Abstract
•A feedforward neural network (FFNN) accurately predicted coal flotation performance (R² > 0.9), identifying flotation time, solids concentration, impeller speed, and reagent dosages as significant factors (p < 0.05).•Flotation kinetics followed the Kelsall model, with reduced rate constants (Kf, Ks) attributed to kaolinite coating on coal particles.•Optimal conditions (7 min, 20 % solids, 1600 rpm, 2000 g/t collector, 150 g/t frother) yielded 34 % coal recovery, 16 % ash, and 24.92 MJ/kg calorific value.
Coal is South Africa’s main energy source, with increasing demand for high-quality products requiring upgrading of low-grade coal fines through flotation. This study developed a feedforward neural network (FFNN) using MATLAB’s Fitnet to model and optimize the flotation process for South African coal fines. Characterization by X-ray fluorescence and diffraction revealed quartz and kaolinite as dominant minerals. The FFNN showed strong prediction accuracy with R² > 0.9, and statistical tests confirmed time, solids concentration, impeller speed, collector, and frother dosages as significant factors (p < 0.05). Correlation analysis indicated that coal yield and calorific value increased with these variables, while ash content decreased. Flotation kinetics fitted well with the Kelsall model (R² > 0.9); however, kinetic constants Kf and Ks were lower than values reported previously, likely due to kaolinite coating on coal particles. Optimal flotation conditions were identified as 7 min flotation time, 20 % solids, 1600 rpm impeller speed, 2000 g/t collector, and 150 g/t frother dosages. Under these conditions, the flotation process achieved a coal yield of 34 %, ash content of 16 %, and calorific value of 24.92 MJ/kg. These results demonstrate that FFNN modeling combined with kinetic analysis effectively optimizes flotation, enhancing coal upgrading for South African fines.
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