Image Processing for the Estimation of Drop Distribution in Agitated Liquid-Liquid Dispersion

Abstract

Image processing of particulate phases can provide an important assistance for the estimation of particle size and shape distributions in multiphase systems. The knowledge of these distributions is of major importance in the modeling of agitated liquid-liquid systems either for hydrodynamic and mass transfer (with or without chemical reaction) simulation. Often, obtaining these distributions implies visual/manual techniques for identifying, counting and measuring the particles. This implies high costs, intensive labor, weariness build-up and consequent high error rates. A largely automated computational approach presents a great potential for better performance. In this paper we describe our technique in image processing for shape discrimination and size classification for liquid drops in monochromatic digitized frames. We empirically evaluate our approach on examples of images.