Classification of granular materials via flowability-based clustering with application to bulk feeding
Author (s): Torres-Serra, J.; Rodríguez-Ferran, A. and Romero, E.
Journal: Powder Technology
Volume: 378
Pages: 288 – 302
Date: 2021
Abstract:
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.
Bibtex:
@article {JTS-TSRFR:21,
Author = {Joel Torres-Serra and Antonio Rodr\’{\i}guez-Ferran and Enrique Romero},
Title = {Classification of granular materials via flowability-based clustering
with application to bulk feeding},
Journal = {Powder Technol.},
Volume = {378},
Number = {},
Pages = {288-302},
Year = {2021},
Doi = {10.1016/j.powtec.2020.09.022},
}