Rapid prototyping of a modular optical flow cell for image-based droplet size measurements in emulsification processes

Burke, Inga; Assies, Christina; Kockmann, Norbert

doi:10.1007/s41981-024-00323-2

J Flow Chem

2024

DOI: 10.1007/s41981-024-00323-2

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Rapid prototyping of a modular optical flow cell for image-based droplet size measurements in emulsification processes

Inga Burke,

Christina Assies,

Norbert Kockmann

Abstract: Emulsification processes are often found in the process industry and their evaluation is crucial for product quality and safety. Numerous methods exist to analyze critical quality attributes (CQA) such as the droplet sizes and droplet size distribution (DSD) of an emulsification process. During the emulsification process, the optical process accessibility may be limited due to high disperse phase content of liquid-liquid systems. To overcome this challenge, a modular, optical measurement flow cell is presented… Show more

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Cited by 4 publications

References 29 publications

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Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

Burke,

Dhayaparan,

Youssef

et al. 2024

J Flow Chem

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For reliable supervision in multiphase processes, the droplet size represents a critical quality attribute and needs to be monitored. A promising approach is the use of smart image flow sensors since optical measurement is the most commonly used technique for droplet size distribution determination. For this, two different AI-based object detection methods, Mask RCNN and YOLOv4, are compared regarding their accuracy and their applicability to an emulsification flow process. Iterative optimization steps, including data diversification and adaption of training parameters, enable the models to achieve robust detection performance across varying image qualities and compositions. YOLOv4 shows better detection performances and more accurate results which leads to a wider application window than Mask RCNN in determining droplet sizes in emulsification processes. The final droplet detection model YOLOv4 with Hough Circle (HC) for feature extraction determines reliable droplet sizes across diverse datasets of liquid-liquid flow systems (disperse phase content 1–15 vol.-%, droplet size range 5–150 μm). Evaluating the adjustment of Confidence Scores (CS) ensures statistical representation of even smaller droplets. The droplet detection performance of the final YOLOv4 model is compared with a manual image processing method to validate the model in general as well as its accuracy and reliability. Since YOLOv4 in combination with Hough Circle (HC) shows an accurate and robust detection and size determination, it is applicable for online monitoring and characterization of various liquid-liquid flow processes. Graphical abstract

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Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

Burke,

Dhayaparan,

Youssef

et al. 2024

J Flow Chem

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Design and Rapid Prototyping of 3D-Printed Microfluidic Systems for Multiphase Flow

Oldach,

Fortmann,

Pleie

et al. 2024

Chemistry

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Since the emergence of microfluidic devices, subtractive manufacturing techniques have dominated their production. Although the conventional manufacturing processes are well established, they come along with some disadvantages that limit the accessibility and hinder the further development of microfluidics. With the rise of additive manufacturing, researchers are focused on developing alternative fabrication methods to promote affordability and accessibility. This paper presents the opportunities and challenges of laser-based stereolithography printers for the fabrication of microfluidic equipment. Emphasis is put on the design and iterative prototyping process from the initial design idea to the final device. To print with adequate and sufficient geometrical accuracy and suitable material, the optimization of the printer’s performance is discussed. Regarding the design of multiphase microfluidics and its complex fluid behavior, suitable surface treatments, including an appropriate cleaning protocol, and coating strategies to make the printed channels either hydrophilic or hydrophobic are presented to ensure applicability. With these fundamentals of additive manufacturing in microfluidic fabrication at hand, the second focus of this contribution is on the application of a modular co-flow device and a monolithic flow-focusing device to generate droplets and slugs in different multiphase flow applications. The presented co-flow setup features a tapered capillary that affects the droplet and slug sizes due to differing diameters, with larger diameters leading to larger droplets and slugs and vice versa. Several design parameters for the flow-focusing device were evaluated to determine the influence of device design on multiphase flow formation. It was found that the diameter of the inlet for the dispersed phase has the greatest effect on the size of the resulting droplets and slugs and covers the largest range of adjustable sizes.

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Investigation of AI Algorithms for Photometric Online Analysis in a Draft Tube Baffle Crystallizer

Marsollek,

Lamprecht,

Kockmann

2024

Crystals

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The rapid advancement of AI algorithms presents new opportunities for sensing technologies based on image recognition, such as real-time crystallization monitoring. This work investigates the use of computer vision to detect and size crystals in a lab scale draft tube baffle crystallizer (DTBC). A specially developed analytical bypass was implemented on the DTBC to enable a low-influence analysis without invasive intrusion into the process. By utilizing AI models such as YouOnlyLookOnce version 8 (YOLOv8), YOLOv8 Segmentation (YOLO8seg), and the convolutional network for biomedical image segmentation U-Net, this study assesses their effectiveness in determining crystal size distributions from photometric images. While U-Net was deemed unsuitable due to computational demands and accuracy issues, YOLOv8 and YOLO8seg performed better in terms of efficiency and precision. YOLO8seg, specifically, achieved the highest accuracy, with a mean average precision (mAP) of 82.3%, and excelling in detecting larger crystals, but underperforming with crystals smaller than 90 µm. Despite this limitation, YOLO8seg was able to compete with the manual methods and was superior to the state-of-the-art algorithm mask region convolutional neural network (Mask R-CNN) in terms of accuracy. The study suggests that specific training and adaptation of the imaging conditions could further improve the crystal detection performance.

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Rapid prototyping of a modular optical flow cell for image-based droplet size measurements in emulsification processes

Cited by 4 publications

References 29 publications

Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

Design and Rapid Prototyping of 3D-Printed Microfluidic Systems for Multiphase Flow

Investigation of AI Algorithms for Photometric Online Analysis in a Draft Tube Baffle Crystallizer

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