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The crushing feed production process is a key link in the beneficiation industrial process, requiring the crusher to be “packed” to the mine to improve the efficiency of the entire crushing process. This paper proposes a control strategy based on neural network PID algorithm for fine-grained system. Using the neural network algorithm to optimize the parameters of the PID controller. The simulation results show that the optimization control method has strong anti-interference, good robust performance and fast convergence, which can be further applied to the crushing process control system. Introduction The crushing production process is the primary part of the entire beneficiation process. It undertakes to separate the raw ore collected from the primary crushing process so that the ore can meet the particle size requirements required for other processes in the subsequent beneficiation process[1].In the process of crushing production, in order to maximize the crushing efficiency of the crusher, controlling the mineral material level of the mineral material in the crusher is the most important control point in the whole crushing process control system, and it is also an important guarantee for the normal production process of the crushing process. If the material level is too high, it will easily cause the accumulation of mineral materials, and it will also easily block the crusher, and it is difficult to reach the particle size requirement of the broken ore. If the level is too low, the crushing output will decrease and the production output will be affected. The broken feedstock control system mainly has the following problems: (1) High level blockage leads to discontinuous production. The manual feeding of the mine does not meet the requirements of the “crowded” feeding condition of the efficient production of the crusher. The direct consequences:1 Because the production conditions are unstable, after the long-term exploration and summarization of the law in production, the "squeezing" con dition is achieved by manual feeding. When the production fluctuates and the manual adjustment lags, the "squeezing type" feeding requirements cannot be dynamically satisfied, which makes the working conditions unstable and cannot guarantee full-load production. 2 Long breaking time, low processing capacity. 3 The adjustment strategy is missing when the current fluctuates, which is not conducive to equipment stability. For the crusher quantificationally of system control, is often the method of manual adjustment, which is obviously difficult to meet the actual requirements of the project[2]. At present, the conventional PID algorithm is used to control the feeding amount in engineering, but for complex working conditions, it is difficult to coordinate the contradiction between rapidity and stability due to the lack of ability to quickly suppress disturbance, and the control effect is not very ideal. This paper combines neural network with PID control. A neural network-based optimization control scheme for ore dressing and crushing system is designed. Further improve the control quality of the ore crushing system, the anti-interference ability and robustness of the control system are improved.

Optimization Control of Mineral Processing and Crushing System Based on Neural Network PID