Automated drop detection using image analysis for online particle size monitoring in multiphase systems
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- 作者：Sebastian Maa? ; J¨¹rgen Rojahn ; Ronny H?nsch ; Matthias Kraume
- 关键词：Particle size distribution ; Image analysis ; Online monitoring ; Sauter mean diameter ; Dispersion ; Automatic particle recognition
- 刊名：Computers and Chemical Engineering
- 出版年：2012
- 出版时间：12 October, 2012
- 年：2012
- 卷：45
- 期：Complete
- 页码：27-37
- 全文大小：1912 K
文摘
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.