Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10267
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Main Title: Characterization of fast-growing foams in bottling processes by endoscopic imaging and convolutional neural networks
Author(s): Panckow, Robert P.
McHardy, Christopher
Rudolph, Alexander
Muthig, Michael
Kostova, Jordanka
Wegener, Mirco
Rauh, Cornelia
Type: Article
Language Code: en
Abstract: Regardless of whether the occurrence of foams in industrial processes is desirable or not, the knowledge about the characteristics of their formation and morphology is crucial. This study addresses the measuring of characteristics in foam and the trailing bubbly liquid that result from air bubble entrainment by a plunging jet in the environment of industry-like bottling process es of non-carbonated beverages. Typically encountered during the bottling of fruit juices, this process configuration is characterized by very fast filling speeds with high dynamic system parameter changes. Especially in multiphase systems with a sensitive disperse phase like gas bubbles, the task of its measurement turns out to be difficult. The aim of the study is to develop and employ an image processing capability in real geometries under realistic industrial conditions, e.g. as opposed to a narrow measurement chamber. Therefore, a typically sized test bottle was only slightly modified to adapt an endoscopic measurement technique and to acquire image data in a minimally invasive way. Two convolutional neural networks (CNNs) were employed to analyze irregular non-overlapping bubbles and circular overlapping bubbles. CNNs provide a robust object recognition for varying image qualities and therefore can cover a broad range of process conditions at the cost of a time-consuming training process. The obtained single bubble and population measurements allow approximation, correlation and interpretation of the bubble size and shape distributions within the foam and in the bubbly liquid. The classification of the measured foam morphologies and the influence of operating conditions are presented. The applicability to the described test case as an industrial multiphase process reveals high potential for a huge field of operations for particle size and shape measurement by the introduced method.
URI: https://depositonce.tu-berlin.de/handle/11303/11383
http://dx.doi.org/10.14279/depositonce-10267
Issue Date: 27-May-2020
Date Available: 15-Jun-2020
DDC Class: 600 Technik, Technologie
Subject(s): plunging jet
filling of beverages
process monitoring
image analysis
particle measurement
bubble size distribution
License: https://creativecommons.org/licenses/by-nc-nd/4.0/
Journal Title: Journal of Food Engineering
Publisher: Elsevier
Publisher Place: Amsterdam [u.a.]
Article Number: 110151
Publisher DOI: 10.1016/j.jfoodeng.2020.110151
EISSN: 1873-5770
ISSN: 0260-8774
Appears in Collections:FG Lebensmittelbiotechnologie und -prozesstechnik » Publications
FG Verfahrenstechnik » Publications

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