Automated drop detection using image analysis for online particle size monitoring in multiphase systems

Image analysis has become a powerful tool for the work with particulate systems, occurring in chemical engineering. A major challenge is still the excessive manual work load which comes with such applications. Additionally manual quantification also generates bias by different observers, as shown in this study. Therefore a full automation of those systems is desirable. A MATLAB® based image recognition algorithm has been implemented to automatically count and measure particles in multiphase systems.A given image series is pre-filtered to minimize misleading information. The subsequent particle recognition consists of three steps: pattern recognition by correlating the pre-filtered images with search patterns, pre-selection of plausible drops and the classification of these plausible drops by examining corresponding edges individually. The software employs a normalized cross correlation procedure algorithm. The program has reached hit rates of 95% with an error quotient under 1% and a detection rate of 250 particles per minute depending on the system.

► We compared manual and computer based automated particle detection on digital images.
► We examined human bias during manual counting.
► Automated detection with R2 = 0.97.
► Computation times of the automated drop detection allow process control.