Keywords and Phrases
Froth Flotation; Machine Learning in Flotation; Novel Dispersant; Phosphate Flotation
”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.
Alagha, Lana Z.
Schlesinger, Mark E.
Nadendla, V. Sriram Siddhardh
Ph. D. in Mining Engineering
Missouri University of Science and Technology
xiii, 103 pages
© 2020 Ashraf Alsafafeh, All rights reserved.
Dissertation - Open Access
Alsafasfeh, Ashraf, "Modeling and optimization of froth flotation of low-grade phosphate ores: Experiments and machine learning" (2020). Doctoral Dissertations. 2948.