CATEGORY: ONLINE MONITORING
Computers & Chemical Engineering, Volume 45,
12 October 2012, Pages 27–37
Automated
drop detection using image analysis for online particle size monitoring in
multiphase systems
Sebastian Maaß a, Jürgen Rojahn a, b, Ronny Hänsch b, Matthias
Kraume a
a Technische Universität Berlin, Straße des 17. Juni 135, Sekr. MA 5-7, Chair
of Chemical and Process Engineering, 10623 Berlin, Germany
b Technische Universität Berlin, Franklinstraße 28/29, Department of Computer
Vision and Remote Sensing, 10587 Berlin, Germany
Abstract
While
image analysis has become a powerful tool for work with particulate systems in
chemical engineering, a major challenge remains. Image analysis entails excessive manual work
load. Manual quantification also
generates bias by different observers. Consequently,
full automation of such systems is desirable.
Authors describe implementation of a MATLAB® based image recognition
algorithm 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.
Full Text Source (Subscription or Fee): http://www.sciencedirect.com/science/article/pii/S0098135412001603
No comments:
Post a Comment