In this study, the effect of ultrasound (US) on the quartz-amine flotation system was investigated in detail by considering various surface chemistry techniques. The effect of ultrasound on particle size, shape factor, and surface roughness were characterized by using Brunauer-Emmett-Teller (BET) surface area measurements and scanning electron microscopy (SEM) analyses. The contact angle and bubble-particle attachment time, as well as adsorption density measurements was carried out to evaluate the effect of ultrasound on quartz surface wetting ability. In addition, atomic force microscopy (AFM) analyses were conducted, and finally micro-flotation studies were performed. As a result, it was found that the micro-flotation recovery at 2 × 10-5 M dodecyl amine hydrochloride (DAH) concentration increased from 45.45% to 63.64% with 30 W ultrasonic application at conditioning step. However, the micro-flotation recovery decreased to 37.50% when the ultrasonic power increased to 150 W. The results showed some effect of ultrasound on particle size, particle shape, and surface roughness in some extent. The increase in the contact angle and the decrease in the bubble-particle attachment time were observed. A slightly high adsorption density was measured. All these show a positive effect of ultrasound on quartz flotation with amine as a collector.

This review aims to advance quartz processing technology by examining the surface properties, flotation behavior, and selective flotation mechanisms of quartz mineral. Characterized by a strong negative charge over a wide pH range and an isoelectric point around pH 2, quartz surfaces allow physical adsorption of cationic collectors, particularly amines, which render the quartz surface hydrophobic and enhance bubble–particle interactions. In contrast, flotation with anionic collectors requires prior surface activation via multivalent metal cations such as Ca²⁺. The pH value of the medium plays a critical role in both collector adsorption and flotation selectivity. Both direct and reverse flotation strategies can be used, depending on whether quartz is targeted as a valuable mineral or a gangue mineral. In direct flotation, depressants like carboxymethyl cellulose and starch are used to depress gangue minerals, while in reverse flotation, quartz is depressed using chemicals such as fluoride ions and cationic polymers. To improve the efficiency and selectivity of quartz flotation, further research is needed on surface chemistry, collector adsorption mechanisms, and the transition from laboratory-scale experiments to industrial applications.

Coals generally exist with clay minerals, which are very fine-sized minerals, and adversely affect the flotation of coal. For instance, they make the coal surface hydrophilic by coating coal surfaces and therefore, they inhibit bubble-particle attachment. Clay minerals can also cause over-consumption of reagents due to their high surface area. Additionally, clay minerals can transfer into the concentrate during the flotation process by entrainment because of their low weight and small particle size, and thus, increase pulp viscosity and changing froth stability. In this study, the influence of various clay minerals (kaolinite, illite, and bentonite) on the flotation behavior of coal sample was studied in detail by conventional flotation tests. In addition, the values obtained from flotation tests were correlated with theoretical energy barrier values established with classical DLVO theory based on zeta potential values of coal and clay samples measured at different pH values. According to the results of this study, no significant effect of clay minerals was observed on coal flotation. This was probably because of the high floatability of the coal used for this study. Both the results obtained from the experiments and theoretical analysis, DLVO calculations, showed that the type of clay mineral is the main indicator parameter for coal flotation.

In cemented paste backfill (CPB), fly ash (FA) can reduce cement costs. However, the chemical compositions of FA and tailings used in the CPB can vary significantly, affecting the strength values of CPBs, which can be determined through laboratory tests and play a crucial role in design operations. Therefore, developing a predictive model would be advantageous in terms of time and cost. The most critical aspect of this study is that machine learning (ML) models demonstrate high accuracy in the performance of strength prediction in experimental studies, especially in nonlinear and complex data structures, and even in the presence of uncertainty in geochemical and geophysical parameters. Among the ML algorithms, random forest (RF), artificial neural network (ANN), linear regression (LR), voting, and extreme gradient boosting (XGBoost) algorithms were used in this study. According to the results obtained, the XGBoost model exhibited the most robust predictive performance, evidenced by the highest correlation coefficient (R) (0.922) and the lowest mean absolute error (0.666). XGBoost also demonstrated its durability and stability by achieving the lowest relative absolute error (18.81%) and root mean square error (41.10%). Therefore, it has been understood that significant time and resource savings can be achieved in important projects by eliminating the need for experimental tests.

Thermal treatment is one of the well-known methods used before froth flotation to remove the polar groups from coal surfaces to make them more hydrophobic. In this study, the effect of conventional and microwave thermal treatments on the wettability of low and high-rank lignite was investigated using flotation experiments, zeta potential, and bubble-particle attachment time measurements, as well as hydrophilicity index analyses. The results showed that both of the thermal treatments increased hydrophobicity, and therefore floatability of the coal samples. The optimum results were achieved with conventional and microwave thermal treatment for the low and high-rank coals, respectively.