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In this work, steady-state droplet size distributions in a DN300 stirred batch vessel with a Rushton turbine impeller are investigated using an insertion probe based on the telecentric transmitted light principle. High-resolution droplet size distributions are extracted from the images using a convolutional neural network for image-analysis in order to investigate the influence of impeller speed and phase fraction (up to 50 vol.-%). In addition, Sauter mean diameters were calculated and correlated with two semi-empirical approaches, while the standard approach only accomplished 5.7% accuracy, and the correlation of Laso et al. provided a relative mean error of 4.0%. In addition, the correlated exponent in the Weber number was fitted to the experimental data of this work yielding a slightly different value than the theoretical (−0.6), which allows a better representation of the low coalescence tendency of the system, which is usually neglected in standard procedures.

applied sciences

Experimental Investigation and Modelling of the Droplet Size in a DN300 Stirred Vessel at High Disperse Phase Content Using a Telecentric Shadowgraphic Probe