Wastes produced from the phosphate industry presents many challenges due to the high economic and environmental impacts involved with their disposal. However, the relative scarcity of high-grade phosphate ores has driven researchers to recover and recycle these valuable wastes (secondary sources). The goal of this study was to investigate the possibility of upgrading the P 2 O 5 content in tailings produced from a phosphorous production plant using direct froth flotation. Characterization assays, including X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray fluorescence (XRF), and mineral liberation analysis (MLA), were first conducted to identify the mineralogical and morphological characteristics of tailings samples and grain properties to better understand the behavior of the flotation feed. Flotation experiments were conducted using Denver D-12 in a 1-L flotation cell in the presence of sodium silicate (dispersant) and sodium oleate (collector). Several parameters, such as the solids content of the flotation pulp, pulp pH and flotation time, were investigated to optimize the recovery and grade of the concentrate products. Results obtained from this study indicated that the P 2 O 5 content in plant tailings could be upgraded from 21.57% to 28.4% at >73% recovery.

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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 (R 2 ), mean absolute error (MAE), and rootmean-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.

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