Energy consumption optimization strategy and application for information are researched. The performance test experiments for the Ball Mill in a power plant are carried out. The optimal equipment parameters and work condition are obtained by the test. The results can be used as guidance for practical operation for ball mill. Aimed at the disadvantage of conventional optimization algorithm for ball mill, a new on-line optimization algorithm combined fill level is proposed. A new neural network called resource optimized networks (RON) is used to build the complex nonlinear relationship between the electric consumption and process parameters. Then the object function is determined based on the nonlinear model. The optimal work parameters of ball mill system are determined by genetic algorithm. The optimization results demonstrate that power consumption can be decreased obviously and the goal of energy consumption optimization is achieved. The optimization results of control variables as fill level, inlet negative press and outlet temperature are obtained.

All pulverizers common feature is: milled parts are by the relative motion of the two parts,Grinding coal to the surface in between the two groups, under the action of the pressing force by the extrusion and milling have been crushed.At the same time, the hot gases pass into the coal mill drying, and sifted through a mill grinding of coal brought to the upper part of the separation device in the area.After separation, the coarse particles which return to repeat the grinding mill area, fine coal powder will be taken out of the mill by still air . Rotating disc in the drive this type of mill spindle are vertical, so it is said 'vertical shaft mill'.Spindle speed range between the low-speed grinding and high-speed mill, medium speed coal mill will be hence the name.

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