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 (R) 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. (C) 2012 Elsevier Ltd. All rights reserved.
image analysis particle size distribution online monitoring sauter mean diameter dispersion automatic particle recognition