Liu, Siying
(2019)
Quantitative Prediction of Segregation at Process Scale.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Segregation, or the separation/stratification of particles with differing properties, can lead to significant handling problems, product non-uniformity, and even complete batches being discarded at huge financial loss in multiple industries. Thus, one could argue that segregation is one of the most important factors in industrial processing of granular materials. There has been a tremendous focus in recent years on granular segregation problems and much has been learned about the mechanisms driving those phenomena. Segregation model development holds promise for translation of academic research into industrial practice; however, experimental validation of dynamic models is extremely difficult and typical segregation models are not inherently built with scale-up in mind. One unique aspect of our work is that we overcome these experimental limitations by exploiting a novel framework for segregation testing based on establishing an “equilibrium” between mixing and segregation in free surface granular flows in order to alter the steady-state distribution of particles. By achieving this balance between the rate of segregation and the perturbation rate, we combine the model expressions that we are interested in testing with dramatically simplified experiments to ultimately deduce the segregation rate and validate the expressions. Moreover, by exploring a novel view of the interplay between granular rheology and segregation, we have introduced a new way of structuring segregation rate models that make them inherently more scalable and accurate for industrial use than any models previously reported. Types of segregation properties studied in this research include density, size, wet and shape. Our results suggest that one can prescribe (or design) industrial operating conditions that will lead to dramatically lower segregation extents.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
| Title | Member | Email Address | Pitt Username | ORCID |
|---|
| Committee Chair | McCarthy, Joseph | | | | | Committee Member | Niepa, Tagbo | | | | | Committee Member | Velankar, Sachin | | | | | Committee Member | Robertson, Anne | | | |
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| Date: |
24 January 2019 |
| Date Type: |
Publication |
| Defense Date: |
16 November 2018 |
| Approval Date: |
24 January 2019 |
| Submission Date: |
7 November 2018 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
135 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Chemical and Petroleum Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Particle Technology, solid transport, Segregation |
| Date Deposited: |
24 Jan 2019 15:42 |
| Last Modified: |
24 Jan 2019 15:42 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/35660 |
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Quantitative Prediction of Segregation at Process Scale. (deposited 24 Jan 2019 15:42)
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