Quality control of coal jigging in a coal-preparing plant using MEBML method

Mind-evolution-based machine learning (MEBML) is a new kind of evolutionary computing algorithm. MEBML substitutes similartaxis and dissimilation for crossover and mutation operators used in GA. It possesses more rapid convergence and higher calculation accuracy. In this paper, MEBML was used as an approach for dynamic quality control of coal jigging in a coal-preparing plant. The dynamic quality control system based on MEBML for jigger can control the ashes of three products and their product ratios, the system can raise the fine coal quality effectively. The system can also realize expert control and neuro-PID control for feeding, discharging, winding, watering loop control. Results presented in the paper clearly demonstrate the feasibility of the proposed scheme.