Classification of granular materials via flowability-based clustering with application to bulk feeding
2020 Torres-Serra et al. - Classification of granular materials via flowability-based clustering with application to bulk feeding.pdf (1,263Mb) (Restricted access) Request copy
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Feeder selection impacts the performance of bagging machinery throughout its life cycle, and yet it is usually based on qualitative assessments of flowability. We propose a data analysis methodology aimed at verifying the feeder-type classification of powders and grains by cluster analysis on their material properties. Results for a first data set of conventional properties show the granular materials clustered into as many groups as main bulk feeding systems. Mismatch between feeder classes and flowability-based clusters is explained by common industrial practice and incomplete material characterisation. For this reason, we introduce a set of specialised properties measured with the granular flow tester we have recently developed. Results for principal component analysis on a second extended property data set show that similarly flowing granular materials are better detected considering the specialised properties. This research contributes to objectify the decision-making process of bulk feeder selection from the quantitative description of granular flow.
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
CitationTorres-Serra, J.; Rodriguez-Ferran, A.; Romero, E. Classification of granular materials via flowability-based clustering with application to bulk feeding. "Powder technology", 22 Gener 2021, vol. 378, núm. A, p. 288-302.
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