Doctoral Dissertations

Keywords and Phrases

Froth Flotation; Machine Learning in Flotation; Novel Dispersant; Phosphate Flotation

Abstract

”In this research work, bench-scale and micro-scale flotation tests were conducted to separate “minerals from silicate minerals using direct and reverse flotation approaches, respectively. Experiments were conducted at different flotation conditions including reagents’ type, reagents’ dosages, pulp’s pH, and flotation time.

In the direct flotation process, two polymers were selected to promote the depression of silicates: hybrid polyacrylamide-based polymers (Hy-PAM) and chitosan. Results indicated that the highest recovery of P2O5 (86.82%) was obtained when the Hy-PAM polymer was used compared with 66.7% and 40% when chitosan and commercial inorganic depressant were used, respectively. The experimental datasets obtained from the direct flotation tests were assimilated to develop an artificial neural network (ANN) model to predict the flotation efficiency of phosphate minerals in relation to various process parameters. The developed ANN model predicted that optimum flotation performance can be achieved at 4 min of flotation time, 250-300 g/ton of reagents’ dosages, and pH 9.

The reverse flotation tests were conducted using two types of ionic liquid collectors (THAI and HMLHF) in micro-flotation system wherein pure apatite and quartz were used as an example of phosphate and silicate minerals, respectively. Results obtained from mixed minerals flotation showed that quartz’s recovery and grade were ~90% and ~64% when HMLHF was used at pH 11. When THAI was used, the recovery and grade of quartz were 87% and 70.3%, respectively, compared with 87% and 65.3% when commercial amine collector was used”--Abstract, page iii.

Advisor(s)

Alagha, Lana Z.

Committee Member(s)

Sherizadeh, Taghi
Schlesinger, Mark E.
Usman, Shoaib
Nadendla, V. Sriram Siddhardh

Department(s)

Mining Engineering

Degree Name

Ph. D. in Mining Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2020

Pagination

xiii, 103 pages

Note about bibliography

Includes bibliographic references (pages 94-102).

Rights

© 2020 Ashraf Alsafafeh, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11770

Electronic OCLC #

1240361902

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