Fardis Nakhaei

Although copper smelter dust (CSD) is classified as hazardous waste, it also serves as a valuable secondary resource, offering potential for the recovery of critical elements. In this study, CSD samples were collected from the waste heat boiler (WHB) and electrostatic precipitator (ESP) units connected to the flash smelting furnace at a copper smelter. The representative samples were extensively characterized by particle size, and chemical and mineralogical analyses. Following sample characterization, leaching experiments were performed to evaluate the potential extraction of Cu, Fe, As, Zn, Pb, In, Ga, and Ge from the flue dusts. Distilled water, H₂SO₄, and HCl were employed as lixiviants. The characterization results showed that 80% of the particles by weight in WHB and ESP samples were smaller than 104 μm and 46 μm, respectively. The WHB sample comprised 23.3% Cu, 20.3% Fe, 7.2% S, 1.8% As, and 1.5% Pb, whereas the ESP sample contained 21.2% Cu, 15.9% Fe, 1.8% As, and 1.5% Pb. The concentrations of Ga, Ge, and In were relatively low and nearly identical in both samples, measuring 6 ppm, 33 ppm, and 18 ppm, respectively. TIMA analysis indicated that the WHB and ESP samples were predominantly composed of CuFe sulfates (25–43 wt%), FeCu oxides (11–16 wt%), FeCu and CuFe silicates (13–28 wt%), and Cu sulfates (10–13 wt%). Additionally, Fe-Cu silicate phases served as the primary host for Ga, Ge, and In in both samples. The leaching efficiencies obtained in water for WHB sample were 43% for Cu, 35% for Zn, and 72% for Cd. In comparison, the ESP sample exhibited higher leaching efficiencies, with values of 62% for Cu, 50% for Zn, and 80% for Cd. Meanwhile, the leaching efficiencies for the rest of target elements in both samples were under 12%. Under high acid concentrations, the WHB and ESP samples demonstrated nearly complete extraction of Cu, As, Cd, and Fe at over 90%, while also achieving high leaching efficiencies for trace elements, with Ga at 75–82%, Ge at 84–86%, and In at 90–93%. Implementing a water pre-leaching stage reduced solid residue mass by 40–50% and yielded a pregnant leach solution enriched with 10,700–14,200 mg L⁻¹ Cu, 101–180 mg L⁻¹ Cd, and 193–294 mg L⁻¹ Zn, facilitating simplified downstream purification for critical elements. Graphical Abstract

As a critical mineral, magnesite plays a vital role in industries such as steelmaking, construction, and advanced technologies due to its high thermal stability and chemical resistance. However, the beneficiation of low-grade magnesite ores (~38.36% MgO) remains challenging due to the presence of iron, silica, and calcium-bearing impurities. This study proposes an integrated beneficiation strategy combining medium-intensity magnetic separation and flotation techniques to upgrade a low-grade magnesite ore. After grinding to 75 µm (d₈₀), the sample was subjected to two-stage magnetic separation at 5000 Gauss to remove Fe-bearing minerals, reducing Fe₂O₃ below 0.5%. The non-magnetic fraction was then treated through a two-stage reverse flotation process using dodecylamine (350 g/t) and diesel oil (60 g/t) at pH 7 to reject silicate gangue. This was followed by a four-stage direct flotation using sodium oleate (250 g/t), sodium silicate (350 g/t), and SHMP (100 g/t) at pH 10 to selectively recover magnesite while suppressing Ca-bearing minerals. The optimized flowsheet achieved a final concentrate with MgO > 46.5%, SiO₂ ≈ 1.05%, Fe₂O₃ ≈ 0.44%, and CaO ≈ 0.73%, meeting the specifications for refractory-grade magnesite. These results highlight the effectiveness of a combined magnetic–flotation route in upgrading complex, low-grade magnesite deposits for commercial use.

Coal resources, such as those from the South African Waterberg coal field and the Mozambique coalfields, often contain highly interlayered structures of coal macerals and mineral matter. The mineral matter, which is incombustible, reduces the coal’s calorific value and poses challenges during coal processing due to its adverse impact on plant yields. Effective liberation of coal from mineral matter is crucial to maximize economic recovery and reduce undesirable ash content. This study investigates the effect of various breakage modes on the liberation of coal to attain a high yield of low-ash coal. Coal samples from the Moatize Coalfield in Mozambique were subjected to impact breakage, compressive breakage, and attrition breakage in different combinations to determine the optimal conditions for maximizing coal yield while minimizing fines formation. The results showed that impact breakage to a top size of 6.7 mm followed by attrition breakage significantly enhanced the coal yield, particularly for material with relative density (RD) < 1.4. The study also identified an optimal particle size of 4.75 mm, where the cumulative yield of liberated coal with RD < 1.4 was maximized. The most effective combination for improving coal yield was impact breakage to 6.7 mm, followed by a short attritioning time of 1 minute. This combination achieved a yield of 61%, a significant increase compared to the raw coal yield of 26%. The findings highlight the importance of breakage mode selection and particle size for optimizing coal liberation and yield in processing plants.

