Characterization of fast-growing foams in bottling processes by endoscopic imaging and convolutional neural networks

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

Photo-Optical In-Situ Measurement of Drop Size Distributions: Applications in Research and Industry : Mesure photo-optique in-situ de la distribution des taille des gouttes : applications dans la recherche et l’industrie

The exact knowledge of Drop Size Distributions (DSD) plays a major role in various fields of applications to control and optimise processes as well as reduce waste. In the microbial production of advanced biofuels, oil droplets are produced under turbulent conditions in an aqueous medium containing many surface active components, which might hinder the recovery of the product. Knowledge of DSD is thus essential for process optimisation. This study demonstrates the capability of a photo-optical measurement method for DSD measurement in fermentation broth and in plate separators aimed at cost reduction in the microbial production of advanced biofuels. Measurements were made with model mixtures in a bioreactor, and at the inlet and outlet of a plate separator. In the bioreactor, the method was effective in detecting a broad range of droplet sizes and in differentiating other disperse components (e.g. microbial cells and gas bubbles). In the plate separator, the method was effective in determining the influence of the varied parameters on the separation efficiency.La connaissance approfondie de la distribution du diamètre des gouttelettes joue un rôle important dans des diverses applications des contrôles et d’optimisations des processus, de plus elle permet de réduire le gaspillage. Pendant la production microbienne de biocarburants avancés, des gouttelettes d’huiles sont produites sous les effets des turbulences du milieu aqueux contenant beaucoup de substances tensio-actives, ce qui pourrait entraver la récupération du produit. La connaissance de la distribution du diamètre des gouttelettes est donc essentielle pour l’optimisation des processus. Cette étude montre les possibilités offertes par une méthode de mesure photo-optique pour les mesures de la taille des gouttelettes dans des bouillons de fermentations et dans des séparateurs à plaques, ceci vise à réduire les coûts dans la production microbienne de biocarburants avancés. Les mesures ont été effectuées avec des mélanges modèles dans un bioréacteur ainsi qu’à l’entrée et à la sortie d’une plaque de séparation. Dans le bioréacteur, le procédé est efficace dans la détection d’une large gamme de tailles de gouttelettes et dans la différenciation des autres composants dispersés (par exemple des cellules microbiennes et des bulles de gaz). Dans le séparateur à plaque, le procédé est efficace, car il détermine l’influence de différents paramètres sur l’efficacité de la séparation

Photo-Optical In-Situ Measurement of Drop Size Distributions: Applications in Research and Industry

The exact knowledge of Drop Size Distributions (DSD) plays a major role in various fields of applications to control and optimize processes, as well as reduce waste. In the microbial production of advanced biofuels, oil droplets are produced under turbulent conditions in an aqueous medium containing many surface active components, making DSD knowledge essential for process optimization. The capability of a photo-optical measurement method for DSD measurement in fermentation broth and in plate separators is illustrated aiming at cost reduction in the microbial production of advanced biofuels. Measurements were carried out with model mixtures in a bioreactor, and at the inlet and outlet of a plate separator. In the bioreactor, the method was effective in detecting a broad range of droplet sizes and in differentiating other disperse components, e.g., microbial cells and gas bubbles. In the plate separator, photo-optical measurement effectively determined the influence of the varied parameters on the separation efficiency.BT/Bioprocess Engineerin