Advanced deep learning for dynamic emulsion stability measurement

Microscopic probes are used to visualise dispersions in fluid-fluid systems to characterize dispersion behaviors. An advanced multi-stage filtered Hough method and a deep learning based neural networking method (Faster R-CNN) has been compared for droplet detection performance on an image set. The comparison was made with respect to an independent manual analysis. A test analysis for droplet detection performance analysis was prepared. The Faster R-CNN method performed better relative to advanced filtered Hough method. The Faster R-CNN had within 12 measurement error with respect to the visual or manual detection standard in terms droplet size measurement. This standardized method was utilized to analyze Exxsol D80 oil and water emulsions for evaluating droplet stability parameters. Accurate droplet stability coefficients in the inertial sub-range was evaluated alongside dependency on dispersion phase fraction. The droplet relaxation time scales for the Exxsol D80 oil and water system has been measured and reported where possible.