关键词: 解耦控制, 制粉系统, 多变量, 神经网络, 自寻优

Abstract: For the low economic performance caused by coal pulverizing system multi-variable, strong coupling and the inaccuracy material level measurement, a multi-neuron PID decoupling control of coal pulverizing system based on the material level’s of self optimizing is proposed. Firstly, the mill maximum output has been found with the real-time material level searching optimization algorithm according to the mill sound and mill load parameters, and the system can run at the best operating point. Then the weight improving neural network PID algorithm is used according to the material level controlling quantity at the best operating point. The simulations and results of actual control show that the algorithm has higher accuracy and is able to adapt to the stable control of exit’s temperature, inlet’s negative pressure and the material level under different conditions. Not only closed-loop automatic control of the milling system has been achieved, but also the efficiency of the pulverizing system has been improved, which makes the consumption of flour milling reduced, in order to achieve the optimization target of energy saving ultimately.

Key words: decoupling control, coal pulverizing system, multivariable, neural network, self optimizing