Ying Chung, Naoki Kondo, Ken’ichiro Kita, Mikinori Hotta, Junichi Tatami, Shinya Kawaguchi
Abstract
A scalable nitrogen‑free granulator that enables freezing temperature to be tuned as a process variable for powder granulation was used to fabricate alumina granules, and the obtained granules were further fabricated into green bodies this study. We quantified links between process conditions, granule properties, and green density using Pearson correlations and principal component analysis (PCA). It is found that higher freezing temperature is likely to produce dried granules with smaller angle of repose and thus results in higher green density. It is suggested that higher freezing temperatures allow surface-level rearrangement to take place easier than lower ones, therefore creating smoother external surfaces of the obtained granules. This is considered as the reason for smaller angle of repose measured in this study. Finally, predictive models of green density were built from granulation conditions and granule properties using multiple regression and artificial neural networks (ANNs). After mitigating multicollinearity among inputs, the ANN achieved an average R2 of 0.84, demonstrating robust predictive capability for process–structure–property relationships in this spray freeze granulation process.
Highlights:
Scalable spray freeze granulation drying with flexible freezing temperature.
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Higher freezing temperature produces granules with smaller angle of repose.
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R2 > 0.8 was obtained when predicting the density of green and bodies.
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Chian of causality successfully established between process-granule-green body.