Optimization of Flotation Efficiency of Phosphate Minerals in Mine Tailings using Polymeric Depressants: Experiments and Machine Learning

Abstract

In this study, direct froth flotation experiments were conducted on silicate-rich phosphate tailing samples. The average grade of P2O5 in the flotation feed was 21.6% as determined using a combination of spectroscopic techniques including X-ray powder diffraction (XRD), mineral liberation analysis (MLA), and scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDS). Two polymers were selected to promote the depression of silicates and enhance the flotation of phosphates: in-house synthesized hybrid polyacrylamide (Hy-PAM) and chitosan. Flotation efficiency of phosphates was evaluated at different flotation conditions including depressant type, depressant dosage, pH, and the flotation time. Results indicated that the optimum flotation efficiency of phosphate minerals (84.6% recovery at 28.6% grade of P2O5) was obtained when Hy-PAM was utilized at the studied range of pH and flotation time. All datasets produced from the flotation experiments were integrated within the framework of machine learning (ML) using artificial neural networks (ANNs). The ANN platform was trained, validated, and successfully employed to predict the process outcomes in relation to the pulp and reagents characteristics, which in turn were used to determine the optimum values of process variables. Coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE) were used as model indicators. Optimization results showed that the peak flotation performance could be achieved at higher dosages of both polymers. However, lower pH and shorter flotation time for Hy-PAM, and higher pH and longer flotation time for chitosan, were predicted to give the optimum process efficiency.

Department(s)

Mining Engineering

Comments

The authors gratefully acknowledge the financial support provided by the Energetic Materials, Rock Characterization, and Geomechanics Research Centre (EMRGe) at Missouri University of Science and Technology.

Keywords and Phrases

Artificial Neural Networks; Chitosan; Froth Flotation; Phosphate Tailing; Polymers

International Standard Serial Number (ISSN)

1643-1049

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2022, All rights reserved.

Publication Date

01 Jan 2022

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