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

作者：Maass Sebastian^*; Rojahn Juergen; Haensch Ronny; Kraume Matthias

来源：COMPUTERS %26 CHEMICAL ENGINEERING, 2012, 45: 27-37.

DOI：10.1016/j.compchemeng.2012.05.014

• Particle size distribution
• Image analysis
• Online monitoring
• Sauter mean diameter
• Dispersion
• Automatic particle recognition

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. %26lt;br%26gt;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.

• 出版日期2012-10-12