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• W4309938392 endingPage "118333" @default.
• W4309938392 startingPage "118333" @default.
• W4309938392 abstract "This study aims at developing an experimental method to characterise the fluid dynamic stress acting on particles in a lab-scale stirred tank reactor. The method is based on a photo-optical inline particle size measurement technique, which is able to capture the complex morphology of a stress-sensitive clay-floc system. The novel image-based analysis uses a Convolutional Neural Network (CNN), which was trained for particle detection. Its output provides a high-quality particle recognition for subsequent size and shape analysis throughout the flocculation process under various experimental conditions. The flocculation system is used to characterise the stress induced by four impeller geometries in the reactor. The obtained changes of floc size and shape allow to evaluate the particle stress in more detail compared to previous studies. The agreement of main findings of the floc breakage process with known literature assesses the developed method to be applied successfully in further studies." @default.
• W4309938392 created "2022-11-30" @default.
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• W4309938392 date "2023-03-01" @default.
• W4309938392 modified "2023-10-16" @default.
• W4309938392 title "Characterisation of particle stress in turbulent impeller flows utilising photo-optical measurements of a flocculation system – PART 1" @default.
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• W4309938392 doi "https://doi.org/10.1016/j.ces.2022.118333" @default.
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