Computers and Chemical Engineering 45 (2012) 27–37 Contents lists available at SciVerse ScienceDirect Computers and Chemical Engineering jo u rn al hom epa ge : www.elsevier.com/locate/compchemeng 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 a r t i c l e i n f o Article history: Received 2 November 2011 Received in revised form 24 March 2012 Accepted 24 May 2012 Available online 2 June 2012 Keywords: Particle size distribution Image analysis Online monitoring Sauter mean diameter Dispersion Automatic particle recognition a b s t r a c t 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 appli- cations. 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 recog- nition 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 algo- rithm. 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. © 2012 Elsevier Ltd. All rights reserved. 1. Introduction The competitive pressure in the chemical industry makes it nec- essary to take measures that enable processes to be drastically improved in order to remain competitive also in the future (Ruscitti et al., 2008). Product quality control is more complex in particu- late than in conventional chemical processes. The key properties of the product are often related to the particle size distribution (PSD) which is influenced by the operating conditions and the history of the process (Zeaiter, Romagnoli, & Gomes, 2006). Disturbances in operating conditions may irreversibly change the quality of the product. Quantitative real-time measuring is needed to enable feedback control. Monitoring and control of such processes have evoked interest in the use of image-based approaches to esti- mate product quality in real time and in situ (Zhou, Srinivasan, & Lakshminarayanan, 2009). During the last decades extensive research has been performed to establish and improve technologies which measure particle properties, such as size, shape, composition, and velocity. Concern- ing the interpretation of particle size distributions using different ∗ Corresponding author. Tel.: +49 30 314 78609. E-mail addresses: sebastian.maass@tu-berlin.de, sebastian.maass@sopatec.com (S. Maaß). physical principles there is still a considerable lack of understand- ing (Leschonski, 1986). Various authors found unsatisfying results, analyzing spherical drops in different liquid/liquid systems, using laser optical mea- surement techniques based on back scattering (Boxall, Koh, Sloan, Sum, & Wu, 2010; Greaves et al., 2008; Honkanen, Eloranta, & Saarenrinne, 2010; Maaß, Wollny, Voigt, & Kraume, 2011). These authors are questioning the reliability of these online probes in gen- eral and reaffirming the need to use image analysis (IA) instead as the particle surface unpredictably influences the signals. A further limitation, according to different authors (Martínez- Bazán, Montanés, & Lasheras, 1999; Niknafs, Spyropoulos, & Norton, 2011; Pacek, Moore, Nienow, & Calabrese, 1994), is the use of external physical sampling. This never can guarantee that the particle size does not change during measuring. Even for sampling times less than a second, significant measurement errors can occur. In order to get reliable drop size distribution (DSD) measurements the technique needs to be chosen carefully. This work is focused on a MATLAB ® based image recognition algorithm, which is able to automatically measure particles robust in different multiphase systems. The paper is structured as follows. The use of image analysis for sizing fluid particles is shortly reviewed in Section 2 followed by an introduction of the used experimental set-ups in Section 3. Image pre-processing and image analysis size measurements are given in Sections 4 and 5. The results achieved by that method are compared with manual results in Section 6. 0098-1354/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compchemeng.2012.05.014