The flotation method is widely used for the preliminary beneficiation of aphanitic (microcrystalline) graphite. However, there is limited literature regarding the effects of flotation reagents on the flotation kinetics of aphanitic graphite. In this study, six commonly used flotation kinetic models were used to fit the flotation experimental data of aphanitic graphite. The classical first-order model was found to be most suitable for describing flotation kinetics of aphanitic graphite. The modified flotation rate constant (K_m) was then applied to evaluate the effects of collector, frother, and inhibitor on aphanitic graphite flotation kinetics. Compared to diesel oil and terpineol oil, kerosene and 2-octanol produced a greater K_m. The highest K_m was obtained at an inhibitor dosage of 15 mg/L.

This study investigated the influence of bulk nanobubbles (NBs) on the flocculation and filtration behavior of kaolin suspensions treated with cationic polyacrylamide (CPAM). Traditionally, flocculation relies on bridging mechanisms by polymers like CPAM. The present work examines the possibility of combining NBs with CPAM to achieve more efficient kaolin separation. The settling behavior of kaolin suspensions with and without bulk nanobubbles was compared. The results with 2 mL CPAM and 300 s settling time revealed that bulk NBs significantly enhanced flocculation efficiency, with supernatant zone height reductions exceeding 50% compared to CPAM alone, indicating a faster settling rate resulting from bulk NBs. This improvement in the settling rate is attributed to NBs’ ability to reduce inter-particle repulsion (as evidenced by a shift in zeta potential from −20 mV to −10 mV) and bridge kaolin particles, complementing the action of CPAM. Additionally, the study demonstrated that bulk NBs improved dewatering characteristics by lowering the medium resistance and specific cake resistance during filtration. These findings pave the way for the utilization of bulk NBs as a novel and efficient strategy for kaolin separation in mineral processing, potentially leading to reduced processing times and lower operational costs.

Accurate assessment of the effects of parameters on the flotation process is important for understanding the complex flotation mechanisms. To address the problem of unsatisfactory prediction of large sample flotation data (641 sets) by traditional machine learning algorithms, four advanced algorithms (GBDT, CatBoost, LightBGM and XGBoost) are used in this paper to investigate the effects of feed properties and flotation conditions on the effectiveness of coal flotation. It was found that the data at flotation recoveries below <40% were difficult to predict effectively by machine learning algorithms due to abnormal flotation results caused by lower flotation reagent dosages. An importance analysis of flotation parameters and prediction of flotation results were carried out based on the reordered data. The results showed that the fraction and ash content of -74 um in the feed are the main factors affecting concentrate yield and ash content. The XGBoost model also achieved the best prediction results compared to other models, and the prediction coefficient of determination R2 reached 0.877 and 0.971 for concentrate yield and ash content, respectively. The results are expected to provide a reference for the intelligent control of coal beneficiation plant by machine learning technology in the future.

The thermal transformation mechanism of pyrite in coal, which governs sulfur emissions and ash deposition, remains highly controversial. There are significant discrepancies in reported activation energies (E_a) (60–310 kJ/mol) and conflicting reaction pathways. To resolve these long-standing controversies, this study proposes a competition-coupling mechanism: pyrolysis and oxidation compete under local O₂ and temperature gradients, while coupling through microstructural evolution. Specifically, pyrolysis generates a porous Fe_1−XS that facilitates oxidation, which in turn can form a passivating oxide/sulfate layer that promotes further pyrolysis. This mechanism reconciles longstanding kinetic controversies by showing that the apparent activation energy is not a fixed value but instead a dynamic parameter, shifting along a continuous curve that bridges pyrolysis and oxidation-dominated regimes. Furthermore, we construct a unified phase diagram by incorporating the competition-coupling mechanism into classical thermodynamic equilibria. This diagram uses the molar ratio FeS₂/(FeS₂ + O₂) and temperature to categorize the transformation process into four distinct regions—pyrolysis-dominated, competition-coupling, oxidation-dominated, and melt-dominated. The key contribution of this work lies in the diagram which offers a practical framework for optimizing combustion and roasting systems, allowing for improved control over sulfur emissions and ash-related issues such as slagging and fouling.

In the study, a pilot-scale liquid-solid fluidized bed (LSFB) apparatus was modified by adding an aeration unit, which enabled us to test the effect of air addition on LSFB separation performance for fine coal (0.25 ~ 1.00 mm). Upward water flow velocity, aeration volume and flotation reagent (Kerosene and 2-Caprylic alcohol) dosage varied throughout the experiment. The optimum results from the three experimental conditions (without aeration, with aeration only, with aeration and reagent) were compared by their respective probable error (Ep) values. The Ep values of the three experimental conditions were 0.109, 0.08 and 0.07, respectively. The results indicated that the addition of air obviously enhanced LSFB performance, while the addition of flotation reagent further improved the separation result.