The ball mill 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 is used to build the complex nonlinear relationship between the electric consumption and process parameters. Then the ball mill object function is determined based on the nonlinear model. The ball mill optimal work parameters of ball mill system are determined by genetic algorithm. The ball mill optimization results demonstrate that power consumption can be decreased obviously and the goal of energy consumption optimization is achieved. The ball mill optimization results of control variables as fill level, inlet negative press and outlet temperature are obtained.In order to overcome the ball mill uncertainty of model parameters and disturbance of system, robust control algorithm for ball mill system is researched. For the load control loop, based on the difference pressure control loop, the ball mill fill level control loop is imported. By choosing proper weighting function, a cascade robust control algorithm is proposed based on H, mixed sensitivity theory. For the coupling loop of inlet negative press and outlet temperature, the ball mill decoupling problem and mixed sensitivity problem design is combined. An H robust decoupling algorithm is proposed.
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