Magnetic Flocculation Treatment of Coal Mine Water and a Comparison of Water Quality Prediction Algorithms

In this study, laboratory-scale magnetic flocculation systems were set up and the optimal coagulant and flocculent dosage, magnetic seed immersion, pH, stirring intensity, and precipitation time were all evaluated. A total of 729 expansion reaction conditions were generated, and 700 of the expansion reaction conditions were used to train a back-propagation neural network (BPNN) and general regression neural network (GRNN). The other 29 conditions were used to verify the networks’ regression ability. The optimum coagulant was poly-aluminum chloride at a dosage of 100 mg/L; the optimum flocculent was anionic polyacrylamide at a dosage of 4 mg/L; the optimum size of the magnetic seed was 200 mesh at a dosage of 1 g/L; and the optimum stirring intensities were 300 r/min for the coagulation tank, 200 r/min for the mixing tank, and 100 r/min for the flocculation tank. Comparing BPNN and GRNN, the GRNN’s regression performance better matched the relationship between the reaction conditions and results.