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Using statistical methods to optimize powder flow measurements and to predict powder processing performance
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Koynov, Sara. Using statistical methods to optimize powder flow measurements and to predict powder processing performance. Retrieved from https://doi.org/doi:10.7282/T32F7Q9K

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TitleUsing statistical methods to optimize powder flow measurements and to predict powder processing performance
NameKoynov, Sara (author); Glasser, Benjamin (chair); Muzzio, Fernando (internal member); Callegari, Gerardo (internal member); Borghard, Bill (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-05 (degree)
SubjectChemical and Biochemical Engineering, Powders, Bulk solids flow, Statistics
Extent1 online resource (xi, 158 p. : ill.)
DescriptionThe flow behavior of powders - key raw materials, intermediates, and final products across many industries - is poorly understood, making the prediction of manufacturability and process performance difficult. Common manufacturing problems include non-uniform flow, jamming, segregation, and content uniformity issues. Due to the complex nature of granular materials, their flow behavior, typically, cannot be described using a single parameter. Many methods have been developed that utilize a range of sample sizes and characterize the material in a variety of consolidation states. The path for using these techniques for increasing process understanding remains unclear since the relationships between material properties and powder processing conditions remain partly unknown. In this work, principal component analysis of large material property datasets was used to identify the most relevant material properties for a given application. This statistical approach was demonstrated using a database of raw material properties. The number of material properties needed to explain the observed variability was reduced to the minimum, while retaining the same predictive capability as the original dataset. Additionally, the three characterization techniques that provided the most predictive capability were identified. Fundamental understanding of the characterization techniques is critical for the successful application of material flow properties to solids processing operations. Two commonly used techniques are the shear cell and compressibility tests. These tests were also among those previously identified as relevant for distinguishing maximally between raw materials. It was found that different shear cells yield statistically different measurements even when testing the same powders under the same consolidation stress. Further, a novel compressibility method for reducing the amount of material required, to less than 50mg, for measuring flow properties was developed. The use of material property characterization to increase process understanding was demonstrated through a case study of axial mixing in a rotating drum. The axial dispersion coefficient was found to be dependent on the material properties and increased with decreasing flowability. In this case, particle size, shear cell, and compressibility measurements explained 95% of the variation observed in the axial dispersion coefficient.
NotePh.D.
NoteIncludes bibliographical references
Noteby Sara Koynov
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T32F7Q9K
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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