L4.4 Zerstäuben von Flüssigkeiten mit Einstoff-Druckdüsen

Walzel, Peter

doi:10.1007/978-3-662-52989-8_95

Cited by 2 publications

(2 citation statements)

References 92 publications

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“…A small droplet size corresponded to a high volume flow, and a large droplet size corresponded to a low volume flow. This observation is consistent with the literature [20,21].…”

Section: Determination Of Droplet Size Distributionsupporting

confidence: 94%

Categorization of Sprays by Image Analysis with Convolutional Neuronal Networks

et al. 2022

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Spray characterization has been an issue for process and product characterization for decades. Because of this, a convolutional neuronal network was developed to determine the droplet size from spray images. The images were taken using a digital camera, a light source, and a dark room. These were subsequently employed to design and train a convolutional neuronal network using open‐source software packages and a desktop computer. The accuracy of the network droplet size determinations was checked with additional, independent images. The median drop size was assessed with a high accuracy of more than 99.8 % as the mean spray performance indicator. Additionally, the droplet size distribution measurements from the neural network method deviated from those from the reference method (laser diffraction) by less than 1.5 %. Convolutional neuronal networks can be applied to determine the spray performance using spray cone images. This approach could be useful for multiple applications.

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“…A small droplet size corresponded to a high volume flow, and a large droplet size corresponded to a low volume flow. This observation is consistent with the literature [20,21].…”

Section: Determination Of Droplet Size Distributionsupporting

confidence: 94%

Categorization of Sprays by Image Analysis with Convolutional Neuronal Networks

et al. 2022

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“…An oil-in-water emulsion is first atomized into fine droplets, which are then dried into particles with encapsulated oil droplets by subsequent contact with a hot air stream [1][2][3]. In the food, pharmaceutical, and chemical industries, pressure-swirl nozzles are commonly used for the atomization step, as they are generally more efficient and provide better atomization results in comparison to solid-cone nozzles [4,5]. During atomization, the liquid is strongly accelerated through a narrow gap and subjected to high shear and elongational stresses.…”

Section: Introductionmentioning

confidence: 99%

Influence of Nozzle Geometry and Scale-Up on Oil Droplet Breakup in the Atomization Step during Spray Drying of Emulsions

Höhne,

Taboada,

Schröder

et al. 2024

Fluids

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Spray drying of oil-in-water emulsions is a widespread encapsulation technique. The oil droplet size (ODS) significantly impacts encapsulation efficiency and other powder properties. The ODS is commonly set to a specific value during homogenization, assuming that it remains unchanged throughout the process, which is often inaccurate. This study investigated the impact of atomizer geometry and nozzle dimensions on oil droplet breakup during atomization using pressure-swirl atomizers. Subject of the investigation were nozzles that differ in the way the liquid is set in motion, as well as different inlet port and outlet orifice dimensions. The results indicate that nozzle inlet port area may have a significant impact on oil droplet breakup, with x90,3 values of the oil droplet size distribution decreasing from 5.29 to 2.30 µm with a decrease of the inlet area from 2.0 to 0.6 mm. Good scalability of the findings from pilot to industrial-scale was shown using larger nozzles. A simplified theoretical model, aiming to predict the ODS as a function of calculated shear rates, showed reasonable agreement to the experimental data for different atomization pressures with coefficients of determination of up to 0.99. However, it was not able to predict the impact of different nozzle dimensions, most likely due to changes in flow characteristics. These results suggest that the stress history of the oil droplets might have a larger influence than expected. Further studies will need to consider other zones of high stress in addition to the outlet orifice.

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L4.4 Zerstäuben von Flüssigkeiten mit Einstoff-Druckdüsen

Cited by 2 publications

References 92 publications

Categorization of Sprays by Image Analysis with Convolutional Neuronal Networks

Categorization of Sprays by Image Analysis with Convolutional Neuronal Networks

Influence of Nozzle Geometry and Scale-Up on Oil Droplet Breakup in the Atomization Step during Spray Drying of Emulsions

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