Optimization of flotation efficiency of phosphate minerals in mine tailings using polymeric depressants : experiments and machine learning

• Autorzy: Alsafasfeh Ashraf , Alagha Lana , Alzidaneen Ala , Nadendla Venkata Sriram Siddhardh

Identyfikatory

DOI

10.37190/ppmp/150477

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

phosphate tailing; froth flotation; polymers; chitosan; artificial neural networks

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2022
• Tom: Vol. 58, iss. 4
• Strony: art. no. 150477
• Opis fizyczny: Bibliogr. 50 poz., rys., tab.

Twórcy

autor

Alsafasfeh Ashraf

• Department of Mining and Explosives Engineering, Missouri University of Science and Technology, MO65409, USA
• Department of Natural Resources and Chemical Engineering, Tafila Technical University, Tafila, Jordan 66110

autor

Alagha Lana

• Department of Mining and Explosives Engineering, Missouri University of Science and Technology, MO65409, USA
• Thomas J. O'Keefe Institute for Sustainable Supply of Strategic Minerals, Rolla, MO, USA, 65409, USA

autor

Alzidaneen Ala

• Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA

autor

Nadendla Venkata Sriram Siddhardh

• Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).