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 P₂O₅ 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 P₂O₅ content in plant tailings could be upgraded from 21.57% to 28.4% at >73% recovery.

Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model—constructed by combining the random forest model and the firefly algorithm—to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated, and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process outcomes with high‐fidelity, while consistently outperforming the standalone ML models.

Rare earth minerals (REMs) such as bastnaesite, monazite, and xenotime are of considerable significance since they are the main commercial sources for rare earth elements (REEs) with cutting-edge applications. Fundamental understanding of surface properties of REMs is essential to identify the reactions taking place at different interfaces to develop more robust technologies for the recovery of REEs. The goal of this study is to provide a comprehensive investigation on the surface energy characteristics of bastnaesite and xenotime, as the primary sources of light and heavy rare earth elements, respectively. Crystal’s orientation of REMs was identified using surface X-ray diffraction analysis, whereas the morphology and elemental composition were characterized using scanning electron microscopy and energy dispersive spectra analyses. Wettability of REMs was studied using sessile drop contact angle measurement technique, and the surface energy and its constituents were evaluated using Fowkes, van Oss–Chaudhury–Good, Owens–Wendt–Rabel–Kaelble, Zisman, and Neumann models. Atomic force microscopy (AFM) was used to compare the local surface properties and work of adhesion of REMs by analyzing the force profile between the mineral surfaces and a n-type silicon tip. Inverse gas chromatography (IGC) was employed to study the surface energy heterogeneity of REM powders and evaluate the dispersive and Lewis acid–base interactions. Results indicated that the dispersion forces have a larger contribution to the surface energy of both REMs in comparison with the polar interactions. The surface energy values obtained using contact angle measurements were lower than those obtained using IGC, however, the IGC results seemed to be closer to reality since the contact angle results showed a strong dependence on probe liquids, roughness, and local properties of the surfaces. Contact angle measurements and AFM analysis indicated that bastnaesite had higher hydrophobic character, whereas the IGC analysis revealed that the surface energy of xenotime was lower than that of bastnaesite at higher surface coverages. Despite the shortcomings of each method, results showed that a combination of these techniques could provide a deeper understanding of surface energy and wetting behavior of minerals.

In this study, chitosan polymer was tested as a potential selective green depressant of pyrite in the bulk flotation of galena (PbS) and chalcopyrite (CuFeS₂) from sphalerite (ZnS) and pyrite (FeS₂) using sodium isopropyl xanthate as a collector and 4-methyl-2-pentanol (MIBC) as a frother. Flotation tests were carried out in a D12-Denver flotation laboratory cell in the presence and absence of chitosan and/or sodium cyanide depressant which is commercially used as pyrite depressant in sulfide mineral flotation process. Flotation recoveries and concentrate grades (assay) were studied as a function of polymer concentration and flotation time. It was found that at 50 g/ton, chitosan depressed 5.6% more pyrite as compared to conventional depressant NaCN at its optimum dosage. Furthermore, the measured assay values of pyrite in concentrates dropped by ~1.2% when NaCN depressant was replaced with chitosan polymer. Zeta potential measurements of galena, chalcopyrite, sphalerite, and pyrite suspensions before and after chitosan’s addition revealed that the polymer has preferential adsorption on pyrite minerals as compared to other sulfide minerals specially galena. Results obtained from this work show that chitosan polymer has a promising future as a biodegradable alternative to sodium cyanide for the purpose of depressing pyrite in sulfide minerals flotation.