the dispersed flow condition plays a central role. Currently, most microdispersion flow characterization works are conducted by manual analysis on microscopic images, suffering from long analysis time and limited accessible information. Herein, taking the advantage of convolutional neural networks, TrackFit is proposed as an efficient video deep learning method for a precise, fast, and comprehensive investigation of microdispersion states. TrackFit is capable of recognizing bubble instances, tracking them in the microscope sight, and unprecedentedly inferring bubble diameter, location, velocity, as well as sphericity in an intelligent way. TrackFit achieves small measurement errors of diameter (8 μm) and velocity (0.08 mm s−1) on the test dataset after deepening the neural network backbone and adopting the data augmentation technique. When applied to inexperienced data, the TrackFit output relationship of bubble diameter to flow rate is consistent with previous manual results, but with a beyond‐human efficiency (≈500 times higher). TrackFit not only works as a core method of an intelligent microdispersed fluid characterization system, but also suggests a promising potential for real‐time microdispersion process analysis in the future.