Flotation is a significant and widely used processing technique, effectively separating valuable and gangue minerals

Three-dimensional (3D) surface imaging systems have gained popularity in monitoring the status and condition of separation processes by characterizing the internal and external structures of particles such as size, shape, density and composition. This review article mainly focuses on demonstrating the state of the art of 3D imaging systems in mineral processing based of the recent growth in 3D sensors. The structure of this manuscript comprises an overview of the two 3D imaging systems, including RhoVol and X-ray computed tomography, based on the basic principles. Their applications in mineral processing are then explained. By integrating with other imaging tools and the 3D printing technology, 3D surface imaging systems will play an important role in the automation and control of mineral processing in the future.

This paper presents the development and validation of five different soft computing methods for flotation performance prediction: (1) two models based on fuzzy logic (Mamdani and Takagi-Sugeno fuzzy inference system) and (2) three models based on artificial neural networks. Copper content in the ore feed, collector dosage in the rougher and the scavenger flotation circuits, slurry pH in the rougher flotation circuit and frother consumption were selected as input parameters to estimate the copper grade and recovery of final concentrate, as well as the copper content in the final tailings of the flotation plant. The training and evaluation of the proposed models were performed on the basis of real process data collected by the multiannual monitoring of industrial flotation plant operation in “Veliki Krivelj Mine”. The results showed that the proposed soft computing-based models well describe the behavior of the industrial flotation plant in a wide range of circumstances. Among the proposed algorithms, artificial neural networks gave the most accurate predictions for the final copper concentrate grade and recovery (R² = 0.98 and R² = 0.99, respectively) and copper content in final tailings (R² = 0.87). At some points, fuzzy logic models are almost equally efficient, but artificial neural networks had lower values for all error functions.

Froth feature extraction plays a significant role in the monitoring and control of the flotation process. Image-based soft sensors have received a great deal of interest in the flotation process due to their low-cost and non-intrusive properties. This study proposes data-driven soft sensor models based on froth images to predict the key performance indicators of the flotation process. The ability of multiple linear regression (MLR), the backpropagation neural network (BPNN), the k-means clustering algorithm, and the convolutional neural network (CNN) to predict the amount of sulfur removal from iron ore concentrate in the column flotation process was examined. A total of 99 experimental results were used to develop the predictive models. Extracted froth features including color, bubble shape and size, texture, stability, and velocity were used to train the traditional predictive models, whereas in the CNN model the froth images were directly fed into the model. The results comparison indicated that the three-layered feedforward NN model (17-10-1 topology) and CNN model provided better predictions than the MLR and k-means algorithm. The BPNN model displayed a correlation coefficient of 0.97 and a root mean square error of 4.84% between the actual data and network output for both training and the testing datasets. The error percentages of the CNN, BPNN, MLR and k-means models were 10, 11, 15 and 18%, respectively. This study can become a key technical support for the application of intelligent models in the control of the operational variables for the flotation process used to desulfurize iron concentrate.

This study aims to investigate the effects of pH and chemical reagents to model and optimize the flotation process of a complex lead ore containing 2.44 wt% Pb. For this purpose, the effects of pulp pH, collectors (PAX and R404), depressant (FeSo₄), and activator (Na₂S) concentrations were studied using response surface methodology. Furthermore, the influence of these parameters was studied on flotation kinetics parameters. The optimization studies indicated that the desired Pb recovery (84.1%) and grade (26.6%) was obtained from the test, which was performed with the optimum flotation conditions: 75 g/t PAX, 80 g/t FeSo₄, 735 g/t Na₂S, 20 g/t R404 and pH 9.5. The results of statistical analyses showed that the pulp pH, PAX concentration, and interactions between pH-R404 concentration and PAX concentration-R404 concentration were the most influential parameters on concentrate grade and recovery. From the kinetic studies, it was observed that the flotation rate coefficient and ultimate recovery were strongly dependent on the pulp pH. The flotation rate coefficient increased with enhancing the pH from 8 to 9.5 and after that reduced with increasing the pH to 11.

Density is one of the key factors of coal that provides useful information about its physical and chemical composition. Traditional coal washability analysis is determined by conducting float–sink test, which is time consuming, destructive, and uses environmentally hazardous chemicals. The motivation for this work is to develop a new technique as a rapid, safe, and viable alternative to the current laboratory of float–sink method in order to avoid harmful chemicals and save time. In this study, an alternative technique based on 3D imaging technology called RhoVol in which the density of individual particles is measured by separate mass and volume measurements to determine coal washability curves is introduced. Four different coal samples were taken from the three coal mines (Highveld, Witbank, and Molteno-Indwe) in South Africa. The coal samples (8 + 2.8 mm) were sorted based on density using zinc chloride float–sink procedures (1.2–1.8 g/cm³) and RhoVol. The true densities of each fraction were determined using helium gas pycnometer for calibration of the RhoVol results. After comparing the results of experiments, it was found that the washability curve and ash analysis obtained from the RhoVol technique was in line with the washability produced by the float–sink test. The successful application of RhoVol as a new nondestructive and nontoxic approach to determine coal washability curves and density of separation for coal sorting would allow selecting the most suitable separation method.